Cognitive Model Research Articles

Emotional Distress and Physical Activity Engagement in U.S. Veterans With Chronic Obstructive Pulmonary Disease: A Qualitative Study.

Physical activity is recommended as part of guideline-based care for managing chronic obstructive pulmonary disease (COPD) at all stages of the disease. However, physical activity interventions are less effective in individuals with co-occurring emotional distress (i.e., depression and/or anxiety symptoms). Interventions that dually promote improved physical and mental health in COPD are needed. The first step for intervention development is understanding individuals' experience of these factors. To understand the experience of emotional distress in U.S. Veterans diagnosed with COPD and to explore how emotional distress impacts physical activity engagement. Structured interviews informed by the cognitive and behavioral models of psychopathology were conducted with 29 United States Veterans with COPD (89.66% male, age 67.72 ± 6.55 years, 93.10% White) at the VA Boston Healthcare System. Interviews were audio recorded, transcribed, and underwent thematic analysis. Three major themes encapsulating 17 codes emerged through thematic analysis: (i) the experience of emotional distress in Veterans with COPD; (ii) the complex relationship between emotional distress, physical activity engagement, and COPD; (iii) contextual and personal factors. Veterans' interpretation of their COPD symptoms as unpredictable and uncontrollable and COPD-related physical limitations influenced their experience of emotional distress, while the experience of emotional distress both promoted and deterred physical activity engagement. Veterans were motivated to engage in physical activity despite experiencing emotional distress when they faced a responsibility in daily life. They identified motivational strategies and self-regulation techniques to manage emotional distress and to foster physical activity.

Artificial Intelligence enabled cognitive computer-centered digital analysis model for examination of the children’s mental health

Artificial Intelligence enabled cognitive computer-centered digital analysis model for examination of the children’s mental health

Temporal heterogeneity in cognitive architectures

In 2020, Mc Fadden published an article in which he discusses how algorithms can be encoded in time and space. By analyzing the topology of the cytoarchitecture of the brain, cognitive architectures can understand the underlying mechanisms that have led to the development of human intelligence in space. In this study, our focus lies in investigating temporal heterogeneity as a mechanism that the brain could have developed not solely as a biological constraint, but also as an evolutionary advantage. To accomplish this, we employed virtual agents within a virtual environment and constructed a prototype cognitive architecture. Subsequently, we compared the benefits and drawbacks of having this cognitive architecture operate under a model of temporal heterogeneity versus one characterized by temporal homogeneity. At the conclusion of the article, we present the results obtained from two perspectives. From a quantitative standpoint, we contrast the agents’ adaptation to the environment based on the cognitive architecture model employed by each agent. On this front, we found evidence that temporal heterogeneity might be useful in finding parameter optimizations faster, amongst other benefits. From a qualitative perspective, we examine the potential of this model to explore the cognitive processes of the virtual agents, concluding that a different representation of percepts is needed, which we further discuss.

A deep learning based cognitive model to probe the relation between psychophysics and electrophysiology of flicker stimulus

The flicker stimulus is a visual stimulus of intermittent illumination. A flicker stimulus can appear flickering or steady to a human subject, depending on the physical parameters associated with the stimulus. When the flickering light appears steady, flicker fusion is said to have occurred. This work aims to bridge the gap between the psychophysics of flicker fusion and the electrophysiology associated with flicker stimulus through a Deep Learning based computational model of flicker perception. Convolutional Recurrent Neural Networks (CRNNs) were trained with psychophysics data of flicker stimulus obtained from a human subject. We claim that many of the reported features of electrophysiology of the flicker stimulus, including the presence of fundamentals and harmonics of the stimulus, can be explained as the result of a temporal convolution operation on the flicker stimulus. We further show that the convolution layer output of a CRNN trained with psychophysics data is more responsive to specific frequencies as in human EEG response to flicker, and the convolution layer of a trained CRNN can give a nearly sinusoidal output for 10 hertz flicker stimulus as reported for some human subjects.

Variation in gaze following across the life span: A process-level perspective.

