Nonvisual motor learning influences abstract action observation

Neuroimaging studies have recently provided support for the existence of a human equivalent of the "mirror-neuron" system as first described in monkeys [1], involved in both the execution of movements as well as the observation and imitation of actions performed by others (e.g., [2-6]). A widely held conception concerning this system is that the understanding of observed actions is mediated by a covert simulation process [7]. In the present fMRI experiment, this simulation process was probed by asking subjects to discriminate between visually presented trajectories that either did or did not match previously performed but unseen continuous movement sequences. A specific network of learning-related premotor and parietal areas was found to be reactivated when participants were confronted with their movements' visual counterpart. Moreover, the strength of these reactivations was dependent on the observers' experience with executing the corresponding movement sequence. These findings provide further support for the emerging view that embodied simulations during action observation engage widespread activations in cortical motor regions beyond the classically defined mirror-neuron system. Furthermore, the obtained results extend previous work by showing experience-dependent perceptual modulations at the neural systems level based on nonvisual motor learning.     

Complementary systems for understanding action intentions

How humans understand the intention of others' actions remains controversial. Some authors have suggested that intentions are recognized by means of a motor simulation of the observed action with the mirror-neuron system [1-3]. Others emphasize that intention recognition is an inferential process, often called "mentalizing" or employing a "theory of mind," which activates areas well outside the motor system [4-6]. Here, we assessed the contribution of brain regions involved in motor simulation and mentalizing for understanding action intentions via functional brain imaging. Results show that the inferior frontal gyrus (part of the mirror-neuron system) processes the intentionality of an observed action on the basis of the visual properties of the action, irrespective of whether the subject paid attention to the intention or not. Conversely, brain areas that are part of a "mentalizing" network become active when subjects reflect about the intentionality of an observed action, but they are largely insensitive to the visual properties of the observed action. This supports the hypothesis that motor simulation and mentalizing have distinct but complementary functions for the recognition of others' intentions.     

The role of social cognition in decision making

Successful decision making in a social setting depends on our ability to understand the intentions, emotions and beliefs of others. The mirror system allows us to understand other people's motor actions and action intentions. 'Empathy' allows us to understand and share emotions and sensations with others. 'Theory of mind' allows us to understand more abstract concepts such as beliefs or wishes in others. In all these cases, evidence has accumulated that we use the specific neural networks engaged in processing mental states in ourselves to understand the same mental states in others. However, the magnitude of the brain activity in these shared networks is modulated by contextual appraisal of the situation or the other person. An important feature of decision making in a social setting concerns the interaction of reason and emotion. We consider four domains where such interactions occur: our sense of fairness, altruistic punishment, trust and framing effects. In these cases, social motivations and emotions compete with each other, while higher-level control processes modulate the interactions of these low-level biases.     

Favouritism in the motor system: social interaction modulates action simulation

The ability to anticipate others'' actions is crucial for social interaction. It has been shown that this ability relies on motor areas of the human brain that are not only active during action execution and action observation, but also during anticipation of another person''s action. Recording electroencephalograms during a triadic social interaction, we assessed whether activation of motor areas pertaining to the human mirror-neuron system prior to action observation depends on the social relationship between the actor and the observer. Anticipatory motor activation was stronger when participants expected an interaction partner to perform a particular action than when they anticipated that the same action would be performed by a third person they did not interact with. These results demonstrate that social interaction modulates action simulation.     

Active inference and agency: optimal control without cost functions

This paper describes a variational free-energy formulation of (partially observable) Markov decision problems in decision making under uncertainty. We show that optimal control can be cast as active inference. In active inference, both action and posterior beliefs about hidden states minimise a free energy bound on the negative log-likelihood of observed states, under a generative model. In this setting, reward or cost functions are absorbed into prior beliefs about state transitions and terminal states. Effectively, this converts optimal control into a pure inference problem, enabling the application of standard Bayesian filtering techniques. We then consider optimal trajectories that rest on posterior beliefs about hidden states in the future. Crucially, this entails modelling control as a hidden state that endows the generative model with a representation of agency. This leads to a distinction between models with and without inference on hidden control states; namely, agency-free and agency-based models, respectively.     

