ORCID Profile
0000-0002-8301-7726
Current Organisation
Vanderbilt University
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Publisher: Springer Science and Business Media LLC
Date: 18-05-2020
Publisher: eLife Sciences Publications, Ltd
Date: 29-04-2019
DOI: 10.7554/ELIFE.46323
Abstract: Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.
Publisher: Frontiers Media SA
Date: 2013
Publisher: Springer Science and Business Media LLC
Date: 15-02-2018
Publisher: American Physiological Society
Date: 04-2019
Abstract: Stochastic accumulator models account for response times and errors in perceptual decision making by assuming a noisy accumulation of perceptual evidence to a threshold. Previously, we explained saccade visual search decision making by macaque monkeys with a stochastic multiaccumulator model in which accumulation was driven by a gated feed-forward integration to threshold of spike trains from visually responsive neurons in frontal eye field that signal stimulus salience. This neurally constrained model quantitatively accounted for response times and errors in visual search for a target among varying numbers of distractors and replicated the dynamics of presaccadic movement neurons hypothesized to instantiate evidence accumulation. This modeling framework suggested strategic control over gate or over threshold as two potential mechanisms to accomplish speed-accuracy tradeoff (SAT). Here, we show that our gated accumulator model framework can account for visual search performance under SAT instructions observed in a milestone neurophysiological study of frontal eye field. This framework captured key elements of saccade search performance, through observed modulations of neural input, as well as flexible combinations of gate and threshold parameters necessary to explain differences in SAT strategy across monkeys. However, the trajectories of the model accumulators deviated from the dynamics of most presaccadic movement neurons. These findings demonstrate that traditional theoretical accounts of SAT are incomplete descriptions of the underlying neural adjustments that accomplish SAT, offer a novel mechanistic account of decision-making mechanisms during speed-accuracy tradeoff, and highlight questions regarding the identity of model and neural accumulators. NEW & NOTEWORTHY A gated accumulator model is used to elucidate neurocomputational mechanisms of speed-accuracy tradeoff. Whereas canonical stochastic accumulators adjust strategy only through variation of an accumulation threshold, we demonstrate that strategic adjustments are accomplished by flexible combinations of both modulation of the evidence representation and adaptation of accumulator gate and threshold. The results indicate how model-based cognitive neuroscience can translate between abstract cognitive models of performance and neural mechanisms of speed-accuracy tradeoff.
Publisher: Springer Science and Business Media LLC
Date: 23-12-2019
DOI: 10.1038/S41562-019-0812-2
Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Publisher: American Psychological Association (APA)
Date: 11-2013
DOI: 10.1037/A0030543
Abstract: The cognitive concept of response inhibition can be measured with the stop-signal paradigm. In this paradigm, participants perform a 2-choice response time (RT) task where, on some of the trials, the primary task is interrupted by a stop signal that prompts participants to withhold their response. The dependent variable of interest is the latency of the unobservable stop response (stop-signal reaction time, or SSRT). Based on the horse race model (Logan & Cowan, 1984), several methods have been developed to estimate SSRTs. None of these approaches allow for the accurate estimation of the entire distribution of SSRTs. Here we introduce a Bayesian parametric approach that addresses this limitation. Our method is based on the assumptions of the horse race model and rests on the concept of censored distributions. We treat response inhibition as a censoring mechanism, where the distribution of RTs on the primary task (go RTs) is censored by the distribution of SSRTs. The method assumes that go RTs and SSRTs are ex-Gaussian distributed and uses Markov chain Monte Carlo s ling to obtain posterior distributions for the model parameters. The method can be applied to in idual as well as hierarchical data structures. We present the results of a number of parameter recovery and robustness studies and apply our approach to published data from a stop-signal experiment.
Publisher: Elsevier BV
Date: 12-2018
Publisher: Springer Science and Business Media LLC
Date: 02-12-2019
No related grants have been discovered for Gordon Logan.