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.

Reliance on Episodic vs. Procedural Systems in Decision-Making Depends on Individual Differences in Their Relative Neural Efficiency

Experiential decision-making can be explained as a result of either memory-based or reinforcement-based processes. Here, for the first time, we show that individual preferences between a memory-based and a reinforcement-based strategy, even when the two are functionally equivalent in terms of expected payoff, are adaptively shaped by individual differences in resting-state brain connectivity between the corresponding brain regions. Using computational cognitive models to identify which mechanism was most likely used by each participant, we found that individuals with comparatively stronger connectivity between memory regions prefer a memory-based strategy, while individuals with comparatively stronger connectivity between sensorimotor and habit-formation regions preferentially rely on a reinforcement-based strategy. These results suggest that human decision-making is adaptive and sensitive to the neural costs associated with different strategies.

Allocating Mental Effort in Cognitive Tasks: A Model of Motivation in the ACT-R Cognitive Architecture

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 Rt 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.

Confidence-driven weighted retraining for predicting safety-critical failures in autonomous driving systems

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.

Inferring a Cognitive Architecture from Multitask Neuroimaging Data: A Data-Driven Test of the Common Model of Cognition Using Granger Causality

Cognitive architectures (i.e., theorized blueprints on the structure of the mind) can be used to make predictions about the effect of multiregion brain activity on the systems level. Recent work has connected one high-level cognitive architecture, known as the "Common Model of Cognition," to task-based functional MRI data with great success. That approach, however, was limited in that it was intrinsically top-down, and could thus only be compared with alternate architectures that the experimenter could contrive. In this paper, we propose a bottom-up method to infer a cognitive architecture directly from brain imaging data itself, overcoming this limitation. Specifically, Granger causality modeling was applied to the same task-based fMRI data to infer a network of causal connections between brain regions based on their functional connectivity. The resulting network shares many connections with those proposed by the Common Model of Cognition but also suggests important additions likely related to the role of episodic memory. This combined top-down and bottom-up modeling approach can be used to help formalize the computational instantiation of cognitive architectures and further refine a comprehensive theory of cognition.

The Structured Mind at Rest: Low-Frequency Oscillations Reflect Interactive Dynamics Between Spontaneous Brain Activity and a Common Architecture for Task Control

The Common Model of Cognition (CMC) has been proposed as a high level framework through which functional neuroimaging data can be predicted and interpreted. Previous work has found the CMC is capable of predicting brain activity across a variety of tasks, but it has not been tested on resting state data. This paper adapts a previously used method for comparing theoretical models of brain structure, Dynamic Causal Modeling, for the task-free environment of resting state, and compares the CMC against six alternate architectural frameworks while also separately modeling spontaneous low-frequency oscillations. For a large sample of subjects from the Human Connectome Project, the CMC provides the best account of resting state brain activity, suggesting the presence of a general purpose structure of connections in the brain that drives activity when at rest and when performing directed task behavior. At the same time, spontaneous brain activity was found to be present and significant across all frequencies and in all regions. Together, these results suggest that, at rest, spontaneous low-frequency oscillations interact with the general cognitive architecture for task-based activity. The possible functional implications of these findings are discussed.

Increased Basal Ganglia Modulatory Effective Connectivity Observed in Resting-State fMRI in Individuals With Parkinson's Disease

