A biophysical neural accumulator model of decision making in an antisaccade task

2007 ◽  
Vol 70 (7-9) ◽  
pp. 1390-1402 ◽  
Author(s):  
Vassilis Cutsuridis ◽  
Ioannis Kahramanoglou ◽  
Nikolaos Smyrnis ◽  
Ioannis Evdokimidis ◽  
Stavros Perantonis
Keyword(s):  
Decision Making ◽  
Antisaccade Task ◽  
Accumulator Model

scholarly journals The dual accumulator model of strategic deliberation and decision making.

2020 ◽  
Vol 127 (4) ◽  
pp. 477-504 ◽  
Author(s):  
Russell Golman ◽  
Sudeep Bhatia ◽  
Patrick Bodilly Kane
Keyword(s):  
Decision Making ◽  
Accumulator Model

A neural model of decision-making by the superior colicullus in an antisaccade task

2007 ◽  
Vol 20 (6) ◽  
pp. 690-704 ◽  
Author(s):  
Vassilis Cutsuridis ◽  
Nikolaos Smyrnis ◽  
Ioannis Evdokimidis ◽  
Stavros Perantonis
Keyword(s):  
Decision Making ◽  
Neural Model ◽  
Antisaccade Task

scholarly journals A Biophysical Model of Decision Making in an Antisaccade Task Through Variable Climbing Activity

2005 ◽  
pp. 205-210 ◽  
Author(s):  
Vassilis Cutsuridis ◽  
Ioannis Kahramanoglou ◽  
Stavros Perantonis ◽  
Ioannis Evdokimidis ◽  
Nikolaos Smyrnis
Keyword(s):  
Decision Making ◽  
Biophysical Model ◽  
Antisaccade Task

scholarly journals Neurally constrained modeling of speed-accuracy tradeoff during visual search: gated accumulation of modulated evidence

2019 ◽  
Vol 121 (4) ◽  
pp. 1300-1314 ◽  
Author(s):  
Mathieu Servant ◽  
Gabriel Tillman ◽  
Jeffrey D. Schall ◽  
Gordon D. Logan ◽  
Thomas J. Palmeri
Keyword(s):  
Decision Making ◽  
Visual Search ◽  
Response Times ◽  
Frontal Eye Field ◽  
Search Performance ◽  
Model Framework ◽  
Accumulator Model ◽  
Eye Field ◽  
Speed Accuracy ◽  
Accuracy Tradeoff

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.

Analytical Calculation of Errors in Time and Value Perception Due to a Subjective Time Accumulator: A Mechanistic Model and the Generation of Weber’s Law

2016 ◽  
Vol 28 (1) ◽  
pp. 89-117 ◽  
Author(s):  
Vijay Mohan K. Namboodiri ◽  
Stefan Mihalas ◽  
Marshall G. Hussain Shuler
Keyword(s):  
Decision Making ◽  
Time Perception ◽  
Experimental Testing ◽  
Mechanistic Model ◽  
Analytical Calculation ◽  
Temporal Discounting ◽  
Subjective Time ◽  
Weber’S Law ◽  
Weber's Law ◽  
Accumulator Model

It has been previously shown (Namboodiri, Mihalas, Marton, & Hussain Shuler, 2014 ) that an evolutionary theory of decision making and time perception is capable of explaining numerous behavioral observations regarding how humans and animals decide between differently delayed rewards of differing magnitudes and how they perceive time. An implementation of this theory using a stochastic drift-diffusion accumulator model (Namboodiri, Mihalas, & Hussain Shuler, 2014a ) showed that errors in time perception and decision making approximately obey Weber’s law for a range of parameters. However, prior calculations did not have a clear mechanistic underpinning. Further, these calculations were only approximate, with the range of parameters being limited. In this letter, we provide a full analytical treatment of such an accumulator model, along with a mechanistic implementation, to calculate the expression of these errors for the entirety of the parameter space. In our mechanistic model, Weber’s law results from synaptic facilitation and depression within the feedback synapses of the accumulator. Our theory also makes the prediction that the steepness of temporal discounting can be affected by requiring the precise timing of temporal intervals. Thus, by presenting exact quantitative calculations, this work provides falsifiable predictions for future experimental testing.

Neurally constrained multiple-accumulator model of perceptual decision making

2010 ◽  
Author(s):  
Thomas J. Palmeri ◽  
Braden A. Purcell ◽  
Richard P. Heitz ◽  
Jeffrey D. Schall ◽  
Gordon D. Logan
Keyword(s):  
Decision Making ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Accumulator Model
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