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Local dynamics in decision making: The evolution of preference within and across decisions.
Denis O'Hora1, Rick Dale, Petri T Piiroinen
1School of Psychology, National University of Ireland, Galway, University Road, Galway, Ireland. denis.ohora@nuigalway.ie
Scientific Reports
|July 18, 2013
Summary
Mouse cursor movements reveal how people make choices. Competition in decision-making is lower for high-reward options compared to low-reward options, offering insights into cognitive processes.
Area of Science:
- Cognitive Science
- Neuroscience
- Human-Computer Interaction
Background:
- Decision-making involves competition between alternatives.
- The context of choices influences preference.
- Mouse cursor trajectories offer continuous data on cognitive processes.
Purpose of the Study:
- To analyze cognitive decision-making processes using mouse cursor trajectories.
- To investigate how reward value affects choice competition.
- To develop a framework for analyzing decision dynamics.
Main Methods:
- Three experiments using a discrimination learning paradigm.
- Recording participants' mouse cursor trajectories during symbol selection.
- Analyzing trajectory data to infer potential fields and decision spaces.
Main Results:
- Decisions with equally high-point rewards showed less competition than those with equally low-point rewards.
- Inferred potential fields revealed choice locations as areas of minimal potential.
- Decision spaces dynamically evolved with participant learning.
Conclusions:
- Mouse cursor dynamics provide a window into real-time decision-making.
- Reward value significantly modulates the competitive nature of choices.
- This visualization approach offers a novel framework for decision science research.
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