[Show abstract][Hide abstract] ABSTRACT: Freezing of gait is a disabling symptom of Parkinson's disease (PD) that involves failure to initiate and continue motor activity appropriately. PD disrupts fronto-basal ganglia circuitries that also implement the inhibition of responses, leading to the hypothesis that freezing of gait may involve fundamental changes in both initiation and inhibition of motor actions. We asked whether PD patients who show freezing of gait show selective deficits in their ability to inhibit upper and lower extremity reactions. We compared older healthy controls, older PD controls without freezing of gait, and older PD participants with freezing of gait, in stop-signal tasks that measured the initiation (go trials) and inhibition (stop trials) of both hand and foot responses. When only go trials were presented, all three groups showed similar initiation speeds across lower and upper extremity responses. When stop-signal trials were introduced, both PD groups slowed their reactions nearly twice as much as healthy controls. While this adjustment helped PD controls stop their actions as quickly as healthy controls, PD patients with freezing showed significantly delayed inhibitory control of both upper and lower extremities. When anticipating the need to stop their actions urgently, PD patients show greater adjustments (i.e., slowing) to reaction speed than healthy controls. Despite these proactive adjustments, PD patients who freeze show marked impairments in inhibiting both upper and lower extremity responses, suggesting that freezing may involve a fundamental disruption to the brain's inhibitory control system.
Journal of Neural Transmission 09/2015; DOI:10.1007/s00702-015-1454-9 · 2.40 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Following exposure to consistent stimulus-stop mappings, response inhibition can become automatized with practice. What is learned is less clear, even though this has important theoretical and practical implications. A recent analysis indicates that stimuli can become associated with a stop signal or with a stop goal. Furthermore, expectancy may play an important role. Previous studies that have used stop or no-go signals to manipulate stimulus-stop learning cannot distinguish between stimulus-signal and stimulus-goal associations, and expectancy has not been measured properly. In the present study, participants performed a task that combined features of the go/no-go task and the stop-signal task in which the stop-signal rule changed at the beginning of each block. The go and stop signals were superimposed over 40 task-irrelevant images. Our results show that participants can learn direct associations between images and the stop goal without mediation via the stop signal. Exposure to the image-stop associations influenced task performance during training, and expectancies measured following task completion or measured within the task. But, despite this, we found an effect of stimulus-stop learning on test performance only when the task increased the task-relevance of the images. This could indicate that the influence of stimulus-stop learning on go performance is strongly influenced by attention to both task-relevant and task-irrelevant stimulus features. More generally, our findings suggest a strong interplay between automatic and controlled processes. (PsycINFO Database Record
(c) 2015 APA, all rights reserved).
Journal of Experimental Psychology Human Perception & Performance 08/2015; DOI:10.1037/xhp0000116 · 3.36 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The interactive race model of saccadic countermanding assumes that response inhibition results from an interaction between a go unit, identified with gaze-shifting neurons, and a stop unit, identified with gaze-holding neurons, in which activation of the stop unit inhibits the growth of activation in the go unit to prevent it from reaching threshold. The interactive race model accounts for behavioral data and predicts physiological data in monkeys performing the stop-signal task. We propose an alternative model that assumes that response inhibition results from blocking the input to the go unit. We show that the blocked-input model accounts for behavioral data as accurately as the original interactive race model and predicts aspects of the physiological data more accurately. We extend the models to address the steady-state fixation period before the go stimulus is presented and find that the blocked-input model fits better than the interactive race model. We consider a model in which fixation activity is boosted when a stop signal occurs and find that it fits as well as the blocked input model but predicts very high steady-state fixation activity after the response is inhibited. We discuss the alternative linking propositions that connect computational models to neural mechanisms, the lessons to be learned from model mimicry, and generalization from countermanding saccades to countermanding other kinds of responses. (PsycINFO Database Record (c) 2015 APA, all rights reserved).
[Show abstract][Hide abstract] ABSTRACT: Two modes of response selection-a mediated route involving categorization and a nonmediated route involving instance-based memory retrieval-have been proposed to explain response congruency effects in task-switching situations. In the present study, we sought a better understanding of the development and characteristics of the nonmediated route. In two experiments involving training and transfer phases, we investigated practice effects at the level of individual target presentations, transfer effects associated with changing category-response mappings, target-specific effects from comparisons of old and new targets during transfer, and the percentages of early responses associated with task-nonspecific response selection (the target preceded the task cue on every trial). The training results suggested that the nonmediated route is quickly learned in the context of target-cue order and becomes increasingly involved in response selection with practice. The transfer results suggested that the target-response instances underlying the nonmediated route involve abstract response labels coding response congruency that can be rapidly remapped to alternative responses, but not rewritten when category-response mappings change after practice. Implications for understanding the nonmediated route and its relationship with the mediated route are discussed.
