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Individuals who develop bilingually typically outperform monolinguals on tests of executive functions. This advantage likely reflects enhanced prefrontal function, but the mechanisms that underlie this improvement are still poorly understood. This article describes a theory on the nature of the neural underpinnings of improved executive function in bilinguals. Specifically, we propose that growing up in a bilingual environment trains a gating system in the striatum that flexibly routes information to the prefrontal cortex. This article is divided into three sections. Firstly, literature establishing a three-way connection between bilingualism, executive function, and fronto-striatal loops is summarized. Secondly, a computational model of information processing in the basal ganglia is described, illustrating how the striatal nuclei function to transfer information between cortical regions under prerequisite conditions. Finally, this model is extended to describe how bilingualism may “train the brain,” enabling improved performance under conditions of competitive information selection during information transfer. Theoretical implications and predictions of this theory are discussed. [ABSTRACT FROM AUTHOR]

The Common Model of Cognition (CMC) is a recently proposed, consensus architecture intended to capture decades of progress in cognitive science on modeling human and human-like intelligence. Because of the broad agreement around it and preliminary mappings of its components to specific brain areas, we hypothesized that the CMC could be a candidate model of the large-scale functional architecture of the human brain. To test this hypothesis, we analyzed functional MRI data from 200 participants and seven different tasks that cover a broad range of cognitive domains. The CMC components were identified with functionally homologous brain regions through canonical fMRI analysis, and their communication pathways were translated into predicted patterns of effective connectivity between regions. The resulting dynamic linear model was implemented and fitted using Dynamic Causal Modeling, and compared against six alternative brain architectures that had been previously proposed in the field of neuroscience (three hierarchical architectures and three hub-and-spoke architectures) using a Bayesian approach. The results show that, in all cases, the CMC vastly outperforms all other architectures, both within each domain and across all tasks. These findings suggest that a common set of architectural principles that could be used for artificial intelligence also underpins human brain function across multiple cognitive domains. [ABSTRACT FROM AUTHOR]

Background. Nowadays the use of measurement instruments that evaluate the patient's condition is very common in the orthopedic field. We know that the most distal district of the lower limb, the osteoarticular complex of the ankle, is one of the most evaluated with rating scales; in fact, there are at least 76 different scoring systems for evaluating ankle problems. Furthermore, there is no measurement tool considered to be the absolute Gold Standard for evaluating problems affecting the ankle joint. Objectives. The main aim of this study was to translate, culturally adapt, and validate in Italian language the Self-Reported Foot and Ankle Score (SEFAS). Methods. The English version of the SEFAS has been translated according to international guidelines. The measurement properties (construct validity and reliability) have been tested according to COSMIN checklists. Cronbach's α was calculated to assess the internal consistency and the Intraclass Correlation Coefficient (ICC) was calculated to estimate the reliability. The Kaiser-Meier-Olkin (KMO), The Short-Form 36 (SF-36) and the EuroQol Health Questionnaire 5 Dimensions 5 Levels (EQ-5D-5L) were used to assess the construct validity and to verify sampling adequacy. A factorial analysis was used to confirm the mono-factorial nature of the scale. Results. All the items were similar in meaning to the originals. Cronbach's α was 0,880 and the ICC was 0.869. The Pearson's Correlation Coefficient showed significant correlations (p < 0.01) between SEFAS and SF-36 and EQ-5D-5L items. Conclusions. Based on the results obtained, we suggest the use of SEFAS in daily clinical practice, also promoting its continuation in the field of scientific research. [ABSTRACT FROM AUTHOR]

Bilingual language control is characterized by the ability to select from amongst competing representations based on the current language in use. According to the Conditional Routing Model (CRM), this feat is underpinned by basal-ganglia signal-routing mechanisms, and may have implications for cognitive flexibility. The current experiment used dynamic causal modeling of fMRI data to compare network-level brain functioning in monolinguals and bilinguals during a task that required productive (semantic decision) and receptive (language) switches. Consistent with the CRM, results showed that: (1) both switch types drove activation in the basal ganglia, (2) bilinguals and monolinguals differed in the strength of influence of dorsolateral prefrontal cortex (DLPFC) on basal ganglia, and (3) differences in bilingual language experience were marginally related to the strength of influence of the switching drives onto basal ganglia. Additionally, a task-by-group interaction was found, suggesting that when bilinguals engaged in language-switching, their task-switching costs were reduced. [ABSTRACT FROM AUTHOR]

