Mice alternate between discrete strategies during perceptual decision-making

Abstract

Classical models of perceptual decision-making assume that animals use a single, consistent strategy to integrate sensory evidence and form decisions during an experiment. Here we provide analyses showing that this common view is incorrect. We use a latent variable modeling framework to show that decision-making behavior in mice reflects an interplay between different strategies that alternate on a timescale of tens to hundreds of trials. This model provides a powerful alternate explanation for "lapses" commonly observed during psychophysical experiments. Formally, our approach consists of a Hidden Markov Model (HMM) with states corresponding to different decision-making strategies, each parameterized by a distinct Bernoulli generalized linear model (GLM). We fit the resulting model (GLM-HMM) to choice data from two large cohorts of mice in different perceptual decision-making tasks. For both datasets, we found that mouse decision-making was far better described by a GLM-HMM with 3 or 4 states than by a traditional psychophysical model with lapses. The identified states were highly consistent across animals, consisting of a single "engaged" state, in which the strategy relied heavily on the sensory stimulus, and multiple biased or disengaged states in which accuracy was low. These states persisted for many trials, suggesting that lapses were not independent, but reflected state dynamics in which animals were relatively engaged or disengaged for extended periods of time. We found that for most animals, response times and violation rates were positively correlated with disengagement, providing independent correlates of the identified changes in strategy. The GLM-HMM framework thus provides a powerful lens for the analysis of decision-making, and suggests that standard measures of psychophysical performance mask the presence of slow but dramatic alternations in strategy across trials.

Prior Experience Modifies Acquisition Trajectories Via Response-Strategy Sampling

“…Most studies make the observation that, on average, criterion are largely stable (e.g., Kuchibhotla et al, 2019). Although the on average claim may be accurate, it belies the underlying criterion dynamics and idiosyncrasies observed here and elsewhere (Ashwood et al, 2020;Heinemann, 1983;Stüttgen et al, 2011).…”

“…This description, however, ignores the possibility that during the pre-solution period individuals do not always sample strategies randomly. Within and across sessions, individuals appear to enter phases of biased responding characterized by persistent emission of "Yes" and "No" responses (Ashwood et al, 2020). Such biased responding is not typically attributed a causal role in acquisition beyond that of hypothesis testing.…”

Probabilistic discrimination of relative stimulus features in mice

“…These fluctuations of the history biases are driven by the internal state of the animal, are independent of the stimulus protocol, and thus will occur in addition to difficulty- or confidence-dependent fluctuations, as recently described (Lak et al, 2020). During periods of decreased performance, higher explanatory power of history terms is not guaranteed, but it is consistent with switching between history-driven and stimulus-driven choice modes (Ashwood et al, 2020).…”

The geometry of cortical representations of touch in rodents

“…One possible explanation of these effects is that in approximately 60% of the trials animals make very accurate choices that are based in properly sampled sensory cues. In the other 40% of the trials, animals still sample information properly but their choice is inaccurate and based on a hidden variable we do not have access to [13,14]. It is important to note that all models were trained on a balanced dataset in which the the number of correct and error trials was exactly the same.…”