Shared population-level dynamics in monkey premotor cortex during solo action, joint action and action observation.

Studies of neural population dynamics of cell activity from monkey motor areas during reaching show that it mostly represents the generation and timing of motor behavior. We compared neural dynamics in dorsal premotor cortex (PMd) during the performance of a visuomotor task executed individually or cooperatively and during an observation task. In the visuomotor conditions, monkeys applied isometric forces on a joystick to guide a visual cursor in different directions, either alone or jointly with a conspecific. In the observation condition, they observed the cursor's motion guided by the partner. We found that in PMd neural dynamics were widely shared across action execution and observation, with cursor motion directions more accurately discriminated than task types. This suggests that PMd encodes spatial aspects irrespective of specific behavioral demands. Furthermore, our results suggest that largest components of premotor population dynamics, which have previously been suggested to reflect a transformation from planning to movement execution, may rather reflect higher cognitive-motor processes, such as the covert representation of actions and goals shared across tasks that require movement and those that do not.

Moral decisions in the age of COVID-19: Your choices really matter.

The moral decisions we make during this period, such as deciding whether to comply with quarantine rules, have unprecedented societal effects. We simulate the "escape from Milan" that occurred on March 7th-8th 2020, when many travelers moved from a high-risk zone (Milan) to southern regions of Italy (Campania and Lazio) immediately after an imminent lockdown was announced. Our simulations show that fewer than 50 active cases might have caused the sudden spread of the virus observed afterwards in these regions. The surprising influence of the actions of few individuals on societal dynamics challenges our cognitive expectations - as in normal conditions, collective dynamics are rather robust to the decisions of few "cheaters". This situation therefore requires novel educational strategies that increase our awareness and understanding of the unprecedented effects of our individual moral decisions.

Hysteresis Modeling in Iron-Dominated Magnets Based on a Multi-Layered NARX Neural Network Approach.

A full-fledged neural network modeling, based on a Multi-layered Nonlinear Autoregressive Exogenous Neural Network (NARX) architecture, is proposed for quasi-static and dynamic hysteresis loops, one of the most challenging topics for computational magnetism. This modeling approach overcomes drawbacks in attaining better than percent-level accuracy of classical and recent approaches for accelerator magnets, that combine hybridization of standard hysteretic models and neural network architectures. By means of an incremental procedure, different Deep Neural Network Architectures are selected, fine-tuned and tested in order to predict magnetic hysteresis in the context of electromagnets. Tests and results show that the proposed NARX architecture best fits the measured magnetic field behavior of a reference quadrupole at CERN. In particular, the proposed modeling framework leads to a percent error below 0.02% for the magnetic field prediction, thus outperforming state of the art approaches and paving a very promising way for future real time applications.

A survey on modern trainable activation functions.

In neural networks literature, there is a strong interest in identifying and defining activation functions which can improve neural network performance. In recent years there has been a renovated interest in the scientific community in investigating activation functions which can be trained during the learning process, usually referred to as trainable, learnable or adaptable activation functions. They appear to lead to better network performance. Diverse and heterogeneous models of trainable activation function have been proposed in the literature. In this paper, we present a survey of these models. Starting from a discussion on the use of the term "activation function" in literature, we propose a taxonomy of trainable activation functions, highlight common and distinctive proprieties of recent and past models, and discuss main advantages and limitations of this type of approach. We show that many of the proposed approaches are equivalent to adding neuron layers which use fixed (non-trainable) activation functions and some simple local rule that constrains the corresponding weight layers.

Hippocampal place cells encode global location but not connectivity in a complex space.

Flexible navigation relies on a cognitive map of space, thought to be implemented by hippocampal place cells: neurons that exhibit location-specific firing. In connected environments, optimal navigation requires keeping track of one's location and of the available connections between subspaces. We examined whether the dorsal CA1 place cells of rats encode environmental connectivity in four geometrically identical boxes arranged in a square. Rats moved between boxes by pushing saloon-type doors that could be locked in one or both directions. Although rats demonstrated knowledge of environmental connectivity, their place cells did not respond to connectivity changes, nor did they represent doorways differently from other locations. Place cells coded location in a global reference frame, with a different map for each box and minimal repetitive fields despite the repetitive geometry. These results suggest that CA1 place cells provide a spatial map that does not explicitly include connectivity.

A framework to identify structured behavioral patterns within rodent spatial trajectories.