Following eye gaze is fundamental for many social-cognitive abilities, for example, when judging what another agent can or cannot know. While the emergence of gaze following has been thoroughly studied on a group level, we know little about (a) the developmental trajectory beyond infancy and (b) the sources of individual differences. In Study 1, we examined gaze following across the lifespan (N=478 3- to 19-year-olds from Leipzig, Germany; and N=240 20- to 80-year-old international, remotely tested adults). We found a steep performance improvement during preschool years, in which children became more precise in locating the attentional focus of an agent. Precision levels then stayed comparably stable throughout adulthood with a minor decline toward old age. In Study 2, we formalized the process of gaze following in a computational cognitive model that allowed us to conceptualize individual differences in a psychologically meaningful way (N=60 3- to 5-year-olds, 50 adults). According to our model, participants estimate pupil angles with varying levels of precision based on observing the pupil location within the agent's eyes. In Study 3, we empirically tested how gaze following relates to vector following in non-social settings and perspective-taking abilities (N=102 4- to 5-year-olds). We found that gaze following is associated with both of these abilities but less so with other Theory of Mind tasks. This work illustrates how the combination of reliable measurement instruments and formal theoretical models allows us to explore the in(ter)dependence of core social-cognitive processes in greater detail. RESEARCH HIGHLIGHTS: Gaze following develops beyond infancy. The highest precision levels in localizing attentional foci are reached in young adulthood with a slight decrease towards old age. We present a computational model that describes gaze following as a process of estimating pupil angles and the corresponding gaze vectors. The model explains individual differences and recovers signature patterns in the data. To estimate the relation between gaze- and vector following, we designed a non-social vector following task. We found substantial correlations between gaze following and vector following, as well as Level 2 perspective-taking. Other Theory of Mind tasks did not correlate.

A Cognitive View on Prosodic Relations

The paper presents a new F0 contour partitioning approach by using the category of prosodic relations also used by Ladd (2008) for improving the F0 contour descriptions based on phonological categories. The new approach involves a cognitive view on the low-high and high-low ’metrical’ structures of prosodic relations, by relating them to the structures of cognitive relations generated during speech object representations at the cortical level. In section 2, the paper presents the information structure model by defining the cognitive categories that describes prosodic relations of F0 contours involving their two overlapped structures and nuclear positions. CU_predicate-CU_argument and CU_theme-CU_rheme are the two structural levels of prosodic relations. The model proposes a binary-tree hierarchy to describe the articulation of prosodic relations within utterances. Two rules are formulated for the identification of nuclear constituents of prosodic relations. The utterances analysed in section 3 illustrate how prosodic and prominence relations can be identified by analysing acoustic cues of their F0 contours. Utterances correspond to English borad focus and narrow focus statements. Focus positions are deduced by only using the rule of the cognitive model.

Psychological Protective and Risk Factors for Depression, Anxiety and Mental Well-Being in Social Workers

Abstract Social workers worldwide experience high rates of depression, anxiety and deficits in well-being. Depression, anxiety or poor mental well-being can have a significant effect on the individual social worker, and the quality of care they can provide. There have been limited attempts to examine and understand the underlying psychological protective and risk factors for depression, anxiety and poor well-being amongst social workers. Using the clinically modified Buddhist psychological model (CBPM) as a theoretical framework, this cross-sectional study examined a range of potential psychological protective and risk factors for depression, anxiety and mental well-being of 121 social workers in Northern Ireland, using structural equation modelling, and conditional process analyses. Mindfulness, acceptance, attention regulation/decentering skills, self-compassion, non-attachment and non-aversion could act as protective factors against depression, anxiety and poor mental well-being in social workers. Results also indicate that each of these variables could reduce the extent to which social workers worry and ruminate, further reducing the risk that social workers might experience depression, anxiety, or poor mental well-being. This study offers promising preliminary evidence that the CBPM is a useful explanatory framework which helps to explain variation in depression, anxiety and mental well-being amongst social workers.

Knowing what to know: Implications of the choice of prior distribution on the behavior of adaptive design optimization.

