Cerebellum Transcriptome of Mice Bred for High Voluntary Activity Offers Insights into Locomotor Control and Reward-Dependent Behaviors.

The role of the cerebellum in motivation and addictive behaviors is less understood than that in control and coordination of movements. High running can be a self-rewarding behavior exhibiting addictive properties. Changes in the cerebellum transcriptional networks of mice from a line selectively bred for High voluntary running (H) were profiled relative to an unselected Control (C) line. The environmental modulation of these changes was assessed both in activity environments corresponding to 7 days of Free (F) access to running wheel and to Blocked (B) access on day 7. Overall, 457 genes exhibited a significant (FDR-adjusted P-value < 0.05) genotype-by-environment interaction effect, indicating that activity genotype differences in gene expression depend on environmental access to running. Among these genes, network analysis highlighted 6 genes (Nrgn, Drd2, Rxrg, Gda, Adora2a, and Rab40b) connected by their products that displayed opposite expression patterns in the activity genotype contrast within the B and F environments. The comparison of network expression topologies suggests that selection for high voluntary running is linked to a predominant dysregulation of hub genes in the F environment that enables running whereas a dysregulation of ancillary genes is favored in the B environment that blocks running. Genes associated with locomotor regulation, signaling pathways, reward-processing, goal-focused, and reward-dependent behaviors exhibited significant genotype-by-environment interaction (e.g. Pak6, Adora2a, Drd2, and Arhgap8). Neuropeptide genes including Adcyap1, Cck, Sst, Vgf, Npy, Nts, Penk, and Tac2 and related receptor genes also exhibited significant genotype-by-environment interaction. The majority of the 183 differentially expressed genes between activity genotypes (e.g. Drd1) were under-expressed in C relative to H genotypes and were also under-expressed in B relative to F environments. Our findings indicate that the high voluntary running mouse line studied is a helpful model for understanding the molecular mechanisms in the cerebellum that influence locomotor control and reward-dependent behaviors

Automatic mental processes, automatic actions and behaviours in game transfer phenomena: an empirical self-report study using online forum data

Previous studies have demonstrated that the playing of videogames can have both intended and unintended effects. The purpose of this study was to investigate the influence of videogames on players’ mental processes and behaviours in day-to-day settings. A total of 1,023 self-reports from 762 gamers collected from online videogame forums were classified, quantified, described and explained. The data include automatic thoughts, sensations and impulses, automatic mental replays of the game in real life, and voluntary/involuntary behaviours with videogame content. Many gamers reported that they had responded – at least sometimes – to real life stimuli as if they were still playing videogames. This included overreactions, avoidances, and involuntary movements of limbs. These experiences lasted relatively short periods of time but in a minority of players were recurrent. The gamers' experiences appeared to be enhanced by virtual embodiment, repetitive manipulation of game controls, and their gaming habits. However, similar phenomena may also occur when doing other non-gaming activities. The implications of these game transfer experiences are discussed

Practopoiesis: Or how life fosters a mind

The mind is a biological phenomenon. Thus, biological principles of organization should also be the principles underlying mental operations. Practopoiesis states that the key for achieving intelligence through adaptation is an arrangement in which mechanisms laying a lower level of organization, by their operations and interaction with the environment, enable creation of mechanisms lying at a higher level of organization. When such an organizational advance of a system occurs, it is called a traverse. A case of traverse is when plasticity mechanisms (at a lower level of organization), by their operations, create a neural network anatomy (at a higher level of organization). Another case is the actual production of behavior by that network, whereby the mechanisms of neuronal activity operate to create motor actions. Practopoietic theory explains why the adaptability of a system increases with each increase in the number of traverses. With a larger number of traverses, a system can be relatively small and yet, produce a higher degree of adaptive/intelligent behavior than a system with a lower number of traverses. The present analyses indicate that the two well-known traverses-neural plasticity and neural activity-are not sufficient to explain human mental capabilities. At least one additional traverse is needed, which is named anapoiesis for its contribution in reconstructing knowledge e.g., from long-term memory into working memory. The conclusions bear implications for brain theory, the mind-body explanatory gap, and developments of artificial intelligence technologies.Comment: Revised version in response to reviewer comment

PRESENCE: A human-inspired architecture for speech-based human-machine interaction

