The long-term goal of our research is to understand how computational models of performance of visual tasks like locating and shifting gaze to a target a visual array map onto specific neural processes producing that performance. Elucidating this mapping provides converging constraints for discriminating between competing model architectures and provides functional explanations of neural circuit function.
The aims of this proposal test, extend, refine, and integrate two major new computational models of target selection during visual search that we have recently developed. Data will consist of performance of monkeys and human participants searching for a target in a visual array in which target location can change unpredictably supplemented by neurophysiological data from FEF that was collected previously. The models provide quantitative accounts of detailed patterns of correct and error saccade behavior during visual search and also provide explanations for the temporal modulation of neurons in frontal eye field (FEF). Unlike previous models of visual search, ours account for the entire range of correct and error response probabilities and response time distributions during efficient and inefficient search, even when the target changes location unexpectedly.
Aim 1 will develop, refine, and extend an INTERACTIVE RACE model of saccade target selection. We will test competing model architectures consisting of multiple stochastic accumulators (GO units) that govern when and where a saccade is made, where the nature of the interactions between GO units and the potential inclusion of a STOP unit for exerting cognitive control is manipulated across model variants. Successful models predict response probabilities and response time distributions in monkeys and humans and neural activity observed previously in monkeys.
Aim 2 will test, refine, and extend a GATED ACCUMULATOR model of how visual salience is translated into a saccade command. The visual salience representation provided by FEF neurons will be the input to a neural network of stochastic GO units with alternative architectures that implement competing hypotheses about the role of feed forward, lateral and gating inhibition.
Aim 3 will integrate these two models. This integration will be guided by new data from human participants performing visual search tasks in which key variables are manipulated to obtain new measures to test competing architectures.
The models tested and refined through this research plan will provide a firm foundation from which to understand disorders of visual attention, orientation and mobility that are consequences of impaired visual search. Elucidation of the mapping between effective mathematical models of behavior and specific brain processes is necessary for translational research seeking to understand how vision and cognition are impacted by injury, disease, or pharmacological interventions.
|Annis, Jeffrey; Palmeri, Thomas J (2017) Bayesian statistical approaches to evaluating cognitive models. Wiley Interdiscip Rev Cogn Sci :|
|Annis, Jeffrey; Miller, Brent J; Palmeri, Thomas J (2017) Bayesian inference with Stan: A tutorial on adding custom distributions. Behav Res Methods 49:863-886|
|Schall, Jeffrey D; Palmeri, Thomas J; Logan, Gordon D (2017) Models of inhibitory control. Philos Trans R Soc Lond B Biol Sci 372:|
|Purcell, Braden A; Palmeri, Thomas J (2017) RELATING ACCUMULATOR MODEL PARAMETERS AND NEURAL DYNAMICS. J Math Psychol 76:156-171|
|Verbruggen, Frederick; Logan, Gordon D (2015) Evidence for capacity sharing when stopping. Cognition 142:81-95|
|Logan, Gordon D (2015) The point of no return: A fundamental limit on the ability to control thought and action. Q J Exp Psychol (Hove) 68:833-57|
|Logan, Gordon D; Yamaguchi, Motonori; Schall, Jeffrey D et al. (2015) Inhibitory control in mind and brain 2.0: blocked-input models of saccadic countermanding. Psychol Rev 122:115-47|
|Bissett, Patrick G; Logan, Gordon D; van Wouwe, Nelleke C et al. (2015) Generalized motor inhibitory deficit in Parkinson's disease patients who freeze. J Neural Transm (Vienna) 122:1693-701|
|Bissett, Patrick G; Logan, Gordon D (2014) Selective stopping? Maybe not. J Exp Psychol Gen 143:455-72|
|Palmeri, Thomas J (2014) An exemplar of model-based cognitive neuroscience. Trends Cogn Sci 18:67-9|
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