Adaptive design optimization (ADO) is a state-of-the-art technique for experimental design (Cavagnaro et al., 2010). ADO dynamically identifies stimuli that, in expectation, yield the most information about a hypothetical construct of interest (e.g., parameters of a cognitive model). To calculate this expectation, ADO leverages the modeler's existing knowledge, specified in the form of a prior distribution. Informative priors align with the distribution of the focal construct in the participant population. This alignment is assumed by ADO's internal assessment of expected information gain. If the prior is instead misinformative, i.e., does not align with the participant population, ADO's estimates of expected information gain could be inaccurate. In many cases, the true distribution that characterizes the participant population is unknown, and experimenters rely on heuristics in their choice of prior and without an understanding of how this choice affects ADO's behavior. Our work introduces a mathematical framework that facilitates investigation of the consequences of the choice of prior distribution on the efficiency of experiments designed using ADO. Through theoretical and empirical results, we show that, in the context of prior misinformation, measures of expected information gain are distinct from the correctness of the corresponding inference. Through a series of simulation experiments, we show that, in the case of parameter estimation, ADO nevertheless outperforms other design methods. Conversely, in the case of model selection, misinformative priors can lead inference to favor the wrong model, and rather than mitigating this pitfall, ADO exacerbates it.

Testing theories of the vocabulary spurt with monolingual and bilingual infants.

Sometime before their second birthday, many children have a period of rapid expressive vocabulary growth called the vocabulary spurt. Theories of the underlying mechanisms differ: Accumulator models emphasize the accumulation of experience with words over time to yield a spurtlike pattern, while cognitive models attribute the spurt to cognitive changes. To test these theories, English-French monolingual and bilingual children with different exposure to each language were studied. Dense, longitudinal data were analyzed from 45 infants aged 16-30 months, whose expressive vocabulary was measured on a total of 617 occasions in English and/or French. Single-language (English and/or French), concept (number of concepts lexicalized across both languages), and word (sum of both languages) vocabulary scores were computed. Infants' exposure to each language and their exposure balance were measured using a language exposure questionnaire. Logistic curves were fitted to each infant's data to estimate the timing (midpoint) and steepness (slope) of the vocabulary spurt in single-language, concept, and word vocabularies. Seventy-six percent of infants showed a spurt in at least one vocabulary type, and bilinguals were less likely to show one in their nondominant than their dominant language. For single-language vocabulary, infants with more exposure to a language had earlier spurts. For combined vocabularies (concept and word), monolinguals and unbalanced bilinguals had earlier and steeper spurts than balanced bilinguals. Results better support the predictions of accumulator models than cognitive theories and show that infants follow different vocabulary acquisition trajectories based on their language background. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Constraining current neuroanatomical models of reading: the view from Arabic.

There is a growing interest in imaging understudied orthographies to unravel their neuronal correlates and their implications for existing computational and neuroanatomical models. Here, we review current brain mapping literature about Arabic words. We first offer a succinct description of some unique linguistic features of Arabic that challenge current cognitive models of reading. We then appraise the existing functional neuroimaging studies that investigated written Arabic word processing. Our review revealed that (1) Arabic is still understudied, (2) the most investigated features concerned the effects of vowelling and diglossia in Arabic reading, (3) findings were not always discussed in the light of existing reading models such as the dual route cascaded, the triangle, and the connectionist dual process models, and (4) current evidence is unreliable when it comes to the exact neuronal pathways that sustain Arabic word processing. Overall, despite the fact that Arabic has some unique linguistic features that challenge and ultimately enrich current reading models, the existing functional neuroimaging literature falls short of offering a reliable evidence about brain networks of Arabic reading. We conclude by highlighting the need for more systematic studies of the linguistic features of Arabic to build theoretical and neuroanatomical models that are concurrently specific and general.

New Paradigm of Identifiable General-response Cognitive Diagnostic Models: Beyond Categorical Data.

Cognitive diagnostic models (CDMs) are a popular family of discrete latent variable models that model students' mastery or deficiency of multiple fine-grained skills. CDMs have been most widely used to model categorical item response data such as binary or polytomous responses. With advances in technology and the emergence of varying test formats in modern educational assessments, new response types, including continuous responses such as response times, and count-valued responses from tests with repetitive tasks or eye-tracking sensors, have also become available. Variants of CDMs have been proposed recently for modeling such responses. However, whether these extended CDMs are identifiable and estimable is entirely unknown. We propose a very general cognitive diagnostic modeling framework for arbitrary types of multivariate responses with minimal assumptions, and establish identifiability in this general setting. Surprisingly, we prove that our general-response CDMs are identifiable under -matrix-based conditions similar to those for traditional categorical-response CDMs. Our conclusions set up a new paradigm of identifiable general-response CDMs. We propose an EM algorithm to efficiently estimate a broad class of exponential family-based general-response CDMs. We conduct simulation studies under various response types. The simulation results not only corroborate our identifiability theory, but also demonstrate the superior empirical performance of our estimation algorithms. We illustrate our methodology by applying it to a TIMSS 2019 response time dataset.

