September 2015
Volume 15, Issue 12
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Evidence of probabilistic representation in assessing visual summary statistics
Author Affiliations
  • Sára Jellinek
    Department of Cognitive Science, Central European University
  • Laurence Maloney
    Department of Cognitive Science, Central European University Department of Psychology, Center for Neural Science, New York University
  • József Fiser
    Department of Cognitive Science, Central European University
Journal of Vision September 2015, Vol.15, 946. doi:
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      Sára Jellinek, Laurence Maloney, József Fiser; Evidence of probabilistic representation in assessing visual summary statistics. Journal of Vision 2015;15(12):946. doi:

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

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People rapidly and precisely extract summary statistics (e.g. mean and variance) of visually presented ensembles. Such statistics are an essential part of their internal representation of the environment. Recently, we reported that human behavior in perceptual decision making tasks indicates that they handle simple visual attributes in a sampling-based probabilistic manner (Popovic et al. Cosyne 2013, VSS 2013; Fiser et al. ECVP 2013; Christensen et al. VSS 2014, ECVP 2014). In this study, we tested whether such probabilistic representations also may underlie the assessment of visual summary statistics. In each trial, subjects saw a group of circles (N=2...10, randomly chosen) of varying sizes and had to estimate either the mean or variance of the ensemble or the size of one individual circle from the group specified after the stimulus was taken off the screen. In addition, they also reported their subjective confidence about their decision on a trial-by-trial basis. Stimuli were presented at nine different durations (50, 75, 100, 133, 167, 200, 300, 400, or 600 msec). In accordance with previous results, participants could accurately estimate the mean, the variance and the size of an element within the ensembles. Interestingly, mean estimation improved significantly as the number of circles in the display increased. More importantly, we found an increasing correlation between error and uncertainty as a function of presentation time, which is the hallmark of sampling-based probabilistic representation. Thus, probabilistic representation is not used exclusively for the simplest visual attributes, such as orientation or the speed of small dots, but also in representing more abstract kind of summary statistics.

Meeting abstract presented at VSS 2015


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