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A Within-Subject Experimental Design using an Object Location Task in Rats
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Choice-induced preference change under a sequential sampling model framework.

Douglas G Lee1, Giovanni Pezzulo2

  • 1Institute of Cognitive Sciences and Technologies, National Research Council, Via Giandomenico Romagnosi 18a, 00196, Rome, Italy. DouglasGLee@gmail.com.

Scientific Reports
|March 22, 2026
PubMed
Summary
This summary is machine-generated.

The drift-diffusion model (DDM) can now explain choice-induced preference change, a phenomenon where preferences shift after decisions. This updated model accounts for spreading of alternatives (SoA) alongside accuracy and response times.

Keywords:
Drift–diffusion modelEvidence accumulationMulti-attribute choicePreferential choiceSpreading of alternatives

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Area of Science:

  • Cognitive psychology
  • Computational neuroscience
  • Decision science

Background:

  • Sequential sampling models, like the drift-diffusion model (DDM), are widely used to analyze choice behavior, including accuracy, response time (RT), and confidence.
  • However, these models traditionally assume fixed option values during deliberation and do not account for choice-induced preference change, where preferences shift post-decision.

Purpose of the Study:

  • To extend sequential sampling models, specifically the DDM, to incorporate and explain choice-induced preference change and the resulting spreading of alternatives (SoA).
  • To demonstrate that relaxing the stationary value assumption in the DDM allows it to account for SoA while maintaining its ability to explain accuracy and RT.

Main Methods:

  • Modified the drift-diffusion model (DDM) by relaxing the assumption of stationary option values during choice deliberation.
  • Simulated choice behavior using the adapted DDM to generate SoA and analyzed its relationship with choice difficulty, attribute disparity, and RT.

Main Results:

  • The modified DDM successfully generated choice-induced preference change (SoA), aligning with empirical findings.
  • The model reproduced known relationships between SoA and factors like choice difficulty, attribute disparity, and RT.
  • While basic DDM versions explained some results, incorporating multi-attribute evidence and differential start times improved the model's fit to experimental data.

Conclusions:

  • Relaxing the stationary value assumption in sequential sampling models, particularly the DDM, enables them to account for choice-induced preference change (SoA).
  • The DDM, when appropriately extended, can simultaneously explain preference shifts, choice consistency, and response times, offering a more comprehensive model of decision-making.
  • Future research should explore multi-attribute evidence and attribute-specific timing within the DDM framework for enhanced empirical accuracy.