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Neuroimage. 2018 Aug 1;176:372-379. doi: 10.1016/j.neuroimage.2018.05.006. Epub 2018 May 4.

Typical retinotopic locations impact the time course of object coding.

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Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany. Electronic address:
Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany.
Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.


In everyday visual environments, objects are non-uniformly distributed across visual space. Many objects preferentially occupy particular retinotopic locations: for example, lamps more often fall into the upper visual field, whereas carpets more often fall into the lower visual field. The long-term experience with natural environments prompts the hypothesis that the visual system is tuned to such retinotopic object locations. A key prediction is that typically positioned objects should be coded more efficiently. To test this prediction, we recorded electroencephalography (EEG) while participants viewed briefly presented objects appearing in their typical locations (e.g., an airplane in the upper visual field) or in atypical locations (e.g., an airplane in the lower visual field). Multivariate pattern analysis applied to the EEG data revealed that object classification depended on positional regularities: Objects were classified more accurately when positioned typically, rather than atypically, already at 140 ms, suggesting that relatively early stages of object processing are tuned to typical retinotopic locations. Our results confirm the prediction that long-term experience with objects occurring at specific locations leads to enhanced perceptual processing when these objects appear in their typical locations. This may indicate a neural mechanism for efficient natural scene processing, where a large number of typically positioned objects needs to be processed.


EEG decoding; Location priors; Multivariate pattern analysis; Object recognition; Real-world regularities; Visual perception

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