Cognitive Neuroscience Models Research Articles

Enhancing Learning Abilities in Students Using a Cognitive Neuroscience Model Based on Brain-Computer Interface Signal Analysis

Computational intelligence is used to create artificially intelligent systems with the ability to learn, adapt, and solve problems. Learners’ computational abilities can be improved with the help of Cognitive Neuroscience techniques. Computational intelligence refers to the ability of a learner based on the complex operations of the brain in the four relevant dimensions: Visual (V), Aural (A), Kinematic (K), and Reading/Writing (R/W). Our proposed framework consists of electroencephalogram (EEG) data acquisition, signal extraction, and EEG signal categorization to assess students’ cognitive learning abilities. Our proposed approach uses an EEG device equipped with a microprocessor, a Think Gear (TGAM) EEG sensor, and a PCB of 16 dry electrodes. The EEG device and the remote processing device are connected by Bluetooth. The EEG signal provides the students with neurofeedback on their cognitive learning capability. The feedback obtained through the learning process will be endowing to improvise computational intelligence. The statistical derivative, Pearson Co-efficient, is used to find the correlation among the derived attributes. The attributes considered are the learner’s gender, stream, age, and geographical region. The results obtained highlight that gender, stream, and age have no correlation with the detectable learning index, and the most accurate learners are kinesthetic ones. Bi-modal learners, who can maintain focus while reading and using their kinematic abilities, had the second highest learning capacity.

Model Virtues in Computational Cognitive Neuroscience.

There is an abundance of computational models in cognitive neuroscience. A framework for what is desirable in a model, what justifies the introduction of a new one, or what makes one better than another is lacking, however. In this article, we examine key qualities ("virtues") that are desirable in computational models, and how these are interrelated. To keep the scope of the article manageable, we focus on the field of cognitive control, where we identified six "model virtues": empirical accuracy, empirical scope, functional analysis, causal detail, biological plausibility, and psychological plausibility. We first illustrate their use in published work on Stroop modeling and then discuss what expert modelers in the field of cognitive control said about them in a series of qualitative interviews. We found that virtues are interrelated and that their value depends on the modeler's goals, in ways that are not typically acknowledged in the literature. We recommend that researchers make the reasons for their modeling choices more explicit in published work. Our work is meant as a first step. Although our focus here is on cognitive control, we hope that our findings will spark discussion of virtues in other fields as well.

Exploring, expounding & ersatzing: a three-level account of deep learning models in cognitive neuroscience

Exploring, expounding & ersatzing: a three-level account of deep learning models in cognitive neuroscience

Attempts to Influence the Value of Alcohol by Manipulating Social Influence and Context

Background : Recent cognitive neuroscience models of value-based decision-making suggest value-based choices for alcohol are sensitive to various inputs, such as context and social influence. In two online experiments, we tested whether manipulating these inputs influenced proxies for alcohol value. Experiment 1 : 157 social drinkers were presented with 4 hypothetical scenarios (drinking alone, with friends who are also drinking, with friends but trying to “cut-down” for health reasons, with friends who aren’t drinking) in a within-subjects design, and completed the Brief Assessment of Alcohol Demand after each as a measure of value. Value for alcohol (number of drinks purchased) was greatest when drinking with friends who were also drinking compared to drinking alone (d = 0.95), friends not drinking (d = 1.49) and friends drinking/health related (d = 1.59). Value for alcohol was also greater when drinking alone compared to with friends who were not drinking (d = 0.55), and also with friends drinking/health related (d = 0.62). Experiment 2 : 241 participants were randomly allocated to see one of four categories of images in a 2 (context: bar vs house) x 2 (social influence: enjoy vs not enjoy) design, before completing a Concurrent Choice Task for alcohol and Visual Analog Scales. There were no significant effects found on either task, both taken as proxies for value. Conclusion : There was inconclusive evidence that the value for alcohol could be manipulated by social context. This could be explained by greater saliency of the manipulation in asking participants to imagine themselves in a hypothetical situation as opposed to presenting images depicting drinking scenarios.

