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Poster

Decoding the perceptual boundary of human/monkey face categories from a population of neurons in the Inferior-Temporal (IT) cortex of the macaque monkey brain

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84221

Sigala,  RA
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84063

Logothetis,  NK
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84154

Rainer,  G
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Sigala, R., Logothetis, N., & Rainer, G. (2008). Decoding the perceptual boundary of human/monkey face categories from a population of neurons in the Inferior-Temporal (IT) cortex of the macaque monkey brain. Poster presented at 1st INCF Congress of Neuroinformatics: Databasing and Modeling the Brain, Stockholm, Sweden.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C769-0
Zusammenfassung
Faces have been intensively used in human and monkey subjects to study visual perception. However, due to the different approaches scientists have had to follow given the implicit differences in these two types of observers, there are few studies comparing face perception in both species, especially perceptual effects in humans and single cell recordings in behaving monkeys. Using a new computer vision algorithm based on Support Vector Machines (SVMs) we created realistic morphs by linearly interpolating three-dimensional information of human and monkey faces. We asked human observers to categorize these morphs as humans or monkeys and we found that they draw the category boundary closer to their own species (at approximately 60human/40monkey). We looked for the neural correlates of this effect recording the single-unit-activity (SUA) of neurons (194 in monkey M1 and 220 in monkey M2) in the IT cortex of two macaque monkeys while they fixate at those faces. Considering all recorded neurons, 85 in monkey M1 and 62 in monkey M2 were visually selective, 14 in monkey M1 and 4 in monkey M2 were face-selective and 8 in monkey M1 and 2 in monkey M2, were category-selective. To find out how these morphs are represented at the level of the population of all recorded neurons, we first reduced the dimensionality of the data applying the Principal Component Analysis (PCA) and using the best 10 of the principal components ranked according to the variance they explained. We used a pattern classifier (Support Vector Machine or SVM) to learn this new representation (form by the principal components) of the responses to human and monkey faces and classify the responses to ambiguous morphs into one of both categories. We found that in both cases (using the neural responses recorded in monkeys M1 and M2), and symmetric to the findings in humans, the classifier drew the category boundary closer to the monkey category (at approximately 40human/60 monkey). These findings suggest an ‘own species’ advantage in the encoding of face stimuli by human and monkey observers. Our findings also indicate that this species-specific advantage is represented by a large fraction of neurons in the inferior temporal (IT) cortex of the monkey brain.