Investigating the 'latent' deficit hypothesis : age at time of head injury, executive and implicit functions and behavioral insight

This study investigated the 'latent deficit' hypothesis in two groups of frontotemporal headinjured patients, those injured prior to steep morphological and corresponding functional maturational periods for frontotemporal networks (≤ age 25), and those injured >28 years. The latent deficit hypothesis proposes that early injuries produce enduring cognitive deficits manifest later in the lifespan with graver consequences for behavior than adult injuries, particularly after frontal pathology (Eslinger, Grattan, Damasio & Damasio, 1992). Implicit and executive deficits both contribute to behavioral insight after frontotemporal head injury (Barker, Andrade, Romanowski, Morton & Wasti, 2006). On the basis of morphological and behavioral data, we hypothesised that early injury would confer greater vulnerability to impairment on tasks associated with frontotemporal regions than later injury. Patients completed experimental tasks of implicit cognition, executive function measures and the DEX measure of behavioural insight (Behavioral Assessment of the Dysexecutive Syndrome: Wilson, Alderman, Burgess, Emslie, & Evans, 1996). The Early Injury group were more impaired on implicit cognition tasks compared to controls that Late Injury patients. There were no marked group differences on most executive function measures. Executive ability only contributed to behavioral awareness in the Early Injury Group. Findings showed that age at injury moderates the relationship between executive and implicit cognition and behavioral insight and that early injuries result in longstanding deficits to functions associated with frontotemporal regions partially supporting the latent deficit hypothesis

Establishing, versus Maintaining, Brain Function: A Neuro-computational Model of Cortical Reorganization after Injury to the Immature Brain

The effect of age at injury on outcome after acquired brain injury (ABI) has been the subject of much debate. Many argue that young brains are relatively tolerant of injury. A contrasting viewpoint due to Hebb argues that greater system integrity may be required for the initial establishment of a function than for preservation of an already-established function. A neuro-computational model of cortical map formation was adapted to examine effects of focal and distributed injury at various stages of development. This neural network model requires a period of training during which it self-organizes to establish cortical maps. Injuries were simulated by lesioning the model at various stages of this process and network function was monitored as "development" progressed to completion. Lesion effects are greater for larger, earlier, and distributed (multifocal) lesions. The mature system is relatively robust, particularly to focal injury. Activities in recovering systems injured at an early stage show changes that emerge after an asymptomatic interval. Early injuries cause qualitative changes in system behavior that emerge after a delay during which the effects of the injury are latent. Functions that are incompletely established at the time of injury may be vulnerable particularly to multifocal injury

A new method calculating load balance of sliding bearing by using neural network PID algorithm

Aiming at low efficiency of existing sliding bearing load balance calculation, a new method that based on neural network proportional-integral-derivative (PID) algorithm is proposed for the first time, in which a compound control algorithm combining neural network and PID algorithm is applied. In this new method, the load error is taken as the input of the system, and the eccentricity of the bearing is used as the input of the controller, and the output of the system is the oil film force of the bearing. Comparing with traditional method, calculation results show that: the number of iterations calculated by neural network PID algorithm is less than traditional one and has higher efficiency and stronger adaptability under different loads

Intelligent control based on fuzzy logic and neural net theory

In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment

Modeling the Synchronization of Multimodal Perceptions as a Basis for the Emergence of Deterministic Behaviors.

Living organisms have either innate or acquired mechanisms for reacting to percepts with an appropriate behavior e.g., by escaping from the source of a perception detected as threat, or conversely by approaching a target perceived as potential food. In the case of artifacts, such capabilities must be built in through either wired connections or software. The problem addressed here is to define a neural basis for such behaviors to be possibly learned by bio-inspired artifacts. Toward this end, a thought experiment involving an autonomous vehicle is first simulated as a random search. The stochastic decision tree that drives this behavior is then transformed into a plastic neuronal circuit. This leads the vehicle to adopt a deterministic behavior by learning and applying a causality rule just as a conscious human driver would do. From there, a principle of using synchronized multimodal perceptions in association with the Hebb principle of wiring together neuronal cells is induced. This overall framework is implemented as a virtual machine i.e., a concept widely used in software engineering. It is argued that such an interface situated at a meso-scale level between abstracted micro-circuits representing synaptic plasticity, on one hand, and that of the emergence of behaviors, on the other, allows for a strict delineation of successive levels of complexity. More specifically, isolating levels allows for simulating yet unknown processes of cognition independently of their underlying neurological grounding

Crystallized intelligence and openness to experience: Drawing on intellectual-investment theories to predict job performance longitudinally

