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Isolating objective and subjective filling-in using the drift diffusion model.

Ron Dekel^1,2, Dov Sagi^1,3, Ativ Zomet^4,5

¹Department of Brain Sciences, The Weizmann Institute of Science, Rehovot, Israel.

Journal of Vision

|December 18, 2023

Summary

This summary is machine-generated.

Spatial context affects visual target detection. Flanker distance influences decision criteria, with randomized distances impairing criterion adjustment. The drift diffusion model explains these effects via evidence accumulation and prior knowledge.

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Area of Science:

Visual perception
Cognitive psychology
Computational neuroscience

Background:

Spatial context, including flanker stimuli, significantly impacts visual target detection.
Signal detection theory (SDT) has been used to model decision-making, but struggles to distinguish criterion shifts from signal/noise variations.
Previous research shows target-flanker distance affects detection rates, with randomization having a greater impact.

Purpose of the Study:

To investigate how spatial context, specifically target-flanker distance, influences the decision criterion in visual detection tasks.
To differentiate between criterion shifts and signal/noise shifts using reaction time (RT) data within the drift diffusion model (DDM).
To explain RT-independent and RT-dependent effects observed in target-flanker interactions.

Main Methods:

Analysis of reaction time (RT) data from existing studies (Polat & Sagi, 2007; Zomet et al., 2008; 2016).
Application of the drift diffusion model (DDM) to analyze target-flanker interactions.
Examination of how flanker distance influences the rate of evidence accumulation and the starting point of decision-making.

Main Results:

A stronger dependence on flankers was observed for faster reaction times (RTs).
A weaker dependence on flankers was observed for slower RTs.
The drift diffusion model successfully explained these findings by incorporating changes in the evidence accumulation rate (signal/noise shift) and the starting point (criterion shift).

Conclusions:

The drift diffusion model provides a more nuanced explanation of spatial context effects than traditional SDT.
RT-independent effects are attributed to the signal/noise shift, while RT-dependent effects relate to criterion shifts.
Observers' difficulty in learning multiple internal distributions underlies the observed distance-dependent response bias.