An optimality principle for unsupervised learning
Advances in neural information processing systems 1
Organization of face and object recognition in modular neural network models
Neural Networks - Special issue on organisation of computation in brain-like systems
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
The Computational Exploration of Visual Word Recognition in a Split Model
Neural Computation
A Magnetic Stimulation Examination of Orthographic Neighborhood Effects in Visual Word Recognition
Journal of Cognitive Neuroscience
EMPATH: A Neural Network that Categorizes Facial Expressions
Journal of Cognitive Neuroscience
The Fusiform "Face Area" is Part of a Network that Processes Faces at the Individual Level
Journal of Cognitive Neuroscience
Hemispheric asymmetry in perception: A differential encoding account
Journal of Cognitive Neuroscience
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Anatomical evidence shows that our visual field is initially split along the vertical midline and contralaterally projected to different hemispheres. It remains unclear at which processing stage the split information converges. In the current study, we applied the Double Filtering by Frequency (DFF) theory (Ivry & Robertson, 1998) to modeling the visual field split; the theory assumes a right-hemisphere/low-frequency bias. We compared three cognitive architectures with different timings of convergence and examined their cognitive plausibility to account for the left-side bias effect in face perception observed in human data. We show that the early convergence model failed to show the left-side bias effect. The modeling, hence, suggests that the convergence may take place at an intermediate or late stage, at least after information has been extracted/encoded separately in the two hemispheres, a fact that is often overlooked in computational modeling of cognitive processes. Comparative anatomical data suggest that this separate encoding process that results in differential frequency biases in the two hemispheres may be engaged from V1 up to the level of area V3a and V4v, and converge at least after the lateral occipital region. The left-side bias effect in our model was also observed in Greeble recognition; the modeling, hence, also provides testable predictions about whether the left-side bias effect may also be observed in (expertise-level) object recognition.