Action and behavior: a free-energy formulation

We have previously tried to explain perceptual inference and learning under a free-energy principle that pursues Helmholtz’s agenda to understand the brain in terms of energy minimization. It is fairly easy to show that making inferences about the causes of sensory data can be cast as the minimization of a free-energy bound on the likelihood of sensory inputs, given an internal model of how they were caused. In this article, we consider what would happen if the data themselves were sampled to minimize this bound. It transpires that the ensuing active sampling or inference is mandated by ergodic arguments based on the very existence of adaptive agents. Furthermore, it accounts for many aspects of motor behavior; from retinal stabilization to goal-seeking. In particular, it suggests that motor control can be understood as fulfilling prior expectations about proprioceptive sensations. This formulation can explain why adaptive behavior emerges in biological agents and suggests a simple alternative to optimal control theory. We illustrate these points using simulations of oculomotor control and then apply to same principles to cued and goal-directed movements. In short, the free-energy formulation may provide an alternative perspective on the motor control that places it in an intimate relationship with perception.     

Bayesian action-perception computational model: interaction of production and recognition of cursive letters

In this paper, we study the collaboration of perception and action representations involved in cursive letter recognition and production. We propose a mathematical formulation for the whole perception-action loop, based on probabilistic modeling and bayesian inference, which we call the Bayesian Action-Perception (BAP) model. Being a model of both perception and action processes, the purpose of this model is to study the interaction of these processes. More precisely, the model includes a feedback loop from motor production, which implements an internal simulation of movement. Motor knowledge can therefore be involved during perception tasks. In this paper, we formally define the BAP model and show how it solves the following six varied cognitive tasks using bayesian inference: i) letter recognition (purely sensory), ii) writer recognition, iii) letter production (with different effectors), iv) copying of trajectories, v) copying of letters, and vi) letter recognition (with internal simulation of movements). We present computer simulations of each of these cognitive tasks, and discuss experimental predictions and theoretical developments.     

Grasping the Intentions of Others with One's Own Mirror Neuron System

Understanding the intentions of others while watching their actions is a fundamental building block of social behavior. The neural and functional mechanisms underlying this ability are still poorly understood. To investigate these mechanisms we used functional magnetic resonance imaging. Twenty-three subjects watched three kinds of stimuli: grasping hand actions without a context, context only (scenes containing objects), and grasping hand actions performed in two different contexts. In the latter condition the context suggested the intention associated with the grasping action (either drinking or cleaning). Actions embedded in contexts, compared with the other two conditions, yielded a significant signal increase in the posterior part of the inferior frontal gyrus and the adjacent sector of the ventral premotor cortex where hand actions are represented. Thus, premotor mirror neuron areas—areas active during the execution and the observation of an action—previously thought to be involved only in action recognition are actually also involved in understanding the intentions of others. To ascribe an intention is to infer a forthcoming new goal, and this is an operation that the motor system does automatically.     

Grasping the intentions of others with one's own mirror neuron system

Understanding the intentions of others while watching their actions is a fundamental building block of social behavior. The neural and functional mechanisms underlying this ability are still poorly understood. To investigate these mechanisms we used functional magnetic resonance imaging. Twenty-three subjects watched three kinds of stimuli: grasping hand actions without a context, context only (scenes containing objects), and grasping hand actions performed in two different contexts. In the latter condition the context suggested the intention associated with the grasping action (either drinking or cleaning). Actions embedded in contexts, compared with the other two conditions, yielded a significant signal increase in the posterior part of the inferior frontal gyrus and the adjacent sector of the ventral premotor cortex where hand actions are represented. Thus, premotor mirror neuron areas—areas active during the execution and the observation of an action—previously thought to be involved only in action recognition are actually also involved in understanding the intentions of others. To ascribe an intention is to infer a forthcoming new goal, and this is an operation that the motor system does automatically.     

