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

Steinke, Alexander; Lange, Florian; Seer, Caroline; Hendel, M; Kopp, Bruno

doi:10.3390/jcm9041158

Cited by 13 publications

(56 citation statements)

References 116 publications

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“…Strong modulations of perseveration propensity by response demands might be specifically associated with increased inertia of MF-feedback expectations (i.e., increased ), or with heightened MF-learning rate after negative feedback (i.e., increased ). These examples illustrate that future computational research of card sorting might contribute a better understanding of behavioral card sorting symptoms (for an illustrative example of the effect of model parameters on feedback expectations, see 79 , 80 ).…”

Section: Discussionmentioning

confidence: 92%

Parallel model-based and model-free reinforcement learning for card sorting performance

Steinke

Lange

Kopp

2020

Sci Rep

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The Wisconsin Card Sorting Test (WCST) is considered a gold standard for the assessment of cognitive flexibility. On the WCST, repeating a sorting category following negative feedback is typically treated as indicating reduced cognitive flexibility. Therefore such responses are referred to as ‘perseveration’ errors. Recent research suggests that the propensity for perseveration errors is modulated by response demands: They occur less frequently when their commitment repeats the previously executed response. Here, we propose parallel reinforcement-learning models of card sorting performance, which assume that card sorting performance can be conceptualized as resulting from model-free reinforcement learning at the level of responses that occurs in parallel with model-based reinforcement learning at the categorical level. We compared parallel reinforcement-learning models with purely model-based reinforcement learning, and with the state-of-the-art attentional-updating model. We analyzed data from 375 participants who completed a computerized WCST. Parallel reinforcement-learning models showed best predictive accuracies for the majority of participants. Only parallel reinforcement-learning models accounted for the modulation of perseveration propensity by response demands. In conclusion, parallel reinforcement-learning models provide a new theoretical perspective on card sorting and it offers a suitable framework for discerning individual differences in latent processes that subserve behavioral flexibility.

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Section: Discussionmentioning

confidence: 92%

Parallel model-based and model-free reinforcement learning for card sorting performance

Steinke

Lange

Kopp

2020

Sci Rep

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“…The insufficient nosological specificity of WCST error propensities may relate to the 'impureness' of behavioral WCST measures [11,12,[40][41][42][43]. That is to say that behavioral WCST measures may originate from a mixture of multiple covert cognitive processes.…”

Section: Introductionmentioning

confidence: 99%

“…Computational cognitive neuropsychology offers an approach to decompose behavior that was observed on neuropsychological assessment instruments into covert cognitive processes [41,46]. Computational cognitive neuropsychology utilizes mathematical formalization of (1) the assumed covert cognitive processes, and (2) the way in which these processes interact [47][48][49][50][51][52][53][54][55][56][57].…”

Section: Introductionmentioning

confidence: 99%

“…In a recently published study [41], we characterized PD patients' alterations in latent variables of the parallel RL model by studying PD patients 'on' and 'off' their clinically prescribed dopamine (DA) medication and a matched healthy control (HC) group on a computerized WCST (cWCST) [88]. Application of the parallel RL model revealed that PD patients showed enhanced cognitive retention rates and reduced sensorimotor retention rates compared to HC participants.…”

Section: Introductionmentioning

confidence: 99%

“…The present study examined the specificity of executive dysfunction in ALS through (1) a computational analysis of WCST behavior in patients with ALS and (2) a comparison between their latent cognitive profile and that of patients with PD from an earlier computational study [41]. In order to achieve these goals, we characterized ALS patients' alterations in latent variables of the parallel RL model [33] by studying ALS patients and a matched HC group on a cWCST variant [15].…”

Section: Introductionmentioning

confidence: 99%

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A Computational Study of Executive Dysfunction in Amyotrophic Lateral Sclerosis

Steinke

Lange

Seer

et al. 2020

JCM

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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.

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Multidimensionality in Executive Function Profiles in Schizophrenia: a Computational Approach Using the Wisconsin Card Sorting Task

Haywood

Baughman

2021

Comput Brain Behav

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Deficits in executive functions (EF) are frequently implicated in the aetiology of schizophrenia. However, no consensus exists regarding the contribution of EF to the dysfunctions observed in these individuals, with competing accounts arguing a primary role for deficits in inhibition, shifting and updating. We examine an alternative proposal referred to as the multidimensional hypothesisrather than performance on cognitive tasks being primarily dependent on a single EF, that performance is multiply realised by a range of uneven EF profiles. We describe a computational approach, using the WCST, that allows manipulations to ability levels of processes relating to each EF component to be explicitly assessed. Our central objective is to determine whether models exhibiting unique profiles of EF abilities may simulate the performance of three target groups: individuals with schizophrenia, healthy first-degree relatives, and controls. For each target group, we show that of the performance measures yielded by the WCST, perseverative errors (PE; commonly assumed to rely heavily on EF) are simulated by multiple models. For a range of secondary measures (cards sorted, categories completed and non-perseverative errors; assumed less specific to EF abilities) our manipulations show little resemblance to the target groups. We view these results to support the multidimensional hypothesis, in that (1) no single EF process accounts for the poorer performance of the schizophrenic group, and(2) performance of PE in the target groups is achieved in models of diverse EF profiles.The implications of these findings for the study of EF in neuropsychological disorders are discussed.

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

Cited by 13 publications

References 116 publications

Parallel model-based and model-free reinforcement learning for card sorting performance

Parallel model-based and model-free reinforcement learning for card sorting performance

A Computational Study of Executive Dysfunction in Amyotrophic Lateral Sclerosis

Multidimensionality in Executive Function Profiles in Schizophrenia: a Computational Approach Using the Wisconsin Card Sorting Task

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