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Related Concept Videos

Decision Making01:20

Decision Making

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.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Reason and Intuition01:37

Reason and Intuition

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 brain can only use...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Heuristics01:21

Heuristics

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.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...

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Related Experiment Video

Updated: Jun 6, 2026

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
10:39

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

Published on: May 3, 2018

Structure learning in human sequential decision-making.

Daniel E Acuña1, Paul Schrater

  • 1Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, United States of America. acuna002@umn.edu

Plos Computational Biology
|December 15, 2010
PubMed
Summary
This summary is machine-generated.

Humans exhibit near-optimal structure learning in sequential decision-making tasks. This challenges previous findings of suboptimal performance by accounting for the complexity of learning reward generation models.

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Last Updated: Jun 6, 2026

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Reinforcement Learning

Background:

  • Human sequential decision-making is often considered suboptimal compared to ideal models.
  • Previous studies overlooked the complexity of learning environmental reward structures.

Purpose of the Study:

  • To investigate structure learning in sequential decision-making.
  • To model human behavior using Bayesian reinforcement learning.
  • To determine if humans can learn reward generation models.

Main Methods:

  • Formulated structure learning using Bayesian reinforcement learning.
  • Designed experiments with mixed one-armed and two-armed bandit reward models.
  • Analyzed behavioral data for evidence of structure learning.

Main Results:

  • Learning the generative model for rewards significantly alters optimal agent behavior.
  • Observed behaviors previously deemed suboptimal are explained by structure learning.
  • Demonstrated that humans can perform structure learning effectively.

Conclusions:

  • Human sequential decision-making performance is better understood through the lens of structure learning.
  • Bayesian reinforcement learning provides a framework for modeling this complex learning process.
  • Future research should consider structure learning when evaluating human decision-making.