Comparing exemplar-retrieval and decision-bound models of speeded perceptual classification

Area of Science:

• Cognitive Psychology
• Computational Neuroscience
• Perception

Background:

• Comparing cognitive models is crucial for understanding decision-making.
• Nosofsky and Palmeri's exemplar-based random-walk (EBRW) and Ashby and Maddox's decision-bound model (DBM) are prominent theories.
• Garner's speeded classification tasks provide a standard paradigm for testing these models.

Purpose of the Study:

• To evaluate the predictive accuracy of the EBRW and DBM in Garner's speeded classification tasks.
• To assess model performance with integral-dimension stimuli, focusing on facilitation and interference.
• To rigorously test models by fitting detailed response time (RT) distribution data.

Main Methods:

• Empirical comparison of EBRW and DBM performance.
• Analysis of Garner's speeded classification tasks with integral-dimension stimuli.
• Fitting of detailed response time (RT) distributions and accuracy data for model evaluation.

Main Results:

• Both EBRW and DBM provided reasonable global fits to RT distributions and accuracy.
• The DBM failed to accurately predict interference effects in the filtering task.
• The DBM incorrectly predicted faster RTs in the filtering task compared to the control task, contrary to observed data.

Conclusions:

• The EBRW model demonstrates superior predictive power over the DBM for Garner's tasks, particularly regarding interference effects.
• The DBM exhibits a fundamental limitation in modeling interference with integral-dimension stimuli.
• Findings necessitate refinement of the DBM or favor the EBRW for explaining performance in these specific classification tasks.