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High Quality but Limited Quantity Perceptual Evidence Produces Neural Accumulation in Frontal and Parietal Cortex
... This second approach is straightforward with EEG measurements, which have high temporal resolution (Nunez et al., 2017;Philiastides et al., 2014;Philiastides et al., 2006). Using fMRI in this way is complicated by its low temporal resolution, but has been overcome using various methodological paradigms (James and Gauthier, 2006;Krueger et al., 2017;McKeeff and Tong, 2006;Wheeler et al., 2015), many of which involve a gradually revealing stimuli over time to artificially lengthen perceptual processes (Carlson et al., 2006;James et al., 1999;James et al., 2000;Morito and Murata, 2022;Ploran et al., 2007;Ploran et al., 2011). In sum, the instantiation of evidence accumulation processes in the brain that produce decision behavior has been an interdisciplinary undertaking spanning decades that has brought the fields of formal cognitive modeling and micro-and meso-scale neural measurement together. ...
... Region of interest characteristics.Notes: Action profiles were labeled Moment of Recognition inPloran et al. (2007) and Commitment inPloran et al. (2011). Action* profiles were labeled Accumulator inPloran et al. (2011). ...
... Region of interest characteristics.Notes: Action profiles were labeled Moment of Recognition inPloran et al. (2007) and Commitment inPloran et al. (2011). Action* profiles were labeled Accumulator inPloran et al. (2011). MNI refers to the MNI coordinates of the center of mass for each ROI. ...
... We initially postulated peak and rise times of activity signals as the parameters to characterize the activity profiles because the region classes of Ploran et al. (2007) were defined by dependence of these parameters on the decision moment: both of the two parameters show the dependence in the moment-of-recognition regions, only the peak time in the accumulators, and neither in the sensory processors. We also evaluated validity of the present method by reproducibility of the classification results of Ploran et al. (2007Ploran et al. ( , 2011, the latter of which was an improved version of the former study showing substantially similar classification results with modified methods of stimulation and clustering. [In this study, our approach basically followed Ploran et al. (2007), while we referred to the results of Ploran et al. (2011) as well.] ...
... [In this study, our approach basically followed Ploran et al. (2007), while we referred to the results of Ploran et al. (2011) as well.] In a heuristic manner, we found that the rising slope was more effective for reproducing the classification results of Ploran et al. (2007Ploran et al. ( , 2011) than the rise time as described in Results, so that we used the rising slope of the response-locked time course (referred to as Slope_rsp) instead of the rise time. The three profile parameters, which were the peak times of the stimulus-locked and response-locked time courses (referred to as Peak_stm and Peak_rsp, respectively) and Slope_rsp, were extracted from each of the RT-group-averaged signal segments, as shown in Figure 6B. ...
... Rise_stm was not used for the classification but to rationalize the present method. To reproduce similar results as the classification of Ploran et al. (2007Ploran et al. ( , 2011, we used four statistical quantities of the profile parameters over RTs as follows: SD of Peak_stm (Peak_stm_SD), SD of Peak_rsp (Peak_rsp_SD), mean of Slope_rsp (Slope_rsp_MN), and mean of Peak_rsp (Peak_rsp_MN). The first three quantities were used to reflect the different dependence on decision moment (i.e., RT), while the fourth was employed in a heuristic manner as described in Results. ...
Neural substrates of evidence accumulation have been a central issue in decision-making studies because of the prominent success of the accumulation model in explaining a wide range of perceptual decision making. Since accumulation-shaped activities have been found in multiple brain regions, which are called accumulators, questions regarding functional relations among these accumulators are emerging. This study employed the deconvolution method of functional magnetic resonance imaging (fMRI) signals from human male and female participants during object-category decision tasks, taking advantage of the whole-brain coverage of fMRI with improved availability of temporal information of the deconvolved activity. We detected the accumulation activity in many non-category-selective regions over the frontal, parietal, and temporal lobes as well as category-selective regions of the categorization task. Importantly, the frontal regions mostly showed activity peaks matching the decision timing (classified as "type-A accumulator"), while activity peaks of the parietal and temporal regions were behind the decision (classified as "type-B accumulator"). The category-selective regions showed activity peaks whose timing depended on both region and stimulus preference, plausibly reflecting the competition among the alternative choices (classified as "type-C accumulator"). The results suggest that these functionally heterogeneous accumulators form a system for evidence accumulation in which the type-A accumulator regions make decisions in a general manner while the type-B and type-C accumulator regions are employed depending on the modality and content of decision tasks. The concept of the accumulation system may provide a key to understanding the universality of the accumulation model over various kinds of decision tasks.Significance StatementPerceptual decision making, such as deciding to walk or stop on seeing the signal colors, has been explained theoretically by the accumulation model, in which sensory information is accumulated to reach a certain threshold for making decisions. Neural substrates of this model, however, are still under elucidation among candidate regions found over the brain. We show here that, taking advantage of the whole-brain coverage of fMRI with improving availability of temporal information by deconvolution method, the accumulation is carried out by a system comprising many regions in different abstraction levels and only a part of these regions in the frontal cortex make decisions. The system concept may provide a key to explain the universality of the accumulation model.
... This has precedent in the analysis of probabilistic tasks, where intrinsic properties of the gamble at hand (such as mean and variance of the uncertain reward) have proven invaluable to deciphering the neural processes leading up to choice (e.g., D' Acremont and Bossaerts, 2008). Likewise, mathematical characteristics of the stimuli in perceptual tasks, such as signal strength, elucidate neural dynamics during deliberation (e.g., Ploran et al., 2011;Hanks and Summerfield, 2017). Drawing on computational complexity theory, we demonstrate here that a mapping exists between intrinsic properties of instances of a problem related to computational hardness and neural dynamics during decision-making. ...
... It is worth noting that the negative pattern found in this period might stem from a different slope in the increased task-related activation and not from differences in the sustained level of activity (Fig 5). This pattern would align with previous results that support that FPN regions encode evidence accumulation towards a particular decision (Ploran et al., 2011;Gratton et al., 2017). Indeed, in the knapsack task we would expect that lower TCC entails faster evidence accumulation towards a solution. ...
... It is The copyright holder for this preprint this version posted January 5, 2022. ; https://doi.org/10.1101/2022.01.05.475102 doi: bioRxiv preprint ratio is modulated (e.g., Aben et al., 2020;Dubis et al., 2016;Hanks and Summerfield, 2017;Ploran et al., 2011); alternatively, in memory retrieval tasks, the amount of information to be stored/retrieved is tuned (e.g., Gratton et al., 2018;Fedorenko et al., 2013). The lack of a generic (problem-independent) definition of cognitive demand hinders the generalization of this approach to new problems. ...
Many everyday tasks require people to solve computationally complex problems. However, little is known about the effects of computational hardness on the neural processes associated with solving such problems. Here, we draw on computational complexity theory to address this issue. We performed an experiment in which participants solved several instances of the 0-1 knapsack problem, a combinatorial optimization problem, while undergoing ultra-high field (7T) functional magnetic resonance imaging (fMRI). Instances varied in two task-independent measures of intrinsic computational hardness: complexity and proof hardness. We characterise a network of brain regions whose activation was correlated with both measures but in distinct ways, including the anterior insula, dorsal anterior cingulate cortex and the intra-parietal sulcus/angular gyrus. Activation and connectivity changed dynamically as a function of complexity and proof hardness, in line with theoretical computational requirements. Overall, our results suggest that computational complexity theory provides a suitable framework to study the effects of computational hardness on the neural processes associated with solving complex cognitive tasks.
... Hebart et al. (2012) also showed that neural signals in parietal cortex allowed for predicting decision outcomes better for less compared to stronger motion coherence, while early visual cortex showed the opposite pattern. It has further been proposed that these regions, particularly PPC and DLPFC, are directly related to perceptual decision-making, potentially in a role such as evidence accumulation and computation of a decision variable (Heekeren et al., 2004(Heekeren et al., , 2006Ploran et al., 2007Ploran et al., , 2011. However, Heekeren et al. (2004) based their argument on the observation that DLPFC was more strongly activated by stronger stimulus evidence, which is the opposite of what we found. ...
... This means that stronger activation in these regions was observed when decisions were made faster for fixed stimulus quality levels, possibly indicating trials with less random noise in stimulus representations. For the high stimulus quality condition, activation in the orbitofrontal cluster extended posterior, possibly to the frontal operculum, which has been suggested as being related to recognition time (Ploran et al., 2007(Ploran et al., , 2011. The same authors have also found the pre-SMA to have a highly similar activation profile. ...
... While our results and the results of previous studies (e.g., Sajda, 2006, 2007;Ploran et al., 2007Ploran et al., , 2011 clearly point towards specialisation of regions for different aspects of the decision process, it should not be overlooked that the overall picture is that a large network is engaged during the entire decision process. This idea is further supported by electrophysiology studies in monkeys, which have shown that neurons in an extended network of brain regions, spanning from sensory regions, posterior parietal and prefrontal areas to regions related to response preparation, encode decision-relevant information (e.g., Britten et al., 1996;Gold, 2012, 2013;Gold and Shadlen, 2007;Huk et al., 2017;Kim and Shadlen, 1999;Romo and de Lafuente, 2013;Shadlen and Kiani, 2013;Shadlen and Newsome, 2001). ...
According to sequential sampling models, perceptual decision-making is based on accumulation of noisy evidence towards a decision threshold. The speed with which a decision is reached is determined by both the quality of incoming sensory information and random trial-by-trial variability in the encoded stimulus representations. To investigate those decision dynamics at the neural level, participants made perceptual decisions while functional magnetic resonance imaging (fMRI) was conducted. On each trial, participants judged whether an image presented under conditions of high, medium, or low visual noise showed a piano or a chair. Higher stimulus quality (lower visual noise) was associated with increased activation in bilateral medial occipito-temporal cortex and ventral striatum. Lower stimulus quality was related to stronger activation in posterior parietal cortex (PPC) and dorsolateral prefrontal cortex (DLPFC). When stimulus quality was fixed, faster response times were associated with a positive parametric modulation of activation in medial prefrontal and orbitofrontal cortex, while slower response times were again related to more activation in PPC, DLPFC and insula. Our results suggest that distinct neural networks were sensitive to the quality of stimulus information, and to trial-to-trial variability in the encoded stimulus representations, but that reaching a decision was a consequence of their joint activity.
... Therefore, it remains unclear whether the CO and FP networks make distinct contributions to transient moment-to-moment processing within correct trials. Some insight into this question may come from a parallel line of research (Wheeler et al. 2008;Ploran et al. 2007Ploran et al. , 2011 which has suggested that regions spread across a number of systems, including the CO and FP networks, show dissociable responses in decision-making. Responses in some regions of the FP, dorsal attention, and visual processing systems were related to "evidence accumulation," or the process of gradually collecting information toward a decision, and responses in some CO network regions appeared as more control-related "moment of recognition" signals, tightly locked to the time a decision is reached. ...
... Here, we use data from 5 separate decision-making tasks (Ploran et al. 2007(Ploran et al. , 2011Wheeler et al. 2008) to examine these differences in more detail. Specifically, we ask: do the CO and FP networks show separable activation profiles within correct trials, and are these differences generalizable across 5 different tasks? ...
... Functional images were acquired with a Blood Oxygen Level Dependent (BOLD) contrast-sensitive scan using a spin-echo echo-planar (Tasks 1, 2, and 5) or a gradient echo echo-planar (Tasks 3-4) T 2 *weighted pulse sequence. See Table 1 for specific scan parameters and details reported in Ploran et al. (2007) (Task 1; Task 2 also had the same scan parameters as Task 1), and Ploran et al. (2011) (Task 5). The first 3-4 volumes from each functional imaging run were discarded to allow the scanner magnetization to reach steady state. ...
Control of goal-directed tasks is putatively carried out via the cinguloopercular (CO) and frontoparietal (FP) systems. However,
it remains unclear whether these systems show dissociable moment-to-moment processing during distinct stages of a trial. Here,
we characterize dynamics in the CO and FP networks in a meta-analysis of 5 decision-making tasks using fMRI, with a specialized
“slow reveal” paradigm which allows us to measure the temporal characteristics of trial responses. We find that activations
in left FP, right FP, and CO systems form separate clusters, pointing to distinct roles in decision-making. Left FP shows
early “accumulator-like” responses, suggesting a role in pre-decision processing. CO has a late onset and transient response
linked to the decision event, suggesting a role in performance reporting. The majority of right FP regions show late onsets
with prolonged responses, suggesting a role in post-recognition processing. These findings expand upon past models, arguing
that the CO and FP systems relate to distinct stages of processing within a trial. Furthermore, the findings provide evidence
for a heterogeneous profile in the FP network, with left and right FP taking on specialized roles. This evidence informs our
understanding of how distinct control networks may coordinate moment-to-moment components of complex actions.
... Other fMRI studies using paradigms based on a progressive and gradual revelation of the stimulus content have also shed light on brain systems involved in the online generation of predictions during perceptual decision making (Carlson, Grol, & Verstraten, 2006;James, et al., 2000;Ploran, et al., 2007;Ploran, Tremel, Nelson, & Wheeler, 2011;Wheeler, Petersen, Nelson, Ploran, & Velanova, 2008). Among them, Ploran, et al. (2007) devised a task in which masked picture stimuli were gradually revealed over eight discrete steps in a maximum revelation time of 14 seconds, and participants had to press a button as soon as they could identify the picture with a reasonable degree of confidence. ...
... Conversely, in inferior temporal, frontal and parietal regions (including the bilateral fusiform gyrus and the DLPFC), a gradual increase in activity peaking at the time of recognition was compatible with the assumption of a genuine accumulation of evidence process taking place in these regions, that was presumably necessary to recognize the identity of the target object (accumulators). Finally, in several regions of medial frontal cortex --including the dorsal anterior cingulate cortex (dACC) and the anterior insula -activity remained near baseline until the time of recognition when a substantial increase in the BOLD signal occurred, suggesting their selective involvement in decision-related, as opposed to evidence accumulation processes (Ploran, et al., 2007; see also Ploran, et al., 2011). ...
... The aforementioned brain imaging studies extended earlier knowledge on mechanisms of perceptual decision making, providing evidence for the idea that accumulation and momentof-recognition processes could be subtended by non-overlapping brain networks (Heekeren, et al., 2004;Ploran, et al., 2007;Ploran, et al., 2011). However, the progressive stimulus revelation procedure used in these studies were adapted to be compatible with the sluggish temporal resolution of fMRI, and only a compound measure of brain activity across many events during a given trial could eventually be modeled with this haemodynamic imaging technique. ...
... In line with previous research, we hypothesized that the integration of categorical evidence would recruit regions of the parietal lobe Ploran et al., 2007;Ploran, Tremel, Nelson, & Wheeler, 2011;Shadlen & Newsome, 2001;Yang & Shadlen, 2007), as well as primary and premotor regions (Gluth et al., 2012;Pastor-Bernier & Cisek, 2011;Thura & Cisek, 2014;Wheeler et al., 2014). We predicted that frontal regions, such as the VMPFC, the OFC and DLPFC would represent effector-independent decision confidence (the unsigned difference in evidence for each of the two categories; Cohen, McClure, & Yu, 2007;De Martino et al., 2013;Heekeren, Marrett, Ruff, Bandettini, & Ungerleider, 2006;Padoa-Schioppa, 2011;Rolls, Grabenhorst, & Deco, 2010a, 2010b. ...
... Notably, this analysis suggested that the bilateral IPS, regions that we predicted would be sensitive to decision evidence Ploran et al., 2007Ploran et al., , 2011Shadlen & Newsome, 2001;Yang & Shadlen, 2007), tracked the EI function instead. We found that this signal could be easily mistaken for confidence as, when we orthogonalized the EI covariate with respect to confidence (thus assigning shared variance to the confidence PM; see Mumford et al., 2015), the IPS appeared to track both confidence and urgency. ...
