Elucidating the Functional Relationship Between Working Memory Capacity and Psychometric Intelligence: A Fixed-Links Modeling Approach for Experimental Repeated-Measures Designs

Numerous studies reported a strong link between working memory capacity (WMC) and fluid intelligence (Gf), although views differ in respect to how close these two constructs are related to each other. In the present study, we used a WMC task with five levels of task demands to assess the relationship between WMC and Gf by means of a new methodological approach referred to as fixed-links modeling. Fixed-links models belong to the family of confirmatory factor analysis (CFA) and are of particular interest for experimental, repeated-measures designs. With this technique, processes systematically varying across task conditions can be disentangled from processes unaffected by the experimental manipulation. Proceeding from the assumption that experimental manipulation in a WMC task leads to increasing demands on WMC, the processes systematically varying across task conditions can be assumed to be WMC-specific. Processes not varying across task conditions, on the other hand, are probably independent of WMC. Fixed-links models allow for representing these two kinds of processes by two independent latent variables. In contrast to traditional CFA where a common latent variable is derived from the different task conditions, fixed-links models facilitate a more precise or purified representation of the WMC-related processes of interest. By using fixed-links modeling to analyze data of 200 participants, we identified a non-experimental latent variable, representing processes that remained constant irrespective of the WMC task conditions, and an experimental latent variable which reflected processes that varied as a function of experimental manipulation. This latter variable represents the increasing demands on WMC and, hence, was considered a purified measure of WMC controlled for the constant processes. Fixed-links modeling showed that both the purified measure of WMC (β = .48) as well as the constant processes involved in the task (β = .45) were related to Gf. Taken together, these two latent variables explained the same portion of variance of Gf as a single latent variable obtained by traditional CFA (β = .65) indicating that traditional CFA causes an overestimation of the effective relationship between WMC and Gf. Thus, fixed-links modeling provides a feasible method for a more valid investigation of the functional relationship between specific constructs.

Verbalization of Dependencies Between Concepts Built Through Fuzzy Cognitive Maps

The new computing paradigm known as cognitive computing attempts to imitate the human capabilities of learning, problem solving, and considering things in context. To do so, an application (a cognitive system) must learn from its environment (e.g., by interacting with various interfaces). These interfaces can run the gamut from sensors to humans to databases. Accessing data through such interfaces allows the system to conduct cognitive tasks that can support humans in decision-making or problem-solving processes. Cognitive systems can be integrated into various domains (e.g., medicine or insurance). For example, a cognitive system in cities can collect data, can learn from various data sources and can then attempt to connect these sources to provide real time optimizations of subsystems within the city (e.g., the transportation system). In this study, we provide a methodology for integrating a cognitive system that allows data to be verbalized, making the causalities and hypotheses generated from the cognitive system more understandable to humans. We abstract a city subsystem—passenger flow for a taxi company—by applying fuzzy cognitive maps (FCMs). FCMs can be used as a mathematical tool for modeling complex systems built by directed graphs with concepts (e.g., policies, events, and/or domains) as nodes and causalities as edges. As a verbalization technique we introduce the restriction-centered theory of reasoning (RCT). RCT addresses the imprecision inherent in language by introducing restrictions. Using this underlying combinatorial design, our approach can handle large data sets from complex systems and make the output understandable to humans.

Decomposing speed of information processing in elementary cognitive tasks

Many studies obtained reliable individual differences in speed of information processing (SIP) as measured by elementary cognitive tasks (ECTs). ECTs usually employ response times (RT) as measure of SIP, but different ECTs target different cognitive processes (e.g., simple or choice reaction, inhibition). Here we used modified versions of the Hick and the Eriksen Flanker task to examine whether these tasks assess dissociable or common aspects of SIP. In both tasks, task complexity was systematically varied across three levels. RT data were collected from 135 participants. Applying fixed-links modeling, RT variance increasing with task complexity was separated from RT variance unchanging across conditions. For each task, these aspects of variance were represented by two independent latent variables. The two latent variables representing RT variance not varying with complexity of the two tasks were virtually identical (r = .83). The latent variables representing increasing complexity in the two tasks were also highly correlated (r = .72) but clearly dissociable. Thus, RT measures contain both task-unspecific, person-related aspects of SIP as well as task-specific aspects indicating the cognitive processes manipulated with the respective task. Separating these aspects of SIP facilitates the interpretation of individual differences in RT.