Alterations to interactions between networked brain regions underlie cognitive impairment in many neurodegenerative diseases, providing an important physiological link between brain structure and cognitive function. Previous attempts to characterize the effects of Parkinson's disease (PD) on network functioning using resting-state functional magnetic resonance imaging (rs-fMRI), however, have yielded inconsistent and contradictory results. Potential problems with prior work arise in the specifics of how the area targeted by the diseases (the basal ganglia) interacts with other brain regions. Specifically, current computational models point to the fact that the basal ganglia contributions should be captured with modulatory (i.e., second-order) rather than direct (i.e., first-order) functional connectivity measures. Following this hypothesis, a principled but manageable large-scale brain architecture, the Common Model of Cognition, was used to identify differences in basal ganglia connectivity in PD by analyzing resting-state fMRI data from 111 participants (70 patients with PD; 41 healthy controls) using Dynamic Causal Modeling (DCM). Specifically, the functional connectivity of the basal ganglia was modeled as two second-level, modulatory connections that control projections from sensory cortices to the prefrontal cortex, and from the hippocampus and medial temporal lobe to the prefrontal cortex. We then examined group differences between patients with PD and healthy controls in estimated modulatory effective connectivity in these connections. The Modulatory variant of the Common Model of Cognition outperformed the Direct model across all subjects. It was also found that these second-level modulatory connections had higher estimates of effective connectivity in the PD group compared to the control group, and that differences in effective connectivity were observed for all direct connections between the PD and control groups.We make the case that accounting for modulatory effective connectivity better captures the effects of PD on network functioning and influences the interpretation of the directionality of the between-group results. Limitations include that the PD group was scanned on dopaminergic medication, results were derived from a reasonable but small number of individuals and the ratio of PD to healthy control participants was relatively unbalanced. Future research will examine if the observed effect holds for individuals with PD scanned off their typical dopaminergic medications.

Core Cognitive Mechanisms Underlying Syntactic Priming: A Comparison of Three Alternative Models

Syntactic priming (SP) is the effect by which, in a dialogue, the current speaker tends to re-use the syntactic constructs of the previous speakers. SP has been used as a window into the nature of syntactic representations within and across languages. Because of its importance, it is crucial to understand the mechanisms behind it. Currently, two competing theories exist. According to the transient activation account, SP is driven by the re-activation of declarative memory structures that encode structures. According to the error-based implicit learning account, SP is driven by prediction errors while processing sentences. By integrating both transient activation and associative learning, Reitter et al.'s hybrid model 2011 assumes that SP is achieved by both mechanisms, and predicts a priming enhancement for rare or unusual constructions. Finally, a recently proposed account, the reinforcement learning account, claims that SP driven by the successful application of procedural knowledge will be reversed when the prime sentence includes grammatical errors. These theories make different assumptions about the representation of syntactic rules (declarative vs. procedural) and the nature of the mechanism that drives priming (frequency and repetition, attention, and feedback signals, respectively). To distinguish between these theories, they were all implemented as computational models in the ACT-R cognitive architecture, and their specific predictions were examined through grid-search computer simulations. Two experiments were then carried out to empirically test the central prediction of each theory as well as the individual fits of each participant's responses to different parameterizations of each model. The first experiment produced results that were best explained by the associative account, but could also be accounted for by a modified reinforcement model with a different parsing algorithm. The second experiment, whose stimuli were designed to avoid the parsing ambiguity of the first, produced somewhat weaker effects. Its results, however, were also best predicted by the model implementing the associative account. We conclude that the data overall points to SP being due to prediction violations that direct attentional resources, in turn suggesting a declarative rather than a RL based procedural representation of syntactic rules.

When Fear Shrinks the Brain: A Computational Model of the Effects of Posttraumatic Stress on Hippocampal Volume

Post-traumatic stress disorder (PTSD) is a psychiatric disorder often characterized by the unwanted re-experiencing of a traumatic event through nightmares, flashbacks, and/or intrusive memories. This paper presents a neurocomputational model using the ACT-R cognitive architecture that simulates intrusive memory retrieval following a potentially traumatic event (PTE) and predicts hippocampal volume changes observed in PTSD. Memory intrusions were captured in the ACT-R rational analysis framework by weighting the posterior probability of re-encoding traumatic events into memory with an emotional intensity term I to capture the degree to which an event was perceived as dangerous or traumatic. It is hypothesized that (1) increasing the intensity I of a PTE will increase the odds of memory intrusions, and (2) increased frequency of intrusions will result in a concurrent decrease in hippocampal size. A series of simulations were run and it was found that I had a significant effect on the probability of experiencing traumatic memory intrusions following a PTE. The model also found that I was a significant predictor of hippocampal volume reduction, where the mean and range of simulated volume loss match results of existing meta-analyses. The authors believe that this is the first model to both describe traumatic memory retrieval and provide a mechanistic account of changes in hippocampal volume, capturing one plausible link between PTSD and hippocampal volume.

