1,721,141 research outputs found
The eye in hand: Predicting others’ behavior by integrating multiple sources of information
No full text The ability to predict the outcome of other beings' actions confers significant adaptive advantages. Experiments have assessed that human action observation can use multiple information sources, but it is currently unknown how they are integrated and how conflicts between them are resolved. To address this issue, we designed an action observation paradigm requiring the integration of multiple, potentially conflicting sources of evidence about the action target: the actor's gaze direction, hand preshape, and arm trajectory, and their availability and relative uncertainty in time. In two experiments, we analyzed participants' action prediction ability by using eye tracking and behavioral measures. The results show that the information provided by the actor's gaze affected participants' explicit predictions. However, results also show that gaze information was disregarded as soon as information on the actor's hand preshape was available, and this latter information source had widespread effects on participants' prediction ability. Furthermore, as the action unfolded in time, participants relied increasingly more on the arm movement source, showing sensitivity to its increasing informativeness. Therefore, the results suggest that the brain forms a robust estimate of the actor's motor intention by integrating multiple sources of information. However, when informative motor cues such as a preshaped hand with a given grip are available and might help in selecting action targets, people tend to capitalize on such motor cues, thus turning out to be more accurate and fast in inferring the object to be manipulated by the other's hand. </jats:p
A Programmer-Interpreter neural network architecture for prefrontal cognitive control
No full textThere is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on behavior by biasing processing toward task-relevant information and by modulating response selection. This idea is typically framed in terms of top-down influences within a cortical control hierarchy, where prefrontal-basal ganglia loops gate multiple input-output channels, which in turn can activate or sequence motor primitives expressed in (pre-)motor cortices. Here we advance a new hypothesis, based on the notion of programmability and an interpreter-programmer computational scheme, on how the PFC can flexibly bias the selection of sensorimotor patterns depending on internal goal and task contexts. In this approach, multiple elementary behaviors representing motor primitives are expressed by a single multi-purpose neural network, which is seen as a reusable area of "recycled" neurons (interpreter). The PFC thus acts as a "programmer" that, without modifying the network connectivity, feeds the interpreter networks with specific input parameters encoding the programs (corresponding to network structures) to be interpreted by the (pre-)motor areas. Our architecture is validated in a standard test for executive function: the 1-2-AX task. Our results show that this computational framework provides a robust, scalable and flexible scheme that can be iterated at different hierarchical layers, supporting the realization of multiple goals. We discuss the plausibility of the "programmer-interpreter" scheme to explain the functioning of prefrontal-(pre)motor cortical hierarchie
The predictive nature of spontaneous brain activity across scales and species
Full text linkEmerging research suggests the brain operates as a “prediction machine,” continuously anticipating sensory, motor, and cognitive outcomes. Central to this capability is the brain's spontaneous activity—ongoing internal processes independent of external stimuli. Neuroimaging and computational studies support that this activity is integral to maintaining and refining mental models of our environment, body, and behaviors, akin to generative models in computation. During rest, spontaneous activity expands the variability of potential representations, enhancing the accuracy and adaptability of these models. When performing tasks, internal models direct brain regions to anticipate sensory and motor states, optimizing performance. This review synthesizes evidence from various species, from C. elegans to humans, highlighting three key aspects of spontaneous brain activity’s role in prediction: the similarity between spontaneous and task-related activity, the encoding of behavioral and interoceptive priors, and the high metabolic cost of this activity, underscoring prediction as a fundamental function of brains across species
Using Domain Information for Word Sense Disambiguation
No full textThe major goal in ITC-irst partecipation at SENSEVAL-2 was to test the role of domain information in word sense disambiguation. The underlying working Hypothesis is that domain labels, such as MEDICINE, ARCHITECTURE and SPORT provide a natural way to establish semantic relations among word senses, which can be profitably used during the disambiguation process. For each task we partecipated (i.e. English all words, English lexical sample and Italian lexical sample) a different mix of knowledge based and statistical techniques have been implemente
The Role of Domain Information in Word Sense Disambiguation
No full textThis paper explores the role of domain information in word sense disambiguation. The underlying hypothesis is that domain labels, such as Medicine, Architecture and Sport, provide a natural way to establish semantic relations among word senses, which can be profitably used during the disambiguation process. Results obtained at the SANSEVAL-2 initiative confirm that for a significant subset of words domain information can be used to disambiguate with a very high level of precisio
Comparing Ontology-Based and Corpus-Based Domain Annotations in WordNet
No full textDomain information has been regarded as an emerging topic of interest in relation to WordNet. A lexical resource, WordNet Domain, is presented, where WordNet synsets have been annotated with domain labels such as medicine, Architecture and Sport. This annotation reflects the lexico-semantic criteria adopted by humans involved in the annotation. However, from a corpus-based perspective, domains reflect term distribution in a given text collection. The paper proposes a preliminary investigation aiming at comparing and integrating ontology-based and corpus-based domain informatio
Sensorimotor coarticulation in the execution and recognition of intentional actions
Full text linkHumans excel at recognizing (or inferring) another\u27s 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\u27s 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\u27s action recognition processes
An active inference model of hierarchical action understanding, learning and imitation
Full text linkWe advance a novel active inference model of the cognitive processing that underlies the acquisition of a hierarchical action repertoire and its use for observation, understanding and imitation. We illustrate the model in four simulations of a tennis learner who observes a teacher performing tennis shots, forms hierarchical representations of the observed actions, and imitates them. Our simulations show that the agent's oculomotor activity implements an active information sampling strategy that permits inferring the kinematic aspects of the observed movement, which lie at the lowest level of the action hierarchy. In turn, this low-level kinematic inference supports higher-level inferences about deeper aspects of the observed actions: proximal goals and intentions. Finally, the inferred action representations can steer imitative responses, but interfere with the execution of different actions. Our simulations show that hierarchical active inference provides a unified account of action observation, understanding, learning and imitation and helps explain the neurobiological underpinnings of visuomotor cognition, including the multiple routes for action understanding in the dorsal and ventral streams and mirror mechanisms
Sensorimotor communication for humans and robots: improving interactive skills by sending coordination signals
No full textDuring joint actions, humans continuously exchange coordination signals and use non-verbal, sensorimotor forms of communication. Here we discuss a specific example of sensorimotor communication – “signaling” – which consists in the intentional modification of one’s own action plan (e.g., a plan for reaching a glass of wine) to make it more predictable or discriminable from alternative action plans that are contextually plausible (e.g., a plan for reaching another glass on the same table). We first review the existing evidence on signaling in human-human interactions, discussing under which conditions humans use signaling. Successively, we distill these insights into a computational theory of signaling during on-line interactions. Central to our approach are the following ideas: (1) signaling endows pragmatic plans with communicative goals; (2) signaling can be understood within a cost-benefit scheme, balancing the costs for the signaling agent against its benefits for interaction success; (3) signaling may be part of an interactive strategy that optimizes success when joint goals are uncertain. Finally, we exemplify the benefits of signaling in a series of simulations and discuss how endowing robots with signaling abilities can increase the quality of HRIs by making their behavior more predictable and “legible” for human
- …