... Taken together, these results suggest that the univariate BOLD response in the IPS may largely reflect processes associated with decision thresholding, rather than the accumulation of decision evidence. Regardless of the function of this signal, however, these results suggest that monotonically-increasing BOLD signals previously observed in the IPS during temporally-extended decision-making tasks (e.g., Ploran et al., 2007Ploran et al., , 2011 may be insensitive to the strength of decision evidence. Our results confirm, however, that other regions indicated in these studies, notably the right middle and inferior frontal gyri, the left cerebellar crus I, the bilateral anterior insula, the medial prefrontal cortex / pre-SMA, the inferior parietal lobe, and the caudate, are sensitive to the strength of decision evidence. ...
We used a temporally-extended categorization task to investigate the neural substrates underlying our ability to integrate information over time and across multiple stimulus features. Using model-based fMRI, we tracked the temporal evolution of two important variables as participants deliberated about impending choices: 1) categorical evidence, and 2) confidence (the total amount of evidence provided by the stimuli, irrespective of the particular category favored). Importantly, in each model, we also included a covariate which allowed us to differentiate signals related to information accumulation from other, evidence-independent functions that increased monotonically with time (such as urgency or cognitive load). We found that somatomotor regions tracked the temporal evolution of categorical evidence, while regions in both medial and lateral prefrontal cortex, inferior parietal cortex, and the striatum tracked decision confidence. As both theory and experimental work suggest that patterns of activity thought to be related to information-accumulation may reflect, in whole or in part, an interaction between sensory evidence and urgency, we additionally investigated whether urgency might modulate the slopes of the two evidence-dependent functions. We found that the slopes of both functions were likely modulated by urgency such that the difference between the high and low evidence states increased as the response deadline loomed.
... During the presentation of shapes, the spiking rate of neurons in area LIP reflected the cumulative log likelihood ratio of evidence favoring one choice over the other, indicating that LIP neurons integrate evidence informing the monkeysʼ decisions. Comparable research in humans has identified accumulating fMRI activity in the parietal lobes and other brain regions during perceptual (Nosofsky, Little, & James, 2012;Ploran, Tremel, Nelson, & Wheeler, 2011;Kayser, Buchsbaum, Erickson, & DʼEsposito, 2010;Tosoni, Galati, Romani, & Corbetta, 2008;Wheeler, Petersen, Nelson, Ploran, & Velanova, 2008;Ploran et al., 2007;James & Gauthier, 2006) and value-based (Gluth, Rieskamp, & Buchel, 2012) decisions. Although the fMRI results are in general accord with an accumulation-to-boundary account, it is not clear whether the fMRI activity is directly related to the accumulation of 1 task-relevant evidence. ...
... Critically, the rate of evidence (RoE) toward a given choice was manipulated so that, as more shapes were presented, evidence favoring the choice grew rapidly, gradually, or switched after first favoring the other choice. On the basis of the findings of Yang and Shadlen (2007) and fMRI studies of perceptual decision-making (Ploran et al., 2011;Kayser et al., 2010;Tosoni et al., 2008;Ploran et al., 2007;Carlson, Grol, & Verstraten, 2006;James, Humphrey, Gati, Menon, & Goodale, 2000), we predicted that activity in regions most sensitive to the impending choice would increase more rapidly on Rapid than Gradual than Switch trials, adhering to the RoE. This outcome would provide additional support for an accumulation account of neural function during human decision-making. ...
... Second, evidence accrual begins after some nominal "nondecision" processing time related to task initiation and encoding of stimulus features (Ratcliff, Cherian, & Segraves, 2003). Third, in past studies, we found that accumulation began at 2-4 sec from trial onset (Ploran et al., 2007(Ploran et al., , 2011. Fourth, later time points could be contaminated by the response. ...
The evolution of neural activity during a perceptual decision is well characterized by the evidence parameter in sequential sampling models. However, it is not known whether accumulating signals in human neuroimaging are related to the integration of evidence. Our aim was to determine whether activity accumulates in a nonperceptual task by identifying brain regions tracking the strength of probabilistic evidence. fMRI was used to measure whole-brain activity as choices were informed by integrating a series of learned prior probabilities. Participants first learned the predictive relationship between a set of shape stimuli and one of two choices. During scanned testing, they made binary choices informed by the sum of the predictive strengths of individual shapes. Sequences of shapes adhered to three distinct rates of evidence (RoEs): rapid, gradual, and switch. We predicted that activity in regions informing the decision would modulate as a function of RoE prior to the choice. Activity in some regions, including premotor areas, changed as a function of RoE and response hand, indicating a role in forming an intention to respond. Regions in occipital, temporal, and parietal lobes modulated as a function of RoE only, suggesting a preresponse stage of evidence processing. In all of these regions, activity was greatest on rapid trials and least on switch trials, which is consistent with an accumulation-to-boundary account. In contrast, activity in a set of frontal and parietal regions was greatest on switch and least on rapid trials, which is consistent with an effort or time-on-task account.
... Instead, it has been popularly used to demonstrate the temporally non-discrete nature of these stages and the existence of multiple sub-processes. A gradual transition between the two stages has been suggested (Bar et al., 2001(Bar et al., , 2006Ploran et al., 2007Ploran et al., , 2011, consistent with the neural-network model assuming mutual facilitation of the two stages (Farah et al., 1993;Humphreys et al., 1999;Bar, 2003). Multiple sophisticated time-series models of expected signal change beyond the two-stage concept have been used, and distinct but overlapping sets of cortical regions were identified for each model (Carlson et al., 2006;Ploran et al., 2007Ploran et al., , 2011. ...
... A gradual transition between the two stages has been suggested (Bar et al., 2001(Bar et al., , 2006Ploran et al., 2007Ploran et al., , 2011, consistent with the neural-network model assuming mutual facilitation of the two stages (Farah et al., 1993;Humphreys et al., 1999;Bar, 2003). Multiple sophisticated time-series models of expected signal change beyond the two-stage concept have been used, and distinct but overlapping sets of cortical regions were identified for each model (Carlson et al., 2006;Ploran et al., 2007Ploran et al., , 2011. ...
... On the other hand, the observation may be explained by a process outside the classical conceptual framework of the two-stage model. For example, the observation is consistent with the proposal that a network of these areas is engaged in the accumulation of object information (Carlson et al., 2006;Ploran et al., 2007Ploran et al., , 2011. Alternatively, some of the sub-processes included in the perceptual or memory stages may, in fact, be supported by the same neural substrate as different computations. ...
Visual object recognition is classically believed to involve two stages: a perception stage in which perceptual information is integrated, and a memory stage in which perceptual information is matched with an object's representation. The transition from the perception to the memory stage can be slowed to allow for neuroanatomical segregation using a degraded visual stimuli (DVS) task in which images are first presented at low spatial resolution and then gradually sharpened. In this functional magnetic resonance imaging study, we characterized these two stages using a DVS task based on the classic model. To separate periods that are assumed to dominate the perception, memory, and post-recognition stages, subjects responded once when they could guess the identity of the object in the image and a second time when they were certain of the identity. Activation of the right medial occipitotemporal region and the posterior part of the rostral medial frontal cortex was found to be characteristic of the perception and memory stages, respectively. Although the known role of the former region in perceptual integration was consistent with the classic model, a likely role of the latter region in monitoring for confirmation of recognition suggests the advantage of recently proposed interactive models.
... Such components can be obtained both by means of a widespread datadriven decomposition (ICA, independent component analysis) and standard task-related protocols (GLM, general linear model). Hence, we adapted the accumulator-based decision architecture used by Ploran et al. (2007), Ploran, Tremel, Nelson, and Wheeler (2011) and extended it to investigate a general mechanism concerning the integration of holistic perceptual evidence, decision signaling and response. ...
... According to Heekeren et al. (2008) four module hypothesis, the insula belongs to the module that detects perceptual uncertainty/difficulty (Grinband, Hirsch, & Ferrera, 2006) and signals when additional attentional resources are required. Other authors (Binder et al., 2004;Ho et al., 2009;Kayser et al., 2010;Ploran et al., 2007Ploran et al., , 2011, have implicated the anterior insular and adjacent frontal opercular cortex with a more important place in the decision hierarchy. This is consistent with a study (Christensen, Ramsøy, Lund, Madsen, & Rowe, 2006) of graded visual perception, where both insular cortex and frontal operculum were listed under the areas which activity modulation obeyed to the rule: clear percept4vague percept4no percept. ...
... Comparing our results to the studies of Ploran et al. (2007Ploran et al. ( , 2011, which established the groundwork to define our set of predictors and decision architecture, there are some relevant differences that need to be addressed. These authors interpreted activity in the bilateral anterior insula as reflecting ''commitment to decision''. ...
The nature of neural processing within category-preferring visual networks remains an open topic in human neuroscience. Although the topography of face, scene, and object-preferring modules in the human brain is well established, the functional characterization, in terms of dynamic selectivity across their nodes is still elusive. Here, we use long trials of perceptually impoverished images of faces and objects to assess the dynamics of BOLD activity and selectivity induced by perceptual closure within these regions of interest. Departing from paradigms involving immediate percepts, we used ambiguous images favoring holistic search and independence from low level stimulus properties. By assessing the neural responses to images that go beyond the preferred category of the studied ROIs we could dissect the specificity of these processes as a function of the timing of perceptual closure and contribute to the debate regarding specialization of these modules. We found that pSTS is a notable exception to the observation that category selective high-level visual areas also participate on the perceptual closure of their non-preferred category. A similar observation was found for PPA responses to faces. Most importantly, these observations directly link the pSTS region with the social processing network, which cannot be engaged by object stimuli.
... According to Heekeren et al. (2008) four module hypothesis, the insula belongs to the module that detects perceptual uncertainty/difficulty (Grinband, Hirsch, & Ferrera, 2006) and signals when additional attentional resources are required. Other authors (Binder et al., 2004; Ho et al., 2009; Kayser et al., 2010; Ploran et al., 2007 Ploran et al., , 2011), have implicated the anterior insular and adjacent frontal opercular cortex with a more important place in the decision hierarchy. This is consistent with a study (Christensen, Ramsøy, Lund, Madsen, & Rowe, 2006) of graded visual perception, where both insular cortex and frontal operculum were listed under the areas which activity modulation obeyed to the rule: clear percept4vague percept4no percept. ...
... Though it is not the primary role of this area, we were also able to identify somatosensory/sensorimotor correlates in the posterior insula as already reported by others (BjörnsdotterBj¨Björnsdotter, L ¨ oken, Olausson, Vallbo, & Wessberg, 2009; Carey, Abbott, Egan, Bernhardt, & Donnan, 2005; Johansen-Berg & Matthews, 2002; Kurth et al., 2010 ), markedly dissociated from the responseindependent pattern observed in the anterior and middle regions of the insula, providing further clues to the functional anatomy of this structure. Comparing our results to the studies of Ploran et al. (2007 Ploran et al. ( , 2011, which established the groundwork to define our set of predictors and decision architecture, there are some relevant differences that need to be addressed. These authors interpreted activity in the bilateral anterior insula as reflecting ''commitment to decision''. ...
... The ''accumulator''-like pattern seen in the reported data (seeFig. 3d in Ploran et al. (2011)) is consistent with our own view and may have been collapsed in their analysis by the clustering algorithm into the ''decision'' group.Fig. 5. Insular activation patterns for the subregions identified with the different contrasts. Different colors, i.e., different conditions, correspond to faces detected at different time slots, from movie to movie:brown 0–2 s; dark blue 2–4 s; cyan 4–6 s; magenta 6–8 s; green 8–10 s; gray 10–12 s; red represents unseen facesand correspond to different decision times. ...
... Indeed, previous human work suggests that higher brain signal variability affords larger cognitive flexibility 13,14 . For example, several studies have found that brain signal variability increases with increasing task demands (at least until processing limits are reached) and that the ability to upregulate variability predicts task performance [15][16][17][18][19][20][21][22] . These studies commonly argue that neural variability supports performance under increasing task demand by allowing the brain to maintain flexible responding to stimulus information. ...
... Previous fMRI studies of perceptual decision-making also report the involvement of a similar set of higher-order brain regions, which have been linked to evidence accumulation, decision formation and response preparation [11][12][13][14][15][16][17][18] . Notably, some studies have reported an increase in average BOLD activity in these brain areas over the sampling period (i.e., with decreasing state uncertainty), which aligns with our findings 11,19 . ...
To make optimal decisions, intelligent agents must learn latent environmental states from discrete observations. Bayesian frameworks argue that integration of evidence over time allows us to refine our state belief by reducing uncertainty about alternate possibilities. How is this increasing belief precision during learning reflected in the brain? We propose that moment-to-moment neural variability provides a signature that scales with the degree of reduction of uncertainty during learning. In a sample of 47 healthy adults, we found that BOLD signal variability (SD BOLD , as measured with functional MRI) indeed compressed with successive exposure to decision-related evidence. Crucially, more accurate participants expressed greater SD BOLD compression primarily in Default Mode Network regions, possibly reflecting the increasing precision of their latent state belief during more efficient learning. Further, computational modeling of behavior suggested that more accurate subjects held a more unbiased (flatter) prior belief over possible states that allowed for larger uncertainty reduction during learning, which was directly reflected in SD BOLD changes. Our results provide first evidence that moment-to-moment neural variability compresses with increasing belief precision during effective learning, proposing a flexible mechanism for how we come to learn the probabilistic nature of the world around us.
... The 'slow reveal' decision making tasks (Tasks 17-21; (Ploran et al. 2007;Ploran et al. 2011;Wheeler et al. 2008) were analyzed using an event-related approach, with separate event regressors for trials sorted by participant response time -i.e., regressors for participants responses at timepoints 4, 5, 6, and 7 (this selection captures the majority of trials; see ). Each trial was again modeled using a finite impulse response approach with 16 timepoints. ...
... A hierarchical clustering analysis (Cordes et al. 2002;Dosenbach et al. 2007;Salvador et al. 2005) was used to identify clusters of regions with similar task activation profiles related to task control. Seven different types of control-related task activations were included in this analysis ( Fig. 2): (i) onset cues at the start of a block indicating the task to be performed (e.g.: "rhyme/no rhyme" in Fig. 2B), (ii) offset cues, indicating the end of the task set period (e.g., a red fixation cross in Fig. 2B), (iii) sustained signals elevated through an entire task block (green boxcar in Fig. 2B), (iv) errors committed by participants (e.g., responding "rhyme" to the word pair "fare/bank"; light gray signals in Fig. 2B), (v) ambiguity in a stimulus (e.g., a rhyme judgment about the word pair "bass/grace"; medium gray signals in Fig. 2B), relative to (vi) unambiguous correct trials (e.g., responding "no rhyme" to "fare/bank") and finally (vii) decision-making timecourses (e.g., recognition judgment during a slow reveal task; yellow and orange signals in Fig. 2C; note that decision-making timecourses allow us to separate transient performancerelated signals that may link to task parameter updates from gradual evidence accumulation responses Ploran et al. 2007;Ploran et al. 2011). These activations are related to the instantiation of task parameters (i,ii), maintenance of task signals (iii), and adjustment of signals based on ongoing performance (iv-vii). ...
The cingulo-opercular (CO) network and its two best studied regions – the dorsal anterior cingulate and anterior insula – have been linked to task control, but also implicated in many additional processes across cognitive, social, and emotional domains. However, most prior work investigating the CO network has used a group-average approach, which may mix signals across nearby regions that vary across individuals. Here, we reevaluate the CO network’s role in task control with both task and rest fMRI, using regions with a high probability of CO network agreement across individuals. Hierarchical clustering analyses suggest heterogeneity in the CO network’s task response properties, with one sub-system (CO1) showing consistency with prior task control characterizations while another sub-system (CO2) has weak task control responses, but preserved ties to pain and motor functions. Resting-state connectivity confirms subtle differences in the architecture of these two sub-systems. This evidence suggests that, when individual variation in network locations is addressed, the CO network includes (at least) two linked sub-systems with differential roles in task control and other cognitive/motor/interoceptive responses, which may help explain varied accounts of its functions. We propose that this fractionation may reflect expansion of primary CO body-oriented control functions to broader domain-general contexts.
