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Summary
This summary is machine-generated.

This study introduces the racing diffusion model, a simpler alternative to complex sequential sampling models. It explains response time data without needing random between-trial variability, offering new insights into decision-making processes.

Keywords:
Decision makingResponse timeSequential sampling models

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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Decision Science

Background:

  • Current sequential sampling models often incorporate random between-trial variability.
  • This variability increases model complexity and has been criticized for excessive flexibility.
  • Such parameters do not fully explain the data generation process.

Purpose of the Study:

  • To investigate the necessity of between-trial variability parameters in sequential sampling models.
  • To propose a simpler model that can account for response time data phenomena.
  • To offer alternative explanations for decision-making processes.

Main Methods:

  • Development of a novel sequential sampling model: the racing diffusion model.
  • The model simulates N-choice speeded decision-making.
  • It employs a race of noisy evidence accumulators within trials, without assuming between-trial variability.

Main Results:

  • The racing diffusion model provides alternative explanations for response time patterns.
  • It accounts for phenomena like faster error response times compared to correct ones.
  • The model demonstrates that between-trial variability is not always necessary.

Conclusions:

  • The racing diffusion model offers a parsimonious explanation for response time data.
  • Rethinking the inclusion of between-trial variability parameters is warranted.
  • This work advances the understanding of evidence accumulation in decision-making.