Search

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.

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.

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. Blurb We propose to incorporate motivation into ACT‐R models as a reward value associated with a goal representation. Multiple simulations show how this mechanism naturally yields flexible control allocation and reproduces a variety of psychological effects. [ABSTRACT FROM AUTHOR]

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. (Contains 3 footnotes, 1 table, and 14 figures.)

Many authors have endorsed the hypothesis that previous emotional experiences may exert a covert influence on behavior, but some findings and replications of the original studies challenged this view. We investigated this topic by carrying out an experiment with the Iowa Gambling Task (IGT), where a dissociation procedure was adopted to successfully isolate possible implicit components. After a typical interaction with the IGT, participants performed a "blind" card selection phase without receiving any feedback. Half of them were instructed to continue choosing as they did before, the other half was told that good card decks turned bad, and vice versa, so that explicit knowledge was necessary to overcome the previously learned deck-outcome associations. The results confirmed the existence of early acquired implicit biases, confirming that previously experienced emotional events may covertly affect subsequent behavior.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. This paper presents a neurocomputational model using the ACT‐R cognitive architecture that simulates intrusive memory retrieval following a potentially traumatic event and predicts hippocampal volume changes observed in Post‐Traumatic Stress Disorder. [ABSTRACT FROM AUTHOR]

Abstract 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.

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. [ABSTRACT FROM AUTHOR]

Summary: JavaScript is a popular programming language that is also error‐prone due to its asynchronous, dynamic, and loosely typed nature. In recent years, numerous techniques have been proposed for analyzing and testing JavaScript applications. However, our survey of the literature in this area revealed that the proposed techniques are often evaluated on different datasets of programs and bugs. The lack of a commonly used benchmark limits the ability to perform fair and unbiased comparisons for assessing the efficacy of new techniques. To fill this gap, we propose BugsJS, a benchmark of 453 real, manually validated JavaScript bugs from 10 popular JavaScript server‐side programs, comprising 444k lines of code (LOC) in total. Each bug is accompanied by its bug report, the test cases that expose it, as well as the patch that fixes it. We extended BugsJS with a rich web interface for visualizing and dissecting the bugs' information, as well as a programmable API to access the faulty and fixed versions of the programs and to execute the corresponding test cases, which facilitates conducting highly reproducible empirical studies and comparisons of JavaScript analysis and testing tools. Moreover, following a rigorous procedure, we performed a classification of the bugs according to their nature. Our internal validation shows that our taxonomy is adequate for characterizing the bugs in BugsJS. We discuss several ways in which the resulting taxonomy and the benchmark can help direct researchers interested in automated testing of JavaScript applications. © 2021 The Authors. Software Testing, Verification & Reliability published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]