Beyond linear regression: mapping models in cognitive neuroscience should align with research goals

Many cognitive neuroscience studies use large feature sets to predict and interpret brain activity patterns. Feature sets take many forms, from human stimulus annotations to representations in deep neural networks. Of crucial importance in all these studies is the mapping model, which defines the space of possible relationships between features and neural data. Until recently, most encoding and decoding studies have used linear mapping models. Increasing availability of large datasets and computing resources has recently allowed some researchers to employ more flexible nonlinear mapping models; however, the question of whether nonlinear mapping models can yield meaningful scientific insights remains debated. Here, we discuss the choice of a mapping model in the context of three overarching desiderata: predictive accuracy, interpretability, and biological plausibility. We show that these desiderata do not map cleanly onto the linear/nonlinear divide; instead, each desideratum can refer to multiple research goals, each of which imposes its own constraints on the mapping model. Moreover, we argue that, instead of categorically treating the mapping models as linear or nonlinear, researchers should report the complexity of these models. We show that, in many cases, complexity provides a more accurate reflection of restrictions imposed by various research goals and outline several complexity metrics that can be used to effectively evaluate mapping models.

Randomly fluctuating neural connections may implement a consolidation mechanism that explains classic memory laws

How can we reconcile the massive fluctuations in neural connections with a stable long-term memory? Two-photon microscopy studies have revealed that large portions of neural connections (spines, synapses) are unexpectedly active, changing unpredictably over time. This appears to invalidate the main assumption underlying the majority of memory models in cognitive neuroscience, which rely on stable connections that retain information over time. Here, we show that such random fluctuations may in fact implement a type of memory consolidation mechanism with a stable very long-term memory that offers novel explanations for several classic memory ‘laws’, namely Jost’s Law (1897: superiority of spaced learning) and Ribot’s Law (1881: loss of recent memories in retrograde amnesia), for which a common neural basis has been postulated but not established, as well as other general ‘laws’ of learning and forgetting. We show how these phenomena emerge naturally from massively fluctuating neural connections.

Variable-Drift Diffusion Models of Pedestrian Road-Crossing Decisions

Human behavior and interaction in road traffic is highly complex, with many open scientific questions of high applied importance, not least in relation to recent development efforts toward automated vehicles. In parallel, recent decades have seen major advances in cognitive neuroscience models of human decision-making, but these models have mainly been applied to simplified laboratory tasks. Here, we demonstrate how variable-drift extensions of drift diffusion (or evidence accumulation) models of decision-making can be adapted to the mundane yet non-trivial scenario of a pedestrian deciding if and when to cross a road with oncoming vehicle traffic. Our variable-drift diffusion models provide a mechanistic account of pedestrian road-crossing decisions, and how these are impacted by a variety of sensory cues: time and distance gaps in oncoming vehicle traffic, vehicle deceleration implicitly signaling intent to yield, as well as explicit communication of such yielding intentions. We conclude that variable-drift diffusion models not only hold great promise as mechanistic models of complex real-world decisions, but that they can also serve as applied tools for improving road traffic safety and efficiency.

Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience.

In cognitive neuroscience, computational modeling can formally adjudicate between theories and affords quantitative fits to behavioral/brain data. Pragmatically, however, the space of plausible generative models considered is dramatically limited by the set of models with known likelihood functions. For many models, the lack of a closed-form likelihood typically impedes Bayesian inference methods. As a result, standard models are evaluated for convenience, even when other models might be superior. Likelihood-free methods exist but are limited by their computational cost or their restriction to particular inference scenarios. Here, we propose neural networks that learn approximate likelihoods for arbitrary generative models, allowing fast posterior sampling with only a one-off cost for model simulations that is amortized for future inference. We show that these methods can accurately recover posterior parameter distributions for a variety of neurocognitive process models. We provide code allowing users to deploy these methods for arbitrary hierarchical model instantiations without further training.

Using reinforcement learning models in social neuroscience: frameworks, pitfalls and suggestions of best practices.