The anatomy of choice: dopamine and decision-making

This paper considers goal-directed decision-making in terms of embodied or active inference. We associate bounded rationality with approximate Bayesian inference that optimizes a free energy bound on model evidence. Several constructs such as expected utility, exploration or novelty bonuses, softmax choice rules and optimism bias emerge as natural consequences of free energy minimization. Previous accounts of active inference have focused on predictive coding. In this paper, we consider variational Bayes as a scheme that the brain might use for approximate Bayesian inference. This scheme provides formal constraints on the computational anatomy of inference and action, which appear to be remarkably consistent with neuroanatomy. Active inference contextualizes optimal decision theory within embodied inference, where goals become prior beliefs. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (associated with softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution. Crucially, this sensitivity corresponds to the precision of beliefs about behaviour. The changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses—and they may provide a new perspective on the role of dopamine in assimilating reward prediction errors to optimize decision-making.     

Reinforcement Learning or Active Inference?

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.     

友好还是敌对?理解互动意图的大脑动态时程

"他们是友好的还是敌对的?"人如何快速识别他人的社会互动意图?基于对他人意图准确而快速的理解,才能正确地选择应对策略和反应方式,做出与对方展开合作还是应对冲突的决策.为了探索大脑如何理解2个个体通过整个身体表现出的不同性质交际意图的动态加工时程,采用事件相关电位技术记录了16名健康被试观察3类不同性质的交际意图动作(友好意图、敌对意图和无互动意图)照片时的脑电成分.电生理学结果表明,在较早阶段(170~270 ms),在大脑的额中区发现,友好意图的N2峰值比敌对意图和无互动意图更负,而敌对意图与无互动意图则没有显著差异.在晚期阶段(270~500 ms),在大脑的顶区、中线和右半球,敌对意图的P3峰值比友好意图和无互动意图更正,而友好意图与无互动意图则没有显著差异.本研究为大脑理解不同性质的社会互动意图(相对于无互动意图)的动态加工时程提供了神经电生理学的初步证据.     

Abnormalities in the awareness and control of action

Much of the functioning of the motor system occurs without awareness. Nevertheless, we are aware of some aspects of the current state of the system and we can prepare and make movements in the imagination. These mental representations of the actual and possible states of the system are based on two sources: sensory signals from skin and muscles, and the stream of motor commands that have been issued to the system. Damage to the neural substrates of the motor system can lead to abnormalities in the awareness of action as well as defects in the control of action. We provide a framework for understanding how these various abnormalities of awareness can arise. Patients with phantom limbs or with anosognosia experience the illusion that they can move their limbs. We suggest that these representations of movement are based on streams of motor commands rather than sensory signals. Patients with utilization behaviour or with delusions of control can no longer properly link their intentions to their actions. In these cases the impairment lies in the representation of intended movements. The location of the neural damage associated with these disorders suggests that representations of the current and predicted state of the motor system are in parietal cortex, while representations of intended actions are found in prefrontal and premotor cortex.     

What are you doing? How active and observational experience shape infants' action understanding

From early in life, infants watch other people''s actions. How do young infants come to make sense of actions they observe? Here, we review empirical findings on the development of action understanding in infancy. Based on this review, we argue that active action experience is crucial for infants'' developing action understanding. When infants execute actions, they form associations between motor acts and the sensory consequences of these acts. When infants subsequently observe these actions in others, they can use their motor system to predict the outcome of the ongoing actions. Also, infants come to an understanding of others’ actions through the repeated observation of actions and the effects associated with them. In their daily lives, infants have plenty of opportunities to form associations between observed events and learn about statistical regularities of others’ behaviours. We argue that based on these two forms of experience—active action experience and observational experience—infants gradually develop more complex action understanding capabilities.     

