Neural theory for the perception of causal actions

Neuro-safety science: an emerging discipline to reveal the neural mechanisms of safety problems

At present, the research of safety science discipline is limited to the level of describing psychology and behaviors, because the cognitive neural mechanisms behind them are unknown. This paper introduces an emerging interdiscipline, namely neuro-safety science, which uses the neuroscientific methods to investigate the neural systems behind safely relevant behaviors. Qualitative methods such as literature review method and theoretical model construction method were adopted for this study. Based on the background of neuro-safety science, the definition of neuro-safety science was defined, its connotation was analyzed, and the research contents from two aspects of theoretical research and practical application research were proposed. Methodology system including research principles, research routes, research procedure and research methods, and the paradigm system of neuro-safety science were put forward. At last, the application research on neuro-safety science was forecasted. This paper opens up a new research perspective for the research of safety science, and provide guidance and reference to develop neuro-safety science.

Consciousness and the rabbit holes of delirium

Delirium is a common disorder in hospitalized older adults and the defining characteristic is a disturbance of consciousness. Unfortunately, there are currently no testable measures of consciousness as pertains to its disruption in delirium. Not surprisingly rates of recognition of delirium suffer. Arguably, a greater understanding of the quantum of consciousness may improve delirium diagnosis through better diagnostic tools. Candidate dimensions of consciousness derived from fields of psychology, psychiatry, and philosophy are discussed and relevance to delirium explored. Based upon existing literature in the field of consciousness we identify the pre-reflective state, experiential awareness, and functional networks as candidate sites that may be affected in delirium. Opportunities for clinical instrument development and how these tools can be tested are discussed. We conclude that consciousness content may not hold to a unitary measurement, but facets of its integrity that are impacted in delirium are open to further exploration. Disorders in pre-reflective status, experiential awareness, and functional networks may represent the measurable "rabbit holes" of consciousness disturbance.

Visual Neuroscience: Seeing Causality with the Motor System?

Understanding how humans perceive cause and effect in visual events has long intrigued philosophers and scientists. A new study in primates reveals the neural correlates of perceived causality at the single-cell level, but in an unexpected place - the motor system.

Toward a Unified Sub-symbolic Computational Theory of Cognition

This paper proposes how various disciplinary theories of cognition may be combined into a unifying, sub-symbolic, computational theory of cognition. The following theories are considered for integration: psychological theories, including the theory of event coding, event segmentation theory, the theory of anticipatory behavioral control, and concept development; artificial intelligence and machine learning theories, including reinforcement learning and generative artificial neural networks; and theories from theoretical and computational neuroscience, including predictive coding and free energy-based inference. In the light of such a potential unification, it is discussed how abstract cognitive, conceptualized knowledge and understanding may be learned from actively gathered sensorimotor experiences. The unification rests on the free energy-based inference principle, which essentially implies that the brain builds a predictive, generative model of its environment. Neural activity-oriented inference causes the continuous adaptation of the currently active predictive encodings. Neural structure-oriented inference causes the longer term adaptation of the developing generative model as a whole. Finally, active inference strives for maintaining internal homeostasis, causing goal-directed motor behavior. To learn abstract, hierarchical encodings, however, it is proposed that free energy-based inference needs to be enhanced with structural priors, which bias cognitive development toward the formation of particular, behaviorally suitable encoding structures. As a result, it is hypothesized how abstract concepts can develop from, and thus how they are structured by and grounded in, sensorimotor experiences. Moreover, it is sketched-out how symbol-like thought can be generated by a temporarily active set of predictive encodings, which constitute a distributed neural attractor in the form of an interactive free-energy minimum. The activated, interactive network attractor essentially characterizes the semantics of a concept or a concept composition, such as an actual or imagined situation in our environment. Temporal successions of attractors then encode unfolding semantics, which may be generated by a behavioral or mental interaction with an actual or imagined situation in our environment. Implications, further predictions, possible verification, and falsifications, as well as potential enhancements into a fully spelled-out unified theory of cognition are discussed at the end of the paper.

Neural and Computational Mechanisms of Action Processing: Interaction between Visual and Motor Representations

Action recognition has received enormous interest in the field of neuroscience over the last two decades. In spite of this interest, the knowledge in terms of fundamental neural mechanisms that provide constraints for underlying computations remains rather limited. This fact stands in contrast with a wide variety of speculative theories about how action recognition might work. This review focuses on new fundamental electrophysiological results in monkeys, which provide constraints for the detailed underlying computations. In addition, we review models for action recognition and processing that have concrete mathematical implementations, as opposed to conceptual models. We think that only such implemented models can be meaningfully linked quantitatively to physiological data and have a potential to narrow down the many possible computational explanations for action recognition. In addition, only concrete implementations allow judging whether postulated computational concepts have a feasible implementation in terms of realistic neural circuits.

Information processing in the mirror neuron system in primates and machines

The mirror neuron system in primates matches observations of actions with the motor representations used for their execution, and is a topic of intense research and debate in biological and computational disciplines. In robotics, models of this system have been used for enabling robots to imitate and learn how to perform tasks from human demonstrations. Yet, existing computational and robotic models of these systems are found in multiple levels of description, and although some models offer plausible explanations and testable predictions, the difference in the granularity of the experimental setups, methodologies, computational structures and selected modeled data make principled meta-analyses, common in other fields, difficult. In this paper, we adopt an interdisciplinary approach, using the BODB integrated environment in order to bring together several different but complementary computational models, by functionally decomposing them into brain operating principles (BOPs) which each capture a limited subset of the model's functionality. We then explore links from these BOPs to neuroimaging and neurophysiological data in order to pinpoint complementary and conflicting explanations and compare predictions against selected sets of neurobiological data. The results of this comparison are used to interpret mirror system neuroimaging results in terms of neural network activity, evaluate the biological plausibility of mirror system models, and suggest new experiments that can shed light on the neural basis of mirror systems.

