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The muscle synergy is a guiding concept in motor control research that relies on the general notion of muscles ‘working together’ towards task performance. However, although the synergy concept has provided valuable insights into motor coordination, muscle interactions have not been fully characterised with respect to task performance. Here, we address this research gap by proposing a novel perspective to the muscle synergy that assigns a specific functional role to each muscle coupling by characterising its task-relevance. Crucially, our novel perspective provides nuance to the muscle synergy concept, demonstrating how muscular interactions can ‘work together’ in different ways: a) irrespective of the task at hand but also b) redundantly or c) complementarily towards common task-goals. To establish this perspective, we leverage information- and network-theory and dimensionality reduction methods to include discrete and continuous task parameters directly during muscle synergy extraction. Specifically, we introduce co-information as a measure of the task relevance of muscle interactions and use it to categorise such interactions as task-irrelevant (present across tasks), redundant (sharing the same task information) or synergistic (representing different aspects of the task). To demonstrate these types of interactions in real data, we apply the framework to large-scale datasets of human movements and extract generalizable and scale-invariant representations consisting of subnetworks of synchronised muscle couplings and distinct temporal patterns. The representations effectively capture the functional interplay between task end-goals and biomechanical affordances and the concurrent processing of functionally similar (redundant) and complementary (synergistic) task information. The proposed framework unifies the capabilities of current approaches in capturing distinct motor features while providing novel insights and research opportunities through a nuanced perspective to the muscle synergy.

The muscle synergy is a guiding concept in motor control research that relies on the general notion of muscles ‘working together’towards task performance. However, although the synergy concept has provided valuable insights into motor coordination, muscle interactions have not been fully characterised with respect to task performance. Here, we address this research gap by proposing a novel perspective to the muscle synergy that assigns a specific functional role to each muscle coupling by characterising its task-relevance. Crucially, our novel perspective provides nuance to the muscle synergy concept, demonstrating how muscular interactions can ‘work together’in different ways: a) irrespective of the task at hand but also b) redundantly or c) complementarily towards common task-goals. To establish this perspective, we leverage information- and network-theory and dimensionality reduction methods to include discrete and continuous task parameters directly during muscle synergy extraction. Specifically, we introduce co-information as a measure of the task relevance of muscle interactions and use it to categorise such interactions as task-irrelevant (present across tasks), redundant (sharing the same task information) or synergistic (representing different aspects of the task). To demonstrate these types of interactions in real data, we apply the framework to large-scale datasets of human movements and extract generalizable and scale-invariant representations consisting of subnetworks of synchronised muscle couplings and distinct temporal patterns. The representations effectively capture the functional interplay between task end-goals and biomechanical affordances and the concurrent processing of functionally similar (redundant) and complementary (synergistic) task information. The proposed framework unifies the capabilities of current approaches in capturing distinct motor features while providing novel insights and research opportunities through a nuanced perspective to the muscle synergy.

The cerebellum is considered a "learning machine" essential for time interval estimation underlying motor coordination and other behaviors. Theoretical work has proposed that the cerebellum's input recipient structure, the granule cell layer (GCL), performs pattern separation of inputs that facilitates learning in Purkinje cells (P-cells). However, the relationship between input reformatting and learning has remained debated, with roles emphasized for pattern separation features from sparsification to decorrelation. We took a novel approach by training a minimalist model of the cerebellar cortex to learn complex time-series data from time-varying inputs, typical during movements. The model robustly produced temporal basis sets from these inputs, and the resultant GCL output supported better learning of temporally complex target functions than mossy fibers alone. Learning was optimized at intermediate threshold levels, supporting relatively dense granule cell activity, yet the key statistical features in GCL population activity that drove learning differed from those seen previously for classification tasks. These findings advance testable hypotheses for mechanisms of temporal basis set formation and predict that moderately dense population activity optimizes learning.