[Show abstract][Hide abstract] ABSTRACT: Abstract Bartlett (1958) described the point of no return as a point of irrevocable commitment to action, which was preceded by a period of gradually increasing commitment. As such, the point of no return reflects a fundamental limit on the ability to control thought and action. I review the literature on the point of no return, taking three perspectives. First, I consider the point of no return from the perspective of the controlled act, as a locus in the architecture and anatomy of the underlying processes. I review experiments from the stop-signal paradigm that suggest that the point of no return is located late in the response system. Then I consider the point of no return from the perspective of the act of control that tries to change the controlled act before it becomes irrevocable. From this perspective, the point of no return is a point in time that provides enough "lead time" for the act of control to take effect. I review experiments that measure the response time to the stop signal as the lead time required for response inhibition in the stop-signal paradigm. Finally, I consider the point of no return in hierarchically controlled tasks, in which there may be many points of no return at different levels of the hierarchy. I review experiments on skilled typing that suggest different points of no return for the commands that determine what is typed and the countermands that inhibit typing, with increasing commitment to action the lower the level in the hierarchy. I end by considering the point of no return in perception and thought as well as action.
[Show abstract][Hide abstract] ABSTRACT: We develop desiderata for a computational theory of response inhibition that links mathematical psychology with neuroscience. The theory must be explicitmathematically and computationally, and grounded in behavior and neurophysiology. The theory must provide quantitative accounts of complexities of behavior in response inhibition tasks and must predict the neural activity that underlies performance. We evaluate three current theories of response inhibition in the stop signal paradigm using these desiderata, and we find that one theory fulfills the desideratabetter than the others.
[Show abstract][Hide abstract] ABSTRACT: The objective of the present study was to compare two components of executive functioning, response monitoring and inhibition in bipolar disorder (BP) and schizophrenia (SZ). The saccadic countermanding task is a translational paradigm optimized for detecting subtle abnormalities in response monitoring and response inhibition. We have previously reported countermanding performance abnormalities in SZ, but the degree to which these impairments are shared by other psychotic disorders is unknown. 18 BP, 17 SZ, and 16 demographically-matched healthy controls (HC) participated in a saccadic countermanding task. Performance on the countermanding task is approximated as a race between movement generation and inhibition processes; this model provides an estimate of the time needed to cancel a planned movement. Response monitoring was assessed by the reaction time (RT) adjustments based on trial history. Like SZ patients, BP patients needed more time to cancel a planned movement. The two patient groups had equivalent inhibition efficiency. On trial history-based RT adjustments, however, we found a trend towards exaggerated trial history-based slowing in SZ compared to BP. Findings have implications for understanding the neurobiology of cognitive control, for defining the etiological overlap between schizophrenia and bipolar disorder and for developing pharmacological treatments of cognitive impairments.
Psychiatry Research 12/2014; 225(3). DOI:10.1016/j.psychres.2014.12.033 · 2.47 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Previous research has revealed that task-switch costs (worse performance for task switches than for task repetitions) at the first position of an explicit task sequence are eliminated or reduced when repeating or switching sequences. The authors hypothesize that such effects are restricted to points in the sequence representation that are associated with sequence-level processing such as chunk retrieval that changes the contents of working memory. In an experiment testing this chunk-point hypothesis, subjects memorized and performed explicit task sequences under different chunking instructions that induced chunk points at different positions within the sequences. Regardless of position, performance was slower at chunk points than at non-chunk points, providing direct evidence of chunking, and task-switch costs were reduced or eliminated at chunk points while they remained large and robust at non-chunk points. These findings support the chunk-point hypothesis and are discussed in relation to task-set inhibition and associative interference.
[Show abstract][Hide abstract] ABSTRACT: Skilled typing is controlled by two hierarchically structured processing loops (Logan & Crump, 2011): The outer loop, which produces words, commands the inner loop, which produces keystrokes. Here, we assessed the interplay between the two loops by investigating how visual feedback from the screen (responses either were or were not echoed on the screen) and the hands (the hands either were or were not covered with a box) influences the control of skilled typing. Our results indicated, first, that the reaction time of the first keystroke was longer when responses were not echoed than when they were. Also, the interkeystroke interval (IKSI) was longer when the hands were covered than when they were visible, and the IKSI for responses that were not echoed was longer when explicit error monitoring was required (Exp. 2) than when it was not required (Exp. 1). Finally, explicit error monitoring was more accurate when response echoes were present than when they were absent, and implicit error monitoring (i.e., posterror slowing) was not influenced by visual feedback from the screen or the hands. These findings suggest that the outer loop adjusts the inner-loop timing parameters to compensate for reductions in visual feedback. We suggest that these adjustments are preemptive control strategies designed to execute keystrokes more cautiously when visual feedback from the hands is absent, to generate more cautious motor programs when visual feedback from the screen is absent, and to enable enough time for the outer loop to monitor keystrokes when visual feedback from the screen is absent and explicit error reports are required.