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.

Repeated Application of Transcranial Diagnostic Ultrasound Towards the Visual Cortex Induced Illusory Visual Percepts in Healthy Participants

Transcranial magnetic stimulation (TMS) of the visual cortex can induce phosphenes as participants look at a visual target. So can non-diagnostic ultrasound (nDU), delivered in a transcranial fashion, while participants have closed their eyes during stimulation. Here, we sought to determine if DU, aimed at the visual cortex, could alter the perception of a visual target. We applied a randomized series of actual or sham DU, transcranially and towards the visual cortex of healthy participants while they stared at a visual target (a white crosshair on a light-blue background), with the ultrasound device placed where TMS elicited phosphenes. These participants observed percepts seven out of ten times, which consisted of extra or extensions of lines relative to the original crosshair, and additional colors, an average of 53.7 ± 2.6% of the time over the course of the experiment. Seven out of ten different participants exposed to sham-only DU observed comparable percepts, but only an average of 36.3 ± 1.9% of the time, a statistically significant difference (p < 0.00001). Moreover, on average, participants exposed to a combination of sham and actual ultrasound reported a net increase of 47.9 percentage points in the likelihood that they would report a percept by the end of the experiment. Our results are consistent with the hypothesis that a random combination of sham-only and actual DU, applied directly over the visual cortex of participants, increased the likelihood that they would observe visual effects, but not the type of effects, with that likelihood increasing over the course of the experiment. From this, we conclude that repeated exposures by DU may make the visual cortex more responsive to stimulation of their visual cortex by the visual target itself. Future studies should identify the biophysical mechanism(s) and neural pathways by which DU, in our hands and others, can generate its observed effects on brain function. These observations, consistent with other’s observation of effects of DU stimulation of the human motor cortex and amygdala, as well as the FDA approved nature of DU, may lead to increased use of DU as a means of altering brain function.

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.

Monitoring of attentional oscillations through Spectral Similarity Analysis predicts reading comprehension

Deviations of attention from the task at hand are often associated with worse reading performance (Schooler, Reichle, & Halpern, 2004). Ironically, current methods for detecting these shifts of attention typically generate task interruptions and further disrupt performance. In the current study, we developed a method to (1) track shifts of attention away from the reading task by examining the similarity between 5 min of eyes-closed-resting-state EEG and 5 min reading EEG; and (2) investigate, during reading, how the ratio between attention shifts and focused reading relates to readers' comprehension. We performed a Spectral Similarity Analysis (SSA) that examined the spectral similarity between EEG recorded during reading and at rest on a moment-by-moment basis. We then recursively applied the algorithm to the resting-state data itself to obtain an individual baseline of the stability of brain activation recorded during rest. We defined any moment in which SSA during reading was greater than the mean correlation between resting-state EEG and itself as an "attentional shift." The results showed that the proportion of such attentional shifts recorded over the left visual region (O1) significantly predicted reading comprehension, with higher ratios (indicative of more frequent attentional shifts) relating to worse comprehension scores on the reading test. As a proof of its validity, the same measure collected during the reading comprehension test also predicted participants' Simon effect (incongruent - congruent response times) which is a common index of selective attention. This novel method allows researchers to detect attention shifts moments during reading without interrupting natural reading process.

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.

