Affordances, context and sociality

Affordances, i.e. the opportunity of actions offered by the environment, are one of the central research topics for the theoretical perspectives that view cognition as emerging from the interaction between the environment and the body. Being at the bridge between perception and action, affordances help to question a dichotomous view of perception and action. While Gibson’s view of affordances is mainly externalist, many contemporary approaches define affordances (and micro-affordances) as the product of long-term visuomotor associations in the brain. These studies have emphasized the fact that affordances are activated automatically, independently from the context and the previous intention to act: for example, affordances related to objects’ size would emerge even if the task does not require focusing on size. This emphasis on the automaticity of affordances has led to overlook their flexibility and contextual-dependency. In this contribution I will outline and discuss recent perspectives and evidence that reveal the flexibility and context-dependency of affordances, clarifying how they are modulated by the physical, cultural and social context. I will focus specifically on social affordances, i.e. on how perception of affordances might be influenced by the presence of multiple actors having different goals

Seeing Seeing

I argue that we can visually perceive others as seeing agents. I start by characterizing perceptual processes as those that are causally controlled by proximal stimuli. I then distinguish between various forms of visual perspective-taking, before presenting evidence that most of them come in perceptual varieties. In doing so, I clarify and defend the view that some forms of visual perspective-taking are “automatic”—a view that has been marshalled in support of dual-process accounts of mindreading

Coding of self and other's future choices in dorsal premotor cortex during social interaction

Representing others’ intentions is central to primate social life. We explored the role of dorsal premotor cortex (PMd) in discriminating between self and others’ behavior while two male rhesus monkeys performed a non-match-to-goal task in a monkey-human paradigm. During each trial, two of four potential targets were randomly presented on the right and left parts of a screen, and the monkey or the human was required to choose the one that did not match the previously chosen target. Each agent had to monitor the other's action in order to select the correct target in that agent's own turn. We report neurons that selectively encoded the future choice of the monkey, the human agent, or both. Our findings suggest that PMd activity shows a high degree of self-other differentiation during face-to-face interactions, leading to an independent representation of what others will do instead of entailing self-centered mental rehearsal or mirror-like activities. Understanding others’ intentions is essential to successful primate social life. Cirillo et al. explore the role of dorsal premotor cortex (PMd) in discriminating between self and others’ behavior while macaques interacted with humans. They show that the majority of neurons encoding the future choice did so selectively for the monkey or the human agent. PMd thus differentiates self from others’ behavior, leading to independent representations of future actions

Potential for social involvement modulates activity within the mirror and the mentalizing systems

Processing biological motion is fundamental for everyday life activities, such as social interaction, motor learning and nonverbal communication. The ability to detect the nature of a motor pattern has been investigated by means of point-light displays (PLD), sets of moving light points reproducing human kinematics, easily recognizable as meaningful once in motion. Although PLD are rudimentary, the human brain can decipher their content including social intentions. Neuroimaging studies suggest that inferring the social meaning conveyed by PLD could rely on both the Mirror Neuron System (MNS) and the Mentalizing System (MS), but their specific role to this endeavor remains uncertain. We describe a functional magnetic resonance imaging experiment in which participants had to judge whether visually presented PLD and videoclips of human-like walkers (HL) were facing towards or away from them. Results show that coding for stimulus direction specifically engages the MNS when considering PLD moving away from the observer, while the nature of the stimulus reveals a dissociation between MNS -mainly involved in coding for PLD- and MS, recruited by HL moving away. These results suggest that the contribution of the two systems can be modulated by the nature of the observed stimulus and its potential for social involvement

Modulation of cortical motor outputs by the symbolic meaning of visual stimuli.

