Motor imagery helps updating internal models during microgravity exposure

Skilled movements result from a mixture of feedforward and feedback mechanisms conceptualized by internal models. These mechanisms subserve both motor execution and motor imagery. Current research suggests that imagery allows updating feedforward mechanisms, leading to better performance in familiar contexts. Does this still hold in radically new contexts? Here, we test this ability by asking participants to imagine swinging arm movements around shoulder in normal gravity condition and in microgravity in which studies showed that movements slow down. We timed several cycles of actual and imagined arm pendular movements in three groups of subjects during parabolic flight campaign. The first, control, group remained on the ground. The second group was exposed to microgravity but did not imagine movements inflight. The third group was exposed to microgravity and imagined movements inflight. All groups performed and imagined the movements before and after the flight. We predicted that a mere exposure to microgravity would induce changes in imagined movement duration. We found this held true for the group who imagined the movements, suggesting an update of internal representations of gravity. However, we did not find a similar effect in the group exposed to microgravity despite the fact participants lived the same gravitational variations as the first group. Overall, these results suggest that motor imagery contributes to update internal representations of movement in unfamiliar environments, while a mere exposure proved to be insufficient.

Gated recurrence enables simple and accurate sequence prediction in stochastic, changing, and structured environments

From decision making to perception to language, predicting what is coming next is crucial. It is also challenging in stochastic, changing, and structured environments; yet the brain makes accurate predictions in many situations. What computational architecture could enable this feat? Bayesian inference makes optimal predictions but is prohibitively difficult to compute. Here, we show that a specific recurrent neural network architecture enables simple and accurate solutions in several environments. This architecture relies on three mechanisms: gating, lateral connections, and recurrent weight training. Like the optimal solution and the human brain, such networks develop internal representations of their changing environment (including estimates of the environment's latent variables and the precision of these estimates), leverage multiple levels of latent structure, and adapt their effective learning rate to changes without changing their connection weights. Being ubiquitous in the brain, gated recurrence could therefore serve as a generic building block to predict in real-life environments.

A Framework for Evolution and Consciousness: Panpsychism Without Tears?

Giving an account of the relation between evolution and consciousness is painted as posing a dilemma between panpsychism, with minimal consciousness in every grain of matter, and radical emergence, with consciousness appearing as from nowhere in living structures. Panpsychism has been seen as suffering from a combination problem and radical emergence as unjustified in physics. The underpinning of physics now lies in field theory, which may provide a way out on both sides. Only, and always, in a field theory account do influences at different points in space-time combine in the same indivisible event. Radical emergence is also inherent to field theory. Moreover, by providing rich patterns of influence involving both discrete identities and quantitative values, field theory might provide a basis for sensed propositional meaning with subjects and predicates. Ordered condensed matter within living tissue may support unusual emergent dynamic units uniquely suited to building representations of the world with sensed meaning. The evolution of consciousness may then be seen as a tractable biological problem centred on increasingly sophisticated ways for external world dynamics to be mirrored by internal representations with semantic content, based in field relations within condensed matter with genetically encoded complex order.

Slow update of internal representations impedes synchronization in autism

Autism is a neurodevelopmental disorder characterized by impaired social skills, motor and perceptual atypicalities. These difficulties were explained within the Bayesian framework as either reflecting oversensitivity to prediction errors or – just the opposite – slow updating of such errors. To test these opposing theories, we administer paced finger-tapping, a synchronization task that requires use of recent sensory information for fast error-correction. We use computational modelling to disentangle the contributions of error-correction from that of noise in keeping temporal intervals, and in executing motor responses. To assess the specificity of tapping characteristics to autism, we compare performance to both neurotypical individuals and individuals with dyslexia. Only the autism group shows poor sensorimotor synchronization. Trial-by-trial modelling reveals typical noise levels in interval representations and motor responses. However, rate of error correction is reduced in autism, impeding synchronization ability. These results provide evidence for slow updating of internal representations in autism.

Evaluating benefits, costs, and social value as predictors of gratitude

Although much is known about cooperation, the internal decision rules that regulate motivations to initiate and maintain cooperative relationships have not been thoroughly explored. Here, we focus on how acts of benefit delivery and perceptions of social value inform gratitude, an emotion that promotes cooperation. We evaluate alternate information-processing models to determine which inputs and internal representations best account for the intensity with which people report experiencing gratitude. Across two experiments (Ns = 257; 208), we test ten models that consider multiple variables: the magnitude of benefits conferred upon beneficiaries, the magnitude of costs incurred by benefactors, beneficiaries’ perception of how much benefactors value their welfare, and beneficiaries’ value for the welfare of their benefactors. Across both studies, only beneficiaries’ change in social valuation for their benefactors consistently predicted gratitude. Results point to future research and contribute to the growing literature linking cooperation, social emotions, and social valuation.