Animal behavior is highly structured. Yet, structured behavioral patterns-or "statistical ethograms"-are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.g., maze) navigation, in terms of movement building blocks or motor primitives. The hypothesis that we pursue is that rodent behavior is characterized by a small number of motor primitives, which are combined over time to produce open-ended movements. We assume motor primitives to be organized in terms of two sparsity principles: each movement is controlled using a limited subset of motor primitives (sparse superposition) and each primitive is active only for time-limited, time-contiguous portions of movements (sparse activity). We formalize this hypothesis using a sparse dictionary learning method, which we use to extract motor primitives from rodent position and velocity data collected during spatial navigation, and successively to reconstruct past trajectories and predict novel ones. Three main results validate our approach. First, rodent behavioral trajectories are robustly reconstructed from incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, or single sparsity. Second, the motor primitives extracted during one experimental session generalize and afford the accurate reconstruction of rodent behavior across successive experimental sessions in the same or in modified mazes. Third, in our approach the number of motor primitives associated with each maze correlates with independent measures of maze complexity, hence showing that our formalism is sensitive to essential aspects of task structure. The framework introduced here can be used by behavioral scientists and neuroscientists as an aid for behavioral and neural data analysis. Indeed, the extracted motor primitives enable the quantitative characterization of the complexity and similarity between different mazes and behavioral patterns across multiple trials (i.e., habit formation). We provide example uses of this computational framework, showing how it can be used to identify behavioural effects of maze complexity, analyze stereotyped behavior, classify behavioral choices and predict place and grid cell displacement in novel environments.

Channel Selection for Optimal EEG Measurement in Motor Imagery-Based Brain-Computer Interfaces.

A method for selecting electroencephalographic (EEG) signals in motor imagery-based brain-computer interfaces (MI-BCI) is proposed for enhancing the online interoperability and portability of BCI systems, as well as user comfort. The attempt is also to reduce variability and noise of MI-BCI, which could be affected by a large number of EEG channels. The relation between selected channels and MI-BCI performance is therefore analyzed. The proposed method is able to select acquisition channels common to all subjects, while achieving a performance compatible with the use of all the channels. Results are reported with reference to a standard benchmark dataset, the BCI competition IV dataset 2a. They prove that a performance compatible with the best state-of-the-art approaches can be achieved, while adopting a significantly smaller number of channels, both in two and in four tasks classification. In particular, classification accuracy is about 77-83% in binary classification with down to 6 EEG channels, and above 60% for the four-classes case when 10 channels are employed. This gives a contribution in optimizing the EEG measurement while developing non-invasive and wearable MI-based brain-computer interfaces.

Social epistemic actions.

We consider the ways humans engage in social epistemic actions, to guide each other's attention, prediction, and learning processes towards salient information, at the timescale of online social interaction and joint action. This parallels the active guidance of other's attention, prediction, and learning processes at the longer timescale of niche construction and cultural practices, as discussed in the target article.

Author Correction: Differential neural dynamics underlying pragmatic and semantic affordance processing in macaque ventral premotor cortex.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Differential neural dynamics underling pragmatic and semantic affordance processing in macaque ventral premotor cortex.

Premotor neurons play a fundamental role in transforming physical properties of observed objects, such as size and shape, into motor plans for grasping them, hence contributing to "pragmatic" affordance processing. Premotor neurons can also contribute to "semantic" affordance processing, as they can discharge differently even to pragmatically identical objects depending on their behavioural relevance for the observer (i.e. edible or inedible objects). Here, we compared the response of monkey ventral premotor area F5 neurons tested during pragmatic (PT) or semantic (ST) visuomotor tasks. Object presentation responses in ST showed shorter latency and lower object selectivity than in PT. Furthermore, we found a difference between a transient representation of semantic affordances and a sustained representation of pragmatic affordances at both the single neuron and population level. Indeed, responses in ST returned to baseline within 0.5 s whereas in PT they showed the typical sustained visual-to-motor activity during Go trials. In contrast, during No-go trials, the time course of pragmatic and semantic information processing was similar. These findings suggest that premotor cortex generates different dynamics depending on pragmatic and semantic information provided by the context in which the to-be-grasped object is presented.

Tracking the Time Course of Bayesian Inference With Event-Related Potentials:A Study Using the Central Cue Posner Paradigm.

