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Related Concept Videos

Instinctive Drift01:05

Instinctive Drift

208
Instinctive drift refers to the tendency of animals to revert to their innate behaviors despite repeated reinforcement. Breland and Breland demonstrated this concept in an experiment with a raccoon. The raccoon was trained to pick up two coins and place them in a container in exchange for food. Initially, the raccoon learned to associate the coins with food, making them a conditioned stimulus or a substitute for food. However, over time, the raccoon became less willing to put the coins into the...
208

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Updated: Jun 25, 2025

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
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Of Rodents and Primates: Time-Variant Gain in Drift-Diffusion Decision Models.

Abdoreza Asadpour1, Hui Tan1,2, Brendan Lenfesty1

  • 1Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland UK.

Computational Brain & Behavior
|May 27, 2024
PubMed
Summary
This summary is machine-generated.

Time-variant gain in drift-diffusion models (DDM) affects choice behavior differently depending on whether it impacts signal or noise. This study shows gain on noise causes slower error reaction times, while gain on signal causes faster error reaction times.

Keywords:
Cognitive computational modellingDrift–diffusion modelPerceptual decision-makingTime-variant gainUrgency signal

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Decision Science

Background:

  • Sequential sampling models, like the drift-diffusion model (DDM), explain decision-making by evidence accumulation.
  • Time-variant gain features in DDM capture urgency signals and can produce slower error reaction times (RTs).
  • It remains unclear if applying time-variant gain to signal versus noise features differentially impacts choice behavior.

Purpose of the Study:

  • To investigate the distinct effects of time-variant gain on signal versus noise components within the DDM.
  • To develop a parsimonious DDM variant focusing on time-variant gain mechanisms.
  • To computationally model and compare choice behavior across species (rats, monkeys, humans).

Main Methods:

  • Developed and applied a DDM variant with constrained time-variant gain mechanisms.
  • Utilized computational modeling to analyze choice behavior data from rats, monkeys, and humans.
  • Systematically evaluated the impact of time-variant gain applied solely to drift rate (signal) or noise.

Main Results:

  • Time-variant gain on DDM's noise alone produced slower error RTs, consistent with monkey behavior.
  • Time-variant gain on DDM's drift rate alone resulted in faster error RTs, mirroring rodent behavior.
  • Minimal effects of time-variant gain were observed in human data.

Conclusions:

  • Time-variant gain applied to different DDM components leads to distinct choice behaviors.
  • This approach elucidates species-specific time-variant gain mechanisms in decision-making.
  • The findings support the utility of group-level modeling for capturing cross-species trends and for systematic data fitting.