To address the limitations of self-report measures, we developed the Pro-Environmental Behavior Task (PEBT) as a computerized paradigm for the assessment of actual pro-environmental behavior under controlled laboratory conditions. On each PEBT trial, participants can either choose the faster car option, which causes a series of lights to be illuminated, or they can save the associated energy by choosing the bicycle option at the expense of spending more time in the laboratory. In two pre-registered studies (both N = 120), we showed that the proportion of environmentally friendly PEBT choices is a valid and reliable measure of pro-environmental behavior. PEBT choices were consistent across trials, correlated to conceptually relevant variables, and sensitive to conceptually relevant manipulations. These effects were replicable and independent of the labelling of PEBT options. Our findings highlight the psychometric quality and utility of the PEBT as a paradigm that can open new avenues for research on pro-environmental behavior.

The Wisconsin Card Sorting Test (WCST) represents the gold standard for the neuropsychological assessment of executive function. However, very little is known about its reliability. In the current study, 146 neurological inpatients received the Modified WCST (M-WCST). Four basic measures (number of correct sorts, categories, perseverative errors, set-loss errors) and their composites were evaluated for split-half reliability. The reliability estimates of the number of correct sorts, categories, and perseverative errors fell into the desirable range ( rel ≥ .90). The study therefore disclosed sufficiently reliable M-WCST measures, fostering the application of this eminent psychological test to neuropsychological assessment. Our data also revealed that the M-WCST possesses substantially better psychometric properties than would be expected from previous studies of WCST test-retest reliabilities obtained from non-patient samples. Our study of split-half reliabilities from discretionary construed and from randomly built M-WCST splits exemplifies a novel approach to the psychometric foundation of neuropsychology.

Rule inference on WCST-like card-sorting tasks appeared to be selectively impaired when the amount of information to be integrated in working memory increases or when working memory capacity is reduced (as in older individuals). Our findings indicate that measuring integration errors as an index of a distinct rule-inference process can improve the understanding and interpretability of WCST performance. (PsycINFO Database Record

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.

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.