One Size Does Not Fit All: Idiographic Computational Models Reveal Individual Differences in Learning and Meta-Learning Strategies.

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.

Computer programmers show distinct, expertise-dependent brain responses to violations in form and meaning when reading code.

As computer programming becomes more central to the workforce, the need for better models of how it is effectively learned has become more apparent. The current study addressed this gap by recording electrophysiological brain responses as 62 Python programmers with varying skill levels read lines of code with manipulations of form (syntax) and meaning (semantics). At the group level, results showed that manipulations of form resulted in P600 effects, with syntactically invalid code generating more positive deflections in the 500-800 ms range than syntactically valid code. Meaning manipulations resulted in N400 effects, with semantically implausible code generating more negative deflections in the 300-500 ms range than semantically plausible code. Greater Python expertise within the group was associated with greater sensitivity to violations in form. These results support the notion that skilled programming, like skilled natural language learning, is associated with the incorporation of rule-based knowledge into online comprehension processes. Conversely, programmers at all skill levels showed neural sensitivity to meaning manipulations, suggesting that reliance on pre-existing semantic relationships facilitates code comprehension across skill levels.

Investigating local and global control mechanisms in bilingual grammatical processing.

Bilinguals employ both global and local control mechanisms to manage coactivated languages that compete for selection, yet little is known about how they operate on morphosyntactic information. The current study investigated bilingual language control mechanisms for a morphosyntactic production task. Across two experiments, 48 early Spanish-English bilinguals completed rapid instructed task learning paradigms with priming-in-item-recognition manipulations that investigated the extent to which parallel activation was observed across languages and across rules of the same type within a language. The results from the current experiments showed that it was more difficult to reject incorrect responses in the correct target language than to reject incorrect responses that contained the correct grammatical manipulation executed in the nondesired language. These results suggest that global control at the level of target language selection is more effective than local control processes during a bilingual morphosyntactic manipulation. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Getting language right: Relating individual differences in right hemisphere contributions to language learning and relearning.

Language, or the diverse set of dynamic processes through which symbolic, perceptual codes are linked to meaning representations in memory, has long been assumed to be lateralized to the left hemisphere (LH). However, after over 150 years of investigation, we still lack a unifying account of when, and for whom, a particular linguistic process relies upon LH or right hemisphere (RH) computations, or both. With a focus on individual differences, this article integrates existing theories of hemispheric contributions to language and cognition into a novel proposed framework for understanding how, when, and for whom the RH contributes to linguistic processes. We use evidence from first and second language learning and language relearning following focal brain damage to highlight the critical contributions of the RH.

Individual Differences in Reward-Based Learning Predict Fluid Reasoning Abilities.

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.

Pyneal: Open Source Real-Time fMRI Software.

Increasingly, neuroimaging researchers are exploring the use of real-time functional magnetic resonance imaging (rt-fMRI) as a way to access a participant's ongoing brain function throughout a scan. This approach presents novel and exciting experimental applications ranging from monitoring data quality in real time, to delivering neurofeedback from a region of interest, to dynamically controlling experimental flow, or interfacing with remote devices. Yet, for those interested in adopting this method, the existing software options are few and limited in application. This presents a barrier for new users, as well as hinders existing users from refining techniques and methods. Here we introduce a free, open-source rt-fMRI package, the Pyneal toolkit, designed to address this limitation. The Pyneal toolkit is python-based software that offers a flexible and user friendly framework for rt-fMRI, is compatible with all three major scanner manufacturers (GE, Siemens, Phillips), and, critically, allows fully customized analysis pipelines. In this article, we provide a detailed overview of the architecture, describe how to set up and run the Pyneal toolkit during an experimental session, offer tutorials with scan data that demonstrate how data flows through the Pyneal toolkit with example analyses, and highlight the advantages that the Pyneal toolkit offers to the neuroimaging community.

Relating Natural Language Aptitude to Individual Differences in Learning Programming Languages.

This experiment employed an individual differences approach to test the hypothesis that learning modern programming languages resembles second "natural" language learning in adulthood. Behavioral and neural (resting-state EEG) indices of language aptitude were used along with numeracy and fluid cognitive measures (e.g., fluid reasoning, working memory, inhibitory control) as predictors. Rate of learning, programming accuracy, and post-test declarative knowledge were used as outcome measures in 36 individuals who participated in ten 45-minute Python training sessions. The resulting models explained 50-72% of the variance in learning outcomes, with language aptitude measures explaining significant variance in each outcome even when the other factors competed for variance. Across outcome variables, fluid reasoning and working-memory capacity explained 34% of the variance, followed by language aptitude (17%), resting-state EEG power in beta and low-gamma bands (10%), and numeracy (2%). These results provide a novel framework for understanding programming aptitude, suggesting that the importance of numeracy may be overestimated in modern programming education environments.