Most perceptual decisions rely on the active acquisition of evidence from the environment involving stimulation from multiple senses. However, our understanding of the neural mechanisms underlying this process is limited. Crucially, it remains elusive how different sensory representations interact in the formation of perceptual decisions. To answer these questions, we used an active sensing paradigm coupled with neuroimaging, multivariate analysis, and computational modeling to probe how the human brain processes multisensory information to make perceptual judgments. Participants of both sexes actively sensed to discriminate two texture stimuli using visual (V) or haptic (H) information or the two sensory cues together (VH). Crucially, information acquisition was under the participants' control, who could choose where to sample information from and for how long on each trial. To understand the neural underpinnings of this process, we first characterized where and when active sensory experience (movement patterns) is encoded in human brain activity (EEG) in the three sensory conditions. Then, to offer a neurocomputational account of active multisensory decision formation, we used these neural representations of active sensing to inform a drift diffusion model of decision-making behavior. This revealed a multisensory enhancement of the neural representation of active sensing, which led to faster and more accurate multisensory decisions. We then dissected the interactions between the V, H, and VH representations using a novel information-theoretic methodology. Ultimately, we identified a synergistic neural interaction between the two unisensory (V, H) representations over contralateral somatosensory and motor locations that predicted multisensory (VH) decision-making performance.SIGNIFICANCE STATEMENTIn real-world settings, perceptual decisions are made during active behaviors, such as crossing the road on a rainy night, and include information from different senses (e.g., car lights, slippery ground). Critically, it remains largely unknown how sensory evidence is combined and translated into perceptual decisions in such active scenarios. Here we address this knowledge gap. First, we show that the simultaneous exploration of information across senses (multi-sensing) enhances the neural encoding of active sensing movements. Second, the neural representation of active sensing modulates the evidence available for decision; and importantly, multi-sensing yields faster evidence accumulation. Finally, we identify a cross-modal interaction in the human brain that correlates with multisensory performance, constituting a putative neural mechanism for forging active multisensory perception.

Do motor patterns of grasp-to-lift movements change as a result of ageing? Previous studies often relied on simple temporal and kinetic variables to unveil differences caused by ageing, yet their neuromuscular origins remain largely unknown. Here we employed a bimanual grasping protocol with younger and older adults and combined measurements of muscle activity with grip and load forces to provide a window into the neuromuscular strategies underlying effective grasping. We introduced a tensor decomposition to identify patterns of muscle activity and grip-load force ratios while also characterising their temporal profiles and relative activation across object weights and participants of different age groups. This approach extracted the motor components underpinning object grasping across participants. We then probed age-induced changes in these components. A classification analysis revealed two motor components that are differentially recruited between the two age groups. Linear regression analyses further showed that advanced age and poorer manual dexterity can be predicted by the coupled activation of forearm and hand muscles which is associated with high levels of grip force. Our findings suggest that ageing may induce stronger muscle couplings in distal aspects of the upper limbs, and a less economic grasping strategy to overcome age-related decline in manual dexterity.

ObjectiveCurrent approaches to muscle synergy extraction rely on linear dimensionality reduction algorithms that make specific assumptions on the underlying signals. However, to capture nonlinear time varying, large-scale but also muscle-specific interactions, a more generalised approach is required.ApproachHere we developed a novel framework for muscle synergy extraction that relaxes model assumptions by using a combination of information- and network theory and dimensionality reduction. We first quantify informational dynamics between muscles, time-samples or muscle-time pairings using a novel mutual information formulation. We then model these pairwise interactions as multiplex networks and identify modules representing the network architecture. We employ this modularity criterion as the input parameter for dimensionality reduction, which verifiably extracts the identified modules, and also to characterise salient structures within each module.Main resultsThis novel framework captures spatial, temporal and spatiotemporal interactions across two benchmark datasets of reaching movements, producing distinct spatial groupings and both tonic and phasic temporal patterns. Readily interpretable muscle synergies spanning multiple spatial and temporal scales were identified, demonstrating significant task dependence, ability to capture trial-to-trial fluctuations and concordance across participants. Furthermore, our framework identifies submodular structures that represent the distributed networks of co-occurring signal interactions across scales.SignificanceThe capabilities of this framework are illustrated through the concomitant continuity with previous research and novelty of the insights gained. Several previous limitations are circumvented including the extraction of functionally meaningful and multiplexed pairwise muscle couplings under relaxed model assumptions. The extracted synergies provide a holistic view of the movement while important details of task performance are readily interpretable. The identified muscle groupings transcend biomechanical constraints and the temporal patterns reveal characteristics of fundamental motor control mechanisms. We conclude that this framework opens new opportunities for muscle synergy research and can constitute a bridge between existing models and recent network-theoretic endeavours.

