A Dynamic Framework for Modelling Set-Shifting Performances

Abstract

Higher-order cognitive functions can be seen as a class of cognitive processes which are crucial in situations requiring a flexible adjustment of behaviour in response to changing demands of the environment. The cognitive assessment of these functions often relies on tasks which admit a dynamic, or longitudinal, component requiring participants to flexibly adapt their behaviour during the unfolding of the task. An intriguing feature of such experimental protocols is that they allow the performance of an individual to change as the task unfolds. In this work, we propose a Latent Markov Model approach to capture some dynamic aspects of observed response patterns of both healthy and substance dependent individuals in a set-shifting task. In particular, data from a Wisconsin Card Sorting Test were analysed in order to represent performance trends in terms of latent cognitive states dynamics. The results highlighted how a dynamic modelling approach can considerably improve the amount of information a researcher, or a clinician, can obtain from the analysis of a set-shifting task.

A Computational Study of Executive Dysfunction in Amyotrophic Lateral Sclerosis

“…Several previously published studies conducted computational modeling of behavioral performance on the WCST [42,[58][59][60][61][62][63][64][65][66][67][68][69][70], but surprisingly none of these earlier studies applied reinforcement learning (RL) models [71][72][73][74][75][76][77]. RL represents a suitable framework for modeling WCST behavior because of the potential reinforcement-quality of WCST feedback stimuli [40,78] that were illustrated in Figure 1.…”

Toward a Computational Neuropsychology of Cognitive Flexibility

“…There are several computational models for the WCST [ 48 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 ]. These computational models typically belong to one of two subclasses: neural network models or mechanistic models [ 48 ].…”