Recent years have seen steady improvements in the quality and performance of speech-based human-machine interaction driven by a significant convergence in the methods and techniques employed. However, the quantity of training data required to improve state-of-the-art systems seems to be growing exponentially and performance appears to be asymptotic to a level that may be inadequate for many real-world applications. This suggests that there may be a fundamental flaw in the underlying architecture of contemporary systems, as well as a failure to capitalize on the combinatorial properties of human spoken language. This paper addresses these issues and presents a novel architecture for speech-based human-machine interaction inspired by recent findings in the neurobiology of living systems. Called PRESENCE-"PREdictive SENsorimotor Control and Emulation" - this new architecture blurs the distinction between the core components of a traditional spoken language dialogue system and instead focuses on a recursive hierarchical feedback control structure. Cooperative and communicative behavior emerges as a by-product of an architecture that is founded on a model of interaction in which the system has in mind the needs and intentions of a user and a user has in mind the needs and intentions of the system

A new perspective for the training assessment: Machine learning-based neurometric for augmented user's evaluation

Inappropriate training assessment might have either high social costs and economic impacts, especially in high risks categories, such as Pilots, Air Traffic Controllers, or Surgeons. One of the current limitations of the standard training assessment procedures is the lack of information about the amount of cognitive resources requested by the user for the correct execution of the proposed task. In fact, even if the task is accomplished achieving the maximum performance, by the standard training assessment methods, it would not be possible to gather and evaluate information about cognitive resources available for dealing with unexpected events or emergency conditions. Therefore, a metric based on the brain activity (neurometric) able to provide the Instructor such a kind of information should be very important. As a first step in this direction, the Electroencephalogram (EEG) and the performance of 10 participants were collected along a training period of 3 weeks, while learning the execution of a new task. Specific indexes have been estimated from the behavioral and EEG signal to objectively assess the users' training progress. Furthermore, we proposed a neurometric based on a machine learning algorithm to quantify the user's training level within each session by considering the level of task execution, and both the behavioral and cognitive stabilities between consecutive sessions. The results demonstrated that the proposed methodology and neurometric could quantify and track the users' progresses, and provide the Instructor information for a more objective evaluation and better tailoring of training programs. © 2017 Borghini, Aricò, Di Flumeri, Sciaraffa, Colosimo, Herrero, Bezerianos, Thakor and Babiloni

Challenging the Computational Metaphor: Implications for How We Think

This paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor--a sequence of steps--with the notion of a community of interacting entities, and examines the ramifications of such a shift on these various ways in which we think

Modelling dynamic decision making with the ACT-R cognitive architecture

This paper describes a model of dynamic decision making in the Dynamic Stocks and Flows (DSF) task, developed using the ACT-R cognitive architecture. This task is a simple simulation of a water tank in which the water level must be kept constant whilst the inflow and outflow changes at varying rates. The basic functions of the model are based around three steps. Firstly, the model predicts the water level in the next cycle by adding the current water level to the predicted net inflow of water. Secondly, based on this projection, the net outflow of the water is adjusted to bring the water level back to the target. Thirdly, the predicted net inflow of water is adjusted to improve its accuracy in the future. If the prediction has overestimated net inflow then it is reduced, if it has underestimated net inflow it is increased. The model was entered into a model comparison competition-the Dynamic Stocks and Flows Challenge-to model human performance on four conditions of the DSF task and then subject the model to testing on five unseen transfer conditions. The model reproduced the main features of the development data reasonably well but did not reproduce human performance well under the transfer conditions. This suggests that the principles underlying human performance across the different conditions differ considerably despite their apparent similarity. Further lessons for the future development of our model and model comparison challenges are considered

Bestial boredom: a biological perspective on animal boredom and suggestions for its scientific investigation

Boredom is likely to have adaptive value in motivating exploration and learning, and many animals may possess the basic neurological mechanisms to support it. Chronic inescapable boredom can be extremely aversive, and understimulation can harm neural, cognitive and behavioural flexibility. Wild and domesticated animals are at particular risk in captivity, which is often spatially and temporally monotonous. Yet biological research into boredom has barely begun, despite having important implications for animal welfare, the evolution of motivation and cognition, and for human dysfunction at individual and societal levels. Here I aim to facilitate hypotheses about how monotony affects behaviour and physiology, so that boredom can be objectively studied by ethologists and other scientists. I cover valence (pleasantness) and arousal (wakefulness) qualities of boredom, because both can be measured, and I suggest boredom includes suboptimal arousal and aversion to monotony. Because the suboptimal arousal during boredom is aversive, individuals will resist low arousal. Thus, behavioural indicators of boredom will, seemingly paradoxically, include signs of increasing drowsiness, alongside bouts of restlessness, avoidance and sensation-seeking behaviour. Valence and arousal are not, however, sufficient to fully describe boredom. For example, human boredom is further characterized by a perception that time ‘drags’, and this effect of monotony on time perception can too be behaviourally assayed in animals. Sleep disruption and some abnormal behaviour may also be caused by boredom. Ethological research into this emotional phenomenon will deepen understanding of its causes, development, function and evolution, and will enable evidence-based interventions to mitigate human and animal boredom