अमूर्त
Evidence Accumulation as a Computationally Defined Neurocognitive Trait: Task-General Efficiency Clinical Neuroscience Implications
Mekonnen LemmaA fundamental area of cognitive science, the ability to quantify individual differences in higher-order cognitive functions has significant repercussions for psychopathology research. In these fields, the predominant strategy has been to attempt to fractionate higher-order functions into hypothesized components through factor analysis and experimental manipulation. However, there has recently been a lot of theoretical and empirical criticism levelled at the putative constructs produced by this paradigm. In parallel, a new strategy centered on the parameters of mathematical psychology-developed formal computational models of cognition has emerged. These models can be used to measure the latent mechanisms that underpin performance because they offer explanations of the data-generating process for cognitive tasks that are both biologically plausible and experimentally validated. Recent applications of this method have revealed that efficiency of evidence accumulation, a computational mechanism defined by sequential sampling models, is largely responsible for individual and clinical differences in performance on a wide range of cognitive tasks, from simple choice tasks to complex executive paradigms. The hypothesis that efficiency of evidence accumulation is a central individual difference dimension that explains neurocognitive deficits in multiple clinical disorders is supported by the evidence presented in this review, which also identifies ways in which this insight can advance clinical neuroscience research. We propose that the field will be able to draw clearer conclusions regarding cognitive abnormalities in psychopathology and their connections to neurobiology if the efficiency of evidence accumulation is recognized as a major driver of neurocognitive differences.