RESUMEN
Object knowledge is hierarchical. Several hypotheses have proposed that this property might be reflected in the spatial organization of ventral visual cortex. For example, all exemplars of a category might activate the same patches of cortex, but with a slightly different position of the peak of activation in each patch. According to this view, category selectivity would be organized at a larger spatial scale compared to exemplar selectivity. No empirical evidence for such proposals is available from experiments with human subjects. Here, we compare the relative scale of organization for category and exemplar selectivity in two datasets with two methods: (i) by investigating the previously reported beneficial effect of spatial smoothing of the fMRI data on the reliability of multi-voxel selectivity patterns; and (ii) by comparing the relative weight of lower and higher spatial frequencies in the spatial frequency spectrum of these selectivity patterns. The findings are consistent with the proposal that selectivity for stimulus properties that underlie finer distinctions between objects is organized at a finer scale than selectivity for stimulus properties that differentiate categories. This finding confirms the existence of multiple scales of organization in the ventral visual pathway.
Asunto(s)
Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética/métodos , Percepción Espacial/fisiología , Corteza Visual/fisiología , Percepción Visual/fisiología , Adolescente , Adulto , Algoritmos , Mapeo Encefálico , Simulación por Computador , Interpretación Estadística de Datos , Femenino , Análisis de Fourier , Humanos , Masculino , Estimulación Luminosa , Vías Visuales/fisiología , Adulto JovenRESUMEN
Visual object recognition is remarkably accurate and robust, yet its neurophysiological underpinnings are poorly understood. Single cells in brain regions thought to underlie object recognition code for many stimulus aspects, which poses a limit on their invariance. Combining the responses of multiple non-invariant neurons via weighted linear summation offers an optimal decoding strategy, which may be able to achieve invariant object recognition. However, because object identification is essentially parameter optimization in this model, the characteristics of the identification task trained to perform are critically important. If this task does not require invariance, a neural population-code is inherently more selective but less tolerant than the single-neurons constituting the population. Nevertheless, tolerance can be learned - provided that it is trained for - at the cost of selectivity. We argue that this model is an interesting null-hypothesis to compare behavioral results with and conclude that it may explain several experimental findings.