https://scholars.lib.ntu.edu.tw/handle/123456789/625601
Title: | Sequential effects: Superstition or rational behavior? | Authors: | ANGELA YU-CHEN LIN Cohen J.D. |
Issue Date: | 2009 | Start page/Pages: | 1873-1880 | Source: | Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference | Abstract: | In a variety of behavioral tasks, subjects exhibit an automatic and apparently suboptimal sequential effect: they respond more rapidly and accurately to a stimulus if it reinforces a local pattern in stimulus history, such as a string of repetitions or alternations, compared to when it violates such a pattern. This is often the case even if the local trends arise by chance in the context of a randomized design, such that stimulus history has no real predictive power. In this work, we use a normative Bayesian framework to examine the hypothesis that such idiosyncrasies may reflect the inadvertent engagement of mechanisms critical for adapting to a changing environment. We show that prior belief in non-stationarity can induce experimentally observed sequential effects in an otherwise Bayes-optimal algorithm. The Bayesian algorithm is shown to be well approximated by linear-exponential filtering of past observations, a feature also apparent in the behavioral data. We derive an explicit relationship between the parameters and computations of the exact Bayesian algorithm and those of the approximate linear-exponential filter. Since the latter is equivalent to a leaky-integration process, a commonly used model of neuronal dynamics underlying perceptual decision-making and trial-to-trial dependencies, our model provides a principled account of why such dynamics are useful. We also show that parameter-tuning of the leaky-integration process is possible, using stochastic gradient descent based only on the noisy binary inputs. This is a proof of concept that not only can neurons implement near-optimal prediction based on standard neuronal dynamics, but that they can also learn to tune the processing parameters without explicitly representing probabilities. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858789760&partnerID=40&md5=d85b5ce6d005e017334090e4bab950f3 https://scholars.lib.ntu.edu.tw/handle/123456789/625601 |
SDG/Keyword: | Bayesian algorithms; Bayesian frameworks; Behavioral data; Binary inputs; Changing environment; Local patterns; Neuronal dynamics; Non-stationarities; Parameter-tuning; Prediction-based; Predictive power; Processing parameters; Proof of concept; Randomized design; Rational behavior; Sequential effects; Stochastic gradient descent; Algorithms; Optimization; Dynamics |
Appears in Collections: | 環境工程學研究所 |
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