August 2024
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4 Reads
Cognition
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August 2024
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4 Reads
Cognition
July 2024
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2 Reads
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1 Citation
The Oxford Handbook of Human Memory covers the science of human memory, its application to clinical disorders, and its broader implications for learning and memory in real-world contexts. Written by field leaders, the handbook integrates behavioral, neural, and computational evidence with current theories of how humans learn and remember. Following a section of foundational chapters, subsequent sections include chapters that cover forms and attributes of memory, encoding and retrieval processes and their interactions, individual differences, memory disorders and therapies, learning and memory in educational settings, and the role of memory in society. The handbook’s authoritative chapters document the current state of knowledge and provide a roadmap for the next generation of memory scientists, established peers, and practitioners.
July 2024
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151 Reads
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2 Citations
Evidence accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behaviour. EAMs have generated significant theoretical advances in psychology, behavioural economics, and cognitive neuroscience, and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues, and on inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, for relating experimental manipulations to EAM parameters, for planning appropriate sample sizes, and for preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the authors’ substantial collective experience with EAMs. By encouraging good task design practices, and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications.
February 2024
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65 Reads
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2 Citations
Psychonomic Bulletin & Review
People vary in their ability to recognize objects visually. Individual differences for matching and recognizing objects visually is supported by a domain-general ability capturing common variance across different tasks (e.g., Richler et al., Psychological Review, 126, 226–251, 2019). Behavioral (e.g., Cooke et al., Neuropsychologia, 45, 484–495, 2007) and neural evidence (e.g., Amedi, Cerebral Cortex, 12, 1202–1212, 2002) suggest overlapping mechanisms in the processing of visual and haptic information in the service of object recognition, but it is unclear whether such group-average results generalize to individual differences. Psychometrically validated measures are required, which have been lacking in the haptic modality. We investigate whether object recognition ability is specific to vision or extends to haptics using psychometric measures we have developed. We use multiple visual and haptic tests with different objects and different formats to measure domain-general visual and haptic abilities and to test for relations across them. We measured object recognition abilities using two visual tests and four haptic tests (two each for two kinds of haptic exploration) in 97 participants. Partial correlation and confirmatory factor analyses converge to support the existence of a domain-general haptic object recognition ability that is moderately correlated with domain-general visual object recognition ability. Visual and haptic abilities share about 25% of their variance, supporting the existence of a multisensory domain-general ability while leaving a substantial amount of residual variance for modality-specific abilities. These results extend our understanding of the structure of object recognition abilities; while there are mechanisms that may generalize across categories, tasks, and modalities, there are still other mechanisms that are distinct between modalities.
November 2023
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19 Reads
Cognitive models, which describe cognition in terms of processes and representations, are ideally suited to help build bridges between “how” cognition works at the level of individual neurons and “why” cognition occurs at the level of goal-directed whole-organism behavior. This chapter presents an illustrative example of such a model, Salience by Competitive and Recurrent Interaction (SCRI; Cox et al. Psychol Rev, 2022), a theory of how neurons in the Frontal Eye Fields (FEF) integrate localization and identification information over time to represent the relative salience of objects in visual search. SCRI is framed in cognitive terms but is able to explain the millisecond-by-millisecond spiking activity of individual FEF neurons. This enables SCRI to help identify differences between neurons in terms of the computational mechanisms they instantiate by means of accounting for their dynamics. Such neural data also provide valuable constraints on SCRI that illuminate the relative importance of different types of competitive and recurrent interactions. Simulated activity from SCRI, coupled with a Gated Accumulator Model (GAM) of FEF movement neurons, reproduces the details of response time distributions in visual search behavior. The chapter includes extensive discussion of the difficult choices and exciting prospects for developing joint neuro-cognitive models like SCRI, developments which are enabled by recent advances in dynamic cognitive models and neural recording technologies.
August 2023
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2 Reads
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1 Citation
Journal of Vision
July 2023
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112 Reads
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8 Citations
Cognition
A general object recognition ability predicts performance across a variety of high-level visual tests, categories, and performance in haptic recognition. Does this ability extend to auditory recognition? Vision and haptics tap into similar representations of shape and texture. In contrast, features of auditory perception like pitch, timbre, or loudness do not readily translate into shape percepts related to edges, surfaces, or spatial arrangement of parts. We find that an auditory object recognition ability correlates highly with a visual object recognition ability after controlling for general intelligence, perceptual speed, low-level visual ability, and memory ability. Auditory object recognition was a stronger predictor of visual object recognition than all control measures across two experiments, even though those control variables were also tested visually. These results point towards a single high-level ability used in both vision and audition. Much work highlights how the integration of visual and auditory information is important in specific domains (e.g., speech, music), with evidence for some overlap of visual and auditory neural representations. Our results are the first to reveal a domain-general ability, o, that predicts object recognition performance in both visual and auditory tests. Because o is domain-general, it reveals mechanisms that apply across a wide range of situations, independent of experience and knowledge. As o is distinct from general intelligence, it is well positioned to potentially add predictive validity when explaining individual differences in a variety of tasks, above and beyond measures of common cognitive abilities like general intelligence and working memory.
