LAUSR

The application of theoretical and computational approaches to the analysis of complex behavior has a rich history in psychology. A shining example of this is the modeling of learning encapsulated elegantly by Rescorla and Wagner (1972), who demonstrated that classical conditioning can be described by a simple mathematical equation. The explanatory power of the Rescorla-Wagner rule and its subsequent expansion into additional areas of behavioral plasticity has enabled the precise mapping of learning parameters onto neural structures and even individual neurons. A plethora of other mathematical models have since been used to describe a variety of behaviors, and to map those behaviors onto their underlying neurobiology. This so-called computational phenotyping is now gaining momentum as a translational tool that can be used to identify process characteristics in both humans and animals with the potential of transforming the field of psychopharmacology. The contributions to this Special Issue on Computational and Translational Psychopharmacology stem from the European Behavioural Pharmacology Society (EBPS) Workshop that was held at the University of Cambridge, in August of 2018. The overarching goal of the workshop was to foster discussion around the nascent subfield we refer to as Computat ional and Translat ional Psychopharmacology, and to identify points of convergence for which computational approaches could be used to enhance the translational value of animal and human studies. The manuscripts contained herein demonstrate the potential utility of such approaches and provide a foundation for continuous growth towards a better mechanistic understanding of the complex behaviors that characterize psychiatric conditions and the development of more predictive translational probes. There are several advantages to using mathematical algorithmic approaches to advance a quantitative mechanistic understanding of the processes that underlie animal and human behavior, mental health, and disease. First, process-based hypotheses can be made explicit and quantitative using mathematical models, which increase the precision of underlying theories. Second, competing models of such processes can be directly compared and evaluated based on the evidence provided by empirical data. Third, computational model parameters can be modified based on experimental observations to arbitrate which processes best describe the experimental results. Fourth, individuals exhibiting maladaptive or psychopathological behaviors can be reconceptualized as exhibiting computational “failure modes”, i.e., a constellation of model parameters associated with dysfunctions. These “failure modes” can then be used across diagnostic categories and levels of analyses to reveal common underlying brain mechanisms. Moreover, using computational models for animal behavior can help to determine whether these “failure modes” occur across species. Finally, computational approaches allow us to arbitrate between individual-level analyses and group level analyses, i.e., one can determine whether a group of individuals that are characterized by similar disorder also show similar “failure modes.” Taken together, computational approaches within a translational framework can provide greater explanatory depth, but also the potential for better translational prediction. The contributions in this special issue highlight some of the emerging (or re-emerging) constructs which provide the basis for current or to-be-developed computational models. A number of these publications extend from the work of Rescorla and Wagner (1972), modeling different aspects of learning. This article belongs to a Special Issue on Translational Computational Psychopharmacology.