A neurally plausible schema-theoretic approach to modelling cognitive dysfunction and neurophysiological markers in Parkinson's disease

Bridging flexible goal-directed cognition and consciousness: The Goal-Aligning Representation Internal Manipulation theory

Goal-directed manipulation of internal representations is a key element of human flexible behaviour, while consciousness is commonly associated with higher-order cognition and human flexibility. Current perspectives have only partially linked these processes, thus preventing a clear understanding of how they jointly generate flexible cognition and behaviour. Moreover, these limitations prevent an effective exploitation of this knowledge for technological scopes. We propose a new theoretical perspective that extends our 'three-component theory of flexible cognition' toward higher-order cognition and consciousness, based on the systematic integration of key concepts from Cognitive Neuroscience and AI/Robotics. The theory proposes that the function of conscious processes is to support the alignment of representations with multi-level goals. This higher alignment leads to more flexible and effective behaviours. We analyse here our previous model of goal-directed flexible cognition (validated with more than 20 human populations) as a starting GARIM-inspired model. By bridging the main theories of consciousness and goal-directed behaviour, the theory has relevant implications for scientific and technological fields. In particular, it contributes to developing new experimental tasks and interpreting clinical evidence. Finally, it indicates directions for improving machine learning and robotics systems and for informing real-world applications (e.g., in digital-twin healthcare and roboethics).

Executive Functions in Aging: An Experimental and Computational Study of the Wisconsin Card Sorting and Brixton Spatial Anticipation Tests

In order to explore the effect of normal aging on executive function, we tested 25 younger adults and 25 neurologically healthy older adults on the Wisconsin Card Sorting Test (WCST) and the Brixton Spatial Anticipation Test (BRXT), two classic tests of executive function. We found that older participants were more likely than younger participants to err on both tasks, but the additional errors of older participants tended to be related to task set maintenance and rule inference rather than perseveration. We further found that the tendency to perseverate (across all participants) on the WCST was related to the tendency to produce stimulus or response perseverations on the BRXT, rather than any tendency to perseverate on BRXT rule application. Finally, on both tasks, older participants were also slower, particularly on trials following an error, than younger participants. To explore the neurocomputational basis for the observed behaviours we then extended an existing model of schema-modulated action selection on the WCST to the BRXT. We argue on the basis of the model that the performance of older participants on both tasks reflects a slower update of schema thresholds within the basal ganglia, coupled with a decrease in sensitivity to feedback.

Internal manipulation of perceptual representations in human flexible cognition: A computational model

Executive functions represent a set of processes in goal-directed cognition that depend on integrated cortical-basal ganglia brain systems and form the basis of flexible human behaviour. Several computational models have been proposed for studying cognitive flexibility as a key executive function and the Wisconsin card sorting test (WCST) that represents an important neuropsychological tool to investigate it. These models clarify important aspects that underlie cognitive flexibility, particularly decision-making, motor response, and feedback-dependent learning processes. However, several studies suggest that the categorisation processes involved in the solution of the WCST include an additional computational stage of category representation that supports the other processes. Surprisingly, all models of the WCST ignore this fundamental stage and they assume that decision making directly triggers actions. Thus, we propose a novel hypothesis where the key mechanisms of cognitive flexibility and goal-directed behaviour rely on the acquisition of suitable representations of percepts and their top-down internal manipulation. Moreover, we propose a neuro-inspired computational model to operationalise this hypothesis. The capacity of the model to support cognitive flexibility was validated by systematically reproducing and interpreting the behaviour exhibited in the WCST by young and old healthy adults, and by frontal and Parkinson patients. The results corroborate and further articulate the hypothesis that the internal manipulation of representations is a core process in goal-directed flexible cognition.

Toward a Computational Neuropsychology of Cognitive Flexibility

Cognitive inflexibility is a well-documented, yet non-specific corollary of many neurological diseases. Computational modeling of covert cognitive processes supporting cognitive flexibility may provide progress toward nosologically specific aspects of cognitive inflexibility. We review computational models of the Wisconsin Card Sorting Test (WCST), which represents a gold standard for the clinical assessment of cognitive flexibility. A parallel reinforcement-learning (RL) model provides the best conceptualization of individual trial-by-trial WCST responses among all models considered. Clinical applications of the parallel RL model suggest that patients with Parkinson's disease (PD) and patients with amyotrophic lateral sclerosis (ALS) share a non-specific covert cognitive symptom: bradyphrenia. Impaired stimulus-response learning appears to occur specifically in patients with PD, whereas haphazard responding seems to occur specifically in patients with ALS. Computational modeling hence possesses the potential to reveal nosologically specific profiles of covert cognitive symptoms, which remain undetectable by traditionally applied behavioral methods. The present review exemplifies how computational neuropsychology may advance the assessment of cognitive flexibility. We discuss implications for neuropsychological assessment and directions for future research.

A Computational Study of Executive Dysfunction in Amyotrophic Lateral Sclerosis

Executive dysfunction is a well-documented, yet nonspecific corollary of various neurological diseases and psychiatric disorders. Here, we applied computational modeling of latent cognition for executive control in amyotrophic lateral sclerosis (ALS) patients. We utilized a parallel reinforcement learning model of trial-by-trial Wisconsin Card Sorting Test (WCST) behavior. Eighteen ALS patients and 21 matched healthy control participants were assessed on a computerized variant of the WCST (cWCST). ALS patients showed latent cognitive symptoms, which can be characterized as bradyphrenia and haphazard responding. A comparison with results from a recent computational Parkinson's disease (PD) study (Steinke et al., 2020, J Clin Med) suggests that bradyphrenia represents a disease-nonspecific latent cognitive symptom of ALS and PD patients alike. Haphazard responding seems to be a disease-specific latent cognitive symptom of ALS, whereas impaired stimulus-response learning seems to be a disease-specific latent cognitive symptom of PD. These data were obtained from the careful modeling of trial-by-trial behavior on the cWCST, and they suggest that computational cognitive neuropsychology provides nosologically specific indicators of latent facets of executive dysfunction in ALS (and PD) patients, which remain undiscoverable for traditional behavioral cognitive neuropsychology. We discuss implications for neuropsychological assessment, and we discuss opportunities for confirmatory computational brain imaging studies.

Computational Modeling for Neuropsychological Assessment of Bradyphrenia in Parkinson's Disease

The neural mechanisms of cognitive dysfunctions in neurological diseases remain poorly understood. Here, we conjecture that this unsatisfying state-of-the-art is in part due to the non-specificity of the typical behavioral indicators for cognitive dysfunctions. Our study addresses the topic by advancing the assessment of cognitive dysfunctions through computational modeling. We investigate bradyphrenia in Parkinson's disease (PD) as an exemplary case of cognitive dysfunctions in neurological diseases. Our computational model conceptualizes trial-by-trial behavioral data as resulting from parallel cognitive and sensorimotor reinforcement learning. We assessed PD patients 'on' and 'off' their dopaminergic medication and matched healthy control (HC) participants on a computerized version of the Wisconsin Card Sorting Test. PD patients showed increased retention of learned cognitive information and decreased retention of learned sensorimotor information from previous trials in comparison to HC participants. Systemic dopamine replacement therapy did not remedy these cognitive dysfunctions in PD patients but incurred non-desirable side effects such as decreasing cognitive learning from positive feedback. Our results reveal novel insights into facets of bradyphrenia that are indiscernible by observable behavioral indicators of cognitive dysfunctions. We discuss how computational modeling may contribute to the advancement of future research on brain-behavior relationships and neuropsychological assessment.