The Role of Basal Ganglia Reinforcement Learning in Lexical Ambiguity Resolution.

The current study aimed to elucidate the contributions of the subcortical basal ganglia to human language by adopting the view that these structures engage in a basic neurocomputation that may account for its involvement across a wide range of linguistic phenomena. Specifically, we tested the hypothesis that basal ganglia reinforcement learning (RL) mechanisms may account for variability in semantic selection processes necessary for ambiguity resolution. To test this, we used a biased homograph lexical ambiguity priming task that allowed us to measure automatic processes for resolving ambiguity toward high-frequency word meanings. Individual differences in task performance were then related to indices of basal ganglia RL, which were used to group subjects into three learning styles: (a) Choosers who learn by seeking high reward probability stimuli; (b) Avoiders, who learn by avoiding low reward probability stimuli; and (c) Balanced participants, whose learning reflects equal contributions of choose and avoid processes. The results suggest that balanced individuals had significantly lower access to subordinate, or low-frequency, homograph word meanings. Choosers and Avoiders, on the other hand, had higher access to the subordinate word meaning even after a long delay between prime and target. Experimental findings were then tested using an ACT-R computational model of RL that learns from both positive and negative feedback. Results from the computational model simulations confirm and extend the pattern of behavioral findings, providing an RL account of individual differences in lexical ambiguity resolution.

Bilingual Language Experience Shapes Resting-State Brain Rhythms.

An increasing body of research has investigated how bilingual language experience changes brain structure and function, including changes to task-free, or "resting-state" brain connectivity. Such findings provide important evidence about how the brain continues to be shaped by different language experiences throughout the lifespan. The neural effects of bilingual language experience can provide evidence about the additional processing demands placed on the linguistic and/or executive systems by dual-language use. While considerable research has used MRI to examine where these changes occur, such methods cannot reveal the temporal dynamics of functioning brain networks at rest. The current study used data from task-free EEGS to disentangle how the linguistic and cognitive demands of bilingual language use impact brain functioning. Data analyzed from 106 bilinguals and 91 monolinguals revealed that bilinguals had greater alpha power, and significantly greater and broader coherence in the alpha and beta frequency ranges than monolinguals. Follow-up analyses showed that higher alpha was related to language control: more second-language use, higher native-language proficiency, and earlier age of second-language acquisition. Bilateral beta power was related to native-language proficiency, whereas theta was related to native-language proficiency only in left-hemisphere electrodes. The results contribute to our understanding of how the linguistic and cognitive requirements of dual-language use shape intrinsic brain activity, and what the broader implications for information processing may be.

Effects of bilingual language experience on basal ganglia computations: A dynamic causal modeling test of the conditional routing model.

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.

Proactive and Reactive Language Control in the Bilingual Brain.

The current experiment investigated bilingual language control within the dual mechanisms framework. In an fMRI investigation of morphosyntactic rule production, the presence or absence of target language cues was manipulated to investigate the neural mechanisms associated with proactive and reactive global language control mechanisms. Patterns of activation across nine regions of interest (ROIs) were investigated in seventeen early Spanish-English bilingual speakers. A cue by phase interaction in the left dorsolateral prefrontal cortex (DLPFC) and pre-supplementary motor area (Pre-SMA) was observed, suggesting that these regions were more active during cue phases, and less active during execution phases, when target language cues were presented. Individual differences analyses showed that variability in proactive control (informative > non-informative cued trial activation during preparation) in the basal ganglia was correlated with proactive control in the left DLPFC, left inferior frontal gyrus (IFG), and right precentral ROIs. In contrast, reactive control (non-informative > informative cued activation during execution) in the anterior cingulate was correlated with reactive control in the Pre-SMA and left orbital frontal ROIs. The results suggest that, consistent with the dual mechanisms framework, bilinguals differ in the degree to which they use cues to proactively prepare to use a target language.

BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains.

We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as "Senders" whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Sender's decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders' decisions are transmitted via the Internet to the brain of a third subject, the "Receiver," who cannot see the game screen. The Senders' decisions are delivered to the Receiver's brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and uses an EEG interface to make a decision about either turning the block or keeping it in the same orientation. A second round of the game provides an additional chance for the Senders to evaluate the Receiver's decision and send feedback to the Receiver's brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game, (2) True/False positive rates of subjects' decisions, and (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the collaborative task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender's signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that like conventional social networks, BrainNet allows Receivers to learn to trust the Sender who is more reliable, in this case, based solely on the information transmitted directly to their brains. Our results point the way to future brain-to-brain interfaces that enable cooperative problem solving by humans using a "social network" of connected brains.

Individual Differences in Resting-state Brain Rhythms Uniquely Predict Second Language Learning Rate and Willingness to Communicate in Adults.