In this study, we asked whether the event-related potentials associated to cue and target stimuli of a Central Cue Posner Paradigm (CCPP) may encode key parameters of Bayesian inference - prior expectation and surprise - on a trial-by-trial basis. Thirty-two EEG channel were recorded in a sample of 19 young adult subjects while performing a CCPP, in which a cue indicated (validly or invalidly) the position of an incoming auditory target. Three different types of blocks with validities of 50%, 64%, and 88%, respectively, were presented. Estimates of prior expectation and surprise were obtained on a trial-by-trial basis from participants' responses, using a computational model implementing Bayesian learning. These two values were correlated on a trial-by-trial basis with the EEG values in all the electrodes and time bins. Therefore, a Spearman correlation metrics of the relationship between Bayesian parameters and the EEG was obtained. We report that the surprise parameter was able to classify the different validity blocks. Furthermore, the prior expectation parameter showed a significant correlation with the EEG in the cue-target period, in which the Contingent Negative Variation develops. Finally, in the post-target period the surprise parameter showed a significant correlation in the latencies and electrodes in which different event-related potentials are induced. Our results suggest that Bayesian parameters are coded in the EEG signals; and namely, the CNV would be related to prior expectation, while the post-target components P2a, P2, P3a, P3b, and SW would be related to surprise. This study thus provides novel support to the idea that human electrophysiological neural activity may implement a (Bayesian) predictive processing scheme.

The body talks: Sensorimotor communication and its brain and kinematic signatures.

Human communication is a traditional topic of research in many disciplines such as psychology, linguistics and philosophy, all of which mainly focused on language, gestures and deictics. However, these do not constitute the sole channels of communication, especially during online social interaction, where instead an additional critical role may be played by sensorimotor communication (SMC). SMC refers here to (often subtle) communicative signals embedded within pragmatic actions - for example, a soccer player carving his body movements in ways that inform a partner about his intention, or to feint an adversary; or the many ways we offer a glass of wine, rudely or politely. SMC is a natural form of communication that does not require any prior convention or any specific code. It amounts to the continuous and flexible exchange of bodily signals, with or without awareness, to enhance coordination success; and it is versatile, as sensorimotor signals can be embedded within every action. SMC is at the center of recent interest in neuroscience, cognitive psychology, human-robot interaction and experimental semiotics; yet, we still lack a coherent and comprehensive synthesis to account for its multifaceted nature. Some fundamental questions remain open, such as which interactive scenarios promote or do not promote SMC, what aspects of social interaction can be properly called communicative and which ones entail a mere transfer of information, and how many forms of SMC exist and what we know (or still don't know) about them from an empirical viewpoint. The present work brings together all these separate strands of research within a unified overarching, multidisciplinary framework for SMC, which combines evidence from kinematic studies of human-human interaction and computational modeling of social exchanges.

Evidence for sparse synergies in grasping actions.

Converging evidence shows that hand-actions are controlled at the level of synergies and not single muscles. One intriguing aspect of synergy-based action-representation is that it may be intrinsically sparse and the same synergies can be shared across several distinct types of hand-actions. Here, adopting a normative angle, we consider three hypotheses for hand-action optimal-control: sparse-combination hypothesis (SC) - sparsity in the mapping between synergies and actions - i.e., actions implemented using a sparse combination of synergies; sparse-elements hypothesis (SE) - sparsity in synergy representation - i.e., the mapping between degrees-of-freedom (DoF) and synergies is sparse; double-sparsity hypothesis (DS) - a novel view combining both SC and SE - i.e., both the mapping between DoF and synergies and between synergies and actions are sparse, each action implementing a sparse combination of synergies (as in SC), each using a limited set of DoFs (as in SE). We evaluate these hypotheses using hand kinematic data from six human subjects performing nine different types of reach-to-grasp actions. Our results support DS, suggesting that the best action representation is based on a relatively large set of synergies, each involving a reduced number of degrees-of-freedom, and that distinct sets of synergies may be involved in distinct tasks.

You cannot speak and listen at the same time: a probabilistic model of turn-taking.

Turn-taking is a preverbal skill whose mastering constitutes an important precondition for many social interactions and joint actions. However, the cognitive mechanisms supporting turn-taking abilities are still poorly understood. Here, we propose a computational analysis of turn-taking in terms of two general mechanisms supporting joint actions: action prediction (e.g., recognizing the interlocutor's message and predicting the end of turn) and signaling (e.g., modifying one's own speech to make it more predictable and discriminable). We test the hypothesis that in a simulated conversational scenario dyads using these two mechanisms can recognize the utterances of their co-actors faster, which in turn permits them to give and take turns more efficiently. Furthermore, we discuss how turn-taking dynamics depend on the fact that agents cannot simultaneously use their internal models for both action (or messages) prediction and production, as these have different requirements-or, in other words, they cannot speak and listen at the same time with the same level of accuracy. Our results provide a computational-level characterization of turn-taking in terms of cognitive mechanisms of action prediction and signaling that are shared across various interaction and joint action domains.