Translational applications of cognitive science depend on having predictive models at the individual, or idiographic, level. However, idiographic model parameters, such as working memory capacity, often need to be estimated from specific tasks, making them dependent on task-specific assumptions. Here, we explore the possibility that idiographic parameters reflect an individual's biology and can be identified from task-free neuroimaging measures. To test this hypothesis, we correlated a reliable behavioral trait, the individual rate of forgetting in long-term memory, with a readily available task-free neuroimaging measure, the resting-state EEG spectrum. Using an established, adaptive fact-learning procedure, the rate of forgetting for verbal and visual materials was measured in a sample of 50 undergraduates from whom we also collected eyes-closed resting-state EEG data. Statistical analyses revealed that the individual rates of forgetting were significantly correlated across verbal and visual materials. Importantly, both rates correlated with resting-state power levels in the low (13-15 Hz) and upper (15-17 Hz) portion of the beta frequency bands. These correlations were particularly strong for visuospatial materials, were distributed over multiple fronto-parietal locations, and remained significant even after a correction for multiple comparisons (False Discovery Rate) and after robust correlation methods were applied. These results suggest that computational models could be individually tailored for prediction using idiographic parameter values derived from inexpensive, task-free imaging recordings. [ABSTRACT FROM AUTHOR]

Complex skill learning depends on the joint contribution of multiple interacting systems: working memory (WM), declarative long-term memory (LTM) and reinforcement learning (RL). The present study aims to understand individual differences in the relative contributions of these systems during learning. We built four idiographic, ACT-R models of performance on the stimulus-response learning, Reinforcement Learning Working Memory task. The task consisted of short 3-image, and long 6-image, feedback-based learning blocks. A no-feedback test phase was administered after learning, with an interfering task inserted between learning and test. Our four models included two single-mechanism RL and LTM models, and two integrated RL-LTM models: (a) RL-based meta-learning, which selects RL or LTM to learn based on recent success, and (b) a parameterized RL-LTM selection model at fixed proportions independent of learning success. Each model was the best fit for some proportion of our learners (LTM: 68.7%, RL: 4.8%, Meta-RL: 13.25%, bias-RL:13.25% of participants), suggesting fundamental differences in the way individuals deploy basic learning mechanisms, even for a simple stimulus-response task. Finally, long-term declarative memory seems to be the preferred learning strategy for this task regardless of block length (3- vs 6-image blocks), as determined by the large number of subjects whose learning characteristics were best captured by the LTM only model, and a preference for LTM over RL in both of our integrated-models, owing to the strength of our idiographic approach., (© 2024 Cognitive Science Society LLC.)

Motivation is the driving force that influences people's behaviors and interacts with many cognitive functions. Computationally, motivation is represented as a cost-benefit analysis that weighs efforts and rewards in order to choose the optimal actions. Shenhav and colleagues proposed an elegant theory, the Expected Value of Control (EVC), which describes the relationship between cognitive efforts, costs, and rewards. In this paper, we propose a more fine-grained and detailed motivation framework that incorporates the principles of EVC into the ACT-R cognitive architecture. Specifically, motivation is represented as a specific slot in the Goal buffer with a corresponding scalar value, M, that is translated into the reward value R t that is delivered when the goal is reached. This implementation is tested in two models. The first model is a high-level model that reproduces the EVC predictions with abstract actions. The second model is an augmented version of an existing ACT-R model of the Simon task. The motivation mechanism is shown to permit optimal effort allocation and reproduce known phenomena. Finally, the broader implications of our mechanism are discussed., (© 2023 Cognitive Science Society LLC.)

Safe handling of hazardous driving situations is a task of high practical relevance for building reliable and trustworthy cyber-physical systems such as autonomous driving systems. This task necessitates an accurate prediction system of the vehicle's confidence to prevent potentially harmful system failures on the occurrence of unpredictable conditions that make it less safe to drive. In this paper, we discuss the challenges of adapting a misbehavior predictor with knowledge mined during the execution of the main system. Then, we present a framework for the continual learning of misbehavior predictors, which records in-field behavioral data to determine what data are appropriate for adaptation. Our framework guides adaptive retraining using a novel combination of in-field confidence metric selection and reconstruction error-based weighing. We evaluate our framework to improve a misbehavior predictor from the literature on the Udacity simulator for self-driving cars. Our results show that our framework can reduce the false positive rate by a large margin and can adapt to nominal behavior drifts while maintaining the original capability to predict failures up to several seconds in advance., (© 2021 The Authors. Journal of Software: Evolution and Process published by John Wiley & Sons Ltd.)