FCHG: Fuzzy Cognitive Hypergraph for interpretable fault detection

While deep learning-based fault detection methods for rolling body mechanical components have achieved good results, their application in real-world scenarios is limited, and their reliability is not satisfactory due to the non-interpretability of deep learning. Meanwhile, automated component vibration signals are usually affected by irregularities, which make it challenging to obtain advanced features by conventional feature extraction methods. To solve the above problems, we develop a new fuzzy cognitive graph model that combines the interpretability of fuzzy cognitive graphs with the high-precision categorization of hypergraphs, called fuzzy cognitive hypergraph (FCHG). First, frequency domain analysis is combined with graph analysis to help the network extract high-level features. Second, causal learning is used to construct the FCHG weight matrix to make the FCHG interpretable and enhance the reliability of fault diagnosis. Finally, the FCHG network is built using spectrogram theory and Chebyshev polynomial expansion to capture multiple feature relationships and improve fault diagnosis accuracy. The practicality and effectiveness of the FCHG model for monitoring the health and stability status of rolling body mechanical components are verified on three datasets.

Active maintenance in working memory reinforces bindings for future retrieval from episodic long-term memory.

Many theories assume that actively maintaining information in working memory (WM) predicts its retention in episodic long-term memory (LTM), as revealed by the beneficial effects of more WM time. In four experiments, we examined whether affording more time for intentional WM maintenance does indeed drive LTM. Sequences of four words were presented during trials of simple span (short time), slow span (long time), and complex span (long time with distraction; Experiments 1-2). Long time intervals entailed a pause of equivalent duration between the words that presented a blank screen (slow span) or an arithmetic problem to read aloud and solve (complex span). In Experiments 1-3, participants either serially recalled the words (intentional encoding) or completed a no-recall task (incidental encoding). In Experiment 4, all participants were instructed to intentionally encode the words, with the trials randomly ending in the serial-recall or no-recall task. To ensure similar processing of the words between encoding groups, participants silently decided whether each word was a living or nonliving thing via key press (i.e., an animacy judgment; Experiments 1 and 3-4) or read the words aloud and then pressed the space bar (Experiment 2). A surprise delayed memory test at the end of the experiment assessed LTM. Applying Bayesian cognitive models to disambiguate binding and item memory revealed consistent benefits of free time to binding memory that were specific to intentional encoding in WM. This suggests that time spent intentionally keeping information in WM is special for LTM because WM is a system that maintains bindings.

To Overcome Ethical Dilemmas in an Organization: Paradox and Ethics of Freedom Can Help

A theoretical model for managers responding to a morally challenging situation is developed in the article. This model explores and examines how managers deal with paradoxes created by ethical dilemmas. To explain their strategy, the authors’ model combines a moral philosophy approach – existentialism ‒ with paradox theories. Starting with an emblematic situation such as a dilemma, the lens of Smith and Tushman’s Paradoxical Cognition is used to develop the theoretical construct. Then, with the help of Sartre’s ethics of freedom, two stages that managers pass through to find a solution are explained. Drawing on those theories, a model of ethical cognition to solve the paradoxical tension issues from ethical dilemmas is submitted. A reflexive process that includes awareness, freedom, and personal projects is combined and described. Every stage with examples of issues from the previous research of human resource managers is illustrated. This model allows people to escape those paradoxical tensions and suggests how a person articulates and creates ethical principles to handle a paradoxical conflict. Based on the in-depth interviews reanalyzed, concrete illustrations about paradoxical cognition are shown. These semi-structured in-depth interviews were conducted to determine how French-Canadian HR managers make ethical decisions (respondents: 37 HR managers working in Montreal and its suburbs (Quebec, Canada); period: 2013‒2017; number of questions: 16, divided into 4 sections; discourse analysis of data: using AtlasTi software). In doing so, the gap between theory and empirical with Sartre’s concepts such as Awareness, Freedom, and Project is overcome. This conceptual model can be used in the individual to solve of a moral dilemma and can be most valuable for manager when they are facing some moral paradoxical situations.

