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How Recent History Affects Perception: The Normative Approach and Its Heuristic Approximation

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  • Ofri Raviv
  • Merav Ahissar
  • Yonatan Loewenstein

Abstract

There is accumulating evidence that prior knowledge about expectations plays an important role in perception. The Bayesian framework is the standard computational approach to explain how prior knowledge about the distribution of expected stimuli is incorporated with noisy observations in order to improve performance. However, it is unclear what information about the prior distribution is acquired by the perceptual system over short periods of time and how this information is utilized in the process of perceptual decision making. Here we address this question using a simple two-tone discrimination task. We find that the “contraction bias”, in which small magnitudes are overestimated and large magnitudes are underestimated, dominates the pattern of responses of human participants. This contraction bias is consistent with the Bayesian hypothesis in which the true prior information is available to the decision-maker. However, a trial-by-trial analysis of the pattern of responses reveals that the contribution of most recent trials to performance is overweighted compared with the predictions of a standard Bayesian model. Moreover, we study participants' performance in a-typical distributions of stimuli and demonstrate substantial deviations from the ideal Bayesian detector, suggesting that the brain utilizes a heuristic approximation of the Bayesian inference. We propose a biologically plausible model, in which decision in the two-tone discrimination task is based on a comparison between the second tone and an exponentially-decaying average of the first tone and past tones. We show that this model accounts for both the contraction bias and the deviations from the ideal Bayesian detector hypothesis. These findings demonstrate the power of Bayesian-like heuristics in the brain, as well as their limitations in their failure to fully adapt to novel environments. Author Summary: In this paper we study how history affects perception using an auditory delayed comparison task, in which human participants repeatedly compare the frequencies of two, temporally-separated pure tones. We demonstrate that the history of the experiment has a substantial effect on participants' performance: when both tones are high relative to past stimuli, people tend to report that the 2nd tone was higher, and when they are relatively low, they tend to report that the 1st tone was higher. Interestingly, only the most recent trials bias performance, which can be interpreted as if the participants assume that the statistics of stimuli in the experiment is highly volatile. Moreover, this bias persists even in settings, in which it is detrimental to performance. These results demonstrate the abilities, as well as limitations, of the cognitive system when incorporating expectations in perception.

Suggested Citation

  • Ofri Raviv & Merav Ahissar & Yonatan Loewenstein, 2012. "How Recent History Affects Perception: The Normative Approach and Its Heuristic Approximation," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-10, October.
  • Handle: RePEc:plo:pcbi00:1002731
    DOI: 10.1371/journal.pcbi.1002731
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    References listed on IDEAS

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    1. Paymon Ashourian & Yonatan Loewenstein, 2011. "Bayesian Inference Underlies the Contraction Bias in Delayed Comparison Tasks," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-8, May.
    2. Tal Neiman & Yonatan Loewenstein, 2011. "Reinforcement learning in professional basketball players," Nature Communications, Nature, vol. 2(1), pages 1-8, September.
    3. Tal Neiman & Yonatan Loewenstein, 2011. "Reinforcement learning in professional basketball players," Discussion Paper Series dp593, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    4. Konrad P. Körding & Daniel M. Wolpert, 2004. "Bayesian integration in sensorimotor learning," Nature, Nature, vol. 427(6971), pages 244-247, January.
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    2. Ofri Raviv & Itay Lieder & Yonatan Loewenstein & Merav Ahissar, 2014. "Contradictory Behavioral Biases Result from the Influence of Past Stimuli on Perception," PLOS Computational Biology, Public Library of Science, vol. 10(12), pages 1-10, December.
    3. Sagi Jaffe-Dax & Ofri Raviv & Nori Jacoby & Yonatan Loewenstein & Merav Ahissar, 2015. "A computational model of implicit memory captures dyslexics’ perceptual deficits," Discussion Paper Series dp690, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    4. Urit Gordon & Shimon Marom & Naama Brenner, 2019. "Visual detection of time-varying signals: Opposing biases and their timescales," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-18, November.
    5. Elyse H Norton & Luigi Acerbi & Wei Ji Ma & Michael S Landy, 2019. "Human online adaptation to changes in prior probability," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-26, July.
    6. Duffy, Sean & Smith, John, 2020. "Omitted-variable bias and other matters in the defense of the category adjustment model: A comment on Crawford (2019)," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 85(C).

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