The Role of Dorsal Premotor Cortex in Resolving Abstract Motor Rules: Converging Evidence From Transcranial Magnetic Stimulation and Cognitive Modeling

In this study, repetitive transcranial magnetic stimulation (rTMS) was applied over left dorsal premotor cortex (PMd) while participants performed a novel task paradigm that required planning of responses in accordance with both instructed rules and present stimuli. rTMS is a noninvasive form of neurostimulation that can interfere with ongoing processing of a targeted cortical region, resulting in a transient "virtual lesion" that can reveal the contribution of the region to ongoing behavior. Increased response times (RTs) were observed specifically when rTMS was applied over PMd while participants were preparing to execute a complex response to an uninstructed stimulus. To further delineate the effect of stimulation, condition-specific RT distributions were modeled as three-parameter Weibull distributions through hierarchical Bayesian modeling (HBM). Comparison of the estimated parameters to those of a paired control demonstrated that while PMd-rTMS slightly decreased nondecision time, it also greatly increased the variability in the RT distribution. This increased variability resulted in an overall increase in predicted mean RT and is consistent with a delay in cognitive processes. In conjunction, an ACT-R cognitive model of the task was developed in order to systematically test alternative hypotheses on the potential cognitive functions that may be affected by stimulation of PMd. ACT-R simulations suggested that participant's behavior was due to an effect of TMS on a "re-planning" process, indicating that PMd may be specifically involved in planning of complex motor responses to specific visual stimuli. In conjunction with the HBM modeling effort, these results suggest that PMd-rTMS is capable of pausing or slowing the execution of a motor response-planning process.

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.

Phase transfer of TiO2 nanoparticles from water to ionic liquid triggered by phosphonic acid grafting

Controlling the interface between TiO₂ nanocrystals and ionic liquids is of high fundamental and applied interest for energy storage and conversion devices. Phase transfer of nanoparticles from a synthesis medium to a processing or an application medium plays a significant role in nanotechnology. Here we demonstrate that surface modification with phosphonic acids bearing cationic end-groups can trigger the phase transfer of TiO₂ nanoparticles from an aqueous sol to a typical water-immiscible ionic liquid, [Emim][NTf₂]. The transfer involves both the grafting of the phosphonic acid moiety and the exchange of the counter ion of the cationic end-group by NTf₂ anions, as demonstrated by solid-state NMR, elemental analysis and independent grafting and ion exchange experiments. Furthermore, the colloidal stability of the TiO₂ sols in [Emim][NTf₂] strongly depends on the hydrophobic character of the cationic end-groups.

Comment on "Brownian diffusion of a particle at an air/liquid interface: elastic (not viscous) response of the surface"

In a recent article Toro-Mendoza et al. considered an elastic response of an interface in order to explain the enhanced lateral drag of solid particles straddling fluid interfaces we recently measured.

A Biologically Plausible Action Selection System for Cognitive Architectures: Implications of Basal Ganglia Anatomy for Learning and Decision-Making Models

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.

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.

Playing 20 Questions with the Mind: Collaborative Problem Solving by Humans Using a Brain-to-Brain Interface

We present, to our knowledge, the first demonstration that a non-invasive brain-to-brain interface (BBI) can be used to allow one human to guess what is on the mind of another human through an interactive question-and-answering paradigm similar to the “20 Questions” game. As in previous non-invasive BBI studies in humans, our interface uses electroencephalography (EEG) to detect specific patterns of brain activity from one participant (the “respondent”), and transcranial magnetic stimulation (TMS) to deliver functionally-relevant information to the brain of a second participant (the “inquirer”). Our results extend previous BBI research by (1) using stimulation of the visual cortex to convey visual stimuli that are privately experienced and consciously perceived by the inquirer; (2) exploiting real-time rather than off-line communication of information from one brain to another; and (3) employing an interactive task, in which the inquirer and respondent must exchange information bi-directionally to collaboratively solve the task. The results demonstrate that using the BBI, ten participants (five inquirer-respondent pairs) can successfully identify a “mystery item” using a true/false question-answering protocol similar to the “20 Questions” game, with high levels of accuracy that are significantly greater than a control condition in which participants were connected through a sham BBI.

