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Evidence that probability estimates and confidence derive from a single process.

By Florent Meyniel (194266), Daniel Schlunegger (755141) and Stanislas Dehaene (6832)

Abstract

<p><i>(A)</i> Subjective confidence is higher for extreme estimates of transition probabilities. The fitted lines correspond to the average of the quadratic fits performed at the subject level: confidence ~ constant + (probability estimate-0.5)<sup>2</sup>. Trials were sorted by subjective probability estimates and, within each bin, into high and low Ideal Observer confidence according to a median split. Equally-filled bins were used for data visualization, not for data analysis. <i>(B)</i> The accuracies of probability estimates and confidence ratings are correlated across subjects. The accuracy of probability estimates was computed <i>per subject</i> as the correlation (across trials) of the subject's and the Ideal Observer's estimates. The same logic was used for confidence. One dot corresponds to one subject. <i>(C)</i> The link between probability estimates and confidence ratings goes beyond any mapping. Within each subject, we computed the correlation across trials between accuracies in probability estimates and confidence ratings. The accuracy of probability estimates was computed <i>at the trial level</i> as the distance between the subject's and the Ideal Observer's estimates. The same logic was used for confidence. The observed results are contrasted to two ways of shuffling the data (p-values are from one-tailed t-test, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004305#sec013" target="_blank">Methods</a>).</p

Topics: Medicine, Sociology, Science Policy, Infectious Diseases, Space Science, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, uncertainty, human, confidence estimates, stimulus occurrence, auditory stimuli, inference strategy, characteristic, Probabilistic Learning, core property, parameter estimates, transition probabilities, inference process, Normative Account Learning, non-sensory parameters, model, results support
Year: 2015
DOI identifier: 10.1371/journal.pcbi.1004305.g005
OAI identifier: oai:figshare.com:article/1450252
Provided by: FigShare
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