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Value construction through sequential sampling explains serial dependencies in decision making
Ariel Zylberberg1, Akram Bakkour1,2,3, Daphna Shohamy1,4,5
1Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States.
Elife
|December 10, 2024
Summary
Subjective values fluctuate during decision-making, influencing choices and brain activity. This study introduces a new algorithm, Reval, to track these dynamic value changes, offering a better explanation than static values.
Area of Science:
- Neuroscience
- Cognitive Science
- Decision Science
Background:
- Decisions between familiar items typically rely on comparing subjective values.
- Similar values prolong decision times and can lead to inconsistent choices, often attributed to noisy evidence accumulation.
Purpose of the Study:
- To investigate if apparent choice stochasticity stems from dynamic value fluctuations rather than just noise.
- To develop and validate a method for tracking real-time changes in subjective value.
Main Methods:
- Analysis of choice data from a snack item selection task.
- Development of the Reval algorithm to detect within-session value fluctuations.
- Modeling choice behavior and neural activity (BOLD signal) using dynamic values.
Main Results:
- The Reval algorithm identified significant dynamic fluctuations in subjective item values.
- These dynamic values more accurately predicted participant choices and response times than static, stated values.
- Dynamic values provided a better explanation for brain activity in the ventromedial prefrontal cortex.
Conclusions:
- Subjective value is not static but dynamically constructed during deliberation.
- These value revaluations influence subsequent decisions, explaining choice variability.
- The findings support a bounded-evidence accumulation model with temporally correlated evidence samples.
Keywords:
choice-induced preference changedecision makingdrift-diffusion modelfood-choice taskhumanneurosciencevalue-based decisionsvmPFCMore Related Videos
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