The current study used quantitative electroencephalography (qEEG) to characterize individual differences in neural rhythms at rest and to relate them to fluid reasoning ability, to first language proficiency, and to subsequent second language (L2) learning ability, with the goal of obtaining a better understanding of the neurocognitive bases of L2 aptitude. Mean spectral power, laterality, and coherence metrics were extracted across theta, alpha, beta, and gamma frequency bands obtained from eyes-closed resting-state qEEG data from 41 adults aged 18-34 years. Participants then completed 8 weeks of French training using a virtual language and cultural immersion software. Results replicate and extend previous studies showing that faster learners have higher beta power recorded over right hemisphere (RH) electrode sites, greater laterality (RH - LH/RH + LH) of alpha and beta bands, and greater coherence between RH frontotemporal sites across all frequencies, although only coherence measures survived multiple comparisons. Increased coherence within and between RH networks was also associated with greater posttest declarative memory scores and with more accurate speech during learning. Total speech attempts, in contrast, correlated with bilaterally distributed small-world network configurations, as indexed by lower power and coherence over high-frequency (beta and gamma) bands recorded over frontotemporal networks in both hemispheres. Results from partial correlations and regression analyses suggest that the neural predictors of L2 learning rate, posttest proficiency, and total speech attempts varied in their degree of overlap with qEEG correlates of first language proficiency and fluid reasoning abilities, but that neural predictors alone explained 26-60% of the variance in L2 outcomes.

Human performance across decision making, selective attention, and working memory tasks: Experimental data and computer simulations.

This article describes the data analyzed in the paper "Individual differences in the Simon effect are underpinned by differences in the competitive dynamics in the basal ganglia: An experimental verification and a computational model" (Stocco et al., 2017) [1]. The data includes behavioral results from participants performing three cognitive tasks (Probabilistic Stimulus Selection (Frank et al., 2004) [2], Simon task (Craft and Simon, 1970) [3], and Automated Operation Span (Unsworth et al., 2005) [4]), as well as simulationed traces generated by a computational neurocognitive model that accounts for individual variations in human performance across the tasks. The experimental data encompasses individual data files (in both preprocessed and native output format) as well as group-level summary files. The simulation data includes the entire model code, the results of a full-grid search of the model's parameter space, and the code used to partition the model space and parallelize the simulations. Finally, the repository includes the R scripts used to carry out the statistical analyses reported in the original paper.

The bilingual language network: Differential involvement of anterior cingulate, basal ganglia and prefrontal cortex in preparation, monitoring, and execution.

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.

Brain-based individual difference measures of reading skill in deaf and hearing adults.

Most deaf children and adults struggle to read, but some deaf individuals do become highly proficient readers. There is disagreement about the specific causes of reading difficulty in the deaf population, and consequently, disagreement about the effectiveness of different strategies for teaching reading to deaf children. Much of the disagreement surrounds the question of whether deaf children read in similar or different ways as hearing children. In this study, we begin to answer this question by using real-time measures of neural language processing to assess if deaf and hearing adults read proficiently in similar or different ways. Hearing and deaf adults read English sentences with semantic, grammatical, and simultaneous semantic/grammatical errors while event-related potentials (ERPs) were recorded. The magnitude of individuals' ERP responses was compared to their standardized reading comprehension test scores, and potentially confounding variables like years of education, speechreading skill, and language background of deaf participants were controlled for. The best deaf readers had the largest N400 responses to semantic errors in sentences, while the best hearing readers had the largest P600 responses to grammatical errors in sentences. These results indicate that equally proficient hearing and deaf adults process written language in different ways, suggesting there is little reason to assume that literacy education should necessarily be the same for hearing and deaf children. The results also show that the most successful deaf readers focus on semantic information while reading, which suggests aspects of education that may promote improved literacy in the deaf population.

Individual differences in the Simon effect are underpinned by differences in the competitive dynamics in the basal ganglia: An experimental verification and a computational model.

Cognitive control is thought to be made possible by the activity of the prefrontal cortex, which selectively uses task-specific representations to bias the selection of task-appropriate responses over more automated, but inappropriate, ones. Recent models have suggested, however, that prefrontal representations are in turn controlled by the basal ganglia. In particular, neurophysiological considerations suggest that the basal ganglia's indirect pathway plays a pivotal role in preventing irrelevant information from being incorporated into a task, thus reducing response interference due to the processing of inappropriate stimuli dimensions. Here, we test this hypothesis by showing that individual differences in a non-verbal cognitive control task (the Simon task) are correlated with performance on a decision-making task (the Probabilistic Stimulus Selection task) that tracks the contribution of the indirect pathway. Specifically, the higher the effect of the indirect pathway, the smaller was the behavioral costs associated with suppressing interference in incongruent trials. Additionally, it was found that this correlation was driven by individual differences in incongruent trials only (with little effect on congruent ones) and specific to the indirect pathway (with almost no correlation with the effect of the direct pathways). Finally, it is shown that this pattern of results is precisely what is predicted when competitive dynamics of the basal ganglia are added to the selective attention component of a simple model of the Simon task, thus showing that our experimental results can be fully explained by our initial hypothesis.