What Are They Up To? The Role of Sensory Evidence and Prior Knowledge in Action Understanding

Explaining or predicting the behaviour of our conspecifics requires the ability to infer the intentions that motivate it. Such inferences are assumed to rely on two types of information: (1) the sensory information conveyed by movement kinematics and (2) the observer's prior expectations--acquired from past experience or derived from prior knowledge. However, the respective contribution of these two sources of information is still controversial. This controversy stems in part from the fact that "intention" is an umbrella term that may embrace various sub-types each being assigned different scopes and targets. We hypothesized that variations in the scope and target of intentions may account for variations in the contribution of visual kinematics and prior knowledge to the intention inference process. To test this hypothesis, we conducted four behavioural experiments in which participants were instructed to identify different types of intention: basic intentions (i.e. simple goal of a motor act), superordinate intentions (i.e. general goal of a sequence of motor acts), or social intentions (i.e. intentions accomplished in a context of reciprocal interaction). For each of the above-mentioned intentions, we varied (1) the amount of visual information available from the action scene and (2) participant's prior expectations concerning the intention that was more likely to be accomplished. First, we showed that intentional judgments depend on a consistent interaction between visual information and participant's prior expectations. Moreover, we demonstrated that this interaction varied according to the type of intention to be inferred, with participant's priors rather than perceptual evidence exerting a greater effect on the inference of social and superordinate intentions. The results are discussed by appealing to the specific properties of each type of intention considered and further interpreted in the light of a hierarchical model of action representation.     

Inferring subjective states through the observation of actions

Estimating another person''s subjective confidence is crucial for social interaction, but how this inference is achieved is unknown. Previous research has demonstrated that the speed at which people make decisions is correlated with their confidence in their decision. Here, we show that (i) subjects are able to infer the subjective confidence of another person simply through the observation of their actions and (ii) this inference is dependent upon the performance of each subject when executing the action. Crucially, the latter result supports a model in which motor simulation of an observed action mediates the successful understanding of other minds. We conclude that kinematic understanding allows access to the higher-order cognitive processes of others, and that this access plays a central role in social interactions.     

Self and other in the human motor system

Observation of another's action can selectively facilitate the brain's motor circuits for making the same action . A "mirror-matching mechanism" might map observed actions onto the observer's own motor representations . Crucially, this view suggests that the brain represents others' actions like one's own. However, this hypothesis has been difficult to test because the experience of one's own body differs from that of others' bodies with respect to viewpoint, morphological features, familiarity, and the hallmark feature of kinaesthetic experience. We used an established method for manipulating the sense of body ownership ("rubber-hand illusion") to compare effects of observing actions that either were or were not illusorily attributed to the subject's own body. We show that observing another's actions facilitated the motor system, whereas observing identical actions, which were illusorily attributed to the subject's own body, showed the opposite pattern. Thus, motor facilitation strongly depends on the agent to whom the observed action is attributed. This result contradicts previous concepts of equivalence between one's own actions and actions of others and suggests that social differentiation, not equivalence, is characteristic of the human action system.     

Viewing lip forms: cortical dynamics

Viewing other persons' actions automatically activates brain areas belonging to the mirror-neuron system (MNS) assumed to link action execution and observation. We followed, by magnetoencephalographic cortical dynamics, subjects who observed still pictures of lip forms, on-line imitated them, or made similar forms in a self-paced manner. In all conditions and in both hemispheres, cortical activation progressed in 20-70 ms steps from the occipital cortex to the superior temporal region (where the strongest activation took place), the inferior parietal lobule, and the inferior frontal lobe (Broca's area), and finally, 50-140 ms later, to the primary motor cortex. The signals of Broca's area and motor cortex were significantly stronger during imitation than other conditions. These results demonstrate that still pictures, only implying motion, activate the human MNS in a well-defined temporal order.     

Observation-based learning for brain-machine interfaces

Canonically, 'mirror neurons' are cells in area F5 of the ventral premotor cortex that are active during both observation and execution of goal-directed movements. Recently, cells with similar properties have been observed in a number of other areas in the motor system, including the primary motor cortex. Mirror neurons are a part of a system whose function is thought to involve the prediction and interpretation of the sensory consequences of our own actions as well as the actions of others. Mirror-like responses are relevant to the development of brain-machine interfaces (BMIs) because they provide a robust way to map neural activity to behavior, and because they represent high-level information about goals and intentions that may have utility in future BMI applications.