Hierarchical Spiking-Based Model for Efficient Image Classification With Enhanced Feature Extraction and Encoding.

Thanks to their event-driven nature, spiking neural networks (SNNs) are surmised to be great computation-efficient models. The spiking neurons encode beneficial temporal facts and possess excessive anti-noise properties. However, the high-quality encoding of spatio-temporal complexity and also its training optimization of SNNs are restricted by means of the contemporary problem, this article proposes a novel hierarchical event-driven visual device to explore how information transmits and signifies in the retina the usage of biologically manageable mechanisms. This cognitive model is an augmented spiking-based framework consisting of the function learning capacity of convolutional neural networks (CNNs) with the cognition capability of SNNs. Furthermore, this visual device is modeled in a biological realism way with unsupervised learning rules and advanced spike firing rate encoding methods. We train and test them on some image datasets (Modified National Institute of Standards and Technology (MNIST), Canadian Institute for Advanced Research (CIFAR)10, and its noisy versions) to show that our mannequin can process greater vital data than present cognitive models. This article also proposes a novel quantization approach to make the proposed spiking-based model more efficient for neuromorphic hardware implementation. The outcomes show this joint CNN-SNN model can reap excessive focus accuracy and get more effective generalization ability.

Insulin attenuates LPS-induced cognitive impairment and ferroptosis through regulation of glucose metabolism in hippocampus.

Neuroinflammation is a recognized contributor to cognitive disorders like Alzheimer's disease, with ferroptosis emerging as a novel mechanism underlying cognitive dysfunction associated with neuroinflammation. Insulin, pivotal in the central nervous system, holds promise for cognitive function enhancement. This study aimed to establish a cognitive impairment model through intracerebroventricular injection of lipopolysaccharide (LPS) and explore the impact of intracerebroventricular insulin injection on cognitive function in mice. We employed diverse experimental techniques, including animal behavior testing, molecular assays, targeted metabolomics, nuclear medicine, and electron microscopy, to assess neurodegenerative changes, brain insulin resistance (IR), glucose uptake and metabolism, and ferroptosis. The model of cognitive impairment was induced via intracerebroventricular injection of LPS, followed by intracerebroventricular administration of insulin to evaluate its effects. Insulin treatment effectively mitigated LPS-induced cognitive decline and safeguarded against neuronal degeneration. Furthermore, insulin alleviated LPS-induced insulin resistance, enhanced glucose uptake in the hippocampus, and promoted the Pentose Phosphate Pathway (PPP) and nicotinamide adenine dinucleotide phosphate (NADPH) production. Additionally, insulin activated the glutathione (GSH)-glutathione peroxidase 4 (GPX4) pathway, reducing lipid peroxidation, and mitochondrial damage characteristic of LPS-induced ferroptosis in the hippocampus. Our findings underscore the therapeutic potential of insulin in alleviating LPS-induced cognitive impairment and ferroptosis by modulating glucose metabolism. This study offers a promising avenue for future interventions targeting cognitive decline.

Cognitions and Metacognitive Beliefs in Posttraumatic Stress Disorder.

There has been an increasing interest in understanding what contributes to the development and what maintains posttraumatic stress disorder (PTSD). The cognitive model emphasizes that it is a disturbance of the autobiographical memory for the trauma, cognitive beliefs and maladaptive behaviour that maintain trauma symptoms. Interventions are based on cognitive restructuring and behaviour experiments to modify these beliefs. In contrast, the metacognitive model emphasizes that it is the metacognitive beliefs that give rise to the cognitive attentional syndrome (CAS) that maintain trauma symptoms. The focus of treatment is reducing CAS and working on metacognitive beliefs. The aim of this study was to explore the contribution of cognitions and metacognitive beliefs to trauma symptoms and investigate what predicts symptom burden in traumatized patients. Participants (N = 290) diagnosed with PTSD were included, and hierarchical multiple regression analyses were performed to explore if cognitions and metacognitive beliefs explained additional and independent variance in trauma symptoms while controlling for age and gender. Both cognitions and metacognitive beliefs contributed independently and significantly to predicting trauma symptoms. The results provide further support for investigating what maintains trauma symptoms and what to target in treatment. This may have clinical implications for our theoretical and practical understanding of PTSD.

