Abstract
Understanding dependencies between brain functioning and cognition is a challenging task which might require more than applying standard statistical models to neural and behavioural measures to be accomplished. Recent developments in computational modelling have demonstrated the advantage to formally account for reciprocal relations between mathematical models of cognition and brain functional, or structural, characteristics to relate neural and cognitive parameters on a model-based perspective. This would allow to account for both neural and behavioural data simultaneously by providing a joint probabilistic model for the two sources of information. In the present work we proposed an architecture for jointly modelling the reciprocal relation between behavioural and neural information in the context of risky decision-making. More precisely, we offered a way to relate Diffusion Tensor Imaging data to cognitive parameters of a computational model accounting for behavioural outcomes in the popular Balloon Analogue Risk Task (BART). Results show that the proposed architecture has the potential to account for individual differences in task performances and brain structural features by letting individual-level parameters to be modelled by a joint distribution connecting both sources of information. Such a joint modelling framework can offer interesting insights in the development of computational models able to investigate correspondence between decision-making and brain structural connectivity.
Highlights
In cognitive neuroscience, relations between neural and behavioural characteristics of individuals are usually analyzed using a two-step approach which first summarizes performances on a given experimental task, and applies standard statistical analysis on the neural and behavioural measures
Some examples are: (1) cognitive modelling [4,5] which formally accounts for the generative cognitive processes which are assumed to produce the observed data; (2) Bayesian graphical models [6,7] which provide a powerful and flexible way to perform hierarchical Bayesian analysis, allowing to account for group and individual differences; (3) joint neurocognitive modelling [1,8,9,10,11] which provides a framework to simultaneously model and analyze neural and behavioural data by allowing the latter to be informative for the former, and vice versa
The main advantage consists of using formal cognitive models as tools to isolate and quantify cognitive processes in order to effectively associate them with some brain measurements [8])
Summary
Relations between neural and behavioural characteristics of individuals are usually analyzed using a two-step approach which first summarizes performances on a given experimental task, and applies standard statistical analysis on the neural and behavioural measures. Some examples are: (1) cognitive modelling [4,5] which formally accounts for the generative cognitive processes which are assumed to produce the observed data; (2) Bayesian graphical models [6,7] which provide a powerful and flexible way to perform hierarchical Bayesian analysis, allowing to account for group and individual differences; (3) joint neurocognitive modelling [1,8,9,10,11] which provides a framework to simultaneously model and analyze neural and behavioural data by allowing the latter to be informative for the former, and vice versa The latter modelling framework has demonstrated to be an effective way to increase knowledge about the underlying neural substrates of cognitive functioning by bridging the gap between. The main advantage consists of using formal cognitive models as tools to isolate and quantify cognitive processes in order to effectively associate them with some brain measurements [8])
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