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Probalistic internal model-based perceptual decision-making| old_uid | 17672 |
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| title | Probalistic internal model-based perceptual decision-making |
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| start_date | 2019/04/08 |
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| schedule | 15h |
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| online | no |
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| location_info | Bât. 452, Salle F28 du neurocampus |
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| summary | Perceptual decision
-
making is typically investigated through multiple repeated test
trials under the tacit assumption that each decision is an independent process or, at most, it
is influenced by decisions made in a few preceding trials.
We investigated t
his issue under
the condition, when more than one aspect of the context could vary during the experiment:
both the level of noise added to the stimulus and the cumulative base rate of appearance
(how often A vs. B stimuli appeared) followed various predefi
ned patterns.
We found that
long
-
term patterns in the context had very significant effects on human decisions.
Despite
being asked about only the identity of the present stimulus, participants’ decisions strongly
reflected summary statistics of noise and
base rates collected dozens to hundreds of trials
before.
Importantly, these effects could not be described simply as cumulative statistics of
earlier trials: for example, a significant spike change in base rate (a virtual change point)
without a true sh
ift in base rates could induce the same effect as a prolonged shift, while a
gradual change did not induce any effect. As standard decision making models cannot
explain these results, we developed a hierarchical Bayesian model that simultaneously
represent
ed the priors over the base rates and a potentially non
-
uniform noise model over
the different stimulus identities. Based on simulations with the model, we conducted
additional experiments and found that when a change occurred in the context that could be
captured equally well by adjusting one or another aspects of the model, participants chose
adjusting the variable that was less reliable as defined by variability in the preceding
extended set of trials.
In general, regardless of the simplicity of a perce
ptual decision
-
making task, humans automatically develop a complex internal model, and in the light of a
detected change, they adaptively alter the component of this model that is implicitly judged
to be the least reliable one. |
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| responsibles | CRNL |
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