An architecturally constrained model of random number generation and its application to modeling the effect of generation rate

Random number generation (RNG) is a complex cognitive task for human subjects, requiring deliberative control to avoid production of habitual, stereotyped sequences. Under various manipulations (e.g., speeded responding, transcranial magnetic stimulation, or neurological damage) the performance of human subjects deteriorates, as reflected in a number of qualitatively distinct, dissociable biases. For example, the intrusion of stereotyped behavior (e.g., counting) increases at faster rates of generation. Theoretical accounts of the task postulate that it requires the integrated operation of multiple, computationally heterogeneous cognitive control (“executive”) processes. We present a computational model of RNG, within the framework of a novel, neuropsychologically-inspired cognitive architecture, ESPro. Manipulating the rate of sequence generation in the model reproduced a number of key effects observed in empirical studies, including increasing sequence stereotypy at faster rates. Within the model, this was due to time limitations on the interaction of supervisory control processes, namely, task setting, proposal of responses, monitoring, and response inhibition. The model thus supports the fractionation of executive function into multiple, computationally heterogeneous processes

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

A developmental psychobiological approach to developmental neuropsychology.

Although both developmental psychobiology and developmental neuropsychology examine the interface between biological and psychological processes, they differ in conceptual framework. This article argues for the incorporation into developmental neuropsychology of certain aspects of the conceptual framework of developmental psychobiology. Three principles of dynamic psychobiological interaction are described and applied to four issues in neuropsychology (handedness, sex differences in behavior, critical periods, and modularity of structure–function relations). Then, it is proposed that developmental psychobiology can make four direct contributions to developmental neuropsychology. Finally, it is argued that the value of the conceptual framework provided by developmental psychobiology depends, in part, on how well it translates into procedures that can be applied in the clinical settings of the developmental neuropsychologist

Evidence-based clinical decision-making : Conceptual and empirical foundations for an integrative psychological and neurobiological transtheoretical metamodel

The dialogue between psychotherapy and neuroscience is ongoing. Previous meta-analytic research suggests that 35% of psychotherapy outcome variance is not fully explained, whereas 30% is attributed to patient variables, 15% to therapeutic relationship, 10% to specific therapeutic techniques, 7% to therapist variables and 3% to other factors (Norcross & Wampold, 2019). Several authors emphasize the need for integrative, metatheoretical or transtheoretical approaches to enhance conceptual understanding of clinical phenomena, augmenting psychotherapy responsiveness to patients’ significant variables, such as maladaptive patterns, states of mind, relational styles, emotional difficulties, neurocognitive deficits, and psychological needs. The present doctoral proposal aims to respond to these claims through the establishment of preliminary conceptual and empirical foundations for an Integrative Psychological and Neurobiological Transtheoretical Metamodel. First, an extensive literature review of the relationships between psychotherapy and neuroscience was performed to establish theoretical and conceptual integration of different components of the presently proposed model. Second, several methodological aspects were described to systematize the complex data acquisition process. Third, seven studies were conducted, and implications of the results were discussed. Fourth, an integrative discussion was elaborated, emphasizing the major and general implications of the results for clinical practice and future research. The first empirical study aimed to develop and/or adapt self-report assessment measures to evaluate several psychological variables (e.g., metacognition, states of mind), which resulted in five scientific articles. Thus, the Metacognitive Self-assessment Scale (Pedone et al., 2017) and the Inventory of Interpersonal Problems – 32 (IIP-32, Barkham et al., 1998) were validated and adapted to European Portuguese. The State of Mind Questionnaire (SMQ, Faustino et al., 2021b, Emotional Processing Difficulties Scale – R (EPDS-R, Faustino et al., in press) and the Clinical Decision-Making Inventory (Faustino & Vasco, in press) were developed. All instruments showed satisfactory psychometric properties. Nevertheless, the SMQ showed low reliability in the composite scales in smaller subsamples. For the second empirical study, the main aims were to explore the complex relationships between early disorder determinants, maladaptive schemas and states of mind, defensive maneuvers and critical consequences, mental skills and processes, and adaptive self-domains. This was performed with Structural Equation Modeling (SEM). Results showed significant sequential and mediational models between maladaptive schemas, defensive maneuvers and dysfunctional consequences, mental abilities and processes, and adaptive self-domains with psychological needs. Maladaptive schemas and states of mind were both predictors and mediators in several models. However, the relationship between maladaptive schematic functioning and symptomatology had less significant mediations with the same variables. For the third study, the main aims were to explore the relationships of early disorder determinants, maladaptive schematic functioning and states of mind, defensive maneuvers and dysfunctional consequences, mental abilities and processes, and adaptive self-domains, with several neurocognitive variables. Executive functions were negatively correlated with maladaptive schematic functioning and with defensive maneuvers and dysfunctional consequences. Memory only correlated with psychological needs, self-confidence and with dysfunctional interpersonal cycles. These results emphasize previous assumptions that there is a difference between self-report questionnaires and neuropsychological assessment measures which may difficult the integrated study of psychological and neurocognitive processes. The fourth study aimed to explore the associations of affective subliminal processing with dispositional states and contextual states, defined in the present work as early disorder determinants, schematic functioning, and defensive maneuvers and dysfunctional consequences, mental abilities and processes, and adaptive self-domains. Results showed strong correlations between maladaptive schematic functioning, coping responses, emotional processing difficulties, and expressive suppression with behavioral responses. Dispositional traits and contextual states seem to be associated with affective processing, especially when it comes to the neutral valence of the subliminal stimuli. ERPs waveforms showed an amplitude modulation with a temporal progression: in the first 100 msec the waveform amplitude was highest to the negative condition; Later on, in the time windows after 350 msec, the neutral condition was the one that elicited the ERPs’ heist amplitude. These indexes a cascade of reactions, first a priority to nonconscious negative stimulation; and after that, a later processing phase of affective-cognitive interpretation (350msc) in which neutral stimuli acquire a meaning according to schemas. The fifth study explored the diagnostic and or transdiagnostic potential of early disorder determinants, maladaptive schematic functioning and states of mind, defensive maneuvers and dysfunctional consequences, mental abilities and processes, and adaptive self-domains. Results showed that only early complex trauma and expressive suppression were not statistically different in two subsamples. Individuals in the low-symptoms sub-sample reported lower levels of maladaptive schematic functioning, defensive maneuvers, and psychological inflexibility than individuals in the higher-symptoms subsample. The sixth study was focused on the exploration of the temporal stability of maladaptive schematic functioning and states of mind, defensive maneuvers and dysfunctional consequences, mental abilities, and adaptive self-domains. Results showed significant differences between moment one and two, with a descending pattern in the mean scores of dysfunctional variables. An inverse pattern was found regarding the adaptive variables. However, mean scores of some variables, such as early maladaptive schemas, emotional schemas, psychological needs, and cognitive reappraisal were not statistically significant. The seventh study aimed to explore associations of early disorder determinants, maladaptive schemas and states of mind, defensive maneuvers and critical consequences, mental skills and processes and adaptive self-domains, with an empirical based clinical profile (e.g., psychotherapy and motivational stage, coping styles). Results showed significant negative correlations between maladaptive schematic functioning and stage process, motivational stage, therapeutic relationship, attachment style, reactance, and coping style. An inverse pattern was found regarding the adaptive variables. These preliminary results seem to support a theoretically- and empirically-based integrative and transtheoretical metamodel focused on unifying psychotherapy and neuroscience into a coherent framework. Further research is required to augment and enhance the presently proposed model

