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Marko Nardini, Pete Jones, Linnea Landin, Mordechai Juni, Laurence Maloney, Tessa Dekker; Learning efficient perceptual sampling. Journal of Vision 2015;15(12):743. doi: https://doi.org/10.1167/15.12.743.
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© ARVO (1962-2015); The Authors (2016-present)
We tested adults and children aged 7-9 and 10-12 years in a stochastic judgment task. Adult observers compensate in part for perceptual uncertainty. However, the manner in which perceptual systems represent and compute with probabilistic estimates remains largely unknown. Developmental studies provide insight into the nature and origins of these capabilities. In our task, subjects could earn a reward by touching an invisible target circle marked by dots (cues) drawn from a Gaussian distribution centred on the target. Subjects could sample up to 20 cues but each cue reduced the possible reward by a fixed amount. Each additional cue improved the reliability of the location estimate by reducing the standard error of the mean. Subjects therefore had to trade off localization accuracy against the cost of additional cues. There were two conditions that differed in the variance of the Gaussian. We computed the optimal sample size that maximized expected reward in each condition: 4 cues (low variance) and 8 cues (high). We assumed that observers aimed for the mean location of each dot cloud; control conditions showed that deviations from this strategy were small across all age groups. Strikingly, across both variance conditions, in both child and adult groups, numbers of cues sampled were indistinguishable from optimal. However, sampling in child groups was more variable trial-to-trial, with a cost to their final rewards as compared with adults. Children’s relatively mature abilities to compute with probabilistic estimates here contrast with their much poorer abilities to take uncertainty into account in difficult perceptual and motor tasks (e.g. Nardini et al, PNAS 2010; Dekker et al, VSS 2012). This apparent dissociation suggests that probabilities dependent mainly on external factors (samples of dots, in this task) are computed separately to those dependent mainly on internal noise (sensory uncertainty, in previous tasks).
Meeting abstract presented at VSS 2015
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