FAMILY MEDICINE RESIDENT SURVEY ON HEALTH PROMOTION (PERSONAL EXPECTATIONS, BEHAVIORS, PERCEIVED SELF-EFFICACY, COGNITIVE ASSESSMENT, OUTCOME EXPECTANCY)

This study was designed to test the theoretical predictors of personal efficacy expectations among family medicine resident physicians for helping their patients change thirteen high risk health behaviors. A survey questionnaire was sent to 781 family medicine residents in the six state south central region. The response rate was 60 percent. The hypothesized relationship between lower levels of difficulty and higher personal efficacy expectations was supported by the data. Effort was a significant predictor of perceived self efficacy for health behaviors considered less difficult to change. Situational support did not prove to be a significant predictor for many of the health behaviors. Rate and pattern of success were consistent and significant predictors of perceived self efficacy for helping patients change all thirteen of the health behaviors. Modeling of effective methods by faculty was a significant predictor of efficacy expectations for several but not all of the behaviors. Personal modeling was a significant predictor of perceived efficacy for helping patients change behaviors related to alcohol misuse and exercise. The respondents personally modeled positive health behaviors more consistently than their older colleagues or the general population.^ The results of this study lend substantially to the usefulness of the cognitive-behavioral theory of perceived self efficacy and provide a mechanism for assessing the predictors of personal efficacy expectations of family medicine resident physicians. The findings are expected to have direct implications for faculty to institute systematic programs of interventions designed to increase residents' perceptions of efficacy in facilitating more positive health behaviors among their patients. ^

Knowledge representation tool for cognitiveprocesses modeling

In the last decades, neuropsychological theories tend to consider cognitive functions as a result of the whole brainwork and not as individual local areas of its cortex. Studies based on neuroimaging techniques have increased in the last years, promoting an exponential growth of the body of knowledge about relations between cognitive functions and brain structures [1]. However, so fast evolution make complicated to integrate them in verifiable theories and, even more, translated in to cognitive rehabilitation. The aim of this research work is to develop a cognitive process-modeling tool. The purpose of this system is, in the first term, to represent multidimensional data, from structural and functional connectivity, neuroimaging, data from lesion studies and derived data from clinical intervention [2][3]. This will allow to identify consolidated knowledge, hypothesis, experimental designs, new data from ongoing studies and emerging results from clinical interventions. In the second term, we pursuit to use Artificial Intelligence to assist in decision making allowing to advance towards evidence based and personalized treatments in cognitive rehabilitation. This work presents the knowledge base design of the knowledge representation tool. It is compound of two different taxonomies (structure and function) and a set of tags linking both taxonomies at different levels of structural and functional organization. The remainder of the abstract is organized as follows: Section 2 presents the web application used for gathering necessary information for generating the knowledge base, Section 3 describes knowledge base structure and finally Section 4 expounds reached conclusions.

Effector dynamics of rhythmic wrist activity and its implications for (modeling) bimanual coordination

To examine the role of the effector dynamics of the wrist in the production of rhythmic motor activity, we estimated the phase shifts between the EMG and the task-related output for a rhythmic isometric torque production task and an oscillatory movement, and found a substantial difference (45-52degrees) between the two. For both tasks, the relation between EMG and task-related output (torque or displacement) was adequately reproduced with a physiologically motivated musculoskeletal model. The model simulations demonstrated the importance of the contribution of passive structures to the overall dynamics and provided an account for the observed phase shifts in the dynamic task. Additional simulations of the musculoskeletal model with added load suggested that particular changes in the phase relation between EMG and movement may follow largely from the intrinsic muscle dynamics, rather than being the result of adaptations in the neural control of joint stiffness. The implications of these results are discussed in relation to (models of) interlimb coordination in rhythmic tasks. (C) 2004 Elsevier B.V. All rights reserved.

