Reward and Punishment Sensitivity are Associated with Cross-disorder Traits

Portengen, Christel M.; Sprooten, Emma; Zwiers, Marcel P.; Hoekstra, Pieter J.; Dietrich, Andrea; Holz, Nathalie E.; Aggensteiner, Pascal-M.; Banaschewski, Tobias; Schulze, Ulrike; Saam, Melanie C; Craig, Michael C.; Sethi, Arjun; Santosh, Paramala; Ouriaghli, Ilyas Sagar; Castro‐Fornieles, Josefina; Rosa, Mireia; Arango, Celso; Penzol, María José; Werhahn, Julia E; Brandeis, Daniel; Walitza, Susanne; Oldehinkel, Marianne; Franke, Barbara; Buitelaar, Jan K.; Naaijen, Jilly

doi:10.1016/j.psychres.2021.113795

Cited by 8 publications

(13 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, such an effect could be explained by noise or an increased tendency to explore the environment [ 92 ], which could reasonably be associated with decreased use of MF strategies [ 28 ]. This possibility is consistent with recent findings showing that ADHD symptoms are negatively correlated with win-stay scores [ 74 ]. Indeed, computational stimulations reveal an effect of altered dopamine levels on the exploration-exploitation trade-off.…”

Section: Discussionsupporting

confidence: 93%

Distinct reinforcement learning profiles distinguish between language and attentional neurodevelopmental disorders

et al. 2023

View full text Add to dashboard Cite

Background Theoretical models posit abnormalities in cortico-striatal pathways in two of the most common neurodevelopmental disorders (Developmental dyslexia, DD, and Attention deficit hyperactive disorder, ADHD), but it is still unclear what distinct cortico-striatal dysfunction might distinguish language disorders from others that exhibit very different symptomatology. Although impairments in tasks that depend on the cortico-striatal network, including reinforcement learning (RL), have been implicated in both disorders, there has been little attempt to dissociate between different types of RL or to compare learning processes in these two types of disorders. The present study builds upon prior research indicating the existence of two learning manifestations of RL and evaluates whether these processes can be differentiated in language and attention deficit disorders. We used a two-step RL task shown to dissociate model-based from model-free learning in human learners. Results Our results show that, relative to neurotypicals, DD individuals showed an impairment in model-free but not in model-based learning, whereas in ADHD the ability to use both model-free and model-based learning strategies was significantly compromised. Conclusions Thus, learning impairments in DD may be linked to a selective deficit in the ability to form action-outcome associations based on previous history, whereas in ADHD some learning deficits may be related to an incapacity to pursue rewards based on the tasks' structure. Our results indicate how different patterns of learning deficits may underlie different disorders, and how computation-minded experimental approaches can differentiate between them.

show abstract

Section: Discussionsupporting

confidence: 93%

Distinct reinforcement learning profiles distinguish between language and attentional neurodevelopmental disorders

et al. 2023

View full text Add to dashboard Cite

show abstract

“…By contrast, prior studies supported the traditional theory of cognitive inflexibility in patients with diagnosis of OCD (Abramovitch et al, 2013; Gruner and Pittenger, 2017; Shin et al, 2014) and ADHD (Willcutt et al 2005), which is inconsistent with our findings. It has been widely reported that ADHD (McCarthy et al, 2018; Portengen et al, 2021) or OCD (Remijnse et al, 2006; Tezcan et al, 2017) patients show underperformance in PRLT. However, our results showed that all groups perform optimally on the task, in spite of finding credible differences between the clinical groups and the healthy controls.…”

Section: Discussionmentioning

confidence: 99%

“…This profile is also consistent with previous studies reporting reward sensitivity in ADHD, which could be understood as a preference for immediate rewards over delayed rewards with more long-term gains (Barkley, 1997; Bari y Robbins, 2013; Sonuga-Barke, 2003). Reward sensitivity in ADHD has been measured through paradigms such as reversal learning task (Alsop et al, 2016; Hauser et al, 2014; Portengen et al, 2021) and delay discounting task (Barkley et al, 2001; Marx et al, 2021). As the meta-control model proposes, the “inflated flexibility” in ADHD (giving more weight to immediate contingency over the past experiences) leads to difficulties in focusing on a goal (Hommel and Cozalto, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…This paradigm explores the ability to change the previously rewarded response when it becomes disadvantageous (Perandrés-Gómez et al, 2021). Patients with a diagnosis of OCD (Remijnse et al, 2006; Tezcan et al, 2017) and ADHD (McCarthy et al, 2016; Chantiluke et al, 2015; Portengen et al, 2021) showed underperformance compared to a matched healthy control group, in terms of lower rewarded correct responses and higher mean errors per reversal. However, the results are inconsistent since other studies were unable to report differences in the performance of the OCD (Chamberlain et al, 2007) and ADHD (Hauser et al, 2014) population compared to healthy controls.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Contingency-based flexibility mechanisms through a reinforcement learning model in adults with Attention-Deficit/Hyperactivity Disorder and Obsessive-Compulsive Disorder

