Goal-directed top-down control of perceptual representations: A computational model of the Wisconsin Card Sorting Test

Granato, Giovanni; Baldassarre, Gianluca

doi:10.32470/ccn.2019.1168-0

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…In (Granato and Baldassarre, 2019), we propose a computational model that starts to illustrate our proposal. The model controls an artificial agent that pivots on a generative neural network (a Restricted Boltzmann Machine (Hinton, 2002)).…”

Section: Flexibility As Manipulation Of Internal Representations: An mentioning

confidence: 99%

Special Issue “On Defining Artificial Intelligence”—Commentaries and Author’s Response

Monett

Lewis²,

Þórisson

et al. 2020

Journal of Artificial General Intelligence

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Pei Wang's paper titled "On Defining Artificial Intelligence" was published in a special issue of the Journal of Artificial General Intelligence (JAGI) in December of last year (Wang, 2019). Wang has been at the forefront of AGI research for over two decades. His non-axiomatic approach to reasoning has stood as a singular example of what may lie beyond narrow AI, garnering interest from NASA and Cisco, among others. We consider his article one of the strongest attempts, since the beginning of the field, to address the long-standing lack of consensus for how to define the field and topic of artificial intelligence (AI). In the recent AGISI survey on defining intelligence (Monett and Lewis, 2018), Pei Wang's definition, The essence of intelligence is the principle of adapting to the environment while working with insufficient knowledge and resources. Accordingly, an intelligent system should rely on finite processing capacity, work in real time, open to unexpected tasks, and learn from experience. This working definition interprets "intelligence" as a form of "relative rationality" (Wang, 2008), 1. Most striking in these numbers is the glaring absence of female authors. A common reason among female academics for rejecting our invitation to contribute was overcommitment. As a community, we may want to think of new, different ways of engaging the full spectrum of AI practitioners if we value inclusion as an essential constituent of a healthy scientific growth. Self determination and willingness to participate are also essential. This is an open access article licensed under the Creative Commons BY-NC-ND License.

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Section: Flexibility As Manipulation Of Internal Representations: An mentioning

confidence: 99%

Special Issue “On Defining Artificial Intelligence”—Commentaries and Author’s Response

Monett

Lewis²,

Þórisson

et al. 2020

Journal of Artificial General Intelligence

Self Cite

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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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“…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 ].…”

Section: Assessing Covert Cognitive Processes On the Wcstmentioning

confidence: 99%

“…These computational models typically belong to one of two subclasses: neural network models or mechanistic models [ 48 ]. Most computational models of the WCST are neural network models (e.g., [ 71 , 72 ]). Neural network models are biologically inspired sets of computational units (referred to as cells or neurons) [ 81 , 82 ].…”

Section: Assessing Covert Cognitive Processes On the Wcstmentioning

confidence: 99%

Toward a Computational Neuropsychology of Cognitive Flexibility

Steinke

Kopp

2020

Brain Sciences

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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.

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Goal-directed top-down control of perceptual representations: A computational model of the Wisconsin Card Sorting Test

Cited by 5 publications

References 10 publications

Special Issue “On Defining Artificial Intelligence”—Commentaries and Author’s Response

Special Issue “On Defining Artificial Intelligence”—Commentaries and Author’s Response

A Computational Study of Executive Dysfunction in Amyotrophic Lateral Sclerosis

Toward a Computational Neuropsychology of Cognitive Flexibility

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