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Related Concept Videos

Instinctive Drift01:05

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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...
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Related Experiment Video

Updated: May 21, 2026

Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model
06:30

Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model

Published on: May 24, 2019

The attentional drift-diffusion model extends to simple purchasing decisions.

Ian Krajbich1, Dingchao Lu, Colin Camerer

  • 1Division of the Humanities and Social Sciences, California Institute of Technology Pasadena, CA, USA.

Frontiers in Psychology
|June 19, 2012
PubMed
Summary
This summary is machine-generated.

The attentional drift-diffusion model (aDDM) accurately describes purchasing decisions, showing similar computational processes for simple buying choices as for binary and trinary decisions. Visual attention significantly influences these purchasing choices.

Keywords:
choicedecision neurosciencedecision-makingdrift-diffusioneye-trackingneuroeonomicspurchasingvaluation

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Area of Science:

  • Cognitive Psychology
  • Neuroeconomics
  • Computational Neuroscience

Background:

  • Simple purchasing decisions involve complex cognitive processes.
  • The attentional drift-diffusion model (aDDM) has successfully modeled binary and trinary value-based choices.
  • Understanding how visual attention influences purchasing is crucial.

Purpose of the Study:

  • To extend the aDDM to model simple purchasing decisions.
  • To quantitatively describe the relationship between choice, reaction time, and visual fixations in purchasing.
  • To compare the attentional mechanisms in purchasing decisions with those in binary choices.

Main Methods:

  • An eye-tracking experiment was conducted to record visual attention during purchasing decisions.
  • The extended aDDM was applied to analyze the collected behavioral and eye-tracking data.
  • Model parameters were fitted to psychometric data and compared with previous studies.

Main Results:

  • The aDDM provided an accurate quantitative description of purchasing decisions, reaction times, and visual fixations.
  • Parameters derived from the purchasing decision model were similar to those from binary and trinary choice models.
  • Attentional biases influencing choices were approximately half as large in purchasing decisions compared to binary choices.

Conclusions:

  • A unified computational process underlies simple purchasing decisions, binary choices, and trinary choices.
  • Visual attention plays a significant, quantifiable role in guiding purchasing decisions.
  • The aDDM serves as a robust framework for understanding value-based decision-making across different choice complexities.