SUMMARYBCG vaccination induces heterologous protection against respiratory tract infections, and in children improves survival independently of tuberculosis prevention. The phase III ACTIVATE-2 study assessed whether BCG could also protect against COVID19 in the elderly. In this double-blind, randomized trial, elderly Greek patients were randomized (1:1) to receive either BCG revaccination or placebo at hospital discharge, followed by 6 months observation for incidence of COVID19 infection. BCG revaccination resulted in 68% risk reduction for total COVID19 clinical and microbiological diagnoses (OR 0.32, 95% CI 0.13-0.79). Five patients in the placebo group and one in the BCG-vaccinated group had severe COVID19 that necessitated hospitalization. 3 months after BCG vaccination 1.3% of placebo and 4.7% of BCG-vaccinated volunteers had anti-SARS-CoV-2 antibodies. These data argue that BCG revaccination is safe and protects the elderly against COVID19. BCG revaccination may represent a viable preventive measure against COVID19.

Voluntary movement is hypothesized to rely on a limited number of muscle synergies, the recruitment of which translates task goals into effective muscle activity. In this study, we investigated how to analytically characterize the functional role of different types of muscle synergies in task performance. To this end, we recorded a comprehensive dataset of muscle activity during a variety of whole-body pointing movements. We decomposed the electromyographic (EMG) signals using a space-by-time modularity model which encompasses the main types of synergies. We then used a task decoding and information theoretic analysis to probe the role of each synergy by mapping it to specific task features. We found that the temporal and spatial aspects of the movements were encoded by different temporal and spatial muscle synergies, respectively, consistent with the intuition that there should a correspondence between major attributes of movement and major features of synergies. This approach led to the development of a novel computational method for comparing muscle synergies from different participants according to their functional role. This functional similarity analysis yielded a small set of temporal and spatial synergies that describes the main features of whole-body reaching movements.

The signals delivered by different sensory modalities provide us with complementary information about the environment. A key component of interacting with the world is how to direct ones’ sensors so as to extract task-relevant information in order to optimize subsequent perceptual decisions. This process is often referred to as active sensing. Importantly, the processing of multisensory information acquired actively from multiple sensory modalities requires the interaction of multiple brain areas over time. Here we investigated the neural underpinnings of active visual-haptic integration during performance of a two-alternative forced choice (2AFC) reaction time (RT) task. We asked human subjects to discriminate the amplitude of two texture stimuli (a) using only visual (V) information, (b) using only haptic (H) information and (c) combining the two sensory cues (VH), while electroencephalograms (EEG) were recorded. To quantify multivariate interactions between EEG signals and active sensory experience in the three sensory conditions, we employed a novel information-theoretic methodology. This approach provides a principled way to quantify the contribution of each one of the sensory modalities to the perception of the stimulus and assess whether the respective neural representations may interact to form a percept of the stimulus and ultimately drive perceptual decisions. Application of this method to our data identified (a) an EEG component (comprising frontal and occipital electrodes) carrying behavioral information that is common to the two sensory inputs and (b) another EEG component (mainly motor) reflecting a synergistic representational interaction between the two sensory inputs. We suggest that the proposed approach can be used to elucidate the neural mechanisms underlying cross-modal interactions in active multisensory processing and decision-making.