[Show abstract][Hide abstract] ABSTRACT: We address the problem of serial order in skilled typing, asking whether typists represent the identity and order of the keystrokes they type jointly by linking successive keystrokes into a chained sequence, or separately by associating keystrokes with position codes. In 4 experiments, typists prepared to type a prime word and were probed to type a target word. We varied the overlap between the identity and order of keystrokes in the prime and the target. Experiment 1 tested whether the identity of keystrokes can be primed separately from their order. Experiments 2 and 3 tested whether keystroke positions can be primed out of sequence. Experiment 4 tested whether keystrokes are primed equally across serial positions. The results were consistent with chaining theories: Keystroke identities were not primed separately from their order, keystroke positions were not primed out of sequence, and priming was graded across the keystroke sequence and depended on the number of keystrokes that were primed in sequence. We conclude by discussing the possibility that the problem of serial order may be solved differently for different sequential tasks. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
Journal of Experimental Psychology Human Perception & Performance 06/2014; 40(4). DOI:10.1037/a0037199 · 3.36 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The present study investigated the way people acquire and control skilled performance in the context of typewriting. Typing skill was degraded by changing the location of a key (target key) while retaining the locations of other keys to disable an association between the letter and the key. We conducted 4 experiments: Experiment 1 demonstrated that disabling a letter-key association affected not only the execution of the target keystroke but also the planning of other keystrokes for words involving the target key. In Experiments 2-4, typists practiced with a new target location and then transferred to a condition in which they typed the practiced words with the original key location (Experiment 2) or typed new words with the practiced key location (Experiments 3 and 4). Experiment 2 showed that the newly acquired letter-key association interfered with the execution of the original keystroke but not planning. Experiments 3 and 4 demonstrated that acquisition of the new letter-key association depended on multiple levels of linguistic units. Experiment 4 demonstrated that acquisition of the new association depended on sequences both before and after the target keystroke. We discuss implications of the results for 2 prominent approaches to modeling sequential behavior: hierarchical control and recurrent network models. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
Journal of Experimental Psychology Learning Memory and Cognition 06/2014; 40(6). DOI:10.1037/xlm0000026 · 2.86 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Decision-making is explained by psychologists through stochastic accumulator models and by neurophysiologists through the activity of neurons believed to instantiate these models. We investigated an overlooked scaling problem: How does a response time (RT) that can be explained by a single model accumulator arise from numerous, redundant accumulator neurons, each of which individually appears to explain the variability of RT? We explored this scaling problem by developing a unique ensemble model of RT, called e pluribus unum, which embodies the well-known dictum "out of many, one." We used the e pluribus unum model to analyze the RTs produced by ensembles of redundant, idiosyncratic stochastic accumulators under various termination mechanisms and accumulation rate correlations in computer simulations of ensembles of varying size. We found that predicted RT distributions are largely invariant to ensemble size if the accumulators share at least modestly correlated accumulation rates and RT is not governed by the most extreme accumulators. Under these regimes the termination times of individual accumulators was predictive of ensemble RT. We also found that the threshold measured on individual accumulators, corresponding to the firing rate of neurons measured at RT, can be invariant with RT but is equivalent to the specified model threshold only when the rate correlation is very high.
Proceedings of the National Academy of Sciences 02/2014; 111(7):2848-53. DOI:10.1073/pnas.1310577111 · 9.67 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Response inhibition is an important act of control in many domains of psychology and neuroscience. It is often studied in a stop-signal task that requires subjects to inhibit an ongoing action in response to a stop signal. Performance in the stop-signal task is understood as a race between a go process that underlies the action and a stop process that inhibits the action. Responses are inhibited if the stop process finishes before the go process. The finishing time of the stop process is not directly observable; a mathematical model is required to estimate its duration. Logan and Cowan (1984) developed an independent race model that is widely used for this purpose. We present a general race model that extends the independent race model to account for the role of choice in go and stop processes, and a special race model that assumes each runner is a stochastic accumulator governed by a diffusion process. We apply the models to 2 data sets to test assumptions about selective influence of capacity limitations on drift rates and strategies on thresholds, which are largely confirmed. The model provides estimates of distributions of stop-signal response times, which previous models could not estimate. We discuss implications of viewing cognitive control as the result of a repertoire of acts of control tailored to different tasks and situations. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
[Show abstract][Hide abstract] ABSTRACT: The stop-signal paradigm is frequently used to study response inhibition. In this paradigm, participants perform a two-choice response time (RT) task where the primary task is occasionally interrupted by a stop-signal that prompts participants to withhold their response. The primary goal is to estimate the latency of the unobservable stop response (stop signal reaction time or SSRT). Recently, Matzke et al. (2013) have developed a Bayesian parametric approach (BPA) that allows for the estimation of the entire distribution of SSRTs. The BPA assumes that SSRTs are ex-Gaussian distributed and uses Markov chain Monte Carlo sampling to estimate the parameters of the SSRT distribution. Here we present an efficient and user-friendly software implementation of the BPA-BEESTS-that can be applied to individual as well as hierarchical stop-signal data. BEESTS comes with an easy-to-use graphical user interface and provides users with summary statistics of the posterior distribution of the parameters as well various diagnostic tools to assess the quality of the parameter estimates. The software is open source and runs on Windows and OS X operating systems. In sum, BEESTS allows experimental and clinical psychologists to estimate entire distributions of SSRTs and hence facilitates the more rigorous analysis of stop-signal data.
Frontiers in Psychology 12/2013; 4:918. DOI:10.3389/fpsyg.2013.00918 · 2.80 Impact Factor