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The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
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A Quantized Representation of Intertemporal Choice in the Brain.

James Tee1, Desmond P Taylor2

  • 1Department of Psychology, New York University.

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications
|April 19, 2021
PubMed
Summary
This summary is machine-generated.

This study suggests intertemporal choices, like time discounting, may be quantized, not continuous. Quantized models better explain behavioral data than traditional continuous models.

Keywords:
continuousdecision-makingdiscreteexponentialhyperbolicintertemporal choicesquantizationrepresentation

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Area of Science:

  • Decision neuroscience
  • Behavioral economics
  • Computational psychiatry

Background:

  • Value and probability are typically modeled as continuous, but recent theories propose discrete or quantized representations.
  • Intertemporal choices involve decisions across different time points and are influenced by value and probability, as described by Prospect Theory.
  • Previous research has explored quantized probability in the brain, but quantized intertemporal choice remains largely unexamined.

Purpose of the Study:

  • To hypothesize and test whether intertemporal choices are quantized.
  • To introduce and evaluate two novel models: quantized hyperbolic discounting and quantized exponential discounting.
  • To re-examine existing fMRI behavioral data using these new quantized models.

Main Methods:

  • Analysis of behavioral data from a prior fMRI study on intertemporal tasks.
  • Development and application of two novel computational models: quantized hyperbolic discounting and quantized exponential discounting.
  • Model comparison using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) tests.

Main Results:

  • Quantized hyperbolic and exponential discounting models provided a better fit to the experimental data than their continuous counterparts.
  • 13 out of 20 participants were best fit by the quantized exponential model, while 7 were best fit by the quantized hyperbolic model.
  • A significant majority (15/20) of participants were best explained by models with 5-bit precision, indicating discrete steps in decision-making.

Conclusions:

  • Quantized versions of both hyperbolic and exponential discounting models significantly outperform their continuous forms in explaining behavioral data.
  • The findings support the hypothesis that intertemporal choices are processed in discrete, quantized steps rather than continuously.
  • This research opens avenues for exploring quantized representations in decision-making and potential applications in understanding neurological processes.