The modular control hypothesis suggests that motor commands are built from precoded modules whose specific combined recruitment can allow the performance of virtually any motor task. Despite considerable experimental support, this hypothesis remains tentative as classical findings of reduced dimensionality in muscle activity may also result from other constraints (biomechanical couplings, data averaging or low dimensionality of motor tasks). Here we assessed the effectiveness of modularity in describing muscle activity in a comprehensive experiment comprising 72 distinct point-to-point whole-body movements during which the activity of 30 muscles was recorded. To identify invariant modules of a temporal and spatial nature, we used a space-by-time decomposition of muscle activity that has been shown to encompass classical modularity models. To examine the decompositions, we focused not only on the amount of variance they explained but also on whether the task performed on each trial could be decoded from the single-trial activations of modules. For the sake of comparison, we confronted these scores to the scores obtained from alternative non-modular descriptions of the muscle data. We found that the space-by-time decomposition was effective in terms of data approximation and task discrimination at comparable reduction of dimensionality. These findings show that few spatial and temporal modules give a compact yet approximate representation of muscle patterns carrying nearly all task-relevant information for a variety of whole-body reaching movements.

Many real-world decisions rely on active sensing, a dynamic process for directing our sensors (e.g. eyes or fingers) across a stimulus to maximize information gain. Though ecologically pervasive, limited work has focused on identifying neural correlates of the active sensing process. In tactile perception, we often make decisions about an object/surface by actively exploring its shape/texture. Here we investigate the neural correlates of active tactile decision-making by simultaneously measuring electroencephalography (EEG) and finger kinematics while subjects interrogated a haptic surface to make perceptual judgments. Since sensorimotor behavior underlies decision formation in active sensing tasks, we hypothesized that the neural correlates of decision-related processes would be detectable by relating active sensing to neural activity. Novel brain-behavior correlation analysis revealed that three distinct EEG components, localizing to right-lateralized occipital cortex (LOC), middle frontal gyrus (MFG), and supplementary motor area (SMA), respectively, were coupled with active sensing as their activity significantly correlated with finger kinematics. To probe the functional role of these components, we fit their single-trial-couplings to decision-making performance using a hierarchical-drift-diffusion-model (HDDM), revealing that the LOC modulated the encoding of the tactile stimulus whereas the MFG predicted the rate of information integration towards a choice. Interestingly, the MFG disappeared from components uncovered from control subjects performing active sensing but not required to make perceptual decisions. By uncovering the neural correlates of distinct stimulus encoding and evidence accumulation processes, this study delineated, for the first time, the functional role of cortical areas in active tactile decision-making.

How to identify the informative dimensions of large-scale neural data is an open research problem. Neural activity carries information across both time (temporal variations in neural responses) and space (differences in the activity of different neurons or brain regions). Here we review a family of analytical methods, termed space-by-time tensor decompositions, which can elucidate how the spatial and temporal dimensions of neural activity interact in order to form robust representations of neural activity in single trials. We present a set of algorithms based on non-negative matrix factorization that implement the space-by-time tensor decomposition and discuss their properties and applicability to different types of neural signals. We then propose a set of measures that can be used to assess the power of tensor decompositions and quantify their effectiveness in capturing neural information. We conclude with a demonstration of the space-by-time decomposition of real neural population spike train data.

Visual categorization is the brain computation that reduces high-dimensional information in the visual environment into a smaller set of meaningful categories. An important problem in visual neuroscience is to identify the visual information that the brain must represent and then use to categorize visual inputs. Here we introduce a new mathematical formalism—termed space-by-time manifold decomposition—that describes this information as a low-dimensional manifold separable in space and time. We use this decomposition to characterize the representations used by observers to categorize the six classic facial expressions of emotion (happy, surprise, fear, disgust, anger, and sad). By means of a Generative Face Grammar, we presented random dynamic facial movements on each experimental trial and used subjective human perception to identify the facial movements that correlate with each emotion category. When the random movements projected onto the categorization manifold region corresponding to one of the emotion categories, observers categorized the stimulus accordingly; otherwise they selected “other.” Using this information, we determined both the Action Unit and temporal components whose linear combinations lead to reliable categorization of each emotion. In a validation experiment, we confirmed the psychological validity of the resulting space-by-time manifold representation. Finally, we demonstrated the importance of temporal sequencing for accurate emotion categorization and identified the temporal dynamics of Action Unit components that cause typical confusions between specific emotions (e.g., fear and surprise) as well as those resolving these confusions.