These confabulations are guaranteed to improve your marriage! Toward a teleological theory of confabulation

Confabulation is typically understood to be dysfunctional. But this understanding neglects the phenomenon’s potential benefits. In fact, we think that the benefits of non-clinical confabulation provide a better foundation for a general account of confabulation. In this paper, we start from these benefits to develop a social teleological account of confabulation. Central to our account is the idea that confabulation manifests a kind of willful ignorance. By understanding confabulation in this way, we can provide principled explanations for the difference between clinical and non-clinical cases of confabulation and the extent to which confabulation is rational

Are developmental disorders like cases of adult brain damage? Implications from connectionist modelling

It is often assumed that similar domain-specific behavioural impairments found in cases of adult brain damage and developmental disorders correspond to similar underlying causes, and can serve as convergent evidence for the modular structure of the normal adult cognitive system. We argue that this correspondence is contingent on an unsupported assumption that atypical development can produce selective deficits while the rest of the system develops normally (Residual Normality), and that this assumption tends to bias data collection in the field. Based on a review of connectionist models of acquired and developmental disorders in the domains of reading and past tense, as well as on new simulations, we explore the computational viability of Residual Normality and the potential role of development in producing behavioural deficits. Simulations demonstrate that damage to a developmental model can produce very different effects depending on whether it occurs prior to or following the training process. Because developmental disorders typically involve damage prior to learning, we conclude that the developmental process is a key component of the explanation of endstate impairments in such disorders. Further simulations demonstrate that in simple connectionist learning systems, the assumption of Residual Normality is undermined by processes of compensation or alteration elsewhere in the system. We outline the precise computational conditions required for Residual Normality to hold in development, and suggest that in many cases it is an unlikely hypothesis. We conclude that in developmental disorders, inferences from behavioural deficits to underlying structure crucially depend on developmental conditions, and that the process of ontogenetic development cannot be ignored in constructing models of developmental disorders

Task rules, working memory, and fluid intelligence

Many varieties of working memory have been linked to fluid intelligence. In Duncan et al. (Journal of Experimental Psychology:General 137:131–148, 2008), we described limited working memory for new task rules: When rules are complex, some may fail in their control of behavior, though they are often still available for explicit recall. Unlike other kinds of working memory, load is determined in this case not by real-time performance demands, but by the total complexity of the task instructions. Here, we show that the correlation with fluid intelligence is stronger for this aspect of working memory than for several other, more traditional varieties—including simple and complex spans and a test of visual short-term memory. Any task, we propose, requires construction of a mental control program that aids in segregating and assembling multiple task parts and their controlling rules. Fluid intelligence is linked closely to the efficiency of constructing such programs, especially when behavior is complex and novel

