RT Journal Article
SR Electronic
T1 Flexible categorization in perceptual decision making
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.05.23.110460
DO 10.1101/2020.05.23.110460
A1 Genís Prat-Ortega
A1 Klaus Wimmer
A1 Alex Roxin
A1 Jaime de la Rocha
YR 2020
UL http://biorxiv.org/content/early/2020/05/25/2020.05.23.110460.abstract
AB Perceptual decisions require the brain to make categorical choices based on accumulated sensory evidence. The underlying computations have been studied using either phenomenological drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both classes of models can account for a large body of experimental data, it remains unclear to what extent their dynamics are qualitatively equivalent. Here we show that, unlike the drift diffusion model, the attractor model can operate in different integration regimes: an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision-states leading to a crossover between weighting mostly early evidence (primacy regime) to weighting late evidence (recency regime). Between these two limiting cases, we found a novel regime, which we name flexible categorization, in which fluctuations are strong enough to reverse initial categorizations, but only if they are incorrect. This asymmetry in the reversing probability results in a non-monotonic psychometric curve, a novel and distinctive feature of the attractor model. Finally, we show psychophysical evidence for the crossover between integration regimes predicted by the attractor model and for the relevance of this new regime. Our findings point to correcting transitions as an important yet overlooked feature of perceptual decision making.Competing Interest StatementThe authors have declared no competing interest.