As a highly social species, humans generate complex facial expressions to communicate a diverse range of emotions. Since Darwin's work, identifying among these complex patterns which are common across cultures and which are culture-specific has remained a central question in psychology, anthropology, philosophy, and more recently machine vision and social robotics. Classic approaches to addressing this question typically tested the cross-cultural recognition of theoretically motivated facial expressions representing 6 emotions, and reported universality. Yet, variable recognition accuracy across cultures suggests a narrower cross-cultural communication supported by sets of simpler expressive patterns embedded in more complex facial expressions. We explore this hypothesis by modeling the facial expressions of over 60 emotions across 2 cultures, and segregating out the latent expressive patterns. Using a multidisciplinary approach, we first map the conceptual organization of a broad spectrum of emotion words by building semantic networks in 2 cultures. For each emotion word in each culture, we then model and validate its corresponding dynamic facial expression, producing over 60 culturally valid facial expression models. We then apply to the pooled models a multivariate data reduction technique, revealing 4 latent and culturally common facial expression patterns that each communicates specific combinations of valence, arousal, and dominance. We then reveal the face movements that accentuate each latent expressive pattern to create complex facial expressions. Our data questions the widely held view that 6 facial expression patterns are universal, instead suggesting 4 latent expressive patterns with direct implications for emotion communication, social psychology, cognitive neuroscience, and social robotics. (PsycINFO Database Record

International audience; Movement generation has been hypothesized to rely on a modular organization of muscle activity. Crucial to this hypothesis is the ability to perform reliably a variety of motor tasks by recruiting a limited set of modules and combining them in a task-dependent manner. Thus far, existing algorithms that extract putative modules of muscle activations, such as Non-negative Matrix Factorization (NMF), identify modular decompositions that maximize the reconstruction of the recorded EMG data. Typically, the functional role of the decompositions, i.e., task accomplishment, is only assessed a posteriori. However, as motor actions are defined in task space, we suggest that motor modules should be computed in task space too. In this study, we propose a new module extraction algorithm, named DsNM3F, that uses task information during the module identification process. DsNM3F extends our previous space-by-time decomposition method (the so-called sNM3F algorithm, which could assess task performance only after having computed modules) to identify modules gauging between two complementary objectives: reconstruction of the original data and reliable discrimination of the performed tasks. We show that DsNM3F recovers the task dependence of module activations more accurately than sNM3F. We also apply it to electromyographic signals recorded during performance of a variety of arm pointing tasks and identify spatial and temporal modules of muscle activity that are highly consistent with previous studies. DsNM3F achieves perfect task categorization without significant loss in data approximation when task information is available and generalizes as well as sNM3F when applied to new data. These findings suggest that the space-by-time decomposition of muscle activity finds robust task-discriminating modular representations of muscle activity and that the insertion of task discrimination objectives is useful for describing the task modulation of module recruitment.

International audience; Modularity in the central nervous system (CNS), i.e., the brain capability to generate a wide repertoire of movements by combining a small number of building blocks ("modules"), is thought to underlie the control of movement. Numerous studies reported evidence for such a modular organization by identifying invariant muscle activation patterns across various tasks. However, previous studies relied on decompositions differing in both the nature and dimensionality of the identified modules. Here, we derive a single framework that encompasses all influential models of muscle activation modularity. We introduce a new model (named space-by-time decomposition) that factorizes muscle activations into concurrent spatial and temporal modules. To infer these modules, we develop an algorithm, referred to as sample-based nonnegative matrix trifactorization (sNM3F). We test the space-by-time decomposition on a comprehensive electromyographic dataset recorded during execution of arm pointing movements and show that it provides a low-dimensional yet accurate, highly flexible and task-relevant representation of muscle patterns. The extracted modules have a wellcharacterized functional meaning and implement an efficient trade-off between replication of the original muscle patterns and task discriminability. Furthermore, they are compatible with the modules extracted from existing models, such as synchronous synergies and temporal primitives, and generalize time-varying synergies. Our results indicate the effectiveness of a simultaneous but separate condensation of spatial and temporal dimensions of muscle patterns. The space-by-time decomposition accommodates a unified view of the hierarchical mapping from task parameters to coordinated muscle activations, which could be employed as a reference framework for studying compositional motor control.