Rodríguez-Herrera,

León,

Fernández-Martína

et al. 2024

Preprint

View full text Add to dashboard Cite

AbtractThe probabilistic reversal learning paradigm is one of the most used to assess cognitive flexibility during a contingency-based learning process. Lack of cognitive flexibility is related to the symptomatology that characterizes disorders such as Attention-Deficit/Hyperactivity Disorder (ADHD) and Obsessive-Compulsive Disorder (OCD). Resting-state functional connectivity (rsFC) could be a specific predictor of performance in contingency-based cognitive flexibility. However, the mechanisms underlying learning and flexibility in an environment of uncertainty with OCD or ADHD adults have not been widely explored. Computational modelling may be helpful to separate components of the behavioural processes and identify the source of the disorders. In the present study, we aimed to identify the mechanisms underlying contingency-based cognitive flexibility in a sample of the impulsive-compulsive spectrum and healthy controls, and explore the rsFC between the frontoparietal networks (FPN) regions as a possible neuromarker. 148 Spanish-speaking participants (43 patients with OCD, 53 with ADHD, and 52 healthy controls) completed a probabilistic reversal learning task (PRLT). Previously, we obtained a record of FNP rsFC using functional near-infrared spectroscopy (fNIRS). For this purpose, we applied the reinforcement learning model in combination with Bayesian GLM. We found that groups showed an optimal performance in the acquisition phase of the PRLT and higher performance of HC compared to the diagnostic groups in the reversal block, although performance is still optimal in all groups. Likewise, we found that the parameters studied (reinforcement learning rate, punishment learning rate and inverse temperature) predict task performance differently by phase and group. Regarding FNP rsFC, we found that rsFC between left posterior parietal cortex (lpPC) and right posterior parietal cortex (rpPC) seems to credibly predict performance in the acquisition block in the healthy controls. These findings suggest that reducing the uncertainty between action-outcome may help to improve the adaptation of ADHD and OCD patients to changing environments. Thus, understanding sensitivity to punishment or reinforcement and its influence on the decision-making may be important for designing case-specific interventions. According to our data, rsFC between lpPC and rpPC could be important for optimal learning of our task.

show abstract

“…Considering past research indicating prevalence estimates of ODD symptoms in DS ranging from 8 to 26%, we hypothesized that prevalence rate of ODD would fell within this range in the current study. Moreover, basing on literature supporting the use of the three parent-report instruments for the detection of ODD symptoms in general population and in other neurodevelopmental disorders ( 58 – 61 ), we hypothesized that these instruments were equally accurate in identifying ODD in children and adolescents with DS.…”

Section: Introductionmentioning

confidence: 99%

Assessment of oppositional defiant disorder and oppositional behavior in children and adolescents with Down syndrome

et al. 2023

View full text Add to dashboard Cite

IntroductionChildren and adolescents with intellectual disability (ID) exhibit higher rates of oppositional defiant disorder (ODD) than typically developing (TD) peers. However, studies focusing on the investigation of ODD prevalence in youth with Down syndrome (DS) are still limited.MethodsThe current study aimed to investigate the prevalence of ODD clinical and subclinical symptoms in a group of 101 youth with DS (63 boys, 38 girls) ranging in age from 6 to 18 years. Moreover, the prevalence of ODD symptoms, as detected by means of three parent-report questionnaires, was compared with that detected by a semi-structured psychopathological interview, namely, the Schedule for Affective Disorders and Schizophrenia for School Aged Children Present and Lifetime (K-SADS) Version Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5).ResultsWe found that 17% of participants met diagnostic criteria for ODD on the K-SADS, whereas 24% exhibited subclinical symptoms. Results also suggest good specificity of Swanson, Nolan, and Pelham-IV Rating Scale (SNAP-IV), Conners’ Parent Rating Scales Long Version (CPRS) and Child Behavior Checklist (CBCL) in detecting ODD symptoms. The investigation of the agreement in the prevalence rates of clinical and subclinical symptoms of ODD between K-SADS and the parent-report questionnaires indicated CPRS as the parent-report questionnaire with the best agreement with K-SADS.DiscussionThis study provides support for the use of parent-report questionnaires to assess ODD symptoms in children and adolescents with DS by evaluating their levels of agreement with a semi-structured psychopathological interview. In particular, our results suggest that CPRS could be considered a suitable screening tool for ODD clinical and subclinical symptoms in youth with DS.

show abstract

Reward and Punishment Sensitivity are Associated with Cross-disorder Traits

Cited by 8 publications

References 38 publications

Distinct reinforcement learning profiles distinguish between language and attentional neurodevelopmental disorders

Distinct reinforcement learning profiles distinguish between language and attentional neurodevelopmental disorders

Contingency-based flexibility mechanisms through a reinforcement learning model in adults with Attention-Deficit/Hyperactivity Disorder and Obsessive-Compulsive Disorder

Assessment of oppositional defiant disorder and oppositional behavior in children and adolescents with Down syndrome

Contact Info

Product

Resources

About