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Statistical learning and adaptive decision-making underlie human response time variability in inhibitory control.

Ning Ma1, Angela J Yu2

  • 1Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.

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|September 1, 2015
PubMed
Summary

Trial-to-trial variability in response time (RT) reflects dynamic statistical learning and cognitive adjustments. Humans learn stop signal frequency and delay, influencing RT, offering insights into cognitive processing.

Keywords:
bayesian modelingdecision makinginhibitory controllearningpsychophysicsresponse timestop signal task

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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Behavioral Science

Background:

  • Response time (RT) variability in experiments is often high, limiting its utility.
  • Understanding cognitive adjustments to environmental statistics is crucial for interpreting behavior.

Purpose of the Study:

  • To investigate if RT variability reflects dynamic statistical learning and cognitive adjustments.
  • To model human learning of stop signal statistics and decision-making processes.

Main Methods:

  • Utilized the stop-signal task (SST) with 20 human subjects.
  • Employed a Bayesian hidden Markov model to learn stop signal frequency and delay.
  • Used an optimal stochastic control model for within-trial decision-making.

Main Results:

  • RT significantly increased with both expected stop signal frequency (P(stop)) and stop-signal delay (SSD).
  • P(stop) and SSD independently predicted RT, with P(stop) being more influential for 75% of subjects.
  • Behavioral data supported the model's predictions regarding RT adjustments.

Conclusions:

  • Humans effectively internalize environmental statistics and adapt their cognitive strategies.
  • RT variability patterns can validate models of statistical learning and decision-making.
  • The developed modeling tools can be applied to diverse behavioral paradigms and neural data analysis.