... Specifically, firing rates of a subset of neurons in these areas build up over time with a rate proportional to the amount of sensory evidence (i.e., difficulty of the task) and eventually converge to a common firing level (decision boundary) as animals commit to a choice. In humans, macroscopic measurements of neural activity using magneto-and electroencephalography (M/EEG; O'Connell, Dockree, & Kelly, 2012; Philiastides, Heekeren, & Sajda, 2014;Pisauro, Fouragnan, Retzler, & Philiastides, 2017;Polania, Krajbich, Grueschow, & Ruff, 2014;Ratcliff, Philiastides, & Sajda, 2009;Wyart, de Gardelle, Scholl, & Summerfield, 2012) and functional magnetic resonance imaging (fMRI) experiments (Heekeren, Marrett, Bandettini, & Ungerleider, 2004;Kayser, Buchsbaum, Erickson, & D'Esposito, 2010;Noppeney, Ostwald, & Werner, 2010;Ploran, Tremel, Nelson, & Wheeler, 2011) have revealed comparable activation patterns in similar brain regions. ...
... The LIT formulation also has some interesting neurobiological implications as it highlights the need to differentiate between two interrelated but largely separate processes that are likely to take place in different brain structures. While the former can be independent of sensory and response modality, as has been reported in regions of the dorsolateral prefrontal cortex (DLPFC) and lateral intraparietal sulcus/cortex (LIP/ IPS; Filimon et al., 2013;Heekeren, Marrett, Ruff, Bandettini, & Ungerleider, 2006;Philiastides, Auksztulewicz, Heekeren, & Blankenburg, 2011;Ploran et al., 2011), the latter would be tightly coupled with structures controlling the specific motor effectors involved in implementing the decision (Donner, Siegel, Fries, & Engel, 2009;Filimon et al., 2013;Tosoni, Galati, Romani, & Corbetta, 2008). ...
A common assumption in choice response time (RT) modeling is that after evidence accumulation reaches a certain decision threshold, the choice is categorically communicated to the motor system that then executes the response. However, neurophysiological findings suggest that motor preparation partly overlaps with evidence accumulation, and is not independent from stimulus difficulty level. We propose to model this entanglement by changing the nature of the decision criterion from a simple threshold to an actual process. More specifically, we propose a secondary, motor preparation related, leaky accumulation process that takes the accumulated evidence of the original decision process as a continuous input, and triggers the actual response when it reaches its own threshold. We analytically develop this Leaky Integrating Threshold (LIT), applying it to a simple constant drift diffusion model, and show how its parameters can be estimated with the D*M method. Reanalyzing 3 different data sets, the LIT extension is shown to outperform a standard drift diffusion model using multiple statistical approaches. Further, the LIT leak parameter is shown to be better at explaining the speed/accuracy trade-off manipulation than the commonly used boundary separation parameter. These improvements can also be verified using traditional diffusion model analyses, for which the LIT predicts the violation of several common selective parameter influence assumptions. These predictions are consistent with what is found in the data and with what is reported experimentally in the literature. Crucially, this work offers a new benchmark against which to compare neural data to offer neurobiological validation for the proposed processes. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
... To focus only on the third type of task-control signal, another line of research using an extended trial paradigm has shown that these transient signals in the cingulo-opercular network come online at or near the end of the trial, further suggesting that these responses serve as performance reporting signals (Ploran et al. 2007;Ploran et al. 2011). Specifically, in a slow reveal design where images are presented behind a black mask which was gradually degraded, participants were required to press a button when they could recognize the object that was being revealed. ...
... The goal of this report is 2-fold: First, we replicated previous work showing that error-related activity comes online at or near the end of the trial (Ploran et al. 2007;Ploran et al. 2011), and extended this to include late ambiguity-related activity in a perceptual decision-making task. Second, we asked whether ambiguity and accuracy processing show interactive effects that come online as a late performance reporting signal. ...
The cingulo-opercular network (including the dorsal anterior cingulate and bilateral anterior insula) shows 3 distinct task-control
signals across a wide variety of tasks, including trial-related signals that appear to come online at or near the end of the
trial. Previous work suggests that there are separable responses in this network for errors and ambiguity, implicating multiple
types of processing units within these regions. Using a unique paradigm, we directly show that these separable responses withhold
activity to the end of the trial, in the service of reporting performance back into the task set. Participants performed a
slow reveal task where images were presented behind a black mask which was gradually degraded, and they pressed a button when
they could recognize the object that was being revealed. A behavioral pilot was used to identify ambiguous stimuli. We found
interactive effects of accuracy and ambiguity, which suggests that these regions are computing and utilizing information,
at one time, about both types of performance indices. Importantly, we showed a relationship between cingulo-opercular activity
and behavioral performance, suggesting a role for these regions in performance reporting, per se. We discuss these results
in the context of task control.
... (a) Correlation of BOLD response and stimulus difficulty (Heekeren et al., 2004(Heekeren et al., , 2006Pleger et al., 2006;Tosoni et al., 2008;Ho et al., 2009;Noppeney et al., 2010;Kayser et al., 2010aKayser et al., , 2010bKovács et al., 2010;Liu and Pleskac, 2011;Erickson and Kayser, 2013) (b) Correlation of BOLD response and choice reaction time (Binder et al., 2004;Thielscher and Pessoa, 2007;McKeeff and Tong, 2007;Noppeney et al., 2010;Ruff et al., 2010;Kayser et al., 2010a) (c) Correlation of BOLD response and performance (Lewandowska et al., 2010;Kayser et al., 2010a) (d) Overall positive BOLD response during task execution (Tosoni et al., 2008;Ho et al., 2009;Kayser et al., 2010aKayser et al., , 2010bErickson and Kayser, 2013;Filimon et al., 2013) (e) Choice probabilities from BOLD response Padmala, 2005, 2007) (f) Gradual BOLD increase in slow decision-making task (Ploran et al., 2007(Ploran et al., , 2011 (g) Comparison of BOLD signal with predicted signal from cognitive models (Ho et al., 2009;Domenech and Dreher, 2010) (h) Choice-predictive brain signals using MVPA (Pessoa and Padmala, 2007;Serences and Boynton, 2007;Li et al., 2009; Of all these approaches, the correlation of BOLD response and stimulus difficulty is the most common and also often the primary indicator of a decision-related brain signal, rather than for example an overall positive BOLD response which is often used as an additional criterion. ...
... One approach to overcome the limit in time resolution has been to extend the duration of the accumulation process and look for gradual increases in BOLD responses 40 (Ploran et al., 2007(Ploran et al., , 2011 rather than steady responses that would rather reflect unspecific processes such as attention, or responses that show a marked increase which probably reflect choice-related processes and those following the choice. Other studies introduced a time between stimulus presentation and response execution to distinguish motor responses from evidence accumulation (Tosoni et al., 2008;Liu and Pleskac, 2011;Erickson and Kayser, 2013). ...
Die Fähigkeit, Zustände in der Außenwelt zu beurteilen und zu kategorisieren, wird unter dem Oberbegriff „perzeptuelles Entscheiden“ zusammengefasst. In der vorliegenden Arbeit wurde funktionelle Magnetresonanztomografie mit multivariater Musteranalyse verbunden, um offene Fragen zur perzeptuellen Entscheidungsfindung zu beantworten. In der ersten Studie (Hebart et al., 2012) wurde gezeigt, dass der visuelle und parietale Kortex eine Repräsentation abstrakter perzeptueller Entscheidungen aufweisen. Im frühen visuellen Kortex steigt die Menge entscheidungsspezifischer Information mit der Menge an verfügbarer visueller Bewegungsinformation, doch der linke posteriore parietale Kortex zeigt einen negativen Zusammenhang. Diese Ergebnisse zeigen, wo im Gehirn abstrakte Entscheidungen repräsentiert werden und deuten darauf hin, dass die gefundenen Hirnregionen unterschiedlich in den Entscheidungsprozess involviert sind, je nach Menge an verfügbarer sensorischer Information. In der zweiten Studie (Hebart et al., submitted) wurde gezeigt, dass sich eine Repräsentation der Entscheidungsvariable (EV) im fronto-parietalen Assoziationskortex finden lässt. Ferner weist die EV im rechten ventrolateralen präfrontalen Kortex (vlPFC) einen spezifischen Zusammenhang mit konfidenzbezogenen Hirnsignalen im ventralen Striatum auf. Die Ergebnisse deuten darauf hin, dass Konfidenz aus der EV im vlPFC berechnet wird. In der dritten Studie (Christophel et al., 2012) wurde gezeigt, dass der Kurzzeitgedächtnisinhalt im visuellen und posterioren parietalen Kortex, nicht jedoch im präfrontalen Kortex repräsentiert wird. Diese Ergebnisse lassen vermuten, dass der Gedächtnisinhalt in denselben Regionen enkodiert wird, die auch perzeptuelle Entscheidungen repräsentieren können. Zusammenfassend geben die hier errungenen Erkenntnisse Aufschluss über den neuronalen Code des perzeptuellen Entscheidens von Menschen und stellen ein vollständigeres Verständnis der zugrundeliegenden Prozesse in Aussicht.
... Conversely, categorical recognition effects were evidenced in medial frontal regions, including the dorsal anterior cingulate cortex (dACC). However, none of these brain effects, albeit consistent with previous neuroimaging results (Carlson et al., 2006;James et al., 2000;Ploran et al., 2007Ploran et al., , 2011, was found to be influenced by the emotional content of the scenes. ...
... Activity in the dACC for neutral scenes was low and close to baseline until one image before recognition, when a sharp increase took place (see Fig. 6A). This specific response profile for the dACC, which is consistent with the involvement of this medial frontal region in higher-order decision making processes (Bush et al., 2002;Ridderinkhof et al., 2004;Seo and Lee, 2007), was already found in previous imaging studies looking at accumulation of evidence processes taking place during object or scene recognition (Ploran et al., 2007(Ploran et al., , 2011Wheeler et al., 2008). This sharp increase in the dACC close to recognition could reflect either uncertainty or conflict, given the urge to stop the stimulus sequence and take a decision before the sequence comes to an end, while the accumulated sensory evidence may not be completed yet (Anderson et al., 2009;Philiastides and Sajda, 2007;Sohn et al., 2007). ...
Visual scene recognition is a proactive process through which contextual cues and top-down expectations facilitate the extraction of invariant features. Whether the emotional content of the scenes exerts a reliable influence on these processes or not, however, remains an open question. Here, topographic ERP mapping analysis and a distributed source localization method were used to characterize the electrophysiological correlates of proactive processes leading to scene recognition, as well as the potential modulation of these processes by memory and emotion. On each trial, the content of a complex neutral or emotional scene was progressively revealed, and participants were asked to decide whether this scene had previously been encountered or not (delayed match-to-sample task). Behavioral results showed earlier recognition for old compared to new scenes, as well as delayed recognition for emotional vs. neutral scenes. Electrophysiological results revealed that, ~400 ms following stimulus onset, activity in ventral object-selective regions increased linearly as a function of accumulation of perceptual evidence prior to recognition of old scenes. The emotional content of the scenes had an early influence in these areas. By comparison, at the same latency, the processing of new scenes was mostly achieved by dorsal and medial frontal brain areas, including the anterior cingulate cortex and the insula. In the latter region, emotion biased recognition at later stages, likely corresponding to decision making processes. These findings suggest that emotion can operate at distinct and multiple levels during proactive processes leading to scene recognition, depending on the extent of prior encounter with these scenes.
... hand movements versus saccades), indicating that it is relatively abstract and not tied to specific motor effectors (Ho et al., 2009;Liu and Pleskac, 2011). FMRI studies using a variety of tasks have found multiple cortical regions that are sensitive to the accumulation of information; in addition to the parietal lobe, these include the dorsolateral prefrontal cortex (Domenech and Dreher, 2010;Ploran et al., 2007), insula (Ho et al., 2009), and higher order visual cortex in the inferior temporal lobe (Ploran et al., 2007;Ploran et al., 2011). ...
... It is an open question whether these systems are equally as flexible in switching between accumulation tasks. Future research may extend and adapt the fMRI methods already developed to examine accumulation of information in perceptual decision making (Ploran et al., 2007;Ploran et al., 2011). For example, studies could examine flexibility by having subjects switch between perceptual decision making tasks (e.g., switch between dot movement and color identification tasks), or between perceptual identification and categorization tasks. ...
We rarely, if ever, repeatedly encounter exactly the same situation. This makes generalization crucial for real world decision making. We argue that categorization, the study of generalizable representations, is a type of decision making, and that categorization learning research would benefit from approaches developed to study the neuroscience of decision making. Similarly, methods developed to examine generalization and learning within the field of categorization may enhance decision making research. We first discuss perceptual information processing and integration, with an emphasis on accumulator models. We then examine learning the value of different decision making choices via experience, emphasizing reinforcement learning modeling approaches. Next we discuss how value is combined with other factors in decision making, emphasizing the effects of uncertainty. Finally, we describe how a final decision is selected via thresholding processes implemented by the basal ganglia and related regions. We also consider how memory related functions in the hippocampus may be integrated with decision making mechanisms and contribute to categorization.
... Overall, the LIT has important neurobiological implications as it highlights the need to differentiate between 2 interrelated but largely separate accumulation processes that are likely to take place in different brain networks [70] or even heterogenous neural populations [71]. While the former could be independent of sensory and response modality [15,39,72,73], the latter would emerge from structures controlling the specific motor effectors involved in implementing the decision [15,22,74], consistent with an embodied cognition model [21]. ...
Sensorimotor decision-making is believed to involve a process of accumulating sensory evidence over time. While current theories posit a single accumulation process prior to planning an overt motor response, here, we propose an active role of motor processes in decision formation via a secondary leaky motor accumulation stage. The motor leak adapts the "memory" with which this secondary accumulator reintegrates the primary accumulated sensory evidence, thus adjusting the temporal smoothing in the motor evidence and, correspondingly, the lag between the primary and motor accumulators. We compare this framework against different single accumulator variants using formal model comparison, fitting choice, and response times in a task where human observers made categorical decisions about a noisy sequence of images, under different speed-accuracy trade-off instructions. We show that, rather than boundary adjustments (controlling the amount of evidence accumulated for decision commitment), adjustment of the leak in the secondary motor accumulator provides the better description of behavior across conditions. Importantly, we derive neural correlates of these 2 integration processes from electroencephalography data recorded during the same task and show that these neural correlates adhere to the neural response profiles predicted by the model. This framework thus provides a neurobiologically plausible description of sensorimotor decision-making that captures emerging evidence of the active role of motor processes in choice behavior.
... Overall, the LIT has important neurobiological implications as it highlights the need to differentiate between two interrelated but largely separate accumulation processes that are likely to take place in different brain networks. While the former could be independent of sensory and response modality (Heekeren et al., 2006;Philiastides et al., 2011;Filimon et al., 2013;Ploran et al., 2011), the latter would emerge from structures controlling the specific motor effectors involved in implementing the decision (Tosoni et al., 2008;Filimon et al., 2013;Donner et al., 2009), consistent with an embodied cognition model (Cisek & Pastor-Bernier, 2014). ...
Most contemporary theories of sensorimotor decision-making formalize the process leading up to a decision as a gradual accumulation of stimulus information over time. In the currently prevailing view, the amount of accumulated evidence required for a decision is independent of the amount of sensory evidence presented by the stimulus, and once that level is reached, a choice is categorically communicated to the motor system to execute an overt response. Recent experiments cast doubt on these assumptions. We adopt a broader perspective by specifying a model with both an evidence and a motor accumulation process. The evidence accumulation signal feeds into a leaky motor accumulator, and it is on the level of the motor accumulation that the final decision criterion is set. We show that this alternative theory describes the behavioral data better, especially when it comes to urgency manipulations, and its neural correlates can be derived from EEG signatures involving systems of both evidence and motor accumulation.
