An information-theoretic perspective on the costs of cognition
Neuropsychologia. 2019-02-01; 123: 5-18
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Zénon A(1), Solopchuk O(2), Pezzulo G(3).
(1)Institut de Neuroscience Cognitive et Intégrative d’Aquitaine, Université de Bordeaux, France; Institute of Neuroscience, Université Catholique de Louvain,
Brussels, Belgium. Electronic address: .
(2)Institut de Neuroscience Cognitive et Intégrative d’Aquitaine, Université de Bordeaux, France; Institute of Neuroscience, Université Catholique de Louvain,
(3)Institute of Cognitive Sciences and Technologies, National Research Council, Via San Martino della Battaglia 44, 00185 Rome, Italy.
In statistics and machine learning, model accuracy is traded off with complexity, which can be viewed as the amount of information extracted from the data. Here, we discuss how cognitive costs can be expressed in terms of similar information costs, i.e. as a function of the amount of information required to update a person’s prior knowledge (or internal model) to effectively solve a task. We then examine the theoretical consequences that ensue from this assumption. This framework naturally explains why some tasks – for example, unfamiliar or dual tasks – are costly and permits to quantify these costs using information-theoretic measures. Finally, we discuss brain implementation of this principle and show that subjective cognitive costs can originate either from local or global capacity limitations on information processing or from increased rate of metabolic alterations. These views shed light on the potential adaptive value of cost-avoidance mechanisms.