January 2023
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1 Read
December 2022
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7 Reads
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1 Citation
Journal of Vision
June 2022
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38 Reads
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6 Citations
Computational Brain & Behavior
Many models of decision-making assume accumulation of evidence to threshold as a core mechanism to predict response probabilities and response times. A spiking neural network model (Wang, 2002) instantiates these mechanisms at the level of biophysically-plausible pools of neurons with excitatory and inhibitory connections and has numerous model parameters tuned by physiological measures. The diffusion model (Ratcliff, 1978) is a cognitive model that can be fitted to a range of behaviors and conditions. We investigated how parameters of the cognitive-level diffusion model relate to the parameters of a neural-level spiking model. In each simulated “experiment,” we generated “data” from the spiking neural network by factorially combining a manipulation of choice difficulty (via the input to the spiking model) and a manipulation of one of the core parameters of the spiking model. We then fitted the diffusion model to these simulated data to observe how manipulation of each core spiking model parameter mapped on to fitted drift rate, response threshold, and non-decision time. Manipulations of parameters in the spiking model related to input sensitivity, threshold, and stimulus processing time mapped on to their conceptual analogues in the diffusion model, namely drift rate, threshold, and non-decision time. Manipulations of parameters in the spiking model with no direct analogue to the diffusion model, non-stimulus-specific background input, strength of recurrent excitation, and receptor conductances mapped on to threshold in the diffusion model. We discuss implications of these results for interpretations of fits of the diffusion model to behavioral data.
... Our study calls for a careful examination of whether the standard DDM is suitable for data generated from the SMT. It is crucial to evaluate if the task settings align with the assumptions of DDM (see Boag et al., 2024;Voss et al., 2013 for details). For instance, there is a relative lack of validation studies on the applicability of the DDM for analysing perceptual decision-making tasks involving stimuli beyond visual or auditory inputs. ...
July 2024
... We will refer to this ability as o (Richler et al., 2019), but note that a general factor in the visual domain has also been referred to as VG (general factor in the visual domain, Hendel et al., 2019). A visual o-factor may explain a large part of individual differences in object perception, is at least partially modality-specific (Chow et al., 2024; but see Chow et al., 2023), and appears to be separable from low-level visual abilities as well as other cognitive abilities such as general intelligence (Chow et al., 2023;Richler et al., 2017Richler et al., , 2019. Individual differences in o are related to neural measures of shape selectivity, including in suggested category-selective regions of the ventral visual pathway (McGugin et al., 2023). ...
February 2024
Psychonomic Bulletin & Review
... Motivation is a possible influence on virtually any performance test, including intelligence tests, where it can affect predictive validity (Duckworth et al., 2011). Studies that use the NOMT to measure object recognition ability can use performance on other performance tests (for instance IQ tests) to attempt to control for differences in motivation (Chow et al., 2021(Chow et al., , 2023. ...
July 2023
Cognition
... In an academic context, image segmentation serves as a critical preprocessing step [5] for various higher-level image analysis [6] and computer vision tasks [7], including object recognition [8], scene understanding [9], and image-based measurements [10]. The ultimate goal of image segmentation is to provide a pixel-level or region-level decomposition of the image, enabling the extraction of relevant information for subsequent analysis and interpretation [11]. ...
December 2022
Journal of Vision
... In 2-alternative forced choice (2-AFC) tasks, researchers often model decisionmaking as evidence accumulation over time, using approaches such as the drift diffusion model (DDM) [47][48][49], racing diffusion model [50,51], and linear ballistic accumulator (LBA) model [52]. The temporal integration in the LIP module resembles the DDM (see discussions in [21][22][23]53]), and can be directly related to it [54,55]. ...
June 2022
Computational Brain & Behavior
... O also predicts the accuracy with which people can estimate summary statistics (e.g. mean) of groups of objects , and the ability to recognize different types of food (Gauthier & Fiestan, 2023), whereas it does not predict experience with visual arts (Chow et al., 2022b). Beyond visual abilities, o is also related to haptic object recognition accuracy (Chow et al., 2022a), and has a strong correlation with auditory object recognition accuracy (Chow et al., 2023). ...
June 2022
Journal of Vision
... Potentially clouding the discussion, however, is that fMRI studies of accumulation have largely followed the modeling literature by testing evidence accumulation with tasks and stimuli that tap memory-driven processes, leaving a gap in knowledge regarding how neural evidence accumulation is influenced by motor actions. A clear path forward to narrow this gap is to bridge the investigation of motor and memory contributions to evidence accumulation processes (Cox et al., 2022). This endeavor may also offer an opportunity to investigate the continually elusive answer of how the dorsal and ventral streams interact (Goodale and Humphrey, 1998). ...
April 2022
Psychological Review
... In contrast, identification of individual faces, or of specific facial expressions, requires re-entrant signalling from other cortical and subcortical brain regions. Sugase et al.'s findings should be considered in the broader context provided by Chow et al. (2022), in which different levels of categorization are shown to follow different time courses. 4 Consistent with the theme of the present work, face perception cannot be wholly explained in terms of feed-forward processes alone. ...
February 2022
Attention Perception & Psychophysics
... To provide an impartial assessment of the histopathological aspects of a given preclinical model, evaluation should be as unbiased, non-subjective and reproducible as possible. Consistency and quantitative measurements are therefore essential for modeling the pathology present in brain regions at defined stages of the disease, as well as for monitoring the progression of disease over time (Belfiore et al., 2019;Carrigan et al. 2022). Moreover, quantitative evaluation of histological tissue biomarkers, such as markers of cell death (Fricker et al., 2018), pathological protein accumulation (Spires-Jones et al., 2017), or markers of inflammation (Rauf et al., 2022), are similarly critical for successful assessments. ...
January 2022
Applied Ergonomics
... Motivation is a possible influence on virtually any performance test, including intelligence tests, where it can affect predictive validity (Duckworth et al., 2011). Studies that use the NOMT to measure object recognition ability can use performance on other performance tests (for instance IQ tests) to attempt to control for differences in motivation (Chow et al., 2021(Chow et al., , 2023. ...
June 2022
Psychological Research