Introduction This study explores the transformative potential of digital, theory-driven, and Bayesian paradigms in neuropsychology by combining digital technologies, a commitment to evaluating ...theoretical frameworks, and Bayesian statistics. The study also examines theories of executive function and cognitive flexibility in a large sample of neurotypical individuals ( N = 489). Methods We developed an internet-based Wisconsin Card-Sorting Task (iWCST) optimized for online assessment of perseveration errors (PE). Predictions of the percentage of PE, PE (%), in non-repetitive versus repetitive situations were derived from the established supervisory attention system (SAS) theory, non-repetitive PE (%) < repetitive PE (%), and the novel goal-directed instrumental control (GIC) theory, non-repetitive PE (%) > repetitive PE (%). Results Bayesian t -tests revealed the presence of a robust error suppression effect (ESE) indicating that PE are less likely in repetitive situations than in non-repetitive situations, contradicting SAS theory with posterior model probability p < 0.001 and confirming GIC theory with posterior model probability p > 0.999. We conclude that repetitive situations support cognitive set switching in the iWCST by facilitating the retrieval of goal-directed, instrumental memory that associates stimulus features, actions, and outcomes, thereby generating the ESE in neurotypical individuals. We also report exploratory data analyses, including a Bayesian network analysis of relationships between iWCST measures. Discussion Overall, this study serves as a paradigmatic model for combining digital technologies, theory-driven research, and Bayesian statistics in neuropsychology. It also provides insight into how this integrative, innovative approach can advance the understanding of executive function and cognitive flexibility and inform future research and clinical applications.
The brain predicts the timing of forthcoming events to optimize responses to them. Temporal predictions have been formalized in terms of the hazard function, which integrates prior beliefs on the ...likely timing of stimulus occurrence with information conveyed by the passage of time. However, how the human brain updates prior temporal beliefs is still elusive. Here we investigated electroencephalographic (EEG) signatures associated with Bayes-optimal updating of temporal beliefs. Given that updating usually occurs in response to surprising events, we sought to disentangle EEG correlates of updating from those associated with surprise. Twenty-six participants performed a temporal foreperiod task, which comprised a subset of surprising events not eliciting updating. EEG data were analyzed through a regression-based massive approach in the electrode and source space. Distinct late positive, centro-parietally distributed, event-related potentials (ERPs) were associated with surprise and belief updating in the electrode space. While surprise modulated the commonly observed P3b, updating was associated with a later and more sustained P3b-like waveform deflection. Results from source analyses revealed that neural encoding of surprise comprises neural activity in the cingulo-opercular network (CON) and parietal regions. These data provide evidence that temporal predictions are computed in a Bayesian manner, and that this is reflected in P3 modulations, akin to other cognitive domains. Overall, our study revealed that analyzing P3 modulations provides an important window into the Bayesian brain. Data and scripts are shared on OSF: https://osf.io/ckqa5/
Empirical support for the Bayesian brain hypothesis, although of major theoretical importance for cognitive neuroscience, is surprisingly scarce. This hypothesis posits simply that neural activities ...code and compute Bayesian probabilities. Here, we introduce an urn–ball paradigm to relate event-related potentials (ERPs) such as the P300 wave to Bayesian inference. Bayesian model comparison is conducted to compare various models in terms of their ability to explain trial-by-trial variation in ERP responses at different points in time and over different regions of the scalp. Specifically, we are interested in dissociating specific ERP responses in terms of Bayesian updating and predictive surprise. Bayesian updating refers to changes in probability distributions given new observations, while predictive surprise equals the surprise about observations under current probability distributions. Components of the late positive complex (P3a, P3b, Slow Wave) provide dissociable measures of Bayesian updating and predictive surprise. Specifically, the updating of beliefs about hidden states yields the best fit for the anteriorly distributed P3a, whereas the updating of predictions of observations accounts best for the posteriorly distributed Slow Wave. In addition, parietally distributed P3b responses are best fit by predictive surprise. These results indicate that the three components of the late positive complex reflect distinct neural computations. As such they are consistent with the Bayesian brain hypothesis, but these neural computations seem to be subject to nonlinear probability weighting. We integrate these findings with the free-energy principle that instantiates the Bayesian brain hypothesis.
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•We apply Bayesian model comparison to neural correlates of Bayesian inference.•We analyze single-trial EEG dynamics during performance on an urn–ball task.•We find dissociable anterior (P3a) and posterior (P3b, Slow Wave) correlates.•Our data are consistent with nonlinear probability weighting.•Our results are consistent with the Bayesian brain hypothesis.
Task switching is often considered for evaluating limitations of cognitive flexibility. Switch costs are behavioural indices of limited cognitive flexibility, and switch costs may be decomposable ...into stimulus- and response-related fractions, as conjectured by the domain hypothesis of cognitive flexibility. According to the domain hypothesis, there exist separable stimulus- and response-related neural networks for cognitive flexibility, which should be discernible as distinct event-related potentials (ERPs). The present card-matching study allowed isolating stimulus- and response-related switch costs, while measuring ERPs evoked by task cues and target stimuli with a focus on the target-locked N2/P3 complex. Behavioural data revealed that both stimulus-task and response-task bindings contribute to switch costs. Cue-locked ERPs yielded larger anterior negativity/posterior positivity in response to switch cues compared to repeat cues. Target-locked ERPs revealed separable ERP correlates of stimulus- and response-related switch costs. P3 waveforms with fronto-central scalp distributions emerged as a corollary of stimulus-related switch costs. Fronto-centrally distributed N2 waveforms occurred when stimulus-task and response-task bindings contributed jointly to switch costs. The reported N2/P3 ERP data are commensurate with the domain hypothesis according to which there exist separable stimulus- and response-related neural networks for cognitive flexibility.
