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Updated: Jun 1, 2026
09:49
Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
Published on: April 16, 2014
Two routes to face perception: evidence from psychophysics and computational modeling
Adrian Schwaninger1, Janek S Lobmaier, Christian Wallraven
1School of Applied Psychology, University of Applied Sciences Northwestern Switzerland Department of Informatics, University of Zurich, Switzerland Department of Psychology, University of Berne, Switzerland Max Planck Institute for Biological Cybernetics, Tübingen, Germany Department of Psychological Medicine, School of Medicine, Cardiff University, UK.
Cognitive Science
|May 19, 2011
Summary
This study shows that both facial features and their spatial arrangement are crucial for face recognition, supporting a dual-mode processing hypothesis. Computational models can replicate these findings.
Area of Science:
- Cognitive Psychology
- Neuroscience
- Computer Vision
Background:
- Human face recognition relies on processing both individual features and their spatial relationships.
- Previous research has not clearly delineated the independent contributions of featural and configural information.
Purpose of the Study:
- To investigate the distinct roles of featural and configural face representations in recognition.
- To validate a novel method for creating stimuli isolating featural or configural information.
- To propose and test a computational model of dual-mode face processing.
Main Methods:
- Creation of stimuli with isolated featural (scrambled parts) or configural (blurred) information.
- Utilizing an old-new recognition task with unfamiliar and familiar faces.
- Developing and validating a computational framework based on appearance-based representation.
Main Results:
- Both featural and configural information enabled above-chance recognition of faces.
- Stimuli validation confirmed the isolation of representation types.
- Computational models successfully replicated the psychophysical findings.
Conclusions:
- Both featural and configural information are essential for effective face recognition.
- A dual-mode hypothesis, integrating both processing routes, is supported.
- The proposed computational framework accurately models human face perception data.
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