August 2016
Volume 16, Issue 12
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2016
Adaptation of numerical magnitude by visual size
Author Affiliations
  • Eckart Zimmermann
    Institute of Biological Psychology, University D�sseldorf, Germany
Journal of Vision September 2016, Vol.16, 1175. doi:
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      Eckart Zimmermann; Adaptation of numerical magnitude by visual size . Journal of Vision 2016;16(12):1175.

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      © ARVO (1962-2015); The Authors (2016-present)

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Recent psychophysical and electrophysiological work in various species suggest the existence of a number sense. Parietal areas in the brain contain neurons specialized for processing the numerosity of elements in the visual scene. Here, I used the technique of size adaptation to investigate its influence on number, size and density judgements of random dot numerosity patches. Size adaptation only changes the apparent, not the physical size of the probe patch, thereby avoiding confounds with changes in physical dot density. The effect of size adaptation on number judgements depended on the numerosity of elements in the probe stimulus: Adaptation magnitude increased logarithmically with numerosity in the probe patch. Judgements of dot density however were unaffected by size adaptation. Effects on size estimations remained constant across the tested numerosities and thus cannot explain the logarithmic effects on number estimations. Since judgements of low number were not affected by modulations of apparent size, it is likely that they are sensed directly, irrespective of low-level features as size and density. However, the influence of low-level features increased as a function of the amount of dots presented in the probe patch. I also measured the spatial spread of number adaptation for low numerosities. The broad spread of adaptation is consistent with processing of number neurons which have large receptive fields. These results suggest the existence of a number sense for low numerosities and a mechanism for high numerosities, which integrates features as size and density.

Meeting abstract presented at VSS 2016


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