Literature DB >> 28242793

Coupled Decision Processes Update and Maintain Saccadic Priors in a Dynamic Environment.

Timothy Doyeon Kim1, Mohammad Kabir2, Joshua I Gold3.   

Abstract

Much of what we know about how the brain forms decisions comes from studies of saccadic eye movements. However, saccadic decisions are often studied in isolation, which limits the insights that they can provide about real-world decisions with complex interdependencies. Here, we used a serial reaction time (RT) task to show that prior expectations affect RTs via interdependent, normative decision processes that operate within and across saccades. We found that human subjects performing the task generated saccades that were governed by a rise-to-threshold decision process with a starting point that reflected expected state-dependent transition probabilities. These probabilities depended on decisions about the current state (the correct target) that, under some conditions, required the accumulation of information across saccades. Without additional feedback, this information was provided by each saccadic decision threshold, which represented the total evidence in favor of the chosen target. Therefore, the output of the within-saccade process was used, not only to generate the saccade, but also to provide input to the across-saccade process. This across-saccade process, in turn, helped to set the starting point of the next within-saccade process. These results imply a novel role for functional information-processing loops in optimizing saccade generation in dynamic environments.SIGNIFICANCE STATEMENT Saccades are the rapid, ballistic eye movements that we make approximately three times every second to scan the visual scene for interesting things to look at. The apparent ease with which we produce saccades belies their computational sophistication, which can be studied quantitatively in the laboratory to provide insights into how our brain manages the interplay between sensory input and motor output. The present work is important because we show for the first time how this interplay operates both within and across saccades to ensure that these eye movements are guided effectively by learned expectations in dynamic environments. More generally, this study shows how sensory-motor decision processes, typically studied in isolation, interact via functional information-processing loops in the brain to produce complex, adaptive behaviors.
Copyright © 2017 the authors 0270-6474/17/373632-14$15.00/0.

Entities:  

Keywords:  decision-making; eye movement; response times; saccade

Mesh:

Year:  2017        PMID: 28242793      PMCID: PMC5373139          DOI: 10.1523/JNEUROSCI.3078-16.2017

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  68 in total

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Authors:  B A Reddi; R H Carpenter
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Authors:  G D Horwitz; W T Newsome
Journal:  Science       Date:  1999-05-14       Impact factor: 47.728

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Authors:  Leo P Sugrue; Greg S Corrado; William T Newsome
Journal:  Science       Date:  2004-06-18       Impact factor: 47.728

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6.  Changes in expectation consequent on experience, modeled by a simple, forgetful neural circuit.

Authors:  Andrew J Anderson; R H S Carpenter
Journal:  J Vis       Date:  2006-07-24       Impact factor: 2.240

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Authors:  M I Posner; C R Snyder; B J Davidson
Journal:  J Exp Psychol       Date:  1980-06

8.  Caudate encodes multiple computations for perceptual decisions.

Authors:  Long Ding; Joshua I Gold
Journal:  J Neurosci       Date:  2010-11-24       Impact factor: 6.167

9.  Rational regulation of learning dynamics by pupil-linked arousal systems.

Authors:  Matthew R Nassar; Katherine M Rumsey; Robert C Wilson; Kinjan Parikh; Benjamin Heasly; Joshua I Gold
Journal:  Nat Neurosci       Date:  2012-06-03       Impact factor: 24.884

10.  Generalized lessons about sequence learning from the study of the serial reaction time task.

Authors:  Hillary Schwarb; Eric H Schumacher
Journal:  Adv Cogn Psychol       Date:  2012-05-21
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  10 in total

1.  Visuomotor Correlates of Conflict Expectation in the Context of Motor Decisions.

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Journal:  J Neurosci       Date:  2018-09-10       Impact factor: 6.167

2.  Optimizing sequential decisions in the drift-diffusion model.

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Review 3.  Optimal models of decision-making in dynamic environments.

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4.  Choice history biases subsequent evidence accumulation.

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5.  Tracking the contribution of inductive bias to individualised internal models.

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6.  Pupil diameter encodes the idiosyncratic, cognitive complexity of belief updating.

Authors:  Alexandre Ls Filipowicz; Christopher M Glaze; Joseph W Kable; Joshua I Gold
Journal:  Elife       Date:  2020-05-18       Impact factor: 8.140

7.  Counterfactual Reasoning Underlies the Learning of Priors in Decision Making.

Authors:  Ariel Zylberberg; Daniel M Wolpert; Michael N Shadlen
Journal:  Neuron       Date:  2018-08-16       Impact factor: 17.173

8.  Suboptimal human inference can invert the bias-variance trade-off for decisions with asymmetric evidence.

Authors:  Tahra L Eissa; Joshua I Gold; Krešimir Josić; Zachary P Kilpatrick
Journal:  PLoS Comput Biol       Date:  2022-07-19       Impact factor: 4.779

9.  Synaptic mechanisms of interference in working memory.

Authors:  Zachary P Kilpatrick
Journal:  Sci Rep       Date:  2018-05-18       Impact factor: 4.379

10.  Adaptive History Biases Result from Confidence-Weighted Accumulation of past Choices.

Authors:  Anke Braun; Anne E Urai; Tobias H Donner
Journal:  J Neurosci       Date:  2018-01-25       Impact factor: 6.167

  10 in total

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