Flexible categorization in perceptual decision making

Perceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making. Attractor networks and drift diffusion models are two approaches to model the perceptual decision making process. Here, the authors identify an intermediate regime only for the attractor model that allows flexible categorization of two choice decisions for long duration and noisy stimuli and validate these model predictions with psychophysical experiments.

Automated summary

The attractor network model demonstrates a novel flexible categorization regime that allows reversals of initial incorrect categorizations, leading to a non-monotonic psychometric curve.