A direct brain-to-brain interface in humans

We describe the first direct brain-to-brain interface in humans and present results from experiments involving six different subjects. Our non-invasive interface, demonstrated originally in August 2013, combines electroencephalography (EEG) for recording brain signals with transcranial magnetic stimulation (TMS) for delivering information to the brain. We illustrate our method using a visuomotor task in which two humans must cooperate through direct brain-to-brain communication to achieve a desired goal in a computer game. The brain-to-brain interface detects motor imagery in EEG signals recorded from one subject (the "sender") and transmits this information over the internet to the motor cortex region of a second subject (the "receiver"). This allows the sender to cause a desired motor response in the receiver (a press on a touchpad) via TMS. We quantify the performance of the brain-to-brain interface in terms of the amount of information transmitted as well as the accuracies attained in (1) decoding the sender's signals, (2) generating a motor response from the receiver upon stimulation, and (3) achieving the overall goal in the cooperative visuomotor task. Our results provide evidence for a rudimentary form of direct information transmission from one human brain to another using non-invasive means.

Bilingualism trains specific brain circuits involved in flexible rule selection and application

Bilingual individuals have been shown to outperform monolinguals on a variety of tasks that measure non-linguistic executive functioning, suggesting that some facets of the bilingual experience give rise to generalized improvements in cognitive performance. The current study investigated the hypothesis that such advantage in executive functioning arises from the need to flexibly select and apply rules when speaking multiple languages. Such flexible behavior may strengthen the functioning of the fronto-striatal loops that direct signals to the prefrontal cortex. To test this hypothesis, we compared behavioral and brain data from proficient bilinguals and monolinguals who performed a Rapid Instructed Task Learning paradigm, which requires behaving according to ever-changing rules. Consistent with our hypothesis, bilinguals were faster than monolinguals when executing novel rules, and this improvement was associated with greater modulation of activity in the basal ganglia. The implications of these findings for language and executive function research are discussed herein.

Inhibitory synapses between striatal projection neurons support efficient enhancement of cortical signals: a computational model

The function of lateral inhibitory synapses between striatal projection neurons is currently poorly understood. This paper puts forward a model suggesting that inhibitory collaterals can be used to enhance the incoming cortical signals. In particular, we propose that lateral inhibition between projection neurons performs a signal-enhancing process that resembles the image processing technique of "unsharp masking", where a blurred copy is used to enhance and sharpen an input image. The paper also presents the results of computer simulations deomsntrating that the proposed mechanisms is compatible with known properties of striatal projection neurons, and outperforms alternative models of lateral inhibition. Finally, this paper illustrates the advantages of the proposed model and discusses the relevance of these conclusions for existing computational models of the basal ganglia and their role in cognition.

Acetylcholine-based entropy in response selection: a model of how striatal interneurons modulate exploration, exploitation, and response variability in decision-making

The basal ganglia play a fundamental role in decision-making. Their contribution is typically modeled within a reinforcement learning framework, with the basal ganglia learning to select the options associated with highest value and their dopamine inputs conveying performance feedback. This basic framework, however, does not account for the role of cholinergic interneurons in the striatum, and does not easily explain certain dynamic aspects of decision-making and skill acquisition like the generation of exploratory actions. This paper describes basal ganglia acetylcholine-based entropy (BABE), a model of the acetylcholine system in the striatum that provides a unified explanation for these phenomena. According to this model, cholinergic interneurons in the striatum control the level of variability in behavior by modulating the number of possible responses that are considered by the basal ganglia, as well as the level of competition between them. This mechanism provides a natural way to account for the role of basal ganglia in generating behavioral variability during the acquisition of certain cognitive skills, as well as for modulating exploration and exploitation in decision-making. Compared to a typical reinforcement learning model, BABE showed a greater modulation of response variability in the face of changes in the reward contingences, allowing for faster learning (and re-learning) of option values. Finally, the paper discusses the possible applications of the model to other domains.

