Computational validity: using computation to translate behaviours across species

Abstract

We propose a new conceptual framework (computational validity) for translation across species and populations based on the computational similarity between the information processing underlying parallel tasks. Translating between species depends not on the superficial similarity of the tasks presented, but rather on the computational similarity of the strategies and mechanisms that underlie those behaviours. Computational validity goes beyond construct validity by directly addressing questions of information processing. Computational validity interacts with circuit validity as computation depends on circuits, but similar computations could be accomplished by different circuits. Because different individuals may use different computations to accomplish a given task, computational validity suggests that behaviour should be understood through the subject's point of view; thus, behaviour should be characterized on an individual level rather than a task level. Tasks can constrain the computational algorithms available to a subject and the observed subtleties of that behaviour can provide information about the computations used by each individual. Computational validity has especially high relevance for the study of psychiatric disorders, given the new views of psychiatry as identifying and mediating information processing dysfunctions that may show high inter-individual variability, as well as for animal models investigating aspects of human psychiatric disorders. This article is part of the theme issue ‘Systems neuroscience through the lens of evolutionary theory’.

Switching to online: Testing the validity of supervised remote testing for online reinforcement learning experiments

“…Second, the application of computational analysis in this study replicated previous data collected in-person and with a longer version of the RLWM task (Collins, 2018), which suggests that the RLWMi model applied here is valid between studies, between distinct methods of data assessment, and for distinct versions of the same behavioral paradigm. This endorses the idea of computational validity (Redish et al, 2022) for the RLWMi model and reinforces the assumption made in previous publications that this model is relevant to comprehend interactions between RL and WM load (Collins, 2018;Collins & Frank, 2012;Collins et al, 2014;Master et al, 2020).…”

“…Furthermore, when more than one computational model exists for the same data, they need to be statistically compared to find out which one is the best to predict behavioral phenomena. These assertions were the basis for the proposal of computational validity, that is, the understanding that computational models can be valid when they predict the behavior of different species or distinct experimental manipulations (Redish et al, 2022 ). Therefore, a model for reinforcement learning can be valid when it predicts behavior under distinct conditions, for instance, as is the case of online and in-person experiments.…”

“…The option for a remote and synchronous experiment was done because the original experiment was planned to do in person and involve multiple steps to be followed in a specific order. Based on the idea of computational validity (Redish et al, 2022 ), it was assumed that our remote experiment and the behavioral and computational results would be valid only if they would replicate previous data using the same behavioral task. The feasibility of our remote and synchronous online experiment and the usefulness of low-cost and open-source programming languages to analyze data are discussed in this paper.…”

A Corticothalamic Circuit Trades off Speed for Safety during Decision-Making under Motivational Conflict

“…It can involve biases, dynamic changes in decision speed, and trade-offs between decision speed and decision outcome. The mechanisms for such choices could differ across tasks (e.g., Pavlovian vs instrumental), but the application of formal cognitive models to behavior in these tasks to jointly model choice outcome and RTs may provide a useful addition to traditional associative approaches in identifying computational similarities in information processing and their underlying circuit mechanisms across these distinct tasks ( Redish et al, 2022 ).…”

Neuroscience needs evolution

“…a visual discrimination task presented to a rodent versus a primate). A. David Redish and colleagues propose that one method for translating across species is to consider the computations that each performs [ 60 ]. That is, some methods establish the validity of a task from one context to another, or one species to another.…”