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József Arató, József Fiser; Short term and baseline effect in the estimation of probabilistic visual event sequences . Journal of Vision 2014;14(10):373. doi: 10.1167/14.10.373.
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© ARVO (1962-2015); The Authors (2016-present)
To understand how people build probabilistic internal representations of their dynamic perceptual environment, it is essential to know how the statistical structure of event sequences is encoded in the brain. Previous attempts either characterized this encoding by the structure of the preceding short-term repetition/alternations or, while they acknowledged the importance of long-term baseline probabilities, they failed to manipulate the baseline statistics properly to explore their effect. We investigated how expectations about the probability of a visual event are affected by varying short-term and unbalanced baseline statistics of their sequential appearance. Participants (N=19) observed sequences of visual presence-absence events and reported about their beliefs by two means: by quickly pressing a key indicating whether or not an object appeared and by giving interspersed numerical estimates of the appearance probability of the event together with their confidence of their answer. Stimuli appeared at random with the baseline probabilities systematically manipulated throughout the experiment. We found that reaction times (RTs) for visual events did not depend exclusively on short-term patterns but were reliably influenced by the baseline appearance probabilities independent of the local history. Error rates, RTs and explicit estimates were similarly influenced by the baseline: subjects were more accurate estimating the probability of very likely and very unlikely events. Furthermore, we found that subjectsâ€™ report of their confidence was systematically related to both their implicit and explicit accuracy measures. Finally, reaction times could be explained by a combined effect of short-term and baseline statistics of the observed events. These results indicate that the perception of probabilistic visual events in a dynamic visual environment is influenced by short-term patterns as well as automatically extracted statistics acquired on the long run. Our findings lend support to proposals that explain behavioral changes based on internal probabilistic models rather than on local adaptation mechanisms.
Meeting abstract presented at VSS 2014
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