The recent years have witnessed a dramatic increase in the use of reinforcement learning (RL) models in social, cognitive and affective neuroscience. This approach, in combination with neuroimaging techniques such as functional magnetic resonance imaging, enables quantitative investigations into latent mechanistic processes. However, increased use of relatively complex computational approaches has led to potential misconceptions and imprecise interpretations. Here, we present a comprehensive framework for the examination of (social) decision-making with the simple Rescorla–Wagner RL model. We discuss common pitfalls in its application and provide practical suggestions. First, with simulation, we unpack the functional role of the learning rate and pinpoint what could easily go wrong when interpreting differences in the learning rate. Then, we discuss the inevitable collinearity between outcome and prediction error in RL models and provide suggestions of how to justify whether the observed neural activation is related to the prediction error rather than outcome valence. Finally, we suggest posterior predictive check is a crucial step after model comparison, and we articulate employing hierarchical modeling for parameter estimation. We aim to provide simple and scalable explanations and practical guidelines for employing RL models to assist both beginners and advanced users in better implementing and interpreting their model-based analyses.

La Revanche de Gall.

Discusses a poem by Thomas Hood (1799-1845), "Craniology," which is a paradigmatic example of parodying psychological faculties for being material things. Franz Joseph Gall's (1758 -1828) term for organology was schädellehre, a German compound from schädel (skull, cranium, or pate) and lehre (teaching or doctrine). Craniology was used by some of Gall's followers, but mostly his critics; phrenology was coined in 1815 by Thomas Forster. In "Craniology," his Horatian-exemplar poem, Hood ridicules Gall's materialism. The present author notes Gall's idea that there were both intellectual and emotional faculties, which resonates well with contemporary cognitive neuroscience models of the mind. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Байесовский разум и природа сознания

В статье оценивается способность программы предсказывающей обработки и байесовских моделей познавательных процессов внести вклад в совре­менные исследования сознания. Автор отталкивается от вопроса, рассмот­ренного в недавних работах философов Э. Кларка и Н. Блока, а именно: если механизмы восприятия, как предполагается сторонниками байесов­ских моделей познавательных процессов, являются вероятностными, то по­чему сознание не отражает в себе характер находящихся в его основании вероятностных перцептивных репрезентаций? При этом, в противополож­ность Кларку и Блоку, автор указывает на отсутствие оснований полагать, что сознание как высокоуровневый феномен в принципе должно отражать в себе характеристики каких-либо более базовых процессов и репрезента­ций. Соответственно предпочтение отдается именно вопросу о возможно­стях и ограничениях байесовских моделей в когнитивной науке, психоло­гии и нейронауке в деле объяснения человеческого опыта. С целью ответа на этот вопрос в работе в первую очередь исследуется статус самих байе­совских моделей познавательных процессов, относительно которого среди исследователей существуют значительные разногласия. Утверждается, что байесовские модели в когнитивной науке, психологии и нейронауке могут формулироваться на всех трех уровнях анализа систем обработки инфор­мации по Д. Марру. При этом приоритетным для байесовских моделей является вычислительный уровень по Марру, предполагающий описание вы­числительной задачи и доступной организму информации в среде. Тем не ме­нее указывается, что к настоящему моменту применительно к исследовани­ям сознания преимущественное развитие получили модели, относящиеся к уровню системы репрезентации и алгоритмов по Марру. В заключение выдвигается предположение, что, чтобы охарактеризовать сознание с по­зиции вычислительного уровня по Марру, программе предсказывающей обработки, вероятно, потребуется интеграция с теорией глобального рабо­чего пространства.

Prefrontal working memory activity predicts episodic memory performance: A neuroimaging study

Working memory (WM) and long term memory (LTM) are different neuropsychological processes, although distinction between these domains is an area of debate. LTM is thought to rely on hippocampal circuitry. Cognitive neuroscience models imply that WM processing may at least partially support LTM within regions of the prefrontal cortex (PFC). We sought to determine the association between PFC based WM processing and LTM in the visuospatial domain. In contrast to prior work, we aimed to query if WM was involved in learning and free recall trials as measured by standard neuropsychological tests of LTM. Forty-three older adults (24 with a diagnosis of amnestic Mild Cognitive Impairment and 19 elderly controls) were included in the analysis. Patients completed a fMRI task of visuospatial maintenance WM in which they were required to match a previously studied complex shape with one of two probes. Extent of activity in the right PFC during the WM task was tabulated for each patient. Hippocampal volume was quantified from T1 scans. On a separate day patients completed neuropsychological testing, including the Brief Visuospatial Memory Test- Revised (BVMT-R), which includes learning trials (total recall), delayed free recall, and recognition. Right PFC activity was associated with performance on BVMT-R total recall and delayed recall. Results from multiple regression showed that PFC activity explained an additional 9 % of the variance in memory performance above right hippocampal volume. These findings suggest that PFC processing that supports WM (including stimuli maintenance, retrieval, and selection) are also involved in LTM learning and recall.