Abstract The observation of an action modulates motor cortical outputs in specific ways, in part through mediation of the mirror neuron system. Sometimes we infer a meaning to an observed action based on integration of the actual percept with memories. Here, we conducted a series of experiments in healthy adults to investigate whether such inferred meanings can also modulate motor cortical outputs in specific ways. We show that brief observation of a neutral stimulus mimicking a hand does not significantly modulate motor cortical excitability (Study 1) although, after prolonged exposure, it can lead to a relatively nonspecific modulation (Study 2). However, when such a neutral stimulus is preceded by exposure to a hand stimulus, the latter appears to serve as a prime, perhaps enabling meaning to the neutral stimulus, which then modulates motor cortical excitability in accordance with mirror neuron-driving properties (Studies 2 and 3). Overall results suggest that a symbolic value ascribed to an otherwise neutral stimulus can modulate motor cortical outputs, revealing the influence of top-down inputs on the mirror neuron system. These findings indicate a novel aspect of the human mirror neuron system: an otherwise neutral stimulus can acquire specific mirror neuron-driving properties in the absence of a direct association between motor practice and perception. This significant malleability in the way that the mirror neuron system can code otherwise meaningless (i.e. arbitrarily associated) stimuli may contribute to coding communicative signals such as language. This may represent a mirror neuron system feature that is unique to humans

How conscious experience and working memory interact

Active components of classical working memory are conscious, but traditional theory does not account for this fact. Global Workspace theory suggests that consciousness is needed to recruit unconscious specialized networks that carry out detailed working memory functions. The IDA model provides a fine-grained analysis of this process, specifically of two classical workingmemory tasks, verbal rehearsal and the utilization of a visual image. In the process, new light is shed on the interactions between conscious and unconscious\ud aspects of working memory

Intention and motor representation in purposive action

Are there distinct roles for intention and motor representation in explaining the purposiveness of action? Standard accounts of action assign a role to intention but are silent on motor representation. The temptation is to suppose that nothing need be said here because motor representation is either only an enabling condition for purposive action or else merely a variety of intention. This paper provides reasons for resisting that temptation. Some motor representations, like intentions, coordinate actions in virtue of representing outcomes; but, unlike intentions, motor representations cannot feature as premises or conclusions in practical reasoning. This implies that motor representation has a distinctive role in explaining the purposiveness of action. It also gives rise to a problem: were the roles of intention and motor representation entirely independent, this would impair effective action. It is therefore necessary to explain how intentions interlock with motor representations. The solution, we argue, is to recognise that the contents of intentions can be partially determined by the contents of motor representations. Understanding this content-determining relation enables better understanding how intentions relate to actions

Neuronal assembly dynamics in supervised and unsupervised learning scenarios

The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions

Observation and imitation of actions performed by humans, androids, and robots : an EMG study

Understanding others’ actions is essential for functioning in the physical and social world. In the past two decades research has shown that action perception involves the motor system, supporting theories that we understand others’ behavior via embodied motor simulation. Recently, empirical approach to action perception has been facilitated by using well-controlled artificial stimuli, such as robots. One broad question this approach can address is what aspects of similarity between the observer and the observed agent facilitate motor simulation. Since humans have evolved among other humans and animals, using artificial stimuli such as robots allows us to probe whether our social perceptual systems are specifically tuned to process other biological entities. In this study, we used humanoid robots with different degrees of human-likeness in appearance and motion along with electromyography (EMG) to measure muscle activity in participants’ arms while they either observed or imitated videos of three agents produce actions with their right arm. The agents were a Human (biological appearance and motion), a Robot (mechanical appearance and motion), and an Android (biological appearance and mechanical motion). Right arm muscle activity increased when participants imitated all agents. Increased muscle activation was found also in the stationary arm both during imitation and observation. Furthermore, muscle activity was sensitive to motion dynamics: activity was significantly stronger for imitation of the human than both mechanical agents. There was also a relationship between the dynamics of the muscle activity and motion dynamics in stimuli. Overall our data indicate that motor simulation is not limited to observation and imitation of agents with a biological appearance, but is also found for robots. However we also found sensitivity to human motion in the EMG responses. Combining data from multiple methods allows us to obtain a more complete picture of action understanding and the underlying neural computations