The ability to reason and problem-solve in novel situations, as measured by the Raven's Advanced Progressive Matrices (RAPM), is highly predictive of both cognitive task performance and real-world outcomes. Here we provide evidence that RAPM performance depends on the ability to reallocate attention in response to self-generated feedback about progress. We propose that such an ability is underpinned by the basal ganglia nuclei, which are critically tied to both reward processing and cognitive control. This hypothesis was implemented in a neurocomputational model of the RAPM task, which was used to derive novel predictions at the behavioral and neural levels. These predictions were then verified in one neuroimaging and two behavioral experiments. Furthermore, an effective connectivity analysis of the neuroimaging data confirmed a role for the basal ganglia in modulating attention. Taken together, these results suggest that individual differences in a neural circuit related to reward processing underpin human fluid reasoning abilities., (© 2021 Cognitive Science Society, Inc.)

Behavioral data, despite being a common index of cognitive activity, is under scrutiny for having poor reliability as a result of noise or lacking replications of reliable effects. Here, we argue that cognitive modeling can be used to enhance the test-retest reliability of the behavioral measures by recovering individual-level parameters from behavioral data. We tested this empirically with the Probabilistic Stimulus Selection (PSS) task, which is used to measure a participant's sensitivity to positive or negative reinforcement. An analysis of 400,000 simulations from an Adaptive Control of Thought-Rational (ACT-R) model of this task showed that the poor reliability of the task is due to the instability of the end-estimates: because of the way the task works, the same participants might sometimes end up having apparently opposite scores. To recover the underlying interpretable parameters and enhance reliability, we used a Bayesian Maximum A Posteriori (MAP) procedure. We were able to obtain reliable parameters across sessions (intraclass correlation coefficient ≈ 0.5). A follow-up study on a modified version of the task also found the same pattern of results, with very poor test-retest reliability in behavior but moderate reliability in recovered parameters (intraclass correlation coefficient ≈ 0.4). Collectively, these results imply that this approach can further be used to provide superior measures in terms of reliability, and bring greater insights into individual differences., (© Society for Mathematical Psychology 2021.)

Research on the neural bases of bilingual language control has largely overlooked the role of preparatory processes, which are central to cognitive control. Additionally, little is known about how the processes involved in global language selection may differ from those involved in the selection of words and morpho-syntactic rules for manipulating them. These processes were examined separately in an fMRI experiment, with an emphasis on understanding how and when general cognitive control regions become activated. Results of region-of-interest analyses on 23 early Spanish-English bilinguals showed that the anterior cingulate cortex (ACC) was primarily engaged during the language preparation phase of the task, whereas the left prefrontal (DLPFC) and pre-supplementary motor areas showed increasing activation from preparation to execution. Activation in the basal ganglia (BG), left middle temporal lobe, and right precentral cortical regions did not significantly differ throughout the task. These results suggest that three core cognitive control regions, the ACC, DLPFC, and BG, which have been previously implicated in bilingual language control, engage in distinct neurocognitive processes. Specifically, the results are consistent with the view that the BG "keep track" of the target language in use throughout various levels of language selection, that the ACC is particularly important for top-down target language preparation, and that the left prefrontal cortex is increasingly involved in selection processes from preparation through task execution., (Published by Elsevier Inc.)

Several attempts have been made previously to provide a biological grounding for cognitive architectures by relating their components to the computations of specific brain circuits. Often, the architecture's action selection system is identified with the basal ganglia. However, this identification overlooks one of the most important features of the basal ganglia-the existence of a direct and an indirect pathway that compete against each other. This characteristic has important consequences in decision-making tasks, which are brought to light by Parkinson's disease as well as genetic differences in dopamine receptors. This paper shows that a standard model of action selection in a cognitive architecture (ACT-R) cannot replicate any of these findings, details an alternative solution that reconciles action selection in the architecture with the physiology of the basal ganglia, and extends the domain of application of cognitive architectures. The implication of this solution for other architectures and existing models are discussed., (Copyright © 2017 Cognitive Science Society, Inc.)