1,721,002 research outputs found
Perceptual Judgements of Plaid Motions Biased by Active Movements
No full textThe interpretation of a plaid stimulus moving through an aperture is inherently ambiguous. It can be perceived either as a coherent pattern moving rigidly or as two gratings sliding over each other. Perceptual uncertainty thresholds can be modulated by changing the relative luminance properties of single gratings. Many studies on action-perception transfer suggested that information required by the motor system to produce movements affects visual motion perception. We reasoned that physical interaction between an observer and the stimulus may influence the perceptual uncertainty associated to the moving plaids. Accordingly, we designed a motor task in which observers actively generate the relative movement between the plaid and the aperture. A two-alternative forced choice task was performed before and after the motor task to assess the motor effect on the perception of plaid motion. Preliminary results show that action biases the perceptual decision in a wide range of conditions and with spatial differences
Motor intelligence in a simple distributed control system: walking machines and stick insects
No full textCruse H, Dean J. Motor intelligence in a simple distributed control system: walking machines and stick insects. In: Morasso P, Sanguineti V, eds. Self-organization, computational maps, and motor control. Advances in psychology. Vol 119. North Holland, Amsterdam: Elsevier; 1997: 239-270
Game theory and partner representation in joint action: toward a computational theory of joint agency
No full textThe sense of agency – the subjective feeling of being in control of our own actions – is one central aspect of the phenomenology of action. Computational models provided important contributions toward unveiling the mechanisms underlying the sense of agency in individual action. In particular, the sense of agency is believed to be related to the match between the actual and predicted consequences of our own actions (comparator model). In the study of joint action, models are even more necessary to understand the mechanisms underlying the development of coordination strategies and how the subjective experiences of control emerge during the interaction. In a joint action, we not only need to predict the consequences of our own actions; we also need to predict the actions and intentions of our partner, and to integrate these predictions to infer their joint consequences. Understanding our partner and developing mutually satisfactory coordination strategies are key components of joint action and in the development of the sense of joint agency. Here we discuss a computational architecture which addresses the sense of agency during intentional, real-time joint action. We first reformulate previous accounts of the sense of agency in probabilistic terms, as the combination of prior beliefs about the action goals and constraints, and the likelihood of the predicted movement outcomes. To look at the sense of joint agency, we extend classical computational motor control concepts - optimal estimation and optimal control. Regarding estimation, we argue that in joint action the players not only need to predict the consequences of their own actions, but also need to predict partner’s actions and intentions (a ‘partner model’) and to integrate these predictions to infer their joint consequences. As regards action selection, we use differential game theory – in which actions develop in continuous space and time - to formulate the problem of establishing a stable form of coordination and as a natural extension of optimal control to joint action. The resulting model posits two concurrent observer-controller loops, accounting for ‘joint’ and ‘self’ action control. The two observers quantify the likelihoods of being in control alone or jointly. Combined with prior beliefs, they provide weighing signals which are used to modulate the ‘joint’ and ‘self’ motor commands. We argue that these signals can be interpreted as the subjective sense of joint and self agency. We demonstrate the model predictions by simulating a sensorimotor interactive task where two players are mechanically coupled and are instructed to perform planar movements to reach a shared final target by crossing two differently located intermediate targets. In particular, we explore the relation between self and joint agency and the information available to each player about their partner. The proposed model provides a coherent picture of the inter-relation of prediction, control, and the sense of agency in a broader range of joint actions
Audio-Visual Localization by Synthetic Acoustic Image Generation
No full textAcoustic images constitute an emergent data modality for multimodal scene understanding. Such images have the peculiarity to distinguish the spectral signature of sounds coming from different directions in space, thus providing richer information than the one derived from mono and binaural microphones. However, acoustic images are typically generated by cumbersome microphone arrays, which are not as widespread as ordinary microphones mounted on optical cameras. To exploit this empowered modality while using standard microphones and cameras we propose to leverage the generation of synthetic acoustic images from common audio-video data for the task of audio-visual localization. The generation of synthetic acoustic images is obtained by a novel deep architecture, based on Variational Autoencoder and U-Net models, which is trained to reconstruct the ground truth spatialized audio data collected by a microphone array, from the associated video and its corresponding monaural audio signal. Namely, the model learns how to mimic what an array of microphones can produce in the same conditions. We assess the quality of the generated synthetic acoustic images on the task of unsupervised sound source localization in a qualitative and quantitative manner, while also considering standard generation metrics. Our model is evaluated by considering both multimodal datasets containing acoustic images, used for the training, and unseen datasets containing just monaural audio signals and RGB frames, showing to reach more accurate localization results as compared to the state of the art
A Parallel Approach to the Simulation of Complex Movements
No full textA simulation environment is presented aimed at representing and simulating complex kinematic structures, based on gradient descent process, operating on a sort of elastic potential field defined over set of involved structures. At higher level a trajectory formation tool, also based on the minimization of global potential energy function and cooperating units, it responsible for organizing and driving the evolution of represented system. The whole model is inherently parallel and distributed; an implementation ion a MIMD transputer-based architecture is presented, as well as some simulation examples
Partner Representation in Competitive Interaction: Implications for Neurorehabilitation
No full textDuring rehabilitation patients are required to perform movements that are difficult for them to do alone. By effectively promoting player engagement and motivation, competitive settings are potentially useful in rehabilitation. However, in these scenarios the mechanisms underlying adapting to each partner are poorly understood. Here we address competitive interaction by using dyadic haptic interfaces, where pairs of participants repeatedly play a ball game (penalty kick). We manipulated the amount of information each participant had about their partner location. We found that in different experimental conditions the participants changed their behavior. We compared these results with simulations based on a computational model that assumes optimal action estimation and selection. Our findings are consistent with the model predictions: the players seem to develop a model of the partner during the interaction and adapt the model to different conditions
Computational joint action: dynamical models to understand the development of joint coordination
No full textPrevious joint action studies using sensorimotor games suggest that human dyads develop coordination strategies which can be interpreted as Nash equilibria. In a previous study, we argued that if players are uncertain about what their partner is doing, they develop a coordination strategy which is more robust to the actual partner actions. This suggested that humans maintain an explicit representation of what the partner will be doing - a partner model - which also accounts for their degree of confidence about it. However, the mechanisms underlying the development of a joint coordination over repeated trials remain unknown. Very much like individual sensorimotor control, dynamical models can be used to understand how joint coordination develops. Here we present a general computational model - based on game theory and Bayesian estimation - to understand the mechanisms underlying the development of a joint coordination. A joint task is modeled as a quadratic game. Each player predicts their partner's next move (partner model) by optimally combining predictions and sensory observations, and selects their actions through a stochastic optimization of its expected cost, given the partner model. We show that the model captures well the temporal evolution of performance in a previous joint action experiment, and the estimated parameters provide a comprehensive characterization of individual participants in a dyad
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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