Making DL-Lite Planning Practical

Planning in the presence of background ontologies is a topic of long-standing interest in AI. It combines the problems of (1) belief update complexity and (2) state-space combinatorics. DL-Lite offers an attractive solution to (1), with belief updates possible at the ABox level. Indeed, it has been shown that DL-Lite planning can be compiled into the commonly used planning language PDDL. Yet that compilation was previously found to be infeasible for off-the-shelf planning systems. Here we analyze the reasons for this problem and find that the bottleneck lies in the planner pre-processes, in particular in the naïve DNF transformations used to compile the PDDL input into the planners' internal representations. Consequently, we design a PDDL pre-compiler realizing a polynomial DNF transformation. We leverage a particular PDDL language feature ("derived predicates") to avoid the need for excessive control structure. Our pre-compiler turns out to be quite effective: the previous bottleneck disappears, and experiments on a broad range of benchmarks demonstrate the first practical technology for DL-Lite planning.

Contrastive Feature Typologies of Arabic Consonant Reflexes

Attempts to classify spoken Arabic dialects based on distinct reflexes of consonant phonemes are known to employ a mixture of parameters, which often conflate linguistic and non-linguistic facts. This article advances an alternative, theory-informed perspective of segmental typology, one that takes phonological properties as the object of investigation. Under this approach, various classificatory systems are legitimate; and I utilize a typological scheme within the framework of feature geometry. A minimalist model designed to account for segment-internal representations produces neat typologies of the Arabic consonants that vary across dialects, namely qāf,ǧīm,kāf, ḍād, the interdentals, the rhotic, and the pharyngeals. Cognates for each of these are analyzed in a typology based on a few monovalent contrastive features. A key benefit of the proposed typologies is that the featural compositions of the various cognates give grounds for their behavior, in terms of contrasts and phonological activity, and potentially in diachronic processes as well. At a more general level, property-based typology is a promising line of research that helps us understand and categorize purely linguistic facts across languages or language varieties.

Indirect causal influence of a single bot on opinion dynamics through a simple recommendation algorithm

The ability of social and political bots to influence public opinion is often difficult to estimate. Recent studies found that hyper-partisan accounts often directly interact with already highly polarised users on Twitter and are unlikely to influence the general population's average opinion. In this study, we suggest that social bots, trolls and zealots may affect people’s views not just via a direct interaction (e.g. retweets, at-mentions and likes) and via indirect causal pathways through infiltrating platforms’ content recommendation systems. Using a simple agent-based opinion-dynamics simulation, we isolate the effect of a single bot – representing only 1% of the population – on the average opinion of Bayesian agents when we remove all direct connections between the bot and human agents. We compare this experimental condition with an identical baseline condition where such a bot is absent. We used the same random seed in both simulations so that all other conditions remained identical. Results show that, even in the absence of direct connections, the presence of the bot is sufficient to shift the average population opinion. Furthermore, we observe that the presence of the bot significantly affects the opinion of almost all agents in the population. Overall, these findings indicate that social bots and hyperpartisan accounts can influence average population opinions by changing platforms’ recommendation engines’ internal representations.

Flexible control of behavioral variability mediated by an internal representation of head direction

Internal representations are thought to support the generation of flexible, long-timescale behavioral patterns in both animals and artificial agents. Here, we present a novel conceptual framework for how Drosophila use their internal representation of head direction to maintain preferred headings in their surroundings, and how they learn to modify these preferences in the presence of selective thermal reinforcement. To develop the framework, we analyzed flies' behavior in a classical operant visual learning paradigm and found that they use stochastically generated fixations and directed turns to express their heading preferences. Symmetries in the visual scene used in the paradigm allowed us to expose how flies' probabilistic behavior in this setting is tethered to their head direction representation. We describe how flies' ability to quickly adapt their behavior to the rules of their environment may rest on a behavioral policy whose parameters are flexible but whose form is genetically encoded in the structure of their circuits. Many of the mechanisms we outline may also be relevant for rapidly adaptive behavior driven by internal representations in other animals, including mammals.