Low dimensional latent structure underlying the choices of mice

An impressive wealth of cognitive neuroscience tasks involves combining perceptual information with an estimate of the latent state of the environment to make a decision. Such tasks have driven the development of theoretically motivated cognitive models which offer compactly parameterised, and thereby insightful, accounts of the internal processes by which this might happen. However, the very large amounts of data that can now be collected present a challenge and an opportunity for this framework. The challenge is that these models can be shown to underfit the data systematically (Nassar Palminteri, Wyart, Wilson & Collins, 2019), particularly in how they characterise the effect of the latent state estimate. But this opens up the opportunity to extend the framework and employ richer, more highly parameterised and flexible models, in a data-driven manner (Dezfouli, Griffiths, Ramos, Dayan, & Balleine, 2019). Unfortunately, it is tremendously difficult to interpret these models, precisely because of their flexibility. Here, we follow a recent approach (Eckstein, Summerfield, Daw, & Miller, 2024), in which components of compact models are progressively replaced with more flexible homologues, but, where possible, the resulting insights are mapped back into theoretically-transparent forms. We prove the effectiveness of our scheme by applying it to one of the largest available decision-making data sets for mice, more than 300,000 choices from 139 subjects studied by the International Brain Lab (The International Brain Laboratory et al., 2021). We found widely generalisable phenomena such as notable effects of continual fluctuations in task engagement and systematic differences between learning and forgetting of chosen and unchosen options in determining the subjective state estimate. Our results show that combining theory-driven and data-driven methods can reveal cognitive processes that would have been difficult to discover using either method on its own.