Dataset Information

Neural responses to visual scenes reveals inconsistencies between fMRI adaptation and multivoxel pattern analysis.

ABSTRACT: Human observers can recognize real-world visual scenes with great efficiency. Cortical regions such as the parahippocampal place area (PPA) and retrosplenial complex (RSC) have been implicated in scene recognition, but the specific representations supported by these regions are largely unknown. We used functional magnetic resonance imaging adaptation (fMRIa) and multi-voxel pattern analysis (MVPA) to explore this issue, focusing on whether the PPA and RSC represent scenes in terms of general categories, or as specific scenic exemplars. Subjects were scanned while viewing images drawn from 10 outdoor scene categories in two scan runs and images of 10 familiar landmarks from their home college campus in two scan runs. Analyses of multi-voxel patterns revealed that the PPA and RSC encoded both category and landmark information, with a slight advantage for landmark coding in RSC. fMRIa, on the other hand, revealed a very different picture: both PPA and RSC adapted when landmark information was repeated, but category adaptation was only observed in a small subregion of the left PPA. These inconsistencies between the MVPA and fMRIa data suggests that these two techniques interrogate different aspects of the neuronal code. We propose three hypotheses about the mechanisms that might underlie adaptation and multi-voxel signals.

SUBMITTER: Epstein RA

PROVIDER: S-EPMC3288674 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Neural responses to visual scenes reveals inconsistencies between fMRI adaptation and multivoxel pattern analysis.

Epstein Russell A RA Morgan Lindsay K LK

Neuropsychologia 20111005 4

Human observers can recognize real-world visual scenes with great efficiency. Cortical regions such as the parahippocampal place area (PPA) and retrosplenial complex (RSC) have been implicated in scene recognition, but the specific representations supported by these regions are largely unknown. We used functional magnetic resonance imaging adaptation (fMRIa) and multi-voxel pattern analysis (MVPA) to explore this issue, focusing on whether the PPA and RSC represent scenes in terms of general cat ...[more]

PMID: 22001314

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Project description:Biologically inspired deep convolutional neural networks (CNNs), trained for computer vision tasks, have been found to predict cortical responses with remarkable accuracy. However, the internal operations of these models remain poorly understood, and the factors that account for their success are unknown. Here we develop a set of techniques for using CNNs to gain insights into the computational mechanisms underlying cortical responses. We focused on responses in the occipital place area (OPA), a scene-selective region of dorsal occipitoparietal cortex. In a previous study, we showed that fMRI activation patterns in the OPA contain information about the navigational affordances of scenes; that is, information about where one can and cannot move within the immediate environment. We hypothesized that this affordance information could be extracted using a set of purely feedforward computations. To test this idea, we examined a deep CNN with a feedforward architecture that had been previously trained for scene classification. We found that responses in the CNN to scene images were highly predictive of fMRI responses in the OPA. Moreover the CNN accounted for the portion of OPA variance relating to the navigational affordances of scenes. The CNN could thus serve as an image-computable candidate model of affordance-related responses in the OPA. We then ran a series of in silico experiments on this model to gain insights into its internal operations. These analyses showed that the computation of affordance-related features relied heavily on visual information at high-spatial frequencies and cardinal orientations, both of which have previously been identified as low-level stimulus preferences of scene-selective visual cortex. These computations also exhibited a strong preference for information in the lower visual field, which is consistent with known retinotopic biases in the OPA. Visualizations of feature selectivity within the CNN suggested that affordance-based responses encoded features that define the layout of the spatial environment, such as boundary-defining junctions and large extended surfaces. Together, these results map the sensory functions of the OPA onto a fully quantitative model that provides insights into its visual computations. More broadly, they advance integrative techniques for understanding visual cortex across multiple level of analysis: from the identification of cortical sensory functions to the modeling of their underlying algorithms.

Dataset Information

Neural responses to visual scenes reveals inconsistencies between fMRI adaptation and multivoxel pattern analysis.

Publications

Neural responses to visual scenes reveals inconsistencies between fMRI adaptation and multivoxel pattern analysis.

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