Action-based effects on music perception

The classical, disembodied approach to music cognition conceptualizes action and perception as separate, peripheral processes. In contrast, embodied accounts of music cognition emphasize the central role of the close coupling of action and perception. It is a commonly established fact that perception spurs action tendencies. We present a theoretical framework that captures the ways in which the human motor system and its actions can reciprocally influence the perception of music. The cornerstone of this framework is the common coding theory, postulating a representational overlap in the brain between the planning, the execution, and the perception of movement. The integration of action and perception in so-called internal models is explained as a result of associative learning processes. Characteristic of internal models is that they allow intended or perceived sensory states to be transferred into corresponding motor commands (inverse modeling), and vice versa, to predict the sensory outcomes of planned actions (forward modeling). Embodied accounts typically refer to inverse modeling to explain action effects on music perception (Leman, 2007). We extend this account by pinpointing forward modeling as an alternative mechanism by which action can modulate perception. We provide an extensive overview of recent empirical evidence in support of this idea. Additionally, we demonstrate that motor dysfunctions can cause perceptual disabilities, supporting the main idea of the paper that the human motor system plays a functional role in auditory perception. The finding that music perception is shaped by the human motor system and its actions suggests that the musical mind is highly embodied. However, we advocate for a more radical approach to embodied (music) cognition in the sense that it needs to be considered as a dynamical process, in which aspects of action, perception, introspection, and social interaction are of crucial importance

Brain-inspired conscious computing architecture

What type of artificial systems will claim to be conscious and will claim to experience qualia? The ability to comment upon physical states of a brain-like dynamical system coupled with its environment seems to be sufficient to make claims. The flow of internal states in such system, guided and limited by associative memory, is similar to the stream of consciousness. Minimal requirements for an artificial system that will claim to be conscious were given in form of specific architecture named articon. Nonverbal discrimination of the working memory states of the articon gives it the ability to experience different qualities of internal states. Analysis of the inner state flows of such a system during typical behavioral process shows that qualia are inseparable from perception and action. The role of consciousness in learning of skills, when conscious information processing is replaced by subconscious, is elucidated. Arguments confirming that phenomenal experience is a result of cognitive processes are presented. Possible philosophical objections based on the Chinese room and other arguments are discussed, but they are insufficient to refute claims articon’s claims. Conditions for genuine understanding that go beyond the Turing test are presented. Articons may fulfill such conditions and in principle the structure of their experiences may be arbitrarily close to human

Grip Force Reveals the Context Sensitivity of Language-Induced Motor Activity during “Action Words

Studies demonstrating the involvement of motor brain structures in language processing typically focus on \ud time windows beyond the latencies of lexical-semantic access. Consequently, such studies remain inconclusive regarding whether motor brain structures are recruited directly in language processing or through post-linguistic conceptual imagery. In the present study, we introduce a grip-force sensor that allows online measurements of language-induced motor activity during sentence listening. We use this tool to investigate whether language-induced motor activity remains constant or is modulated in negative, as opposed to affirmative, linguistic contexts. Our findings demonstrate that this simple experimental paradigm can be used to study the online crosstalk between language and the motor systems in an ecological and economical manner. Our data further confirm that the motor brain structures that can be called upon during action word processing are not mandatorily involved; the crosstalk is asymmetrically\ud governed by the linguistic context and not vice versa

Implicit memory

The Encyclopedia of Human Behavior, Second Edition is a comprehensive three-volume reference source on human action and reaction, and the thoughts, feelings, and physiological functions behind those actions

Mirroring, mindreading and smart behaviour-reading

This paper examines the claim that mirror neuron activity is the mechanism by which we come to know about the action-related intentions of others (e.g. Gallese et al 1996, Rizzolatti et al 2009), i.e. that they are a mechanism for ‘mindreading’. I agree with recent authors (e.g. Hickok 2008, Jacob 2008) who reject this view but nevertheless I argue that mirror neurons may still have a role to play in the ways in which we understand one another (social cognition). If we adopt a certain kind of pluralism about social cognition then the mirror neuron system could play a role in social cognition even if it provides no access to the minds of others at all. I argue for this view and consider what the approach might entail for the ontology of the mirror neuron system