Salience network dynamics underlying successful resistance of temptation.

Self-control and the ability to resist temptation are critical for successful completion of long-term goals. Contemporary models in cognitive neuroscience emphasize the primary role of prefrontal cognitive control networks in aligning behavior with such goals. Here, we use gaze pattern analysis and dynamic functional connectivity fMRI data to explore how individual differences in the ability to resist temptation are related to intrinsic brain dynamics of the cognitive control and salience networks. Behaviorally, individuals exhibit greater gaze distance from target location (e.g. higher distractibility) during presentation of tempting erotic images compared with neutral images. Individuals whose intrinsic dynamic functional connectivity patterns gravitate toward configurations in which salience detection systems are less strongly coupled with visual systems resist tempting distractors more effectively. The ability to resist tempting distractors was not significantly related to intrinsic dynamics of the cognitive control network. These results suggest that susceptibility to temptation is governed in part by individual differences in salience network dynamics and provide novel evidence for involvement of brain systems outside canonical cognitive control networks in contributing to individual differences in self-control.

Cerebral metabolic correlates of attention networks in Alzheimer's Disease: A study of the Stroop

Patients with Alzheimer's Disease (AD) show difficulties with attention. Cognitive neuroscience models posit that attention can be broken down into alerting, orienting, and executive networks. We used the Stroop Color-Word test to interrogate the neural correlates of attention deficits in AD. We hypothesized that the Word, Color, and Color-Word conditions of the Stroop would all tap into the alerting and orienting networks. The Color-Word condition would additionally tap into the executive network. A ratio of Color-Word to Color naming performance would isolate the executive network from the others. To identify the neural underpinnings of attention in AD we correlated performance on the Stroop with brain metabolic activity. Sixty-six patients with probable AD completed [18F] fluorodeoxyglucose PET scanning and neuropsychological testing. Analysis was conducted with SPM12 (p<0.001 uncorrected, extent threshold 50 voxels). Performance on the Word, Color, and Color-Word conditions directly correlated with metabolic rate in right inferior parietal lobules/intraparietal sulci. The Color-Word/Color ratio revealed associations with metabolic rate in right medial prefrontal cortex and insula/operculum. Overall findings were largely consistent with the hypothesized neuroanatomical substrates of the alerting, orienting, and executive networks. As such, attention deficits in AD reflect compromise to multiple large-scale networks.

The Effort Paradox: Effort is Both Costly and Valued

According to prominent models in cognitive psychology, neuroscience, and economics, effort—be it physical or mental—is costly: when given a choice, humans and non-human animals alike tend to avoid effort. Here, we suggest that the opposite is also true and review extensive evidence that effort can also add value. Not only can the same outcomes be more rewarding if we apply more (not less) effort, sometimes we select options precisely because they require effort. Given the increasing recognition of effort’s role in motivation, cognitive control, and value-based decision-making, considering this neglected side of effort will not only improve formal computational models, but also provide clues about how to promote sustained mental effort across time.

Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach

A fundamental challenge in cognitive neuroscience is to develop theoretical frameworks that effectively span the gap between brain and behavior, between neuroscience and psychology. Here, we attempt to bridge this divide by formalizing an integrative cognitive neuroscience approach using dynamic field theory (DFT). We begin by providing an overview of how DFT seeks to understand the neural population dynamics that underlie cognitive processes through previous applications and comparisons to other modeling approaches. We then use previously published behavioral and neural data from a response selection Go/Nogo task as a case study for model simulations. Results from this study served as the ‘standard’ for comparisons with a model-based fMRI approach using dynamic neural fields (DNF). The tutorial explains the rationale and hypotheses involved in the process of creating the DNF architecture and fitting model parameters. Two DNF models, with similar structure and parameter sets, are then compared. Both models effectively simulated reaction times from the task as we varied the number of stimulus–response mappings and the proportion of Go trials. Next, we directly simulated hemodynamic predictions from the neural activation patterns from each model. These predictions were tested using general linear models (GLMs). Results showed that the DNF model that was created by tuning parameters to capture simultaneously trends in neural activation and behavioral data quantitatively outperformed a Standard GLM analysis of the same dataset. Further, by using the GLM results to assign functional roles to particular clusters in the brain, we illustrate how DNF models shed new light on the neural populations’ dynamics within particular brain regions. Thus, the present study illustrates how an interactive cognitive neuroscience model can be used in practice to bridge the gap between brain and behavior.