... Perceptual decision-making processes have been associated with activity in distinct largescale cortical networks or systems based on functional neuroimaging studies (Gratton et al., 2016;Hanks and Summerfield, 2017). Evidence accumulation occurs with a build-up of activity within a fronto-parietal network that ceases when object recognition occurs, in contrast with primary sensory cortex activity that remains elevated throughout stimulus presentations (Brosnan et al., 2020;Gratton et al., 2016;Ploran et al., 2011Ploran et al., , 2007; c.f., Rowe et al., 2010). More cautious or higher decision criteria occur with increased activity in cingulo-opercular regions during visual judgement tasks (Cavanagh et al., 2011;Rahnev et al., 2016;Van Maanen et al., 2011). ...
Extensive increases in cingulo-opercular frontal activity are typically observed during speech recognition in noise tasks. This elevated activity has been linked to a word recognition benefit on the next trial, termed “adaptive control,” but how this effect might be implemented has been unclear. The established link between perceptual decision making and cingulo-opercular function may provide an explanation for how those regions benefit subsequent word recognition. In this case, processes that support recognition such as raising or lowering the decision criteria for more accurate or faster recognition may be adjusted to optimize performance on the next trial. The current neuroimaging study tested the hypothesis that pre-stimulus cingulo-opercular activity reflects criterion adjustments that determine how much information to collect for word recognition on subsequent trials. Participants included middle-age and older adults (N = 30; age = 58.3 ± 8.8 years; m ± sd) with normal hearing or mild sensorineural hearing loss. During a sparse fMRI experiment, words were presented in multitalker babble at +3 dB or +10 dB signal-to-noise ratio (SNR), which participants were instructed to repeat aloud. Word recognition was significantly poorer with increasing participant age and lower SNR compared to higher SNR conditions. A perceptual decision-making model was used to characterize processing differences based on task response latency distributions. The model showed that significantly less sensory evidence was collected (i.e., lower criteria) for lower compared to higher SNR trials. Replicating earlier observations, pre-stimulus cingulo-opercular activity was significantly predictive of correct recognition on a subsequent trial. Individual differences showed that participants with higher criteria also benefitted the most from pre-stimulus activity. Moreover, trial-level criteria changes were significantly linked to higher versus lower pre-stimulus activity. These results suggest cingulo-opercular cortex contributes to criteria adjustments to optimize speech recognition task performance.
... Since fMRI distorts the timescale, one strategy to increase chronometric sensitivity is to modify a paradigm's timescale to be more compatible with that of the Blood-Oxygen-Level-Dependent (BOLD) signal (E. Formisano et al., 2002;Gratton et al., 2017;Ploran et al., 2007;Ploran, Tremel, Nelson, & Wheeler, 2011;Richter et al., 2000). However, fMRI's timescale distortion poses a problem illustrated by the following scenario. ...
The timeline of brain-wide neural activity relative to a behavioral event is crucial when decoding the neural implementation of a cognitive process. Yet, fMRI assesses neural processes indirectly via delayed and regionally variable hemodynamics. This method-inherent temporal distortion impacts the interpretation of behavior-linked neural timing. Here we describe a novel behavioral protocol that aims at disentangling the BOLD dynamics of the pre-and post-response periods in Response Time tasks. We tested this response-locking protocol in a perceptual decision-making (random dot) task. Increasing perceptual difficulty produced expected activity increases over a broad network involving the lateral/medial prefrontal cortex and the anterior insula. However, response-locking revealed a previously unreported functional dissociation within this network. preSMA and anterior premotor cortex (prePMV) showed post-response activity modulations while anterior insula and anterior cingulate cortex did not. Furthermore, post-response BOLD activity at preSMA showed a modulation in timing but not amplitude while this pattern was reversed at prePMV. These timeline dissociations with response-locking thus revealed three functionally distinct sub-networks in what was seemingly one shared distributed network modulated by perceptual difficulty. These findings suggest that our novel response-locked protocol could boost the timing-related sensitivity of fMRI.
... Accuracy was reflected in the cuneus and cerebellum. The cuneus has previously been implicated in learning rates (Payzan-LeNestour et al., 2013) and belief updating (Kobayashi and Hsu, 2017), in line with results in our study and has also been implicated in perceptual evidence accumulation (Ploran et al., 2011;FitzGerald et al., 2015), however this region also correlated with stimulus-bound surprise in another fMRI study (O'Reilly et al., 2013). The cerebellum on the other hand showed the strongest response to information gain. ...
Uncertainty presents a problem for both human and machine decision-making. While utility maximization has traditionally been viewed as the motive force behind choice behavior, it has been theorized that uncertainty minimization may supersede reward motivation. Beyond reward, decisions are guided by belief, i.e., confidence-weighted expectations. Evidence challenging a belief evokes surprise, which signals a deviation from expectation (stimulus-bound surprise) but also provides an information gain. To support the theory that uncertainty minimization is an essential drive for the brain, we probe the neural trace of uncertainty-related decision variables, namely confidence, surprise, and information gain, in a discrete decision with a deterministic outcome. Confidence and surprise were elicited with a gambling task administered in a functional magnetic resonance imaging experiment, where agents start with a uniform probability distribution, transition to a non-uniform probabilistic state, and end in a fully certain state. After controlling for reward expectation, we find confidence, taken as the negative entropy of a trial, correlates with a response in the hippocampus and temporal lobe. Stimulus-bound surprise, taken as Shannon information, correlates with responses in the insula and striatum. In addition, we also find a neural response to a measure of information gain captured by a confidence error, a quantity we dub accuracy. BOLD responses to accuracy were found in the cerebellum and precuneus, after controlling for reward prediction errors and stimulus-bound surprise at the same time point. Our results suggest that, even absent an overt need for learning, the human brain expends energy on information gain and uncertainty minimization.
... Given the regular presentation of pictures of the same valence category, one could wonder whether the observed 2 Hz SSVEP response is a consequence of predictability rather than perceptual categorization. Indeed, recent studies have shown that temporal expectations could aid visual facilitation of regularly presented items (Breska & Deouell, 2014;Cravo, Haddad, Claessens, & Baldo, 2013), and that perceptual expectations may influence recognition processes at different stages (Carlson, Grol, & Verstraten, 2006;Ploran, Tremel, Nelson, & Wheeler, 2011;Summerfield & de Lange, 2014;Summerfield & Egner, 2009) depending on the emotional connotation of the stimuli (Barrett & Bar, 2009). Temporal expectations might indeed have had an impact on previous studies (Peyk, Schupp, Keil, Elbert, & Junghöfer, 2009) reporting emotional cue extraction up to 12 Hz (e.g., 83 ms per image), because of the presentation of simple and predictable stimulus sequences (e.g., pleasant -neutral -pleasant -neutral -…). ...
Adaptive behavior requires the rapid extraction of behaviorally relevant information in the environment, with particular emphasis on emotional cues. However, the speed of emotional feature extraction from complex visual environments is largely undetermined. Here we use objective electrophysiological recordings in combination with frequency tagging to demonstrate that the extraction of emotional information from neutral, pleasant, or unpleasant naturalistic scenes can be completed at a presentation speed of 167 ms (i.e., 6 Hz) under high perceptual load. Emotional compared to neutral pictures evoked enhanced electrophysiological responses with distinct topographical activation patterns originating from different neural sources. Cortical facilitation in early visual cortex was also more pronounced for scenes with pleasant compared to unpleasant or neutral content, suggesting a positivity offset mechanism dominating under conditions of rapid scene processing. These results significantly advance our knowledge of complex scene processing in demonstrating rapid integrative content identification, particularly for emotional cues relevant for adaptive behavior in complex environments.
... The purpose of the combined-events GLM was primarily to validate the effectiveness of the separated-events GLM. We also ran a second version of the separated-events GLM, in which stimulus events were coded by Fast (>median) and Slow (<median) RTs, to attempt to replicate the finding that the rising slope of the BOLD signal in ITC was steeper on Fast than Slow RT trials, a pattern that is consistent with an accumulation-to-boundary account (see Supplementary Materials) 1,16 . ...
As we gather noisy sensory information from the environment, prior knowledge about the likely cause(s) of sensory input can be leveraged to facilitate perceptual judgments. Here, we investigated the computational and neural manifestation of cued expectations in human subjects as they performed a probabilistic face/house discrimination task in which face and house stimuli were preceded by informative or neutral cues. Drift-diffusion modeling of behavioral data showed that cued expectations biased both the baseline (pre-sensory) and drift-rate (post-sensory) of evidence accumulation. By employing a catch-trial functional MRI design we were able to isolate neural signatures of expectation during pre- and post-sensory stages of decision processing in face- and house-selective areas of inferior temporal cortex (ITC). Cue-evoked timecourses were modulated by cues in a manner consistent with a pre-sensory prediction signal that scaled with probability. Sensory-evoked timecourses resembled a prediction-error signal, greater in magnitude for surprising than expected stimuli. Individual differences in baseline and drift-rate biases showed a clear mapping onto pre- and post-sensory fMRI activity in ITC. These findings highlight the specificity of perceptual expectations and provide new insight into the convergence of top-down and bottom-up signals in ITC and their distinct interactions prior to and during sensory processing.
... The functions that the brain utilizes to integrate such sensory information over time (Gold and Shadlen 2007;Heekeren et al. 2008) and parcel it into abstract categories (Freedman and Assad 2011) are part of the process known as perceptual decision making. Previous neuroimaging investigations have used visual (Heekeren et al. 2004;Grinband et al. 2006;Ho et al. 2009;Ploran et al. 2011), auditory (Binder et al. 2004;Kaiser et al. 2007;Myers et al. 2009;Lee et al. 2012), and tactile (Pleger et al. 2006;Preuschhof et al. 2006;Van Kemenade et al. 2014) stimulation to probe the cortical mechanisms that underlie the process of categorization in the human brain. Furthermore, some studies have even sought to understand perceptual decisions and categorical representations irrespective of sensory modalities, providing evidence that frontal (Ivanoff et al. 2009;Noppeney et al. 2010;Tamber-Rosenau et al. 2013), temporal (Simanova et al. 2014), and parietal (O'Connell et al. 2012;Handjaras et al. 2017;Levine and Schwarzbach 2017) regions are implicated in some form of "supramodal" information representations (Binder 2016). ...
Perceptual decision making is the cognitive process wherein the brain classifies stimuli into abstract categories for more efficient downstream processing. A system that, during categorization, can process information regardless of the information’s original sensory modality (i.e., a supramodal system) would have a substantial advantage over a system with dedicated processes for specific sensory modalities. While many studies have probed decision processes through the lens of one sensory modality, it remains unclear whether there are such supramodal brain areas that can flexibly process task-relevant information regardless of the original “format” of the information. To investigate supramodality, one must ensure that supramodal information exists somewhere within the functional architecture by rendering information from multiple sensory systems necessary but insufficient for categorization. To this aim, we tasked participants with categorizing auditory and tactile frequency-modulated sweeps according to learned, supramodal categories in a delayed match-to-category paradigm while we measured their blood-oxygen-level dependent signal with functional MRI. To detect supramodal information, we implemented a set of cross-modality pattern classification analyses, which demonstrated that the left caudate nucleus encodes category-level information but not stimulus-specific information (such as spatial directions and stimulus modalities), while the right inferior frontal gyrus, showing the opposite pattern, encodes stimulus-specific information but not category-level information. Given our paradigm, these results reveal abstract representations in the brain that are independent of motor, semantic, and sensory-specific processing, instead reflecting supramodal, categorical information, which points to the caudate nucleus as a locus of cognitive processes involved in complex behavior.
... However, we agree with Potts and Tucker (2001), suggesting that the parietal areas process the physical features of the stimuli (modalityspecific activity), while the anterior areas are involved in higher level of processing (multimodal activity), such as mapping between the stimulus and the associated choice. However, the detection of task-relevant stimuli requires the interaction between parietal and frontal areas (Liu and Pleskac 2011;Ploran et al. 2011;. ...
Using two independent electrical neuroimaging techniques (BESA and sLORETA), we tested a fMRI-seeded source modeling indicating that in visual discriminative tasks the anterior insula (aIns) participates in the generation of three prefrontal ERP components: the pN1 (at 115 ms), the pP1 (at 170 ms), and the pP2 (at 300 ms). This latter component represented the focus of the present study. Results showed that the pP2 had different activation profiles across hemispheres. The left aIns activity peaked at 420 ms (30 ms before the response) for both Go and No-go trials, that is independently from the ultimate choice (response or inhibition). The right aIns activity started at about 250 ms and progressively increased for a time interval extending after the motor response; its amplitude was larger in case of Go than No-go stimuli. We suggest that the activation of the left aIns reflected the timing of the decision, and the right aIns the categorization and the performance monitoring processes. A control experiment requiring simple (not discriminative) motor response revealed that the pP2 and the aIns activity were nearly absent after the 250 ms; this result confirmed that the aIns activity at this stage is associated with the decisional processes, and not with the motor response per se. The present investigation shed new lights on the insular contribution to perceptual decision-making, and opens to the possibility of assessing the aIns activity via ERP analysis.
... One way parietal cortex could "clean up" noisy representations is through accumulating evidence to aid in perceptual decision-making 34 . Children with reading disability may not be able to make efficient use of working memory to accumulate sensory evidence for mapping orthographic and phonologic information (Poor Readers and Poor Decoders) or for following the meaning of connected speech or text (Poor Readers and Poor Comprehenders). ...
Dyslexia is a developmental disorder in reading that exhibits varied patterns of expression across children. Here we examined the degree to which different kinds of reading disabilities (defined as profiles or patterns of reading problems) contribute to brain morphology results in Jacobian determinant images that represent local brain shape and volume. A matched-pair brain morphometry approach was used to control for confounding from brain size and research site effects in this retrospective multi-site study of 134 children from eight different research sites. Parietal operculum, corona radiata, and internal capsule differences between cases and controls were consistently observed across children with evidence of classic dyslexia, specific comprehension deficit, and language learning disability. Thus, there can be common brain morphology findings across children with quite varied reading disability profiles that we hypothesize compound the developmental difficulties of children with unique reading disability profiles and reasons for their reading disability.
... A natural starting point for such a theory can be found within the family of models involving sequential analysis, a statistical decision process that can be viewed as an extension of SDT into the time domain ( Fig. 2A). Enthusiasm for SA models-which include bounded evidence accumulation, drift-diffusion, and race models-derives from their effectiveness in explaining choice and RT distributions across many domains 5 (Busemeyer 1985;Luce 1986;Reddi et al. 2003;Ratcliff and Smith 2004;Krajbich and Rangel 2011;DasGupta et al. 2014), as well as their robust neural correlates Gold and Shadlen 2007;Heekeren et al. 2008;Ploran et al. 2011). When monkeys are trained to report the outcome of a decision with an eye movement, neurons related to oculomotor planning show persistent activity resembling the accumulation of noisy evidence toward a threshold or bound (Hanes and Schall 1996;Shadlen and Newsome 1996;Kim and Shadlen 1999;Horwitz and Newsome 2001;Roitman and Shadlen 2002;Ratcliff et al. 2003). ...
The quantitative study of decision-making has traditionally rested on three key behavioral measures: accuracy, response time, and confidence. Of these, confidence-defined as the degree of belief, prior to feedback, that a decision is correct-is least well understood at the level of neural mechanism, although recent years have seen a surge in interest in the topic among theoretical and systems neuroscientists. Here we review some of these developments and highlight a particular candidate mechanism for assigning confidence in a perceptual decision. The mechanism is appealing because it is rooted in the same decision-making framework-bounded accumulation of evidence-that successfully explains accuracy and reaction time in many tasks, and it is validated by neurophysiology and microstimulation experiments.
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... Neural activity in the lateral intraparietal area has been studied exten- sively in the context of perceptual decision-making tasks, and activity in these neurons has been related to continuously-graded integration of sensory evidence (e.g., Shadlen and Newsome, 2001;Bollimunta et al., 2012;see Gold and Shadlen, 2007) as well as the degree of confidence in perceptual decisions (Kiani and Shadlen, 2009). In humans, BOLD activity in the parietal cortex -specifi- cally, the intraparietal sulcus and inferior parietal lobule -has similarly been related to accumulation of sensory evidence in the service of perceptual decision-making (e.g., Heekeren et al., 2006;Ploran et al., 2007Ploran et al., , 2011Kayser et al., 2010;Liu and Pleskac, 2011). Although on markedly different timescales and levels of analysis, these behavioral and neural results raise the possibility that graded signals in parietal areas may be related to graded levels of strength-based perception. ...
