Drift diffusion model of reward and punishment learning in schizophrenia: Modeling and experimental data

Moustafa, Ahmed A.; Kéri, Szabolcs; Somlai, Zsuzsanna; Balsdon, Tarryn; Frydecka, Dorota; Misiak, Błażej; White, Corey N.

doi:10.1016/j.bbr.2015.05.024

Cited by 50 publications

(49 citation statements)

References 46 publications

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“…Patients might also have used a cognitive compensatory strategy favouring accuracy over speed to maintain performance; this strategy has been involved in the improvement of several cognitive processes in patients with schizophrenia. [30][31][32] However, our results suggest that mentalizing was slowed down in patients with schizophrenia, even when executive contextual control was taken into account. Many studies have reported increased response times during ToM tasks in patients with schizophrenia, [33][34][35] although the specificity of this slowdown to the domain of social cognition has remained unclear.…”

Section: Slowdown Of Intention Processing In Patients With Schizophreniacontrasting

confidence: 56%

Eye-tracking reveals a slowdown of social context processing during intention attribution in patients with schizophrenia

Roux

Brunet-Gouet

Passerieux

et al. 2016

jpn

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Section: Slowdown Of Intention Processing In Patients With Schizophreniacontrasting

confidence: 56%

Eye-tracking reveals a slowdown of social context processing during intention attribution in patients with schizophrenia

Roux

Brunet-Gouet

Passerieux

et al. 2016

jpn

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“…Arguably one of the best ways to incorporate accuracy and speed of performance into a single set of indices is through a computational approach known as diffusion modelling (Ratcliff & McKoon, 2008). This approach, which has long been used in the cognitive sciences and cognitive neurosciences, has recently begun to be adopted in the developmental (Cohen-Gilbert et al, 2014; Ratcliff, Love, Thompson, & Opfer, 2012), aging (Ratcliff, Thapar, & McKoon, 2004, 2011; Starns & Ratcliff, 2010), and clinical (Huang-Pollock et al, 2016; Huang-Pollock, Karalunas, Tam, & Moore, 2012; Karalunas, Huang-Pollock, & Nigg, 2012; Moustafa et al, 2015; Weigard, Huang-Pollock, & Brown, 2016; Weigard & Huang-Pollock, 2014; Wiecki, Poland, & Frank, 2015) literatures.…”

Section: Discussionmentioning

confidence: 99%

Is Poor Working Memory a Transdiagnostic Risk Factor for Psychopathology?

Huang-Pollock

Shapiro

Galloway-Long

et al. 2016

J Abnorm Child Psychol

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In contrast to historical conceptualizations that framed psychological disorders as distinct, categorical conditions, it is now widely understood that co- and multi-morbidities between disorders are extensive. As a result, there has been a call to better understand the dimensional liabilities that are common to and influence the development of multiple psychopathologies, as supported and exemplified by the National Institutes of Mental Health (NIMH) Research Domain Criteria (RDoC) framework. We use a latent variable SEM approach to examine the degree to which working memory deficits represent a cognitive liability associated with the development of common and discrete dimensions of psychopathology. In a sample of 415 community recruited children aged 8-12 (n = 170 girls), we fit a bi-factor model to parent reports of behavior from the DISC-4 and BASC-2, and included a latent working memory factor as a predictor of the internalizing, externalizing, and general “p-factor.” We found that both the general “p-factor” and externalizing (but not internalizing) latent factor were significantly associated with working memory. When a bi-factor model of externalizing symptomology was fit to further explore this relationship, working memory was only correlated with the general externalizing dimension; correlation with specific inattention, hyperactive/impulsive, and oppositional factors did not survive once the general externalizing dimension was taken into consideration. These findings held regardless of the sex of the child. Our results suggest that working memory deficits represent both a common cognitive liability for mental health disorders, and a specific liability for externalizing disorders.

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“…They have been used to study individual differences in IQ, working memory, and reading measures [122-125], and to examine deficits in populations such as aphasics [166], older adults and children [13-14,123-124], children [167], low literacy adults [168], dyslexics [169], ADHD [170-171], schizophrenia [172], and in depressed and anxious individuals [116,173]. …”

Section: Figurementioning

confidence: 99%

Diffusion Decision Model: Current Issues and History

Ratcliff

Smith

Brown

et al. 2016

Trends in Cognitive Sciences

1,338

1,481

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There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in psychology. They view decision making as a process of noisy accumulation of evidence from a stimulus. The standard model assumes that evidence accumulates at a constant rate during the second or two it takes to make a decision. This process can be linked to the behaviors of populations of neurons and to theories of optimality. Diffusion models have been used successfully in a range of cognitive tasks and as psychometric tools in clinical research to examine individual differences. In this article, we relate the models to both earlier and more recent research in psychology.

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Drift diffusion model of reward and punishment learning in schizophrenia: Modeling and experimental data

Cited by 50 publications

References 46 publications

Eye-tracking reveals a slowdown of social context processing during intention attribution in patients with schizophrenia

Eye-tracking reveals a slowdown of social context processing during intention attribution in patients with schizophrenia

Is Poor Working Memory a Transdiagnostic Risk Factor for Psychopathology?

Diffusion Decision Model: Current Issues and History

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