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Temporal integration of feature probability distributions

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Abstract

Humans are surprisingly good at learning the statistical characteristics of their visual environment. Recent studies have revealed that not only can the visual system learn repeated features of visual search distractors, but also their actual probability distributions. Search times were determined by the frequency of distractor features over consecutive search trials. The search displays applied in these studies involved many exemplars of distractors on each trial and while there is clear evidence that feature distributions can be learned from large distractor sets, it is less clear if distributions are well learned for single targets presented on each trial. Here, we investigated potential learning of probability distributions of single targets during visual search. Over blocks of trials, observers searched for an oddly colored target that was drawn from either a Gaussian or a uniform distribution. Search times for the different target colors were clearly influenced by the probability of that feature within trial blocks. The same search targets, coming from the extremes of the two distributions were found significantly slower during the blocks where the targets were drawn from a Gaussian distribution than from a uniform distribution indicating that observers were sensitive to the target probability determined by the distribution shape. In Experiment 2, we replicated the effect using binned distributions and revealed the limitations of encoding complex target distributions. Our results demonstrate detailed internal representations of target feature distributions and that the visual system integrates probability distributions of target colors over surprisingly long trial sequences.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the Open Science Framework repository, available at: https://www.osf.io/hjpb2/.

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Funding

SHR and AK were supported by Grant IRF #173947-052 from the Icelandic research fund, and by a grant from the Research Fund of the University of Iceland. JJG was supported by a grant from the NIH (R01 MH113855).

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Authors and Affiliations

  1. Icelandic Vision Lab, School of Health Sciences, University of Iceland, Reykjavík, Iceland

    Sabrina Hansmann-Roth, Sóley Þorsteinsdóttir & Árni Kristjánsson

  2. Université de Lille, CNRS, UMR 9193—SCALab—Sciences Cognitives et Sciences Affectives, 59000, Lille, France

    Sabrina Hansmann-Roth

  3. Center for Mind and Brain, University of California Davis, Davis, CA, USA

    Joy J. Geng

  4. Department of Psychology, University of California Davis, Davis, CA, USA

    Joy J. Geng

  5. School of Psychology, National Research University Higher School of Economics, Moscow, Russia

    Árni Kristjánsson

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  1. Sabrina Hansmann-Roth
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Correspondence to Sabrina Hansmann-Roth.

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Our study was conducted in compliance with ethical standards. All participants gave informed consent to participate in the experiment, and there was no conflict of interest to declare. No animals were used in the study and all procedures performed involving human participants were approved by the ethics committee of the National Bioethics committee in Iceland (Vísindasiðanefnd, http://www.vsn.is) and performed in accordance with their requirements and guidelines and the Declaration of Helsinki.

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A preprint of this manuscript has been recently published on PsyArXiv: Hansmann-Roth, S., Thorsteinsdóttir, S., Geng, J., & Kristjansson, A. Temporal integration of feature probability distributions in working memory. https://doi.org/10.31234/osf.io/2uy57.

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Hansmann-Roth, S., Þorsteinsdóttir, S., Geng, J.J. et al. Temporal integration of feature probability distributions. Psychological Research 86, 2030–2044 (2022). https://doi.org/10.1007/s00426-021-01621-3

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