... These neurons accelerate their firing at a rate proportional to the motion coherence in a random dot stimulus until reaching a set threshold at decision time and returning to baseline. In addition, human neuroimaging studies have localized patterns of evidence accumulation in the dorsolateral prefrontal cortex (dlPFC) (Bowman, Kording, & Gottfried, 2012;Heekeren, Marrett, Bandettini, & Ungerleider, 2004;Ploran et al., 2007;Philiastides, Auksztulewicz, Heekeren, & Blankenburg, 2011;Ploran, Tremel, Nelson, & Wheeler, 2011). However, due in part to the generality and simple assumptions of the diffusion model, it has proven more challenging to characterize the neural implementation of other proposed decision mechanisms, such as those that mediate decision bias. ...
... Consistent with the above ideas, the findings in this study accord well with other studies that have included duration considerations. Activity in the anterior insula in this study, for example, correlates well with activity that defines ''decision commitment regions'' in Ploran et al. [32] – but see also [7] – while other areas that show stronger effects of duration (PM, IFS, mIPS) correspond more strongly to their accumulator regions. In the larger sense, these data also provide quantitative evidence for the commonly held idea that perceptual decisions identify a large-scale anterior-posterior gradient within the brain. ...
In perceptual decision making, the selection of an appropriate action depends critically on an organism's ability to use sensory inputs to accumulate evidence for a decision. However, differentiating decision-related processes from effects of "time on task" can be difficult. Here we combine the response signal paradigm, in which the experimenter rather than the subject dictates the time of the response, and independent components analysis (ICA) to search for signatures consistent with time on task and decision making, respectively, throughout the brain. Using this novel approach, we identify two such independent components from BOLD activity related to a random dot motion task: one sensitive to the main effect of stimulus duration, and one to both the main effect of motion coherence and its interaction with duration. Furthermore, we demonstrate that these two components are expressed differently throughout the brain, with activity in occipital regions most reflective of the former, activity within intraparietal sulcus modulated by both factors, and more anterior regions including the anterior insula, pre-SMA, and inferior frontal sulcus driven almost exclusively by the latter. Consistent with these ICA findings, cluster analysis identifies a posterior-to-anterior gradient that differentiates regions sensitive to time on task from regions whose activity is strongly tied to motion coherence. Together, these findings demonstrate that progressively more anterior regions are likely to participate in progressively more proximate decision-related processes.
... Functionally, this activation may have reflected the selection of details regarding the probe representation to determine whether the item was old or new. Previous research has demonstrated that similar left VLPFC areas are involved in evidence accumulation in the perceptual domain (Ploran et al., 2007(Ploran et al., , 2011. Furthermore, we demonstrated that during verbal STM retrieval, the left VLPFC shows functional connectivity with various sources of visual, semantic, and phonological information that may underlie evidence accumulation processes in STM . ...
Recent research has suggested that short-term memory (STM) can be partitioned into three distinct states. By this model, a single item is held in the focus of attention making it available for immediate processing (focus of attention), a capacity-limited set of additional items is actively maintained for future processing (direct access region), and other recently presented information is passively active, but can nevertheless influence ongoing cognition (activated portion of long-term memory). While there is both behavioral and neural support for this 3-state model in verbal STM, it is unclear whether the model generalizes to non-verbal STM. Here, we tested a 3-state model of visual STM using fMRI. We found a triple dissociation of regions involved in the access of each hypothesized state. The inferior parietal cortex mediated access to the focus of attention, the medial temporal lobe (MTL) including the hippocampus mediated access to the direct access region, and the left ventrolateral prefrontal cortex (VLPFC) mediated access to the activated portion of long-term memory. Direct comparison with previously collected verbal STM data revealed overlapping neural activations involved in the access of each state across different forms of content suggesting that mechanisms of access are domain general. These data support a 3-state model of STM.
... As noted above, the AG region displayed differential strength-based effects early in the time course for foils, suggesting that the momentary signal in this region may be related to the impending memory decision. This finding is similar to the patterns of accumulating activity in a perceptual identification task where participants identify an item that is masked and slowly revealed (Ploran et al. 2007;Wheeler et al. 2008;Ploran et al. 2011). In those studies, the onset of activity in left parietal cortex ROIs occurred early in the trial and increased at a rate that correlated with the time of identification: activity increased faster when identification occurred earlier in the trial. ...
Differentiation models of recognition memory predict a strength-based mirror effect in the distributions of subjective memory strength. Subjective memory strength should increase for targets and simultaneously decrease for foils following a strongly encoded list compared with a weakly encoded list. An alternative explanation for the strength-based mirror effect is that participants adopt a stricter criterion following a strong list than a weak list. Behavioral experiments support the differentiation account. The purpose of this study was to identify the neural bases for these differences. Encoding strength was manipulated (strong, weak) in a rapid event-related fMRI paradigm. To investigate the effect of retrieval context on foils, foils were presented in test blocks containing strong or weak targets. Imaging analyses identified regions in which activity increased faster for foils tested after a strong list than a weak list. The results are interpreted in support of a differentiation account of memory and are suggestive that the angular gyrus plays a role in evaluating evidence related to the memory decision, even for new items.
... Guerin and Miller 2011). This interpretation is supported by prior research suggesting that IPS activity during perceptual decisions in humans (Ploran et al. 2007; Heekeren et al. 2008; Tosoni et al. 2008; Kayser et al. 2010; Ploran et al. 2011, cf. Ho et al. 2009 Guerin and Miller 2011 ) and lateral intraparietal activity in nonhuman primates ( Newsome 1996, 2001; Schall 2003; Kiani et al. 2008; Kiani and Shadlen 2009) is associated with the accumulation of evidence to guide perceptual decision making. ...
While attention is critical for event memory, debate has arisen regarding the extent to which posterior parietal cortex (PPC) activation during episodic retrieval reflects engagement of PPC-mediated mechanisms of attention. Here, we directly examined the relationship between attention and memory, within and across subjects, using functional magnetic resonance imaging attention-mapping and episodic retrieval paradigms. During retrieval, 4 functionally dissociable PPC regions were identified. Specifically, 2 PPC regions positively tracked retrieval outcomes: lateral intraparietal sulcus (latIPS) indexed graded item memory strength, whereas angular gyrus (AnG) tracked recollection. By contrast, 2 other PPC regions demonstrated nonmonotonic relationships with retrieval: superior parietal lobule (SPL) tracked retrieval reaction time, consistent with a graded engagement of top-down attention, whereas temporoparietal junction displayed a complex pattern of below-baseline retrieval activity, perhaps reflecting disengagement of bottom-up attention. Analyses of retrieval effects in PPC topographic spatial attention maps (IPS0-IPS5; SPL1) revealed that IPS5 and SPL1 exhibited a nonmonotonic relationship with retrieval outcomes resembling that in the SPL region, further suggesting that SPL activation during retrieval reflects top-down attention. While demands on PPC attention mechanisms vary during retrieval attempts, the present functional parcellation of PPC indicates that 2 additional mechanisms (mediated by latIPS and AnG) positively track retrieval outcomes.
... When discriminability is very low, medial parietal regions, as well as medial prefrontal regions, may become informative about decision outcomes, because the recent choice history might prime the decision system and magnify choice-related preexisting activity. Consistent with this hypothesis, areas in prefrontal cortex and posterior parietal cortex (Heekeren et al., 2004; Philiastides et al., 2011; Ploran et al., 2011), predominantly in monkey parietal area LIP, have been linked theoretically to diffusive information accumulation (Shadlen and Newsome, 2001; Mazurek et al., 2003; Gold and Shadlen, 2007), decision confidence (Kiani and Shadlen, 2009), as well as to the representation of generic categorical associations (Fitzgerald et al., 2011 ). Our EEGdecoding approach lacks the spatial resolution to identify the sources of information encoding precisely. ...
Perceptual decision making is believed to be driven by the accumulation of sensory evidence following stimulus encoding. More controversially, some studies report that neural activity preceding the stimulus also affects the decision process. We used a multivariate pattern classification approach for the analysis of the human electroencephalogram (EEG) to decode choice outcomes in a perceptual decision task from spatially and temporally distributed patterns of brain signals. When stimuli provided discriminative information, choice outcomes were predicted by neural activity following stimulus encoding; when stimuli provided no discriminative information, choice outcomes were predicted by neural activity preceding the stimulus. Moreover, in the absence of discriminative information, the recent choice history primed the choices on subsequent trials. A diffusion model fitted to the choice probabilities and response time distributions showed that the starting point of the evidence accumulation process was shifted toward the previous choice, consistent with the hypothesis that choice priming biases the accumulation process toward a decision boundary. This bias is reflected in prestimulus brain activity, which, in turn, becomes predictive of future decisions. Our results provide a model of how non-stimulus-driven decision making in humans could be accomplished on a neural level.
Objective: Decision-making is an integrative process during which multiple sources of available evidence are combined into a singular response. Importantly, subconscious processes occur in perceptual decisions that may influence interpretations of visually displayed data such as fetal heart rate tracings (FHRT), which are typically presented together for twins. To examine the potential impact of subconscious perceptual influences on fetal well-being, differences in assessments of FHRTs for twin gestations presented singly or paired were evaluated for baseline fetal heart rate, variability, accelerations, decelerations, and overall concern.
Study design: Obstetrical nurses (N = 27) assessed FHRTs from 20 twin gestations (each of which had at least one live birth with a 5-min Apgar <7) presented either on the same tracing or as singletons on separate tracings. Nurses were naïve to the fact that the fetal heart rate tracings presented in the unpaired condition were the same as those presented in the paired condition. Assessments were then compared between the two conditions.
Results: Each nurse participant completed ratings on five metrics for each of 20 twin gestations across two conditions (80 FHRT assessments, 400 metrics total per participant). The intraobserver impact of visual context was calculated as the frequency of changed opinions regarding an individual metric (e.g. variability) between the paired and unpaired contexts for each individual fetal heart rate. Assessments of variability (average Kappa = 0.59), decelerations (average Kappa = 0.34), and overall level of concern (average Kappa = 0.33) were moderately to heavily impacted by viewing condition (unpaired vs. paired FHRT). Analysis of interobserver agreement using intraclass correlations (two-way random effect, absolute agreement) indicates poor agreement on unpaired assessments for both accelerations (ICC = 0.01, 95% CI −0.01–0.04) and decelerations (ICC = 0.22, 95% CI 0.15–0.33). These results are mirrored in poor agreement on paired assessments for both accelerations (ICC = 0.00, 95% CI −0.01–0.03) and decelerations (ICC = 0.27, 95% CI 0.19–0.39). There was moderate agreement on overall level of concern for unpaired assessments (ICC = 0.55, 95% CI 0.44–0.67) and near moderate agreement for the paired condition (ICC = 0.45, 95% CI 0.34–0.58).
Conclusions: The simultaneous presentation of fetal heart rate tracings in twin gestations introduces both intraobserver and interobserver variances in the interpretation of variability, accelerations, and decelerations, likely due to the influence of subconscious perceptual decision-making. This may theoretically affect outcomes in cases in which visual information is nuanced. More research is necessary to determine whether the standard protocol of simultaneous assessment of FHRT in twins is subliminally affected by perceptual decision-making.
Cingulo-opercular activity is hypothesized to reflect an adaptive control function that optimizes task performance through adjustments in attention and behavior, and outcome monitoring. While auditory perceptual task performance appears to benefit from elevated activity in cingulo-opercular regions of frontal cortex before stimuli are presented, this association appears reduced for older adults compared to younger adults. However, adaptive control function may be limited by difficult task conditions for older adults. An fMRI study was used to characterize adaptive control differences while 15 younger (average age = 24 years) and 15 older adults (average age = 68 years) performed a gap detection in noise task designed to limit age-related differences. During the fMRI study, participants listened to a noise recording and indicated with a button-press whether it contained a gap. Stimuli were presented between sparse fMRI scans (TR = 8.6 s) and BOLD measurements were collected during separate listening and behavioral response intervals. Age-related performance differences were limited by presenting gaps in noise with durations calibrated at or above each participant's detection threshold. Cingulo-opercular BOLD increased significantly throughout listening and behavioral response intervals, relative to a resting baseline. Correct behavioral responses were significantly more likely on trials with elevated pre-stimulus cingulo-opercular BOLD, consistent with an adaptive control framework. Cingulo-opercular adaptive control estimates appeared higher for participants with better gap sensitivity and lower response bias, irrespective of age, which suggests that this mechanism can benefit performance across the lifespan under conditions that limit age-related performance differences.
Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task. Behavioral measures were then analyzed with both a drift-diffusion model and a reinforcement learning model. Parameter values from each were then used as fMRI regressors to identify regions whose activity fluctuates with specific cognitive processes described by the models. The patterns of intersecting neural effects across models support two main inferences about the influence of feedback on decision-making. First, frontal, anterior insular, fusiform, and caudate nucleus regions behave like performance monitors, reflecting errors in performance predictions that signal the need for changes in control over decision-making. Second, temporoparietal, supplementary motor, and putamen regions behave like mnemonic storage sites, reflecting differences in learned item values that inform optimal decision choices. As information about optimal choices is accrued, these neural systems dynamically adjust, likely shifting the burden of decision processing from controlled performance monitoring to bottom-up, stimulus-driven choice selection. Collectively, the results provide a detailed perspective on the fundamental ability to use past experiences to improve future decisions.
Effective generalization in a multiple-category situation involves both assessing potential membership in individual categories and resolving conflict between categories while implementing a decision bound. We separated generalization from decision bound implementation using an information integration task in which category exemplars varied over two incommensurable feature dimensions. Human subjects first learned to categorize stimuli within limited training regions, and then, during fMRI scanning, they also categorized transfer stimuli from new regions of perceptual space. Transfer stimuli differed both in distance from the training region prototype and distance from the decision bound, allowing us to independently assess neural systems sensitive to each. Across all stimulus regions, categorization was associated with activity in the extrastriate visual cortex, basal ganglia, and the bilateral intraparietal sulcus. Categorizing stimuli near the decision bound was associated with recruitment of the frontoinsular cortex and medial frontal cortex, regions often associated with conflict and which commonly coactivate within the salience network. Generalization was measured in terms of greater distance from the decision bound and greater distance from the category prototype (average training region stimulus). Distance from the decision bound was associated with activity in the superior parietal lobe, lingual gyri, and anterior hippocampus, whereas distance from the prototype was associated with left intraparietal sulcus activity. The results are interpreted as supporting the existence of different uncertainty resolution mechanisms for uncertainty about category membership (representational uncertainty) and uncertainty about decision bound (decisional uncertainty).
During a perceptual decision, neuronal activity can change as a function of time-integrated evidence. Such neurons may serve as decision variables, signaling a choice when activity reaches a boundary. Because the signals occur on a millisecond timescale, translating to human decision-making using functional neuroimaging has been challenging. Previous neuroimaging work in humans has identified patterns of neural activity consistent with an accumulation account. However, the degree to which the accumulating neuroimaging signals reflect specific sources of perceptual evidence is unknown. Using an extended face/house discrimination task in conjunction with cognitive modeling, we tested whether accumulation signals, as measured using functional magnetic resonance imaging (fMRI), are stimulus-specific. Accumulation signals were defined as a change in the slope of the rising edge of activation corresponding with response time (RT), with higher slopes associated with faster RTs. Consistent with an accumulation account, fMRI activity in face- and house-selective regions in the inferior temporal cortex increased at a rate proportional to decision time in favor of the preferred stimulus. This finding indicates that stimulus-specific regions perform an evidence integrative function during goal-directed behavior and that different sources of evidence accumulate separately. We also assessed the decision-related function of other regions throughout the brain and found that several regions were consistent with classifications from prior work, suggesting a degree of domain generality in decision processing. Taken together, these results provide support for an integration-to-boundary decision mechanism and highlight possible roles of both domain-specific and domain-general regions in decision evidence evaluation.
