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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

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The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
08:24

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies

Published on: August 25, 2023

Goal-proximity decision-making.

Vladislav D Veksler1, Wayne D Gray, Michael J Schoelles

  • 1Air Force Research Laboratory, AFB, OH, USA. vdv718@gmail.com

Cognitive Science
|April 5, 2013
PubMed
Summary
This summary is machine-generated.

Reinforcement learning (RL) models fail when humans make decisions without rewards. Goal-Proximity Decision-making (GPD) uses object proximity to guide choices, outperforming RL in simulations and human experiments.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Reinforcement learning (RL) models are limited in explaining human decision-making when prior rewards or punishments are absent.
  • Existing models struggle to account for choices based on learned associations between goals and options derived from experience.

Purpose of the Study:

  • To propose and evaluate a novel decision-making mechanism, Goal-Proximity Decision-making (GPD), that addresses the limitations of RL in scenarios lacking explicit reward feedback.
  • To implement GPD within the ACT-R cognitive architecture and compare its efficiency and accuracy against RL.

Main Methods:

  • Developed the Goal-Proximity Decision-making (GPD) mechanism, which relies on association strengths reflecting experienced object proximity to guide choices.
  • Implemented GPD within the ACT-R cognitive framework.
  • Conducted three maze-navigation simulations to compare GPD's efficiency against RL.
  • Designed and executed an experiment where human participants made choices without prior reward information.

Main Results:

  • GPD demonstrated superior efficiency compared to RL across three maze-navigation simulations.
  • The performance advantage of GPD over RL increased with greater task difficulty.
  • GPD accurately captured human decision-making performance in an experiment where choices were made without explicit reward feedback, outperforming RL.

Conclusions:

  • Goal-Proximity Decision-making (GPD) offers a more effective approach than traditional RL for modeling human choices, particularly in situations devoid of immediate reward or punishment.
  • The ACT-R implemented GPD provides a computationally plausible mechanism for understanding how humans leverage past experiences of object proximity for goal-directed decision-making.
  • GPD's ability to explain human behavior in reward-absent scenarios highlights the importance of associative learning and spatial memory in cognitive decision processes.