Motion as manipulation: Implementation of motion and force analogies by event-file binding and action planning\ud

Tool improvisation analogies are a special case of motion and force analogies that appear to be implemented pre-conceptually, in many species, by event-file binding and action planning. A detailed reconstruction of the analogical reasoning steps involved in Rutherford's and Bohr's development of the first quantized-orbit model of atomic structure is used to show that human motion and force analogies generally can be implemented by the event-file binding and action planning mechanism. Predictions that distinguish this model from competing concept-level models of analogy are discussed, available data pertaining to them are reviewed, and further experimental tests are proposed

Motivations, Values and Emotions: 3 sides of the same coin

This position paper speaks to the interrelationships between the three concepts of motivations, values, and emotion. Motivations prime actions, values serve to choose between motivations, emotions provide a common currency for values, and emotions implement motivations. While conceptually distinct, the three are so pragmatically intertwined as to differ primarily from our taking different points of view. To make these points more transparent, we briefly describe the three in the context a cognitive architecture, the LIDA model, for software agents and robots that models human cognition, including a developmental period. We also compare the LIDA model with other models of cognition, some involving learning and emotions. Finally, we conclude that artificial emotions will prove most valuable as implementers of motivations in situations requiring learning and development

Automatic imitation of multiple agents: a computational model

There is accumulating evidence that the actions of others are represented in the motor system, leading to automatic imitation. However, whereas early work focused mainly on the effects of observing a single agent, recent studies indicate that the actions of multiple agents can be represented simultaneously. Yet, theorizing has lagged behind. The current study extends the dual-route model of automatic imitation to include multiple agents, and demonstrates, in five simulation studies, that the extended model is able to capture four critical multi-agent effects. Importantly, however, it was necessary to augment the model with a control mechanism regulating response inhibition based on the number of observed actions. Furthermore, additional simulation indicated that this mechanism could be driven by response conflict. Together, our results demonstrate how theories of automatic imitation can be extended from single- to multi-agent settings. As such, they constitute an important step towards a mechanistic understanding of social interaction beyond the dyad

A neurocomputational account of self-other distinction: from cell to society

Human social systems are unique in the animal kingdom. Social norms, constructed at a higher level of organisation, influence individuals across vast spatiotemporal scales. Characterising the neurocomputational processes that enable the emergence of these social systems could inform holistic models of human cognition and mental illness. Social neuroscience has shown that the processing of ‘social’ information demands many of the same computations as those involved in reasoning about inanimate objects in ‘non-social’ contexts. However, for people to reason about each other’s mental states, the brain must be able to distinguish between one mind and another. This ability, to attribute a mental state to a specific agent, has long been studied by philosophers under the guise of ‘meta-representation’. Empathy research has taken strides in describing the neural correlates of representing another person’s affective or bodily state, as distinct from one’s own. However, Self-Other distinction in beliefs, and hence meta-representation, has not figured in formal models of cognitive neuroscience. Here, I introduce a novel behavioural paradigm, which acts as a computational assay for Self-Other distinction in a cognitive domain. The experiments in this thesis combine computational modelling with magnetoencephalography and functional magnetic resonance imaging to explore how basic units of computation, predictions and prediction errors, are selectively attributed to Self and Other, when subjects have to simulate another agent’s learning process. I find that these low-level learning signals encode information about agent identity. Furthermore, the fidelity of this encoding is susceptible to experience-dependent plasticity, and predicts the presence of subclinical psychopathological traits. The results suggest that the neural signals generating an internal model of the world contain information, not only about ‘what’ is out there, but also about ‘who’ the model belongs to. That this agent-specificity is learnable highlights potential computational failure modes in mental illnesses with an altered sense of Self