Basal ganglia impairments in autism spectrum disorder are related to abnormal signal gating to prefrontal cortex.

Research on the biological basis of autism spectrum disorder has yielded a list of brain abnormalities that are arguably as diverse as the set of behavioral symptoms that characterize the disorder. Among these are patterns of abnormal cortical connectivity and abnormal basal ganglia development. In attempts to integrate the existing literature, the current paper tests the hypothesis that impairments in the basal ganglia's function to flexibly select and route task-relevant neural signals to the prefrontal cortex underpins patterns of abnormal synchronization between the prefrontal cortex and other cortical processing centers observed in individuals with autism spectrum disorder (ASD). We tested this hypothesis using a Dynamic Causal Modeling analysis of neuroimaging data collected from 16 individuals with ASD (mean age=25.3 years; 6 female) and 17 age- and IQ-matched neurotypical controls (mean age=25.6, 6 female), who performed a Go/No-Go test of executive functioning. Consistent with the hypothesis tested, a random-effects Bayesian model selection procedure determined that a model of network connectivity in which basal ganglia activation modulated connectivity between the prefrontal cortex and other key cortical processing centers best fit the data of both neurotypicals and individuals with ASD. Follow-up analyses suggested that the largest group differences were observed for modulation of connectivity between prefrontal cortex and the sensory input region in the occipital lobe [t(31)=2.03, p=0.025]. Specifically, basal ganglia activation was associated with a small decrease in synchronization between the occipital region and prefrontal cortical regions in controls; however, in individuals with ASD, basal ganglia activation resulted in increased synchronization between the occipital region and the prefrontal cortex. We propose that this increased synchronization may reflect a failure in basal ganglia signal gating mechanisms, resulting in a non-selective copying of signals to prefrontal cortex. Such a failure to prioritize and filter signals to the prefrontal cortex could result in the pervasive impairments in cognitive flexibility and executive functioning that characterize autism spectrum disorder, and may offer a mechanistic explanation of some of the observed abnormalities in patterns of cortical synchronization in ASD.

Resting-state qEEG predicts rate of second language learning in adults.

Understanding the neurobiological basis of individual differences in second language acquisition (SLA) is important for research on bilingualism, learning, and neural plasticity. The current study used quantitative electroencephalography (qEEG) to predict SLA in college-aged individuals. Baseline, eyes-closed resting-state qEEG was used to predict language learning rate during eight weeks of French exposure using an immersive, virtual scenario software. Individual qEEG indices predicted up to 60% of the variability in SLA, whereas behavioral indices of fluid intelligence, executive functioning, and working-memory capacity were not correlated with learning rate. Specifically, power in beta and low-gamma frequency ranges over right temporoparietal regions were strongly positively correlated with SLA. These results highlight the utility of resting-state EEG for studying the neurobiological basis of SLA in a relatively construct-free, paradigm-independent manner.

A network-level analysis of cognitive flexibility reveals a differential influence of the anterior cingulate cortex in bilinguals versus monolinguals.

Mounting evidence suggests that bilingual development may change the brain in a way that gives rise to differences in non-linguistic cognitive functioning; however, only a limited number of studies have investigated the mechanism by which bilingualism shapes the brain. The current study used a network-level analysis to investigate differences in the mechanisms by which bilinguals and monolinguals flexibly adapt their neural networks in the face of novel task demands. Three competing hypotheses concerning differences in network-level adaptation were examined using Dynamic Causal Modeling of data from 15 bilinguals and 14 monolinguals who performed a Rapid Instructed Task Learning paradigm. The results demonstrated that the best-fitting model for the data from both groups specified that novel task execution is accomplished through a modulation of the influence of the anterior cingulate cortex (ACC) on the dorsolateral prefrontal cortex (DLPFC) and on the striatum. Further examination of the best-fitting model revealed that ACC activity increased DLPFC and striatal activity in bilinguals but decreased activity in these regions in monolinguals. Interestingly, an increased positive connection between the ACC and striatum was associated with decreased accuracy across groups. Taken together, the results suggest that regardless of language experience, the ACC plays a critical role in cognitive flexibility, but the exact influence of the ACC on other primary control regions seems to be dependent on language experience. When paired with the behavioral results, these results suggest that bilinguals and monolinguals may employ different neurocognitive mechanisms for conflict monitoring to flexibly adapt to novel situations.