Familial influence on offspring gambling: a cognitive mechanism for transmission of gambling behavior in families

Background. The problem-gambling literature has identified a range of individual, cognitive, behavioral and emotional factors as playing important roles in the development, maintenance and treatment of problem gambling. However, familial factors have often been neglected. The current study aims to investigate the possible influence of parental factors on offspring gambling behavior. Method. A total of 189 families (546 individuals) completed several questionnaires including the South Oaks Gambling Screen (SOGS) and the Gambling Related Cognition Scale (GRCS). The relationships were examined using Pearson product-moment correlations and structural equation modeling (SEM) analyses. Results. Results showed that generally parents' (especially fathers') gambling cognitions and gambling behaviors positively correlated with offspring gambling behaviors and cognitions. However, SEM analyses showed that although parental gambling behavior was directly related to offspring gambling behavior, parental cognitions were not related to offspring gambling behavior directly but indirectly via offspring cognitions. Conclusion. The findings show that the influence of parental gambling cognition on offspring gambling behavior is indirect and via offspring cognitions. The results suggest a possible cognitive mechanism of transmission of gambling behavior in the family from one generation to the next.

Cognitive fit between conceptual schemas and internal problem representations: The case of geospatio-temporal conceptual schema comprehension

Geospatio-temporal conceptual models provide a mechanism to explicitly represent geospatial and temporal aspects of applications. Such models, which focus on both what and when/where, need to be more expressive than conventional conceptual models (e.g., the ER model), which primarily focus on what is important for a given application. In this study, we view conceptual schema comprehension of geospatio-temporal data semantics in terms of matching the external problem representation (that is, the conceptual schema) to the problem-solving task (that is, syntactic and semantic comprehension tasks), an argument based on the theory of cognitive fit. Our theory suggests that an external problem representation that matches the problem solver's internal task representation will enhance performance, for example, in comprehending such schemas. To assess performance on geospatio-temporal schema comprehension tasks, we conducted a laboratory experiment using two semantically identical conceptual schemas, one of which mapped closely to the internal task representation while the other did not. As expected, we found that the geospatio-temporal conceptual schema that corresponded to the internal representation of the task enhanced the accuracy of schema comprehension; comprehension time was equivalent for both. Cognitive fit between the internal representation of the task and conceptual schemas with geospatio-temporal annotations was, therefore, manifested in accuracy of schema comprehension and not in time for problem solution. Our findings suggest that the annotated schemas facilitate understanding of data semantics represented on the schema.

Cognitive neuroscience: the evolution of a discipline

Preface. The evolution of cognitive neuroscience has been spurred by the development of increasingly sophisticated investigative techniques to study human cognition. In Methods in Mind, experts examine the wide variety of tools available to cognitive neuroscientists, paying particular attention to the ways in which different methods can be integrated to strengthen empirical findings and how innovative uses for established techniques can be developed. The book will be a uniquely valuable resource for the researcher seeking to expand his or her repertoire of investigative techniques. Each chapter explores a different approach. These include transcranial magnetic stimulation, cognitive neuropsychiatry, lesion studies in nonhuman primates, computational modeling, psychophysiology, single neurons and primate behavior, grid computing, eye movements, fMRI, electroencephalography, imaging genetics, magnetoencephalography, neuropharmacology, and neuroendocrinology. As mandated, authors focus on convergence and innovation in their fields; chapters highlight such cross-method innovations as the use of the fMRI signal to constrain magnetoencephalography, the use of electroencephalography (EEG) to guide rapid transcranial magnetic stimulation at a specific frequency, and the successful integration of neuroimaging and genetic analysis. Computational approaches depend on increased computing power, and one chapter describes the use of distributed or grid computing to analyze massive datasets in cyberspace. Each chapter author is a leading authority in the technique discussed.

Information Processing in a Cognitive Model of NLP

A model of the cognitive process of natural language processing has been developed using the formalism of generalized nets. Following this stage-simulating model, the treatment of information inevitably includes phases, which require joint operations in two knowledge spaces – language and semantics. In order to examine and formalize the relations between the language and the semantic levels of treatment, the language is presented as an information system, conceived on the bases of human cognitive resources, semantic primitives, semantic operators and language rules and data. This approach is applied for modeling a specific grammatical rule – the secondary predication in Russian. Grammatical rules of the language space are expressed as operators in the semantic space. Examples from the linguistics domain are treated and several conclusions for the semantics of the modeled rule are made. The results of applying the information system approach to the language turn up to be consistent with the stages of treatment modeled with the generalized net.