Extension of action rule grammar and implementation of processing engine of a DEMO based low-code platform

Numerosos estudos afirmam que muitos projetos de software ficam aquém das expetativas inici ais dos utilizadores finais. Causas comuns para estas falhas são objetivos irrealistas do projeto e requisitos incompletos, entre outros. O trabalho desenvolvido nesta tese ocorre no contexto do projeto DISME, uma plataforma low-code para modelação e execução de processos de negócio que pretende ultrapassar alguns destes problemas comuns em sistemas de informação, de modo a tornar a sua utilização para apoio à decisão mais intuitiva, personalizável e adaptável, de forma dinâmica e sem necessidade de programação. No âmbito do DISME, estendeu-se e aprimorou-se um novo meta-modelo para o Modelo de Ação do DEMO, e desenvolveu-se o componente referente ao Executor do Sistema, cuja função é interpretar e executar as Regras de Ação. Foi depois integrado num Dashboard, que permite uma gestão de tarefas e processos de fácil utilização. No decorrer deste desenvolvimento, notou-se ser de igual importância a extensão de outros com ponentes relativos ao desenho e execução de Regras de Ação, mais concretamente os componentes de gestão de Regras de Ação e de formulários do mesmo projeto, respetivamente, e a criação de um componente de parametrização para facilitar a gestão da especificação do sistema. Para comprovar a eficácia da plataforma, foi realizada uma experiência comparando a abor dagem tradicional de desenvolvimento com uma abordagem low-code utilizando a plataforma DISME. Para o caso específico utilizado, observou-se uma redução de 94,63% no esforço necessário, e uma redução de 86% relativamente à complexidade. A usabilidade da plataforma foi também avaliada via métodos qualitativos e quantitativos. A avaliação qualitativa através do método Think Aloud deu feedback valioso sobre vários aspetos da plataforma. Os participantes consideraram a plataforma cativante, intuitiva e de fácil utilização. A avaliação quantitativa utilizando a System Usability Scale confirmou estas conclusões, com uma pontuação global de usabilidade de 89,25%.Numerous studies find that many software projects fall short of end customers’ initial expectations. Common causes for software project failures are unrealistic project objectives and incomplete requirements, among others. The work developed in this thesis occurs in the context of the DISME project, a low-code platform for the modelling and execution of business processes that intends to overcome some of these common problems in information systems, in order to make their use for decision support more intuitive, customizable and adaptable, dynamically and without the need for programming. In the scope of DISME, a new meta-model was extended and improved for DEMO’s Action Model, and the component related to the System Executor was developed, whose function is to interpret and run the Action Rules. It was then integrated with a Dashboard, which allows user friendly task and process management to the platform’s users. During this development, it was noted that it was equally important to extend other compo nents relative to the design and execution of Action Rules, more specifically the components of Action Rule management and form management of the same project, respectively, and to create a parameterization component for easier management of the system’s specification. To prove the efficacy of the platform, an experiment was made, comparing the traditional development approach with a low-code one using DISME. For the specific case used, our findings showed a 94.63% reduction in the needed effort. Regarding complexity, a reduction of 86% was observed. The usability of the platform was then evaluated using both qualitative and quantitative meth ods. The qualitative evaluation through the Think Aloud method provided valuable feedback on various aspects of the platform. Participants found the platform engaging, intuitive, visually ap pealing, and user-friendly. The quantitative evaluation using the System Usability Scale confirmed these findings, with an overall usability score of 89.25%

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 314)

This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August, 1988

Current trends on the early diagnosis of Alzheimer\u27s Disease by means of neural computation methods

The prevalence of dementia is expected to increment in the next decades as the elderly population grows and ages. Hence, Alzheimer’s Disease (AD), as the most frequent dementia, will be more problematic from a socioeconomic point of view. Different diagnostic criteria have been proposed by clinicians for the early diagnosis of AD. After discarding the longitudinal and prognosis articles, a selection of articles from the last decade and based on Artificial Neural Networks (ANNs) was collated from the PubMed database, and complemented with researches extracted from others. The latest trends on this field were discovered in these selected articles, which were later discussed. Only articles based whether on shallow ANNs, Deep Learning (DL) or a mix of both were included. The total number of cross-sectional articles that complied with our selection criteria was 154. Convolutional Neural Networks (CNNs) combined with neuroimaging has been the most popular approach, yielding very good performance results. Approaches based on non- neuroimaging techniques, such as gait, genetics, speech and neuropsychological tests, were less common but have their own advantages. Multimodality solutions may become even more prevalent in the near future. Similarly, novel diagnostic criteria will appear and the popularity of currently not-so-common ones will expand. A new proposal emerged from these trends, which is based on ontogenetic ANNs