Nonlinear associations between human values and neuroanatomy

ABSTRACTHuman values guide behavior and the smooth functioning of societies. Schwartz’s circumplex model of values predicts a sinusoidal waveform in relations between ratings of the importance of diverse human value types (e.g., achievement, benevolence) and any variables psychologically relevant to them. In this neuroimaging study, we examined these nonlinear associations between values types and brain structure. In 85 participants, we found the predicted sinusoidal relationship between ratings of values types and two measures of white matter (WM), volume and myelin volume fraction, as well as for grey matter (GM) parameters in several frontal regions. These effects reveal new functional associations for structural brain parameters and provide a novel cross-validation of Schwartz’s model. Moreover, the sinusoidal waveform test can be applied to other circumplex models in social, affective and cognitive neuroscience.

Applying a Reading Program Based on Cognitive Science in Rural Areas of Malawi: Preliminary Results

Reading fluency is a skill foundational to academic performance, and acquiring this skill in early grades is crucial. Throughout sub-Saharan Africa, reading levels of students are far below grade level, and Malawi is no exception. Research suggests that students, particularly in consistently spelled languages, acquire automaticity most easily by starting from individual letters, combining them in increasingly larger chunks, and getting plenty of practice in decoding them. Due to working memory capacity limits, students must attain a minimum reading speed in order to understand text. These concepts were tested in a rural district in Malawi. The study helps to explain the components of this science-based “learning package” needed for reading fluency. Volunteers held the classes in communities as an after school program for children from grades 1 to 5. The goal behind this innovative six-month program is students’ recognition of all letters used in Chichewa language and progress in text decoding. This study helps to explain the various components of the learning intervention and the results show that there has been improvement in knowledge of letter sounds. This study uses an already tried and tested cognitive neuroscience model, which was contextualized and implemented in the Malawi setting.

Cognition and impulsivity in adults with attention deficit hyperactivity disorder with and without cocaine and/or crack dependence

BackgroundSubstance use disorder (SUD) is a common comorbidity in adults with attention deficit-hyperactivity disorder (ADHD). However,there have been few studies on cognitive profiles of these patients. Impulsivity is also commonly increased in both disorders. The central aim of this study was to compare cognition and impulsivity in subjects who had ADHD and cocaine dependence (ADHD+COC group) to those with ADHD only (ADHD-noSUD group). We hypothesized that the ADHD+COC group would show more marked cognitive dysfunction and greater impulsivity than their counterparts with ADHD only. MethodsA total of 70 adult patients diagnosed with ADHD according to (DSM-IV-TR) criteria were enrolled; 36 with ADHD+COC and 34 with ADHD-noSUD. All study participants were evaluated with a sociodemographic questionnaire; the Mini International Neuropsychiatric Interview; the Adult ADHD Self-Report Scale; the Addiction Severity Index; the Alcohol, Smoking and Substance Involvement Screening Test; the Barratt Impulsiveness Scale; and a comprehensive neurocognitive battery. ResultsCompared to individuals with ADHD-noSUD, ADHD+COC individuals had significantly lower mean IQ and higher motor impulsivity. On average, the ADHD+COC group also performed more poorly on tasks assessing verbal skills, vigilance, implicit learning during decision making, and ADHD-noSUD performed more poorly on selective attention, information processing, and visual search. ConclusionsOur results support the integrative theory of ADHD based on the cognitive and affective neuroscience model, and suggests that ADHD-noSUD patients have impairments in cognitive regulation, while ADHD+COC patients have impairments in both cognitive and affective regulation.

Graph theoretic analysis on large DCM models

Graph theoretic analysis on large DCM models