Various approaches to conceptualizing and measuring intelligence have been utilized throughout history. Despite the plethora of intelligence theories, the field of industrial and organizational (I-O) psychology has been largely dominated by the psychometric tradition of intelligence and Spearman\u27s general factor theory of intelligence (g). Moreover, other approaches to intelligence (e.g., the developmental perspective) have generally been ignored by I-O psychology. This is puzzling given the widespread acceptance among I-O psychologists of intelligence\u27s substantial and increasing importance in the modern workplace. Supported by a vast amount of research, g has often been recognized as the single best predictor of job performance. However, traditional measures of g have reached a plateau in terms of predictive validity for work-related criteria. Although g is not the sole determinate of job performance, failing to incorporate advancements from other fields (e.g., developmental psychology, cognitive psychology) is a potential limitation to continued improvement of job-performance prediction. One modern approach to intelligence that holds promise for improving our prediction of performance in the workplace is known collectively as the intellectual-investment theories, which posit that intellectual development is partially influenced by investment traits (e.g., Openness to Experience) that guide how, where, and when individuals invest their cognitive ability

Brain enhancement through cognitive training: A new insight from brain connectome

Owing to the recent advances in neurotechnology and the progress in understanding of brain cognitive functions, improvements of cognitive performance or acceleration of learning process with brain enhancement systems is not out of our reach anymore, on the contrary, it is a tangible target of contemporary research. Although a variety of approaches have been proposed, we will mainly focus on cognitive training interventions, in which learners repeatedly perform cognitive tasks to improve their cognitive abilities. In this review article, we propose that the learning process during the cognitive training can be facilitated by an assistive system monitoring cognitive workloads using electroencephalography (EEG) biomarkers, and the brain connectome approach can provide additional valuable biomarkers for facilitating leaners' learning processes. For the purpose, we will introduce studies on the cognitive training interventions, EEG biomarkers for cognitive workload, and human brain connectome. As cognitive overload and mental fatigue would reduce or even eliminate gains of cognitive training interventions, a real-time monitoring of cognitive workload can facilitate the learning process by flexibly adjusting difficulty levels of the training task. Moreover, cognitive training interventions should have effects on brain sub-networks, not on a single brain region, and graph theoretical network metrics quantifying topological architecture of the brain network can differentiate with respect to individual cognitive states as well as to different individuals' cognitive abilities, suggesting that the connectome is a valuable approach for tracking the learning progress. Although only a few studies have exploited the connectome approach for studying alterations of the brain network induced by cognitive training interventions so far, we believe that it would be a useful technique for capturing improvements of cognitive function

The Moral Dimensions of Boredom: A call for research

Despite the impressive progress that has been made on both the empirical and conceptual fronts of boredom research, there is one facet of boredom that has received remarkably little attention. This is boredom's relationship to morality. The aim of this article is to explore the moral dimensions of boredom and to argue that boredom is a morally relevant personality trait. The presence of trait boredom hinders our capacity to flourish and in doing so hurts our prospects for a moral life

Approccio modellistico del sistema di controllo motorio nella malattia di parkinson

Parkinson’s disease is a neurodegenerative disorder due to the death of the dopaminergic neurons of the substantia nigra of the basal ganglia. The process that leads to these neural alterations is still unknown. Parkinson’s disease affects most of all the motor sphere, with a wide array of impairment such as bradykinesia, akinesia, tremor, postural instability and singular phenomena such as freezing of gait. Moreover, in the last few years the fact that the degeneration in the basal ganglia circuitry induces not only motor but also cognitive alterations, not necessarily implicating dementia, and that dopamine loss induces also further implications due to dopamine-driven synaptic plasticity got more attention. At the present moment, no neuroprotective treatment is available, and even if dopamine-replacement therapies as well as electrical deep brain stimulation are able to improve the life conditions of the patients, they often present side effects on the long term, and cannot recover the neural loss, which instead continues to advance. In the present thesis both motor and cognitive aspects of Parkinson’s disease and basal ganglia circuitry were investigated, at first focusing on Parkinson’s disease sensory and balance issues by means of a new instrumented method based on inertial sensor to provide further information about postural control and postural strategies used to attain balance, then applying this newly developed approach to assess balance control in mild and severe patients, both ON and OFF levodopa replacement. Given the inability of levodopa to recover balance issues and the new physiological findings than underline the importance in Parkinson’s disease of non-dopaminergic neurotransmitters, it was therefore developed an original computational model focusing on acetylcholine, the most promising neurotransmitter according to physiology, and its role in synaptic plasticity. The rationale of this thesis is that a multidisciplinary approach could gain insight into Parkinson’s disease features still unresolved