Delis, Ioannis | Berret, Bastien | Pozzo, Thierry | Panzeri, Stefano; International audience; ''Muscle synergies have been hypothesized to be the building blocks used by the central nervous system to generate movement. According to this hypothesis, the accomplishment of various motor tasks relies on the ability of the motor system to recruit a small set of synergies on a single-trial basis and combine them in a task-dependent manner. It is conceivable that this requires a fine tuning of the trial-to-trial relationships between the synergy activations. Here we develop an analytical methodology to address the nature and functional role of trial-to-trial correlations between synergy activations, which is designed to help to better understand how these correlations may contribute to generating appropriate motor behavior. The algorithm we propose first divides correlations between muscle synergies into types (noise correlations, quantifying the trial-to-trial covariations of synergy activations at fixed task, and signal correlations, quantifying the similarity of task tuning of the trial-averaged activation coefficients of different synergies), and then uses single-trial methods (task-decoding and information theory) to quantify their overall effect on the task-discriminating information carried by muscle synergy activations. We apply the method to both synchronous and time-varying synergies and exemplify it on electromyographic data recorded during performance of reaching movements in different directions. Our method reveals the robust presence of information-enhancing patterns of signal and noise correlations among pairs of synchronous synergies, and shows that they enhance by 9-15% (depending on the set of tasks) the task-discriminating information provided by the synergy decompositions. We suggest that the proposed methodology could be useful for assessing whether single-trial activations of one synergy depend on activations of other synergies and quantifying the effect of such dependences on the task-to-task differences in muscle activation patterns.''

Delis, Ioannis | Berret, Bastien | Pozzo, Thierry | Panzeri, Stefano; International audience; ''Muscle synergies, i.e., invariant coordinated activations of groups of muscles, have been proposed as building blocks that the central nervous system (CNS) uses to construct the patterns of muscle activity utilized for executing movements . Several efficient dimensionality reduction algorithms that extract putative synergies from electromyographic (EMG) signals have been developed. Typically, the quality of synergy decompositions is assessed by computing the Variance Accounted For (VAF). Yet, little is known about the extent to which the combination of those synergies en codes task discriminating variations of muscle activity in individual trials. To address this question, here we conceive and develop an ovel computational framework to evaluate muscle synergy decompositions in task space. Unlike previous methods considering the total variance of muscle patterns (VAF based metrics), our approach focuses on variance discriminating execution of different tasks. The procedure is based on single-trial task decoding from muscle synergy activation features. The task decoding based metric evaluates quantitatively the mapping between synergy recruitment and task identification and automatically determines the minimal number of synergies that captures all the task discriminating variability in the synergy activations. In this paper, we first validate the method on plausibly simulated EMG datasets. We then show that it can be applied to different types of muscle synergy decomposition and illustrate its applicability to real data by using it for the analysis of EMG recordings during an arm pointing task. We find that time varying and synchronous synergies with similar number of parameters are equally efficient in task decoding, suggesting that in this experimental paradigm they are equally valid representations of muscle synergies. Overall, these findings stress the effectiveness of the decoding metric in systematically assessing muscle synergy decompositions in task space.''

