September 2019
Volume 19, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2019
Reliability-based arbitration between noise and event-based component of observers’ internal model during perceptual decision making
Author Affiliations & Notes
  • Jozsef Fiser
    Department of Cognitive Science, CEU
  • Adam Koblinger
    Department of Cognitive Science, CEU
  • Jozsef Arato
    Department of Cognitive Science, CEU
Journal of Vision September 2019, Vol.19, 59c. doi:https://doi.org/10.1167/19.10.59c
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      Jozsef Fiser, Adam Koblinger, Jozsef Arato; Reliability-based arbitration between noise and event-based component of observers’ internal model during perceptual decision making. Journal of Vision 2019;19(10):59c. doi: https://doi.org/10.1167/19.10.59c.

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

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Abstract

The effects of long-term history on sequentially performed perceptual decision making are typically investigated either under the simplest stationary condition or in the context of changing volatility of the event statistics defined by the generative process. We investigated the rules of human decision making in the more natural situation when changes in the external conditions could be explained away by multiple equally feasible adjustment of the internal model. In each of four experiments, observers performed 500 trials of 2AFC visual discrimination between two arbitrary shapes that could appear with different frequency across trials and were corrupted by various amount of Gaussian noise in each trial. Trials were split to practice and test, where at the transition between the two, the appearance probability of the shapes (AP) changed either abruptly or gradually, their relative noise characteristics (NOISE) were altered, and feedback stopped. Using hierarchical Bayesian modeling, we showed that in this setup, the same perceptual experience can be explained by assuming a change in either AP or NOISE, but the two alternatives induce opposite long-term biases and consequently, different behavior under uncertain conditions. Interestingly, we found that observers strongly preferred one of the two alternatives. However, by manipulating the nature of the AP and the NOISE transition, and the volatility of AP during training, observers’ behavioral biases and hence their implicit choice of explaining the situation changed toward the other alternative as predicted by the model based on the newly introduced uncertainty. This suggests that similarly to arbitration between habitual and model-based explicit learning, humans adjust their implicit internal model during perceptual decision making based on the reliability of the various components, which reliability is assessed across detected change points during the sequence of events.

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