SNMCF: A Scalable Non-Negative Matrix Co-Factorization for Student Cognitive Modeling

SNMCF: A Scalable Non-Negative Matrix Co-Factorization for Student Cognitive Modeling

'Who will I become?': possible selves and depression symptoms in adolescents.

Adolescence is an important period for the development of the possible self. It is also a time when depression is prevalent. The cognitive theory of depression proposes that a negative view of the future is a key feature of depression. Targeting these negative thoughts about the future during cognitive behavioural therapy may be helpful in depression. However, little is known about how adolescents envisage their future (i.e. possible) self, or if the content is associated with affect. The aim of this quantitative study is to describe how adolescents describe their 'possible self' and examine the relationship between the valence of the possible self and depression in adolescents. Adolescents (n = 584) aged 13-18 years were recruited via opportunity sampling via their schools and completed measures of depression symptoms (the Mood and Feelings Questionnaire) and the 'possible self' (a variant of the 'I Will Be' task). Possible selves were coded for content and valence. Despite depression severity, the most common possible selves generated by adolescents were positive and described interpersonal roles. The valence of the possible self was associated with depression severity but only accounted for 3.4% of the variance in severity. The results support the cognitive model of depression. However, adolescents with elevated symptoms of depression were able to generate positive, possible selves and therefore may remain somewhat 'hopeful' about their future despite clinically significant depression symptoms. Future-oriented treatment approaches such as cognitive behavioural therapy that focus on changing unhelpful negative future thinking may not be appropriate for this population.

Attentional Biases and Nonsuicidal Self-Injury Urges in Adolescents

Nonsuicidal self-injury (NSSI) is a significant clinical concern among adolescents. Exposure to NSSI-related content on social media platforms has been suspected to potentially act as a trigger for NSSI. To use free-viewing eye-tracking and dot-probe paradigms to examine attentional bias and psychophysiological responses to NSSI-related pictorial and textual stimuli in adolescents with and without a history of NSSI. From June 2022 to April 2023, adolescent participants in Vienna, Austria with and without a history of NSSI were exposed to NSSI-related stimuli in this nonrandomized controlled trial. Data were analyzed from December 2023 to January 2024. Exposure to NSSI-related stimuli. During both tasks, subjective arousal, NSSI urges, and autonomic nervous system activity were assessed. A total of 50 adolescents in 2 groups, 25 who engaged in NSSI (mean [SD] age 15.86 [1.14] years; 19 female participants [76%]) and 25 who did not (mean [SD] age 16.40 [1.71] years; 19 female participants [76%]) were included. Adolescents with a history of NSSI-but not those without a history of NSSI-showed a clear attentional bias toward NSSI-related images during eye-tracking, as indicated by increased initial fixations (500 ms stimulus presentation mean difference, 28.64%; 95% CI, 18.31%-38.98%; P < .001; 1000 ms stimulus presentation mean difference, 18.50%; 95% CI, 9.05%-27.95%; P < .001) and longer fixation durations (500 ms mean difference, 29.51 ms; 95% CI, 4.3-54.72 ms; P < .001; 1000 ms mean difference, 39.83 ms; 95% CI, 6.90-72.76 ms; P < .001), regardless of stimulus duration. This bias was associated with a heightened urge to engage in NSSI (d = 1.22; 95% CI, 0.69-1.73; P < .001), a trend not seen in adolescents without a history of NSSI. Similarly, in the dot-probe task, only the NSSI group showed an attentional bias toward NSSI images but not toward trauma images, emphasizing the specificity of their attentional bias. Physiological measures revealed no significant differences, suggesting that viewing NSSI images is not associated with increased autonomic arousal. Textual NSSI content did not provoke an attentional bias or heighten NSSI urges in either group. In this nonrandomized controlled trial of 50 adolescents, results highlighted a specific attentional bias toward NSSI-related pictorial stimuli in adolescents with a history of NSSI, particularly a difficulty in disengaging from NSSI images. These findings contribute to understanding maladaptive information processing in NSSI and suggest implications for clinical management and cognitive models addressing NSSI triggers. German Clinical Trials Register identifier: DRKS00025905.