Visual categorization seeks to understand how logical combinations of visual cues (e.g. "wide opened left eye" and "opened mouth") provide singly necessary and jointly sufficient conditions to access categories in memory (e.g. "surprise"). Such combinations of visual cues constitute the categorization manifold underlying the information goals of face categorization mechanisms and are therefore critical for their understanding. Yet, no method currently exists to reliably characterize the visual cues of the categorization manifold (i.e. its dimensions, such as "wide opened eyes" and "opened mouth") and how they combine (i.e. the manifold topology which dictates e.g. that "wide opened eyes" and "opened mouth" can be used independently or must be used jointly). Here we present a generic method to characterize categorization manifolds and apply it to observers categorizing dynamic facial expressions of emotion. To generate information, we used the Generative Face Grammar (GFG) platform (Yu et al., 2012) that selects on each trial (N = 2,400 trials/observer) a random set of Action Units (AUs) and values for their parametric activation (Jack et al., 2012). We asked 60 naive Western Caucasian observers to categorize the presented random facial animation according to one of six classic emotions ("happy", "surprise", "fear", "disgust", "anger", "sad", plus "don't know"). For each observer, we used a Non-negative Matrix Factorization (NMF) algorithm to extract AU x Time components of facial expression signals associated with the pereceptual categorization of each emotion. We then performed a Linear Discriminant Analysis (LDA) to select the components (i.e. manifold dimensions) that discriminate between six emotion categories (Quian Quiroga and Panzeri, 2009; Delis et al., 2013). Hence, the dimensions of the resultant categorization manifolds represent the strategies observers use to categorize the emotions. Our data show that observers use multiple categorization strategies, which constitute the atomic signals of emotion communication via facial expressions.

The brain ages as dramatically as the body, manifesting itself in the reorganization of brain networks mediating interpretation of social signals. In particular, previous research has indicated that older adults are less accurate in categorizing negative facial emotional expressions, but the specific nature of these changes is unclear. Here, using the 4D Generative Face Grammar (GFG) platform (Yu et al., 2012) we generated a set of random combinations of facial movements, which coupled with reverse correlation informed the subsets constituting socially meaningful facial expressions. Eighteen young (10 female, median age=21, 18-25) and eighteen older (10 female, median age=65, 56-88) observers each categorized 4,992 animations of young and old faces into six basic emotions (i.e. 'happy', 'surprise', 'fear', 'disgust', 'anger', and 'sad') plus 'other/don't know', and rated the intensity of each expression (Jack et al., 2012). We then reverse correlated the random facial movements and their temporal parameters with the observers' responses. This resulted in dynamic models of the 6 basic facial expressions at five levels of intensity for each observer. We then performed a group-level k-means clustering analysis on the individual models. Young observers showed high homogeneity within expression categories, with a characteristic confusion between 'anger' and 'disgust'. Older observers showed well-preserved homogeneity for the expression models of 'disgust' and 'happiness'. By contrast, models of negative emotions ('fear', 'anger', 'sadness', and 'surprise') were less homogeneous across older observers. To examine whether this arose from a higher diversity of models of negative emotions in older observers, we identified the recognition strategies of each individual observer (Delis et al., submitted) and found that older observers deploy more strategies, in particular for models of 'sadness'.

Motor primitives and modularity may be important in biological movement control. However, their neural basis is not understood. To investigate this, we recorded 302 neurons, making multielectrode recordings in the spinal cord gray of spinalized frogs, at 400, 800, and 1200 μm depth, at the L2/L3 segment border. Simultaneous muscle activity recordings were used with independent components analysis to infer premotor drive patterns. Neurons were divided into groups based on motor pattern modulation and sensory responses, depth recorded, and behavior. The 187 motor pattern modulated neurons recorded comprised 14 cutaneous neurons and 28 proprioceptive neurons at 400 μm in the dorsal horn, 131 intermediate zone interneurons from ∼800 μm depth without sensory responses, and 14 motoneuron-like neurons at ∼1200 μm. We examined all such neurons during spinal behaviors. Mutual information measures showed that cutaneous neurons and intermediate zone neurons were related better to premotor drives than to individual muscle activity. In contrast, proprioceptive-related neurons and ventral horn neurons divided evenly. For 46 of the intermediate zone interneurons, we found significant postspike facilitation effects on muscle responses using spike-triggered averages representing short-latency postspike facilitations to multiple motor pools. Furthermore, these postspike facilitations matched significantly in both their patterns and strengths with the weighting parameters of individual primitives extracted statistically, although both were initially obtained without reference to one another. Our data show that sets of dedicated interneurons may organize individual spinal primitives. These may be a key to understanding motor development, motor learning, recovery after CNS injury, and evolution of motor behaviors.