Copyright © 2014. Published by Elsevier Inc.
Everyday actions often require fast and efficient error detection and error correction. For this, the brain has to accumulate evidence for errors as soon as it becomes available. This study used multivariate pattern classification techniques for event-related potentials to track the accumulation of error-related brain activity before an overt response was made. Upcoming errors in a digit-flanker task could be predicted after the initiation of an erroneous motor response, ∼90 ms before response execution. Channels over motor and parieto-occipital cortices were most important for error prediction, suggesting ongoing perceptual analyses and comparisons of initiated and appropriate motor programmes. Lower response force on error trials as compared to correct trials was observed, which indicates that this early error information was used for attempts to correct for errors before the overt response was made. In summary, our results suggest an early, automatic accumulation of error-related information, providing input for fast correction processes.
Humans can quickly engage a neural network to transform complex visual stimuli into a motor response. Activity from a key region within this network, the intraparietal sulcus (IPS), has been associated with evidence accumulation and motor planning, thus implicating it in sensorimotor transformations. If such transformations occur within a brain region, a key and untested prediction is that neural activity reflecting both the parametric amount of evidence available and the timing of motor planning can be independently manipulated. To investigate these ideas, we constructed a dot-motion discrimination task in which information about response modality (what to use) and response mapping (how to use it) was provided independently either before or after presentation of a dot motion coherence stimulus whose strength varied across trials. Consistent with our hypothesis, activity within IPS covaried with dot motion coherence during the stimulus phase, and as information necessary for the response was delayed, the peak of IPS activity shifted to the response phase. In contrast, areas such as the motion-sensitive region MT+ and the supplementary motor area demonstrated activity limited to the stimulus and response phases of the task, respectively. These results show that activity in IPS correlates with temporally dissociable representations consistent with both evidence accumulation and motor planning, and suggest that IPS is a core component for sensorimotor transformations within the perceptual decision-making network.
Perceptual decision making is the process by which information from sensory systems is combined and used to influence our behavior. In addition to the sensory input, this process can be affected by other factors, such as reward and punishment for correct and incorrect responses. To investigate the temporal dynamics of how monetary punishment influences perceptual decision making in humans, we collected electroencephalography (EEG) data during a perceptual categorization task whereby the punishment level for incorrect responses was parametrically manipulated across blocks of trials. Behaviorally, we observed improved accuracy for high relative to low punishment levels. Using multivariate linear discriminant analysis of the EEG, we identified multiple punishment-induced discriminating components with spatially distinct scalp topographies. Compared with components related to sensory evidence, components discriminating punishment levels appeared later in the trial, suggesting that punishment affects primarily late postsensory, decision-related processing. Crucially, the amplitude of these punishment components across participants was predictive of the size of the behavioral improvements induced by punishment. Finally, trial-by-trial changes in prestimulus oscillatory activity in the alpha and gamma bands were good predictors of the amplitude of these components. We discuss these findings in the context of increased motivation/attention, resulting from increases in punishment, which in turn yields improved decision-related processing.
The extent to which different cognitive processes are "embodied" is widely debated. Previous studies have implicated sensorimotor regions such as lateral intraparietal (LIP) area in perceptual decision making. This has led to the view that perceptual decisions are embodied in the same sensorimotor networks that guide body movements. We use event-related fMRI and effective connectivity analysis to investigate whether the human sensorimotor system implements perceptual decisions. We show that when eye and hand motor preparation is disentangled from perceptual decisions, sensorimotor areas are not involved in accumulating sensory evidence toward a perceptual decision. Instead, inferior frontal cortex increases its effective connectivity with sensory regions representing the evidence, is modulated by the amount of evidence, and shows greater task-positive BOLD responses during the perceptual decision stage. Once eye movement planning can begin, however, an intraparietal sulcus (IPS) area, putative LIP, participates in motor decisions. Moreover, sensory evidence levels modulate decision and motor preparation stages differently in different IPS regions, suggesting functional heterogeneity of the IPS. This suggests that different systems implement perceptual versus motor decisions, using different neural signatures.
When facing perceptual choices under challenging conditions we might believe to be purely guessing. But which brain regions determine the outcome of our guesses? One possibility is that higher-level, domain-general brain regions might help break the symmetry between equal-appearing choices. Here we directly investigated whether perceptual guesses share brain networks with other types of free decisions. We trained an fMRI-based pattern classifier to distinguish between two perceptual guesses and tested whether it was able to predict the outcome of similar non-perceptual choices, as in conventional free choice tasks. Activation patterns in the medial posterior parietal cortex cross-predicted free decisions from perceptual guesses and vice versa. This inter-changeability strongly speaks for a similar neural code for both types of decisions. The posterior parietal cortex might be part of a domain-general system that helps resolve decision conflicts when no choice option is more or less likely or valuable, thus preventing behavioural stalemate.
Studies of autism spectrum disorders (ASDs) reveal dysfunction in the neural systems mediating object processing (particularly
faces) and social cognition, but few investigations have systematically assessed the specificity of the dysfunction. We compared
cortical responses in typically developing adolescents and those with ASD to stimuli from distinct conceptual domains known
to elicit category-related activity in separate neural systems. In Experiment 1, subjects made category decisions to photographs,
videos, and point-light displays of people and tools. In Experiment 2, subjects interpreted displays of simple, geometric
shapes in motion depicting social or mechanical interactions. In both experiments, we found a selective deficit in the ASD
subjects for dynamic social stimuli (videos and point-light displays of people, moving geometric shapes), but not static images,
in the functionally localized lateral region of the right fusiform gyrus, including the fusiform face area. In contrast, no
group differences were found in response to either static images or dynamic stimuli in other brain regions associated with
face and social processing (e.g. posterior superior temporal sulcus, amygdala), suggesting disordered connectivity between
these regions and the fusiform gyrus in ASD. This possibility was confirmed by functional connectivity analysis.
The time course of perceptual choice is discussed in a model of gradual, leaky, stochastic, and competitive information accumulation in nonlinear decision units. Special cases of the model match a classical diffusion process, but leakage and competition work together to address several challenges to existing diffusion, random walk, and accumulator models. The model accounts for data from choice tasks using both time-controlled (e.g., response signal) and standard reaction time paradigms and its adequacy compares favorably with other approaches. A new paradigm that controls the time of arrival of information supporting different choice alternatives provides further support. The model captures choice behavior regardless of the number of alternatives, accounting for the log-linear relation between reaction time and number of alternatives (Hick's law) and explains a complex pattern of visual and contextual priming in visual word identification.
In humans, the anterior insula (aI) has been the topic of considerable research and ascribed a vast number of functional properties by way of neuroimaging and lesion studies. Here, we argue that the aI, at least in part, plays a role in domain-general attentional control and highlight studies (Dosenbach et al. 2006; Dosenbach et al. 2007) supporting this view. Additionally, we discuss a study (Ploran et al. 2007) that implicates aI in processes related to the capture of focal attention. Task-level control and focal attention may or may not reflect information processing supported by a single functional area (within the aI). Therefore, we apply a novel technique (Cohen et al. 2008) that utilizes resting state functional connectivity MRI (rs-fcMRI) to determine whether separable regions exist within the aI. rs-fcMRI mapping suggests that the ventral portion of the aI is distinguishable from more dorsal/anterior regions, which are themselves distinct from more posterior parts of the aI. When these regions are applied to functional MRI (fMRI) data, the ventral and dorsal/anterior regions support processes potentially related to both task-level control and focal attention, whereas the more posterior aI regions did not. These findings suggest that there exists some functional heterogeneity within aI that may subserve related but distinct types of higher-order cognitive processing.
Electronic supplementary material
The online version of this article (doi:10.1007/s00429-010-0260-2) contains supplementary material, which is available to authorized users.
To form perceptual decisions in our multisensory environment, the brain needs to integrate sensory information derived from a common source and segregate information emanating from different sources. Combining fMRI and psychophysics in humans, we investigated how the brain accumulates sensory evidence about a visual source in the context of congruent or conflicting auditory information. In a visual selective attention paradigm, subjects (12 females, 7 males) categorized video clips while ignoring concurrent congruent or incongruent soundtracks. Visual and auditory information were reliable or unreliable. Our behavioral data accorded with accumulator models of perceptual decision making, where sensory information is integrated over time until a criterion amount of information is obtained. Behaviorally, subjects exhibited audiovisual incongruency effects that increased with the variance of the visual and the reliability of the interfering auditory input. At the neural level, only the left inferior frontal sulcus (IFS) showed an "audiovisual-accumulator" profile consistent with the observed reaction time pattern. By contrast, responses in the right fusiform were amplified by incongruent auditory input regardless of sensory reliability. Dynamic causal modeling showed that these incongruency effects were mediated via connections from auditory cortex. Further, while the fusiform interacted with IFS in an excitatory recurrent loop that was strengthened for unreliable task-relevant visual input, the IFS did not amplify and even inhibited superior temporal activations for unreliable auditory input. To form decisions that guide behavioral responses, the IFS may accumulate audiovisual evidence by dynamically weighting its connectivity to auditory and visual regions according to sensory reliability and decisional relevance.
Our ability to make rapid decisions based on sensory information belies the complexity of the underlying computations. Recently, "accumulator" models of decision making have been shown to explain the activity of parietal neurons as macaques make judgments concerning visual motion. Unraveling the operation of a decision-making circuit, however, involves understanding both the responses of individual components in the neural circuitry and the relationships between them. In this functional magnetic resonance imaging study of the decision process in humans, we demonstrate that an accumulator model predicts responses to visual motion in the intraparietal sulcus (IPS). Significantly, the metrics used to define responses within the IPS also reveal distinct but interacting nodes in a circuit, including early sensory detectors in visual cortex, the visuomotor integration system of the IPS, and centers of cognitive control in the prefrontal cortex, all of which collectively define a perceptual decision-making network.
To successfully interact with objects in the environment, sensory evidence must be continuously acquired, interpreted, and used to guide appropriate motor responses. For example, when driving, a red light should motivate a motor command to depress the brake pedal. Single-unit recording studies have established that simple sensorimotor transformations are mediated by the same neurons that ultimately guide the behavioral response. However, it is also possible that these sensorimotor regions are the recipients of a modality-independent decision signal that is computed elsewhere. Here, we used functional magnetic resonance imaging and human observers to show that the time course of activation in a subregion of the right insula is consistent with a role in accumulating sensory evidence independently from the required motor response modality (saccade vs manual). Furthermore, a combination of computational modeling and simulations of the blood oxygenation level-dependent response suggests that this region is not simply recruited by general arousal or by the tonic maintenance of attention during the decision process. Our data thus raise the possibility that a modality-independent representation of sensory evidence may guide activity in effector-specific cortical areas before the initiation of a behavioral response.
Event-related fMRI studies reveal that episodic memory retrieval modulates lateral and medial parietal cortices, dorsal middle frontal gyrus (MFG), and anterior PFC. These regions respond more for recognized old than correctly rejected new words, suggesting a neural correlate of retrieval success. Despite significant efforts examining retrieval success regions, their role in retrieval remains largely unknown. Here we asked the question, to what degree are the regions performing memory-specific operations? And if so, are they all equally sensitive to successful retrieval, or are other factors such as error detection also implicated? We investigated this question by testing whether activity in retrieval success regions was associated with task-specific contingencies (i.e., perceived targetness) or mnemonic relevance (e.g., retrieval of source context). To do this, we used a source memory task that required discrimination between remembered targets and remembered nontargets. For a given region, the modulation of neural activity by a situational factor such as target status would suggest a more domain-general role; similarly, modulations of activity linked to error detection would suggest a role in monitoring and control rather than the accumulation of evidence from memory per se. We found that parietal retrieval success regions exhibited greater activity for items receiving correct than incorrect source responses, whereas frontal retrieval success regions were most active on error trials, suggesting that posterior regions signal successful retrieval whereas frontal regions monitor retrieval outcome. In addition, perceived targetness failed to modulate fMRI activity in any retrieval success region, suggesting that these regions are retrieval specific. We discuss the different functions that these regions may support and propose an accumulator model that captures the different pattern of responses seen in frontal and parietal retrieval success regions.
A theory of discrimination which assumes that subjects compare psychological values evoked by a stimulus to a subjective referent is proposed. Momentary differences between psychological values for the stimulus and the referent are accumulated over time until one or the other of two response thresholds is first exceeded. The theory is analyzed as a random walk bounded between two absorbing barriers. A general solution to response conditioned expected response times is computed and the important role played by the moment generating function (mgf) for increments to the random walk is examined. From considerations of the mgf it is shown that unlike other random walk models [Stone, 1960; Laming, 1968] the present theory does not imply that response conditioned mean correct and error times must be equal. For two fixed stimuli and a fixed referent it is shown that by controlling values of response thresholds, subjects can produce Receiver Operating Characteristics similar or identical to those predicted by Signal Detection Theory, High Threshold Theory, or Low Threshold Theory.
The neural mechanism underlying simple perceptual decision-making in monkeys has been recently conceptualized as an integrative process in which sensory evidence supporting different response options accumulates gradually over time. For example, intraparietal neurons accumulate motion information in favor of a specific oculomotor choice over time. It is unclear, however, whether this mechanism generalizes to more complex decisions that are based on arbitrary stimulus-response associations. In a task requiring arbitrary association of visual stimuli (faces or places) with different actions (eye or hand-pointing movements), we found that activity of effector-specific regions in human posterior parietal cortex reflected the 'strength' of the sensory evidence in favor of the preferred response. These regions did not respond to sensory stimuli per se but integrated sensory evidence toward the decision outcome. We conclude that even arbitrary decisions can be mediated by sensory-motor mechanisms that are completely triggered by contextual stimulus-response associations.
When humans respond to sensory stimulation, their reaction times tend to be long and variable relative to neural transduction
and transmission times. The neural processes responsible for the duration and variability of reaction times are not understood.
Single-cell recordings in a motor area of the cerebral cortex in behaving rhesus monkeys (Macaca mulatta) were used to evaluate two alternative mathematical models of the processes that underlie reaction times. Movements were
initiated if and only if the neural activity reached a specific and constant threshold activation level. Stochastic variability
in the rate at which neural activity grew toward that threshold resulted in the distribution of reaction times. This finding
elucidates a specific link between motor behavior and activation of neurons in the cerebral cortex.
Recent functional neuroimaging results implicate part of the ventral temporal lobe of the brain in face recognition, and have, together with neurophysiological findings, been used as evidence for a face-specific neural module in the brain. Experimental designs, however, have often failed to distinguish between the class of the object used as the stimulus (face or non-face) and the level of categorization at which the stimulus is recognized (the 'basic' level, such as 'bird', at which familiar objects are first recognized, or more subordinate levels - 'sparrow', for example - which require additional perceptual processing). We have used echo-planar functional magnetic resonance imaging to compare brain activation for the matching of non-face objects with subordinate-level and basic-level descriptors.
The additional visual processing required to verify the subordinate level of a picture over its basic level was associated with activation of the fusiform and inferior temporal gyri (FIT) as well as the temporal poles. These areas correspond closely to those previously implicated in the processing of facial images.
Our results indicate that areas of the ventral visual pathway that have been associated with face recognition are sensitive to manipulations of the categorization level of non-face objects. This idea offers an alternative to the dominant view that FIT may be organized according to conceptual categories, and our results establish the importance of manipulating task requirements when evaluating a 'neural module' hypothesis.