Group Comparisons on Cognitive Attributes Using the Least Squares Distance Model of Cognitive Diagnosis

2010 Mathematics Subject Classification: 62P15.

Performance analysis of IEEE 802.11 cognitive radio ad hoc networks

IEEE 802.11 standard is the dominant technology for wireless local area networks (WLANs). In the last two decades, the Distributed coordination function (DCF) of IEEE 802.11 standard has become the one of the most important media access control (MAC) protocols for mobile ad hoc networks (MANETs). The DCF protocol can also be combined with cognitive radio, thus the IEEE 802.11 cognitive radio ad hoc networks (CRAHNs) come into being. There were several literatures which focus on the modeling of IEEE 802.11 CRAHNs, however, there is still no thorough and scalable analytical models for IEEE 802.11 CRAHNs whose cognitive node (i.e., secondary user, SU) has spectrum sensing and possible channel silence process before the MAC contention process. This paper develops a unified analytical model for IEEE 802.11 CRAHNs for comprehensive MAC layer queuing analysis. In the proposed model, the SUs are modeled by a hyper generalized 2D Markov chain model with an M/G/1/K model while the primary users (PUs) are modeled by a generalized 2D Markov chain and an M/G/1/K model. The performance evaluation results show that the quality-of-service (QoS) of both the PUs and SUs can be statistically guaranteed with the suitable settings of duration of channel sensing and silence phase in the case of under loading.

Family and group cognitive behavior therapy: Evaluation of treatment outcome and treatment specificity

This dissertation examined the efficacy of family cognitive behavior treatment (FCBT) and group cognitive behavior treatment (GBCT) for reducing anxiety disorders in children and adolescents using several approaches: clinical significant change, equivalence testing, and analyses of variance. It also examined treatment specificity in terms of targeting family/parents (in FCBT) and peers/group (in GCBT) contextual variables using two main approaches: analyses of variance and structural equation modeling (SEM). The sample consisted of 143 children and their parents who presented to the Child Anxiety and Phobia Program housed within the Child and Family Psychosocial Research Center at Florida International University. Diagnostic interviews and questionnaires were administered to assess youth anxiety. Questionnaires were administered to assess child and parent views of family/parents and peers/group contextual variables. In terms of clinical significant change, results indicated that 84.6% of youth in FCBT and 71.2% of youth in GBCT no longer met diagnostic criteria for their primary/targeted anxiety disorder. In addition, results from analyses of variance indicated that FCBT and GCBT were both efficacious in reducing anxiety disorders in youth across both child and parent ratings. Results using both analyses of variance and structural equation modeling also indicated that there was no meaningful treatment specificity between FCBT and GCBT in terms of either family/parents or peers/group contextual variables. That is, child social skills improved in GCBT in which these skills were targeted and in FCBT in which these skills were not targeted; parenting skills improved in FCBT in which these skills were targeted and in GCBT in which these skills were not targeted. Clinical implications and future research recommendations are discussed.

Parental involvement and group cognitive behavioral treatment for anxiety disorders in children and adolescents: Treatment specificity and mediation effects of parent and peer variables

Phobic and anxiety disorders are one of the most common, if not the most common and debilitating psychopathological conditions found among children and adolescents. As a result, a treatment research literature has accumulated showing the efficacy of cognitive behavioral treatment (CBT) for reducing anxiety disorders in youth. This dissertation study compared a CBT with parent and child (i.e., PCBT) and child group CBT (i.e., GCBT). These two treatment approaches were compared due to the recognition that a child’s context has an effect on the development, course, and outcome of childhood psychopathology and functional status. The specific aims of this dissertation were to examine treatment specificity and mediation effects of parent and peer contextual variables. The sample consisted of 183 youth and their mothers. Research questions were analyzed using analysis of variance for treatment outcome, and structural equation modeling, accounting for clustering effects, for treatment specificity and mediation effects. Results indicated that both PCBT and GCBT produced positive treatment outcomes across all indices of change (i.e., clinically significant improvement, anxiety symptom reduction) and across all informants (i.e., youths and parents) with no significant differences between treatment conditions. Results also showed partial treatment specific effects of positive peer relationships in GCBT. PCBT also showed partial treatment specific effects of parental psychological control. Mediation effects were only observed in GCBT; positive peer interactions mediated treatment response. The results support the use CBT with parents and peers for treating childhood anxiety. The findings’ implications are further discussed in terms of the need to conduct further meditational treatment outcome designs in order to continue to advance theory and research in child and anxiety treatment.