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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Signatures of Perseveration and Heuristic-Based Directed Exploration in Two-Step Sequential Decision Task Behaviour.

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  • 1Biological Psychology, Department of Psychology, University of Cologne, Germany.

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Summary

This study enhances computational models of reinforcement learning (RL) to better understand exploration and perseveration in psychiatric disorders. Findings reveal a more complex RL model best explains decision-making, offering insights for computational psychiatry.

Keywords:
computational psychiatryexplorationhabitshigher-order perseverationmodel-basedneurocomputational endophenotypestwo-step task

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Area of Science:

  • Cognitive Neuroscience
  • Computational Psychiatry
  • Reinforcement Learning Theory

Background:

  • Reinforcement Learning (RL) processes like model-based (MB) control and exploration are crucial in neuroscience and psychiatry.
  • Dysregulation in these RL processes is linked to psychiatric disorders, but they are often studied in isolation.
  • Standard hybrid models of the two-step task (TST) are used to measure MB control.

Purpose of the Study:

  • To extend standard hybrid models of the TST to quantify exploration and perseveration mechanisms.
  • To compare different computational model extensions for the TST.
  • To investigate the neurocomputational underpinnings of decision-making in psychiatric contexts.

Main Methods:

  • Implemented and compared various computational model extensions for a sequential RL task (two-step task).
  • Utilized two independent datasets from different task variants.
  • Employed posterior predictive checks to validate model performance.

Main Results:

  • An extended hybrid RL model incorporating higher-order perseveration and heuristic-based exploration provided the best fit to the data.
  • A simpler model with complex perseveration alone was also a good fit.
  • A significant positive effect of directed exploration on stage-one choice probabilities was identified.

Conclusions:

  • The extended RL model successfully reproduced choice patterns across both datasets.
  • Findings highlight the importance of considering combined exploration and perseveration mechanisms.
  • Results have implications for computational psychiatry and identifying neurocognitive endophenotypes.