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.
Environmental events often occur on a probabilistic basis but can sometimes be predicted based on specific cues and thus approached proactively. Incidental statistical learning enables the ...acquisition of knowledge about probabilistic cue-target contingencies. However, the neural mechanisms of statistical learning about contingencies (SLC), the required conditions for successful learning, and the role of implicit processes in the resultant proactive behavior are still debated. We examined changes in behavior and cortical activity during an SLC task in which subjects responded to visual targets. Unbeknown to them, there were three types of target cues associated with high-, low-, and zero target probabilities. About half of the subjects spontaneously gained explicit knowledge about the contingencies (contingency-aware group), and only they showed evidence of proactivity: shortened response times to predictable targets and enhanced event-related brain responses (cue-evoked P300 and contingent negative variation, CNV) to high probability cues. The behavioral and brain responses were strictly associated on a single-trial basis. Source reconstruction of the brain responses revealed activation of fronto-parietal brain regions associated with cognitive control, particularly the anterior cingulate cortex and precuneus. We also found neural correlates of SLC in the contingency-unaware group, but these were restricted to post-target latencies and visual association areas. Our results document a qualitative difference between explicit and implicit learning processes and suggest that in certain conditions, proactivity may require explicit knowledge about contingencies.
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.
This event-related brain potential (ERP) study aimed at bridging two hitherto widely separated domains of cognitive neuroscience. Specifically, we combined the analysis of cognitive control in a cued ...task-switching paradigm with the fundamental question of how uncertainty is encoded in the brain. Two functional models of P3 amplitude variation in cued task-switching paradigms were put to an empirical test: (1) According to the P3b surprise hypothesis, parietal P3b waveforms are related to surprise over switch cues. (2) According to the P3a entropy hypothesis, frontal P3a waveforms are associated with entropy over switch outcomes. In order to examine these hypotheses, we measured the EEG while sixteen healthy young participants performed cued task-switching paradigms closely modeled to the Wisconsin Card Sorting Test (WCST). We applied a factorial design, with number of tasks (two vs. three viable tasks), cue explicitness (task cuing vs. transition cuing), and cue contingency (prospectively-signaled cuing vs. feedback-based cuing) as independent variables. The ERP results replicated the commonly reported P3b effect associated with task switches, and further showed that P3a amplitudes were related to the entropy of switch outcomes, thereby supporting both hypotheses. Based on these ERP data, we suggest that surprise over task switches, and entropy over switch outcomes, constitute dissociable functional correlates of P3b and P3a ERP components in task-switching paradigms, respectively. Finally, a theoretical integration of the findings is proposed within the framework of Sokolov's (1966) entropy model of the orienting response (OR).
Self-administered computerized assessment has the potential to increase the reach of neuropsychological assessment. The present study reports the first split-half reliability estimates for a ...self-administered computerized variant of the Wisconsin Card Sorting Test (WCST), which is considered as a gold standard for the neuropsychological assessment of executive functions. We analyzed data from a large sample of young volunteers (
= 375). Split-half reliability estimates for perseveration errors, set-loss errors, and inference errors were all above 0.90. Split-half reliability estimates for response time measures on switch and repeat trials exceeded 0.95. Our results indicated sufficient split-half reliability for a self-administered computerized WCST, paving the way for an advanced digital assessment of executive functions. We discuss potential effects of test formats, administration variants, and sample characteristics on split-half reliability.
Wisconsin card-sorting tasks provide unique opportunities to study cognitive flexibility and its limitations, which express themselves behaviorally as perseverative errors (PE). PE refer to those ...behavioral errors on Wisconsin card-sorting tasks that are committed when cognitive rules are maintained even though recently received outcomes demand to switch to other rules (i.e., cognitive perseveration). We explored error-suppression effects (ESE) across three Wisconsin card-sorting studies. ESE refer to the phenomenon that PE are reduced on repetitive trials compared to non-repetitive trials. We replicated ESE in all three Wisconsin card-sorting studies. Study 1 revealed that non-associative accounts of ESE, in particular the idea that cognitive inhibition may account for them, are not tenable. Study 2 suggested that models of instrumental learning are among the most promising associative accounts of ESE. Instrumental learning comprises goal-directed control and the formation of corresponding associative memories over and above the formation of habitual memories according to dual-process models of instrumental learning. Study 3 showed that cognitive, rather than motor, representations of responses should be conceptualized as elements entering goal-directed instrumental memories. Collectively, the results imply that ESE on Wisconsin card-sorting tasks are not only a highly replicable phenomenon, but they also indicate that ESE provide an opportunity to study cognitive mechanisms of goal-directed instrumental control. Based on the reported data, we present a novel theory of cognitive perseveration (i.e., the '
oal-directed
nstrumental
ontrol' GIC model), which is outlined in the Concluding Discussion.
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