An anti-saccade, which is a saccade directed toward a mirror-symmetrical position in the opposite visual field relative to the visual stimulus, involves at least three separate operations: covert orienting, response suppression, and coordinate transformation. The distinction between pro- and anti-saccades can also be applied to pointing. We used fMRI to compare patterns of brain activation during pro- and anti-movements, to determine whether or not additional areas become active during the production of anti-movements. In parietal cortex, an inferior network was active during both saccades and pointing that included three foci along the intraparietal sulcus: 1) a posterior superior parietal area (pSPR), more active during the anti-tasks; 2) a middle inferior parietal area (mIPR), active only during the anti-tasks; and 3) an anterior inferior parietal area (aIPR), equally active for pro- and anti-movement. A superior parietal network was active during pointing but not saccades and included the following: 1) a medial region, active during anti- but not pro-pointing (mSPR); 2) an anterior and medial region, more active during pro-pointing (aSPR); and 3) an anterior and lateral region, equally active for pro- and anti-pointing (lSPR). In frontal cortex, areas selectively active during anti-movement were adjacent and anterior to areas that were active during both the anti- and pro-tasks, i.e., were anterior to the frontal eye field and the supplementary motor area. All saccade areas were also active during pointing. In contrast, foci in the dorsal premotor area, the anterior superior frontal region, and anterior cingulate were active during pointing but not saccades. In summary, pointing with central gaze activates a frontoparietal network that includes the saccade network. The operations required for the production of anti-movements recruited additional frontoparietal areas.
The authors describe and illustrate an integrated trio of software programs for carrying out surface-based analyses of cerebral cortex. The first component of this trio, SureFit (Surface Reconstruction by Filtering and Intensity Transformations), is used primarily for cortical segmentation, volume visualization, surface generation, and the mapping of functional neuroimaging data onto surfaces. The second component, Caret (Computerized Anatomical Reconstruction and Editing Tool Kit), provides a wide range of surface visualization and analysis options as well as capabilities for surface flattening, surface-based deformation, and other surface manipulations. The third component, SuMS (Surface Management System), is a database and associated user interface for surface-related data. It provides for efficient insertion, searching, and extraction of surface and volume data from the database.
The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance
imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made
objects, and nonsense pictures. A distinct pattern of response was found for each stimulus category. The distinctiveness of
the response to a given category was not due simply to the regions that responded maximally to that category, because the
category being viewed also could be identified on the basis of the pattern of response when those regions were excluded from
the analysis. Patterns of response that discriminated among all categories were found even within cortical regions that responded
maximally to only one category. These results indicate that the representations of faces and objects in ventral temporal cortex
are widely distributed and overlapping.
The present study employed event-related fMRI and EEG to investigate the biological basis of the cognitive control of behavior. Using a GO/NOGO task optimized to produce response inhibitions, frequent commission errors, and the opportunity for subsequent behavioral correction, we identified distinct cortical areas associated with each of these specific executive processes. Two cortical systems, one involving right prefrontal and parietal areas and the second regions of the cingulate, underlay inhibitory control. The involvement of these two systems was predicated upon the difficulty or urgency of the inhibition and each was employed to different extents by high- and low-absent-minded subjects. Errors were associated with medial activation incorporating the anterior cingulate and pre-SMA while behavioral alteration subsequent to errors was associated with both the anterior cingulate and the left prefrontal cortex. Furthermore, the EEG data demonstrated that successful response inhibition depended upon the timely activation of cortical areas as predicted by race models of response selection. The results highlight how higher cognitive functions responsible for behavioral control can result from the dynamic interplay of distinct cortical systems.
Recent studies combining psychophysical and neurophysiological experiments in behaving monkeys have provided new insights into how several cortical areas integrate efforts to solve a vibrotactile discrimination task. In particular, these studies have addressed how neural codes are related to perception, working memory and decision making in this model. The primary somatosensory cortex drives higher cortical areas where past and current sensory information are combined, such that a comparison of the two evolves into a behavioural decision. These and other observations in visual tasks indicate that decisions emerge from highly-distributed processes in which the details of a scheduled motor plan are gradually specified by sensory information.
An essential reference book for visual science. Visual science is the model system for neuroscience, its findings relevant to all other areas. This massive collection of papers by leading researchers in the field will become an essential reference for researchers and students in visual neuroscience, and will be of importance to researchers and professionals in other disciplines, including molecular and cellular biology, cognitive science, ophthalmology, psychology, computer science, optometry, and education.
Over 100 chapters cover the entire field of visual neuroscience, from its historical foundations to the latest research and findings in molecular mechanisms and network modeling. The book is organized by topic—different sections cover such subjects as the history of vision science; developmental processes; retinal mechanisms and processes; organization of visual pathways; subcortical processing; processing in the primary visual cortex; detection and sampling; brightness and color; form, shape, and object recognition; motion, depth, and spatial relationships; eye movements; attention and cognition; and theoretical and computational perspectives. The list of contributors includes leading international researchers in visual science.
Bradford Books imprint
Controlled processing is central to episodic memory retrieval. In the present study, neural correlates of sustained, as well as transient, processing components were explored during controlled retrieval using a mixed blocked event-related functional magnetic resonance imaging paradigm. Results from 29 participants suggest that certain regions in prefrontal cortex, including anterior left inferior prefrontal cortex near Brodmann's Area (BA) 45/47 and more posterior and dorsal left prefrontal cortex near BA 44, increase activity on a trial-by-trial basis when high levels of control are required during retrieval. Providing direct evidence for control processes that participate on an ongoing basis, right frontal-polar cortex was strongly associated with a sustained temporal profile during high control retrieval conditions, as were several additional posterior regions, including those within left parietal cortex. These results provide evidence for functional dissociation within prefrontal cortex. Frontal-polar regions near BA 10 associate with temporally extended control processes that may underlie an attentional set, or retrieval mode, during controlled retrieval, whereas more posterior prefrontal regions associate with individual retrieval attempts. In particular, right frontal-polar cortex involvement in sustained processes reconciles a number of disparate findings that have arisen when contrasting blocked-trial paradigms with event-related paradigms.
We recorded the activity of single neurons in the posterior parietal cortex (area LIP) of two rhesus monkeys while they discriminated the direction of motion in random-dot visual stimuli. The visual task was similar to a motion discrimination task that has been used in previous investigations of motion-sensitive regions of the extrastriate cortex. The monkeys were trained to decide whether the direction of motion was toward one of two choice targets that appeared on either side of the random-dot stimulus. At the end of the trial, the monkeys reported their direction judgment by making an eye movement to the appropriate target. We studied neurons in LIP that exhibited spatially selective persistent activity during delayed saccadic eye movement tasks. These neurons are thought to carry high-level signals appropriate for identifying salient visual targets and for guiding saccadic eye movements. We arranged the motion discrimination task so that one of the choice targets was in the LIP neuron's response field (RF) while the other target was positioned well away from the RF. During motion viewing, neurons in LIP altered their firing rate in a manner that predicted the saccadic eye movement that the monkey would make at the end of the trial. The activity thus predicted the monkey's judgment of motion direction. This predictive activity began early in the motion-viewing period and became increasingly reliable as the monkey viewed the random-dot motion. The neural activity predicted the monkey's direction judgment on both easy and difficult trials (strong and weak motion), whether or not the judgment was correct. In addition, the timing and magnitude of the response was affected by the strength of the motion signal in the stimulus. When the direction of motion was toward the RF, stronger motion led to larger neural responses earlier in the motion-viewing period. When motion was away from the RF, stronger motion led to greater suppression of ongoing activity. Thus the activity of single neurons in area LIP reflects both the direction of an impending gaze shift and the quality of the sensory information that instructs such a response. The time course of the neural response suggests that LIP accumulates sensory signals relevant to the selection of a target for an eye movement.
In recent years, many new cortical areas have been identified in the macaque monkey. The number of identified connections between areas has increased even more dramatically. We report here on (1) a summary of the layout of cortical areas associated with vision and with other modalities, (2) a computerized database for storing and representing large amounts of information on connectivity patterns, and (3) the application of these data to the analysis of hierarchical organization of the cerebral cortex. Our analysis concentrates on the visual system, which includes 25 neocortical areas that are predominantly or exclusively visual in function, plus an additional 7 areas that we regard as visual-association areas on the basis of their extensive visual inputs. A total of 305 connections among these 32 visual and visual-association areas have been reported. This represents 31% of the possible number of pathways it each area were connected with all others. The actual degree of connectivity is likely to be closer to 40%. The great majority of pathways involve reciprocal connections between areas. There are also extensive connections with cortical areas outside the visual system proper, including the somatosensory cortex, as well as neocortical, transitional, and archicortical regions in the temporal and frontal lobes. In the somatosensory/motor system, there are 62 identified pathways linking 13 cortical areas, suggesting an overall connectivity of about 40%. Based on the laminar patterns of connections between areas, we propose a hierarchy of visual areas and of somato sensory/motor areas that is more comprehensive than those suggested in other recent studies. The current version of the visual hierarchy includes 10 levels of cortical processing. Altogether, it contains 14 levels if one includes the retina and lateral geniculate nucleus at the bottom as well as the entorhinal cortex and hippocampus at the top. Within this hierarchy, there are multiple, intertwined processing streams, which, at a low level, are related to the compartmental organization of areas V1 and V2 and, at a high level, are related to the distinction between processing centers in the temporal and parietal lobes. However, there are some pathways and relationships (about 10% of the total) whose descriptions do not fit cleanly into this hierarchical scheme for one reason or another. In most instances, though, it is unclear whether these represent genuine exceptions to a strict hierarchy rather than inaccuracies or uncertainties in the reported assignment.
An unresolved question in neuroscience and psychology is how the brain monitors performance to regulate behavior. It has been proposed that the anterior cingulate cortex (ACC), on the medial surface of the frontal lobe, contributes to performance monitoring by detecting errors. In this study, event-related functional magnetic resonance imaging was used to examine ACC function. Results confirm that this region shows activity during erroneous responses. However, activity was also observed in the same region during correct responses under conditions of increased response competition. This suggests that the ACC detects conditions under which errors are likely to occur rather than errors themselves.
The diffusion model for two-choice real-time decisions is applied to four psychophysical tasks. The model reveals how stimulus information guides decisions and shows how the information is processed through time to yield sometimes correct and sometimes incorrect decisions. Rapid two-choice decisions yield multiple empirical measures: response times for correct and error responses, the probabilities of correct and error responses, and a variety of interactions between accuracy and response time that depend on instructions and task difficulty. The diffusion model can explain all these aspects of the data for the four experiments we present. The model correctly accounts for error response times, something previous models have failed to do. Variability within the decision process explains how errors are made, and variability across trials correctly predicts when errors are faster than correct responses and when they are slower.
This chapter presents a mathematical analysis of common modality image registration. Two alternative formulas are compared for computing registration error, the basis of these being the computation of image differences versus ratios. The theory suggests that the difference image method should be numerically more stable as well as more immune to noise. To test the theory, a Hoffman brain phantom is repeatedly scanned in one position, and images of variable statistical quality and spatial frequency content are prepared. These data are then submitted for realignment according to either the difference or the ratio image method. The difference image error function yielded consistently better precision in all but the most blurred data, in which case the two methods performed comparably. The precision advantage of the difference image method was attributable mainly to better determination of angular registration error as a consequence of reduced sensitivity to noise, and therefore greater tolerance of increased spatial frequency bandwidth. These results indicate that a modest improvement in realignment precision can be achieved by the use of error functions based on image differences as opposed to ratios.
A modality‐independent approch for interactive spatial normalization of tomographic images of the human brain is described and its performance evaluated. Spatial normalization is accomplished using a nine‐parameter affine transformation to interactively align and adjust the shape of a subject brain to the reference brain detailed in the 1988 atlas of Talairach et al. A user‐friendly software application was developed using the X‐windows Motif environment to guide the user through this process. This software supports data types from a wide variety of tomographic imagers and produces output in spatially concise formats.
The parameters used for spatial alignment and shape normalization are presented and methods to apply them discussed. Where normalization parameters cannot be obtained directly from the image, as with positron emission tomography (PET), methods for estimating them are given. Evaluation of a new four‐landmark method to fit the AC‐PC line in 16 magnetic resonance imaging (MRI) studies indicated an average difference assessed as the distance between the true and fitted AC‐PC line at four locations of 0.82 mm when using a 2‐D weighted fit. The same landmarks were evaluated using lower spatial resolution PET‐like images simulated from the 16 MRI studies. The difference between the PET and MR image volumes following alignment was minimal, with mean rotational differences of less than 0.2 deg and mean translational differences of generally less than 2 mm. Spatial normalization is illustrated for single photon emission computed tomography (SPECT), X‐ray computed tomography (CT), PET, and MR image volumes. Modality‐independent spatial normalization can be consistently and reliably performed with the methods and software presented. © 1995 Wiley‐Liss, Inc.
A method for detecting significant and regionally specific correlations between sensory input and the brain's physiological response, as measured with functional magnetic resonance imaging (MRI), is presented in this paper. The method involves testing for correlations between sensory input and the hemodynamic response after convolving the sensory input with an estimate of the hemodynamic response function. This estimate is obtained without reference to any assumed input. To lend the approach statistical validity, it is brought into the framework of statistical parametric mapping by using a measure of cross-correlations between sensory input and hemodynamic response that is valid in the presence of intrinsic autocorrelations. These autocorrelations are necessarily present, due to the hemodynamic response function or temporal point spread function. © 1994 Wiley-Liss, Inc.
When performing tasks, humans are thought to adopt task sets that configure moment-to-moment data processing. Recently developed mixed blocked/event-related designs allow task set-related signals to be extracted in fMRI experiments, including activity related to cues that signal the beginning of a task block, "set-maintenance" activity sustained for the duration of a task block, and event-related signals for different trial types. Data were conjointly analyzed from mixed design experiments using ten different tasks and 183 subjects. Dorsal anterior cingulate cortex/medial superior frontal cortex (dACC/msFC) and bilateral anterior insula/frontal operculum (aI/fO) showed reliable start-cue and sustained activations across all or nearly all tasks. These regions also carried the most reliable error-related signals in a subset of tasks, suggesting that the regions form a "core" task-set system. Prefrontal regions commonly related to task control carried task-set signals in a smaller subset of tasks and lacked convergence across signal types.
Rapid-presentation event-related functional MRI (ER-fMRI) allows neuroimaging methods based on hemodynamics to employ behavioral task paradigms typical of cognitive settings. However, the sluggishness of the hemodynamic response and its variance provide constraints on how ER-fMRI can be applied. In a series of two studies, estimates of the hemodynamic response in or near the primary visual and motor cortices were compared across various paradigms and sampling procedures to determine the limits of ER-fMRI procedures and, more generally, to describe the behavior of the hemodynamic response. The temporal profile of the hemodynamic response was estimated across overlapping events by solving a set of linear equations within the general linear model. No assumptions about the shape were made in solving the equations. Following estimation of the temporal profile, the amplitude and timing were modeled using a γ function. Results indicated that (1) within a region, for a given subject, estimation of the hemodynamic response is extremely stable for both amplitude (r2 = 0.98) and time to peak (r2 = 0.95), from one series of measurements to the next, and slightly less stable for estimation of time to onset (r2 = 0.60). (2) As the trial presentation rate changed (from those spaced 20 s apart to temporally overlapping trials), the hemodynamic response amplitude showed a small, but significant, decrease. Trial onsets spaced (on average) 5 s apart showed a 17–25% reduction in amplitude compared to those spaced 20 s apart. Power analysis indicated that the increased number of trials at fast rates outweighs this decrease in amplitude if statistically reliable response detection is the goal. (3) Knowledge of the amplitude and timing of the hemodynamic response in one region failed to predict those properties in another region, even for within-subject comparisons. (4) Across subjects, the amplitude of the response showed no significant correlation with timing of the response, for either time-to-onset or time-to-peak estimates. (5) The within-region stability of the response was sufficient to allow offsets in the timing of the response to be detected that were under a second, placing event-related fMRI methods in a position to answer questions about the change in relative timing between regions.
A procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale (n > 100) studies when a precise optimal solution for a specified number of groups is not practical. Given n sets, this procedure permits their reduction to n − 1 mutually exclusive sets by considering the union of all possible n(n − 1)/2 pairs and selecting a union having a maximal value for the functional relation, or objective function, that reflects the criterion chosen by the investigator. By repeating this process until only one group remains, the complete hierarchical structure and a quantitative estimate of the loss associated with each stage in the grouping can be obtained. A general flowchart helpful in computer programming and a numerical example are included.
