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Brian Maniscalco, Brian Odegaard, Piercesare Grimaldi, Seong Hah Cho, Michele A. Basso, Hakwan Lau, Megan A.K. Peters; Tuned normalization in perceptual decision-making circuits can explain seemingly suboptimal confidence behavior. Journal of Vision 2019;19(10):289b. doi: https://doi.org/10.1167/19.10.289b.
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Current dominant views hold that perceptual confidence – e.g., in visual perceptual decisions – reflects the probability that the relevant decision is correct. Although these views have enjoyed some empirical support, recent behavioral results indicate that confidence and the probability of being correct can be dissociated. An alternative hypothesis suggests that confidence instead reflects the magnitude of evidence in favor of a perceptual decision while being relatively insensitive to the evidence opposing the decision. We considered how this alternative hypothesis might be biologically instantiated by developing a simple leaky competing accumulator neural network model incorporating a known property of sensory neurons: tuned normalization. The key idea of the model is that each accumulator neuron’s normalization ‘tuning’ dictates its contribution to perceptual decisions versus confidence judgments. We demonstrate that this biologically plausible model can account for several counterintuitive findings reported in the literature, where confidence and decision accuracy in visual tasks were shown to dissociate -- and that the differential contribution a neuron makes to decisions versus confidence judgments based on its normalization tuning is vital to capturing some of these effects. One critical prediction of the model is that systematic variability in normalization tuning exists not only in sensory cortices but also in the decision-making circuitry. We tested and validated this prediction in macaque superior colliculus (SC; a region implicated in decision-making). The confirmation of this novel prediction provides direct support for our model. We will also present pilot data from an exploratory fMRI paradigm investigating this Tuned Normalization model’s predictions in humans. These findings suggest that the brain has developed and implements this alternative, heuristic theory of perceptual confidence computation by capitalizing on the diversity of neural resources available
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