Conditional routing of information to the cortex: a model of the basal ganglia's role in cognitive coordination

The basal ganglia play a central role in cognition and are involved in such general functions as action selection and reinforcement learning. Here, we present a model exploring the hypothesis that the basal ganglia implement a conditional information-routing system. The system directs the transmission of cortical signals between pairs of regions by manipulating separately the selection of sources and destinations of information transfers. We suggest that such a mechanism provides an account for several cognitive functions of the basal ganglia. The model also incorporates a possible mechanism by which subsequent transfers of information control the release of dopamine. This signal is used to produce novel stimulus-response associations by internalizing transferred cortical representations in the striatum. We discuss how the model is related to production systems and cognitive architectures. A series of simulations is presented to illustrate how the model can perform simple stimulus-response tasks, develop automatic behaviors, and provide an account of impairments in Parkinson's and Huntington's diseases.

High-resolution ellipsometric studies on fluid interfaces

In this article, highly accurate experimental results reveal the interfacial profile between different macroscopic fluid phases. The deviation from a step profile, quantified by the ellipsometric quantity J(1), shows a strong correlation with the cohesive energy quantified by the Gordon parameter G . Surprisingly, at high values of G , J (1)( < 0) deviates significantly from any predictions. Findings for water and water-like interfaces can be interpreted in terms of the strength of hydrogen bonding at the surface.

Dynamics at the air-water interface revealed by evanescent wave light scattering

A new tool to study surface phenomena by evanescent wave light scattering is employed for an investigation of an aqueous surface through the water phase. When the angle of incidence passes the critical angle of total internal reflection, a high and narrow scattering peak is observed. It is discussed as an enhancement of scattering at critical angle illumination. Peak width and height are affected by the interfacial profile and the focusing of the beam. In addition, the propagation of capillary waves was studied at the surface of pure water and in the presence of latex particles and amphiphilic diblock copolymers. The range of the scattering vectors where propagating surface waves were detected is by far wider than standard surface quasi-elastic light scattering (SQELS) and comparable with those of X-ray photon correlation spectroscopy (XPCS).

Dissociable processes underlying decisions in the Iowa Gambling Task: a new integrative framework

BACKGROUND

The Iowa Gambling Task (IGT) is a common paradigm used to study the interactions between emotions and decision making, yet little consensus exists on the cognitive process determining participants' decisions, what affects them, and how these processes interact with each other. A novel conceptual framework is proposed according to which behavior in the IGT reflects a balance between two dissociable processes; a cognitively demanding process that tracks each option's long-term payoff, and a lower-level, automatic process that is primarily sensitive to loss frequency and magnitude.

METHODS

A behavioral experiment was carried out with a modified version of IGT. In this modified version, participants went through an additional phase of interaction, designed to measure performance without further learning, in which no feedback on individual decisions was given. A secondary distractor task was presented in either the first or the second phase of the experiment. Behavioral measures of performance tracking both payoff and frequency sensitivity in choices were collected throughout the experiment.

RESULTS

Consistent with our framework, the results confirmed that: (a) the two competing cognitive processes can be dissociated; (b) that learning from decision outcomes requires central cognitive resources to estimate long-term payoff; and (c) that the decision phase itself can be carried out during an interfering task once learning has occurred.

CONCLUSION

The experimental results support our novel description of the cognitive processes underlying performance in the Iowa Gambling Task. They also suggest that patients' impairments in this and other gambling paradigms can originate from a number of different causes, including a failure in allocating resources among cognitive strategies. This latter interpretation might be particularly useful in explaining the impairments of patients with ventromedial prefrontal cortex lesions and, by extension, the contribution of this brain region to human decision making.