Putting actions in context: visual action adaptation aftereffects are modulated by social contexts

The social context in which an action is embedded provides important information for the interpretation of an action. Is this social context integrated during the visual recognition of an action? We used a behavioural visual adaptation paradigm to address this question and measured participants’ perceptual bias of a test action after they were adapted to one of two adaptors (adaptation after-effect). The action adaptation after-effect was measured for the same set of adaptors in two different social contexts. Our results indicate that the size of the adaptation effect varied with social context (social context modulation) although the physical appearance of the adaptors remained unchanged. Three additional experiments provided evidence that the observed social context modulation of the adaptation effect are owed to the adaptation of visual action recognition processes. We found that adaptation is critical for the social context modulation (experiment 2). Moreover, the effect is not mediated by emotional content of the action alone (experiment 3) and visual information about the action seems to be critical for the emergence of action adaptation effects (experiment 4). Taken together these results suggest that processes underlying visual action recognition are sensitive to the social context of an action.

Differences and commonalities in the judgment of causality in physical and social contexts: an fMRI study

Understanding cause and effect is a fundamental aspect of human cognition. When shown videos of simple two-dimensional shapes colliding, humans experience one object causing the other to move, e.g., one billiard-like ball seeming to hit and move the other. The impression of causality can also occur when people attribute social interactions to moving objects. Whether the judgment of social and physical causality engages distinct or shared neural networks is not known. In a functional magnetic imaging (fMRI) study, participants were presented with two types of dynamic videos: a blue ball colliding with a red ball (P; physical condition) and a blue ball ("Mr. Blue") passing a red ball ("Mrs. Red") without making contact (S; social condition). Participants judged causal relationships (C) or movement direction (D; control task) in both video types, resulting in four conditions (PC; SC; PD; SD). We found common neural activations for physical and social causality judgments (SC > SD)∩(PC > PD) in the right middle/inferior frontal gyrus, right inferior parietal lobule, the right supplementary motor area, and bilateral insulae. For social causal judgments (SC > PC), we found distinct neural activity in the right temporo-parietal junction (rTPJ). These results provide evidence for a common neural network underlying judgments of causality that apply to both physical and social situations. The results also indicate that social causality judgments recruit additional neural resources in an area critical for determining animacy and intentionality.

Action simulation: time course and representational mechanisms

The notion of action simulation refers to the ability to re-enact foreign actions (i.e., actions observed in other individuals). Simulating others' actions implies a mirroring of their activities, based on one's own sensorimotor competencies. Here, we discuss theoretical and experimental approaches to action simulation and the study of its representational underpinnings. One focus of our discussion is on the timing of internal simulation and its relation to the timing of external action, and a paradigm that requires participants to predict the future course of actions that are temporarily occluded from view. We address transitions between perceptual mechanisms (referring to action representation before and after occlusion) and simulation mechanisms (referring to action representation during occlusion). Findings suggest that action simulation runs in real-time; acting on newly created action representations rather than relying on continuous visual extrapolations. A further focus of our discussion pertains to the functional characteristics of the mechanisms involved in predicting other people's actions. We propose that two processes are engaged, dynamic updating and static matching, which may draw on both semantic and motor information. In a concluding section, we discuss these findings in the context of broader theoretical issues related to action and event representation, arguing that a detailed functional analysis of action simulation in cognitive, neural, and computational terms may help to further advance our understanding of action cognition and motor control.

Physiologically inspired model for the visual recognition of transitive hand actions

The visual recognition of actions is an important visual function that is critical for motor learning and social communication. Action-selective neurons have been found in different cortical regions, including the superior temporal sulcus, parietal and premotor cortex. Among those are mirror neurons, which link visual and motor representations of body movements. While numerous theoretical models for the mirror neuron system have been proposed, the computational basis of the visual processing of goal-directed actions remains largely unclear. While most existing models focus on the possible role of motor representations in action recognition, we propose a model showing that many critical properties of action-selective visual neurons can be accounted for by well-established visual mechanisms. Our model accomplishes the recognition of hand actions from real video stimuli, exploiting exclusively mechanisms that can be implemented in a biologically plausible way by cortical neurons. We show that the model provides a unifying quantitatively consistent account of a variety of electrophysiological results from action-selective visual neurons. In addition, it makes a number of predictions, some of which could be confirmed in recent electrophysiological experiments.

From simple innate biases to complex visual concepts

Early in development, infants learn to solve visual problems that are highly challenging for current computational methods. We present a model that deals with two fundamental problems in which the gap between computational difficulty and infant learning is particularly striking: learning to recognize hands and learning to recognize gaze direction. The model is shown a stream of natural videos and learns without any supervision to detect human hands by appearance and by context, as well as direction of gaze, in complex natural scenes. The algorithm is guided by an empirically motivated innate mechanism--the detection of "mover" events in dynamic images, which are the events of a moving image region causing a stationary region to move or change after contact. Mover events provide an internal teaching signal, which is shown to be more effective than alternative cues and sufficient for the efficient acquisition of hand and gaze representations. The implications go beyond the specific tasks, by showing how domain-specific "proto concepts" can guide the system to acquire meaningful concepts, which are significant to the observer but statistically inconspicuous in the sensory input.