Sensorimotor Coarticulation in the Execution and Recognition of Intentional Actions.

Humans excel at recognizing (or inferring) another's distal intentions, and recent experiments suggest that this may be possible using only subtle kinematic cues elicited during early phases of movement. Still, the cognitive and computational mechanisms underlying the recognition of intentional (sequential) actions are incompletely known and it is unclear whether kinematic cues alone are sufficient for this task, or if it instead requires additional mechanisms (e.g., prior information) that may be more difficult to fully characterize in empirical studies. Here we present a computationally-guided analysis of the execution and recognition of intentional actions that is rooted in theories of motor control and the coarticulation of sequential actions. In our simulations, when a performer agent coarticulates two successive actions in an action sequence (e.g., "reach-to-grasp" a bottle and "grasp-to-pour"), he automatically produces kinematic cues that an observer agent can reliably use to recognize the performer's intention early on, during the execution of the first part of the sequence. This analysis lends computational-level support for the idea that kinematic cues may be sufficiently informative for early intention recognition. Furthermore, it suggests that the social benefits of coarticulation may be a byproduct of a fundamental imperative to optimize sequential actions. Finally, we discuss possible ways a performer agent may combine automatic (coarticulation) and strategic (signaling) ways to facilitate, or hinder, an observer's action recognition processes.

Action perception as hypothesis testing.

We present a novel computational model that describes action perception as an active inferential process that combines motor prediction (the reuse of our own motor system to predict perceived movements) and hypothesis testing (the use of eye movements to disambiguate amongst hypotheses). The system uses a generative model of how (arm and hand) actions are performed to generate hypothesis-specific visual predictions, and directs saccades to the most informative places of the visual scene to test these predictions - and underlying hypotheses. We test the model using eye movement data from a human action observation study. In both the human study and our model, saccades are proactive whenever context affords accurate action prediction; but uncertainty induces a more reactive gaze strategy, via tracking the observed movements. Our model offers a novel perspective on action observation that highlights its active nature based on prediction dynamics and hypothesis testing.

Avoiding Accidents at the Champagne Reception.

Using a lifting and balancing task, we contrasted two alternative views of planning joint actions: one postulating that joint action involves distinct predictions for self and other, the other postulating that joint action involves coordinated plans between the coactors and reuse of bimanual models. We compared compensatory movements required to keep a tray balanced when 2 participants lifted glasses from each other's trays at the same time (simultaneous joint action) and when they took turns lifting (sequential joint action). Compared with sequential joint action, simultaneous joint action made it easier to keep the tray balanced. Thus, in keeping with the view that bimanual models are reused for joint action, predicting the timing of their own lifting action helped participants compensate for another person's lifting action. These results raise the possibility that simultaneous joint actions do not necessarily require distinguishing between one's own and the coactor's contributions to the action plan and may afford an agent-neutral stance.

Problem Solving as Probabilistic Inference with Subgoaling: Explaining Human Successes and Pitfalls in the Tower of Hanoi.

How do humans and other animals face novel problems for which predefined solutions are not available? Human problem solving links to flexible reasoning and inference rather than to slow trial-and-error learning. It has received considerable attention since the early days of cognitive science, giving rise to well known cognitive architectures such as SOAR and ACT-R, but its computational and brain mechanisms remain incompletely known. Furthermore, it is still unclear whether problem solving is a "specialized" domain or module of cognition, in the sense that it requires computations that are fundamentally different from those supporting perception and action systems. Here we advance a novel view of human problem solving as probabilistic inference with subgoaling. In this perspective, key insights from cognitive architectures are retained such as the importance of using subgoals to split problems into subproblems. However, here the underlying computations use probabilistic inference methods analogous to those that are increasingly popular in the study of perception and action systems. To test our model we focus on the widely used Tower of Hanoi (ToH) task, and show that our proposed method can reproduce characteristic idiosyncrasies of human problem solvers: their sensitivity to the "community structure" of the ToH and their difficulties in executing so-called "counterintuitive" movements. Our analysis reveals that subgoals have two key roles in probabilistic inference and problem solving. First, prior beliefs on (likely) useful subgoals carve the problem space and define an implicit metric for the problem at hand-a metric to which humans are sensitive. Second, subgoals are used as waypoints in the probabilistic problem solving inference and permit to find effective solutions that, when unavailable, lead to problem solving deficits. Our study thus suggests that a probabilistic inference scheme enhanced with subgoals provides a comprehensive framework to study problem solving and its deficits.