Endogenous control and task representation: an fMRI study in algebraic problem-solving

The roles of prefrontal and anterior cingulate cortices have been widely studied, yet little is known on how they interact to enable complex cognitive abilities. We investigated this issue in a complex yet well-defined symbolic paradigm: algebraic problem solving. In our experimental problems, the demands for retrieving arithmetic facts and maintaining intermediate problem representations were manipulated separately. An analysis of functional brain images acquired while participants were solving the problems confirmed that prefrontal regions were affected by the retrieval of arithmetic facts, but only scarcely by the need to manipulate intermediate forms of the equations, hinting at a specific role in memory retrieval. Hemodynamic activity in the dorsal cingulate, on the contrary, increased monotonically as more information processing steps had to be taken, independent of their nature. This pattern was essentially mimicked in the caudate nucleus, suggesting a related functional role in the control of cognitive actions. We also implemented a computational model within the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, which was able to reproduce both the behavioral data and the time course of the hemodynamic activity in a number of relevant regions of interest. Therefore, imaging results and computer simulation provide evidence that symbolic cognition can be explained by the functional interaction of medial structures supporting control and serial execution, and prefrontal cortices engaged in the on-line retrieval of specific relevant information.

Persistence of chromosomal lesions induced in mouse bone marrow cells by mitomycin C, as evaluated by SCE analysis

The frequency of sister-chromatid exchanges (SCE) was evaluated in mouse bone marrow cells at different time intervals (from 19 h to 10 days) after treatment i.p. with mitomycin C (MMC; 1 and 2 mg/kg body weight). Significantly higher frequencies of SCE were found during the first week after treatment, at both doses tested. This result confirms that chromosomal lesions induced by MMC in the mouse may persist in bone marrow cells, in agreement with previous evidence based on chromosomal aberration analysis in the same cell population. In addition, the observation of a unimodal distribution of SCE/cell frequencies at each time tested indicates that the bone marrow cell population on the whole is affected by increased SCE frequency, i.e., that persistent chromosomal lesions may be transmitted along with cell proliferation.

Persistence of chromosomal lesions induced in actively proliferating bone marrow cells of the mouse

The persistence of chromosomal lesions induced in vivo by mitomycin C (MMC) was evaluated by cytogenetic analysis of mouse bone marrow cells. Chromosome aberration (CA) and micronucleus (MN) frequencies were estimated at different times after treatment, up to 42 days. The frequency of CA per cell decreased in the first 3 days after treatment, but a secondary peak appeared on the 4th day, followed by a stabilization around 0.03 CA per cell (significantly different from the control value), which persisted up to 17 days. At the next time intervals tested (28 and 42 days), the CA frequency returned to the control level. In disagreement with these data obtained directly on metaphases, the MN frequency, as evaluated in polychromatic erythrocytes, decreased quickly after treatment, reaching the control value on the 5th day. We attempted to enhance the sensitivity of the MN test by using CREST antibodies and indirect immunofluorescence. However, higher proportions of CREST- MN in treated than in control animals were observed only at short time intervals, confirming the results obtained with the conventional MN assay.

Identification of kinetochore-containing (CREST+) micronuclei in mouse bone marrow erythrocytes

A methodology for the characterization of kinetochore-containing (CREST+) micronuclei (MN), based on the use of antikinetochore antibodies (derived from CREST patients) and indirect immunofluorescence, was applied to mouse bone marrow erythrocytes. The proposed protocol allows us to obtain fluorescent signals of good quality and highly reproducible data. The clastogenic agent mitomycin C (MMC; 1 mg/kg body wt) and the two aneugenic compounds chloral hydrate (CH; 200 mg/kg body wt) and colchicine (COL; 1 mg/kg body wt) were used to verify the sensitivity of this approach to chemicals with different mechanisms of action. These compounds were tested at a 20 h time interval from treatment and all of them were able to significantly increase (P less than 0.001) the frequency of MN in polychromatic erythrocytes. Of the MN observed in preparations from control animals, 45% were CREST+ and this percentage increased significantly (P less than 0.001) after treatment with CH or COL. On the contrary, only 22% CREST+ MN were found after treatment with MMC (statistical comparison with the control value: P less than 0.001). The CREST characterization of MN induced in vivo in mouse bone marrow allows us to infer the origin of MN formation, thus contributing to the identification of aneugenic agents.