We investigated large-scale systems organization of the whole human brain using functional magnetic resonance imaging (fMRI) data acquired from healthy volunteers in a no-task or ‘resting’ state. Images were parcellated using a prior anatomical template, yielding regional mean time series for each of 90 regions (major cortical gyri and subcortical nuclei) in each subject. Significant pairwise functional connections, defined by the group mean inter-regional partial correlation matrix, were mostly either local and intrahemispheric or symmetrically interhemispheric. Low-frequency components in the time series subtended stronger inter-regional correlations than highfrequency components. Intrahemispheric connectivity was generally related to anatomical distance by an inverse square law; many symmetrical interhemispheric connections were stronger than predicted by the anatomical distance between bilaterally homologous regions. Strong interhemispheric connectivity was notably absent in data acquired from a single patient, minimally conscious following a brainstem lesion. Multivariate analysis by hierarchical clustering and multidimensional scaling consistently defined six major systems in healthy volunteers — corresponding approximately to four neocortical lobes, medial temporal lobe and subcortical nuclei — that could be further decomposed into anatomically and functionally plausible subsystems, e.g. dorsal and ventral divisions of occipital cortex. An undirected graph derived by thresholding the healthy group mean partial correlation matrix demonstrated local clustering or cliquishness of connectivity and short mean path length compatible with prior data on small world characteristics of non-human cortical anatomy. Functional MRI demonstrates a neurophysiological architecture of the normal human brain that is anatomically sensible, strongly symmetrical, disrupted by acute brain injury, subtended predominantly by low frequencies and consistent with a small world network topology.
The anterior insular cortex (AIC) is implicated in a wide range of conditions and behaviours, from bowel distension and orgasm, to cigarette craving and maternal love, to decision making and sudden insight. Its function in the re-representation of interoception offers one possible basis for its involvement in all subjective feelings. New findings suggest a fundamental role for the AIC (and the von Economo neurons it contains) in awareness, and thus it needs to be considered as a potential neural correlate of consciousness.
Decision making has recently emerged as a central theme in neurophysiological studies of cognition, and experimental and computational work has led to the proposal of a cortical circuit mechanism of elemental decision computations. This mechanism depends on slow recurrent synaptic excitation balanced by fast feedback inhibition, which not only instantiates attractor states for forming categorical choices but also long transients for gradually accumulating evidence in favor of or against alternative options. Such a circuit endowed with reward-dependent synaptic plasticity is able to produce adaptive choice behavior. While decision threshold is a core concept for reaction time tasks, it can be dissociated from a general decision rule. Moreover, perceptual decisions and value-based economic choices are described within a unified framework in which probabilistic choices result from irregular neuronal activity as well as iterative interactions of a decision maker with an uncertain environment or other unpredictable decision makers in a social group.
In recent years, many new cortical areas have been identified in the macaque monkey. The number of identified connections between areas has increased even more dramatically. We report here on (1) a summary of the layout of cortical areas associated with vision and with other modalities, (2) a computerized database for storing and representing large amounts of information on connectivity patterns, and (3) the application of these data to the analysis of hierarchical organization of the cerebral cortex. Our analysis concentrates on the visual system, which includes 25 neocortical areas that are predominantly or exclusively visual in function, plus an additional 7 areas that we regard as visual-association areas on the basis of their extensive visual inputs. A total of 305 connections among these 32 visual and visual-association areas have been reported. This represents 31% of the possible number of pathways if each area were connected with all others. The actual degree of connectivity is likely to be closer to 40%. The great majority of pathways involve reciprocal connections between areas. There are also extensive connections with cortical areas outside the visual system proper, including the somatosensory cortex, as well as neocortical, transitional, and archicortical regions in the temporal and frontal lobes. In the somatosensory/motor system, there are 62 identified pathways linking 13 cortical areas, suggesting an overall connectivity of about 40%. Based on the laminar patterns of connections between areas, we propose a hierarchy of visual areas and of somatosensory/motor areas that is more comprehensive than those suggested in other recent studies. The current version of the visual hierarchy includes 10 levels of cortical processing. Altogether, it contains 14 levels if one includes the retina and lateral geniculate nucleus at the bottom as well as the entorhinal cortex and hippocampus at the top. Within this hierarchy, there are multiple, intertwined processing streams, which, at a low level, are related to the compartmental organization of areas V1 and V2 and, at a high level, are related to the distinction between processing centers in the temporal and parietal lobes. However, there are some pathways and relationships (about 10% of the total) whose descriptions do not fit cleanly into this hierarchical scheme for one reason or another. In most instances, though, it is unclear whether these represent genuine exceptions to a strict hierarchy rather than inaccuracies or uncertainities in the reported assignment.
Functional magnetic resonance imaging (fMRI) techniques, such as echo-planar imaging, can permit rapid, sensitive, whole-brain measurements of local blood flow-induced MR signal changes seen during cognitive paradigms. Changes in blood oxygenation due to mismatch of flow and oxygen metabolism cause dynamic variations in microscopic susceptibility effects, leading to the blood oxygenation level-dependent (BOLD) signal measured by fMRI techniques. A related static macroscopic susceptibility effect is known to cause artifacts that attenuate the MR signal, leading to "blind spots" in some regions of brain adjacent to bone and air sinuses. The anatomical location, spatial extent, and magnitude of signal loss artifact are quantitated for a common whole-brain fMRI technique. Resting gradient-echo EPI studies were obtained in four healthy volunteers. Signal loss was primarily localized to inferior frontal regions (medial orbital gyri and gyrus rectus) and to inferior lateral temporal lobe (including part of fusiform gyrus) bilaterally. Increased echo time (TE) uniformly produced larger artifacts. The orientation of acquired slices and choice of phase-encoding direction influenced the location, shape, and extent of the artifacts. Regions of the brain with severe artifact may have attenuated activation signal, with potential implications for the design and interpretation of fMRI studies targeting activations in these areas.
An unresolved question in neuroscience and psychology is how the brain monitors performance to regulate behavior. It has been proposed that the anterior cingulate cortex (ACC), on the medial surface of the frontal lobe, contributes to performance monitoring by detecting errors. In this study, event-related functional magnetic resonance imaging was used to examine ACC function. Results confirm that this region shows activity during erroneous responses. However, activity was also observed in the same region during correct responses under conditions of increased response competition. This suggests that the ACC detects conditions under which errors are likely to occur rather than errors themselves.
It has been shown in nonhuman primates that the posterior parietal cortex is involved in coordination of arm and eye movements in space, whereas the anterior intraparietal area in the anterior lateral bank of the intraparietal sulcus plays a crucial role in fine finger movements, such as grasping. In this study we show by optoelectronic movement recordings that patients with cortical lesions involving the anterior lateral bank of the intraparietal sulcus have selective deficits in the coordination of finger movements required for object grasping, whereas reaching is much less disturbed. Patients with parietal lesions sparing the cortex lining the anterior intraparietal sulcus showed intact grasping behavior. Complementary evidence was obtained from functional MRI in normal control subjects showing a specific activation of the anterior lateral bank of the intraparietal sulcus during grasping. In conclusion, this combined lesion and activation study suggests that the anterior lateral bank of the intraparietal sulcus, possibly including the human homologue of the anterior intraparietal area, mediates the processing of sensorimotor integration of precisely tuned finger movements in humans.
An important challenge in the design and analysis of event-related or single-trial functional magnetic resonance imaging (fMRI) experiments is to optimize statistical efficiency, i.e., the accuracy with which the event-related hemodynamic response to different stimuli can be estimated for a given amount of imaging time. Several studies have suggested that using a fixed inter-stimulus-interval (ISI) of at least 15 sec results in optimal statistical efficiency or power and that using shorter ISIs results in a severe loss of power. In contrast, recent studies have demonstrated the feasibility of using ISIs as short as 500 ms while still maintaining considerable efficiency or power. Here, we attempt to resolve this apparent contradiction by a quantitative analysis of the relative efficiency afforded by different event-related experimental designs. This analysis shows that statistical efficiency falls off dramatically as the ISI gets sufficiently short, if the ISI is kept fixed for all trials. However, if the ISI is properly jittered or randomized from trial to trial, the efficiency improves monotonically with decreasing mean ISI. Importantly, the efficiency afforded by such variable ISI designs can be more than 10 times greater than that which can be achieved by fixed ISI designs. These results further demonstrate the feasibility of using identical experimental designs with fMRI and electro-/magnetoencephalography (EEG/MEG) without sacrificing statistical power or efficiency of either technique, thereby facilitating comparison and integration across imaging modalities.
Rapid-presentation event-related functional MRI (ER-fMRI) allows neuroimaging methods based on hemodynamics to employ behavioral task paradigms typical of cognitive settings. However, the sluggishness of the hemodynamic response and its variance provide constraints on how ER-fMRI can be applied. In a series of two studies, estimates of the hemodynamic response in or near the primary visual and motor cortices were compared across various paradigms and sampling procedures to determine the limits of ER-fMRI procedures and, more generally, to describe the behavior of the hemodynamic response. The temporal profile of the hemodynamic response was estimated across overlapping events by solving a set of linear equations within the general linear model. No assumptions about the shape were made in solving the equations. Following estimation of the temporal profile, the amplitude and timing were modeled using a gamma function. Results indicated that (1) within a region, for a given subject, estimation of the hemodynamic response is extremely stable for both amplitude (r(2) = 0.98) and time to peak (r(2) = 0.95), from one series of measurements to the next, and slightly less stable for estimation of time to onset (r(2) = 0.60). (2) As the trial presentation rate changed (from those spaced 20 s apart to temporally overlapping trials), the hemodynamic response amplitude showed a small, but significant, decrease. Trial onsets spaced (on average) 5 s apart showed a 17-25% reduction in amplitude compared to those spaced 20 s apart. Power analysis indicated that the increased number of trials at fast rates outweighs this decrease in amplitude if statistically reliable response detection is the goal. (3) Knowledge of the amplitude and timing of the hemodynamic response in one region failed to predict those properties in another region, even for within-subject comparisons. (4) Across subjects, the amplitude of the response showed no significant correlation with timing of the response, for either time-to-onset or time-to-peak estimates. (5) The within-region stability of the response was sufficient to allow offsets in the timing of the response to be detected that were under a second, placing event-related fMRI methods in a position to answer questions about the change in relative timing between regions.
Many cognitive processes occur on time scales that can significantly affect the shape of the blood oxygenation level-dependent (BOLD) response in event-related functional MRI. This shape can be estimated from event related designs, even if these processes occur in a fixed temporal sequence (J. M. Ollinger, G. L. Shulman, and M. Corbetta. 2001. NeuroImage 13: 210-217). Several important considerations come into play when interpreting these time courses. First, in single subjects, correlations among neighboring time points give the noise a smooth appearance that can be confused with changes in the BOLD response. Second, the variance and degree of correlation among estimated time courses are strongly influenced by the timing of the experimental design. Simulations show that optimal results are obtained if the intertrial intervals are as short as possible, if they follow an exponential distribution with at least three distinct values, and if 40% of the trials are partial trials. These results are not particularly sensitive to the fraction of partial trials, so accurate estimation of time courses can be obtained with lower percentages of partial trials (20-25%). Third, statistical maps can be formed from F statistics computed with the extra sum of square principle or by t statistics computed from the cross-correlation of the time courses with a model for the hemodynamic response. The latter method relies on an accurate model for the hemodynamic response. The most robust model among those tested was a single gamma function. Finally, the power spectrum of the measured BOLD signal in rapid event-related paradigms is similar to that of the noise. Nevertheless, high-pass filtering is desirable if the appropriate model for the hemodynamic response is used.
Many behavioral paradigms involve temporally overlapping sensory, cognitive, and motor components within a single trial. The complex interplay among these factors makes it desirable to separate the components of the total response without assumptions about shape of the underlying hemodynamic response. We present a method that does this. Four conditions were studied in four subjects to validate the method. Two conditions involved rapid event-related studies, one with a low-contrast (5%) flickering checkerboard and another with a high-contrast (95%) checkerboard. In the third condition, the same high-contrast checkerboard was presented with widely spaced trials. Finally, multicomponent trials were formed from temporally adjacent low-contrast and high-contrast stimuli. These trials were presented as a rapid event-related study. Low-contrast stimuli presented in isolation (partial trials) made it possible to uniquely estimate both the low-contrast and high-contrast responses. These estimated responses matched those measured in the first three conditions, thereby validating the method. Nonlinear interactions between adjacent low-contrast and high-contrast responses were shown to be significant but weak in two of the four subjects.
Here we review recent findings that reveal the functional properties of extra-striate regions in the human visual cortex that are involved in the representation and perception of objects. We characterize both the invariant and non-invariant properties of these regions and we discuss the correlation between activation of these regions and recognition. Overall, these results indicate that the lateral occipital complex plays an important role in human object recognition.
The time course of perceptual choice is discussed in a model of gradual, leaky, stochastic, and competitive information accumulation in nonlinear decision units. Special cases of the model match a classical diffusion process, but leakage and competition work together to address several challenges to existing diffusion, random walk, and accumulator models. The model accounts for data from choice tasks using both time-controlled (e.g., response signal) and standard reaction time paradigms and its adequacy compares favorably with other approaches. A new paradigm that controls the time of arrival of information supporting different choice alternatives provides further support. The model captures choice behavior regardless of the number of alternatives, accounting for the log-linear relation between reaction time and number of alternatives (Hick's law) and explains a complex pattern of visual and contextual priming in visual word identification.
We recorded the activity of single neurons in the posterior parietal cortex (area LIP) of two rhesus monkeys while they discriminated the direction of motion in random-dot visual stimuli. The visual task was similar to a motion discrimination task that has been used in previous investigations of motion-sensitive regions of the extrastriate cortex. The monkeys were trained to decide whether the direction of motion was toward one of two choice targets that appeared on either side of the random-dot stimulus. At the end of the trial, the monkeys reported their direction judgment by making an eye movement to the appropriate target. We studied neurons in LIP that exhibited spatially selective persistent activity during delayed saccadic eye movement tasks. These neurons are thought to carry high-level signals appropriate for identifying salient visual targets and for guiding saccadic eye movements. We arranged the motion discrimination task so that one of the choice targets was in the LIP neuron's response field (RF) while the other target was positioned well away from the RF. During motion viewing, neurons in LIP altered their firing rate in a manner that predicted the saccadic eye movement that the monkey would make at the end of the trial. The activity thus predicted the monkey's judgment of motion direction. This predictive activity began early in the motion-viewing period and became increasingly reliable as the monkey viewed the random-dot motion. The neural activity predicted the monkey's direction judgment on both easy and difficult trials (strong and weak motion), whether or not the judgment was correct. In addition, the timing and magnitude of the response was affected by the strength of the motion signal in the stimulus. When the direction of motion was toward the RF, stronger motion led to larger neural responses earlier in the motion-viewing period. When motion was away from the RF, stronger motion led to greater suppression of ongoing activity. Thus the activity of single neurons in area LIP reflects both the direction of an impending gaze shift and the quality of the sensory information that instructs such a response. The time course of the neural response suggests that LIP accumulates sensory signals relevant to the selection of a target for an eye movement.
A brightness discrimination experiment was performed to examine how subjects decide whether a patch of pixels is "bright" or "dark," and stimulus duration, brightness, and speed versus accuracy instructions were manipulated. The diffusion model (Ratcliff, 1978) was fit to the data, and it accounted for all the dependent variables: mean correct and error response times, the shapes of response time distributions for correct and error responses, and accuracy values. Speed-accuracy manipulations affected only boundary separation (response criteria settings) in the model. Drift rate (the rate of accumulation of evidence) in the diffusion model, which represents stimulus quality, increased as a function of stimulus duration and stimulus brightness but asymptoted as stimulus duration increased from 100 to 150 msec. To address the argument that the diffusion model can fit any pattern of data, simulated patterns of plausible data are presented that the model cannot fit.