An integrated theory of language production and comprehension

Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume that actors construct forward models of their actions before they execute those actions, and that perceivers of others' actions covertly imitate those actions, then construct forward models of those actions. We use these accounts of action, action perception, and joint action to develop accounts of production, comprehension, and interactive language. Importantly, they incorporate well-defined levels of linguistic representation (such as semantics, syntax, and phonology). We show (a) how speakers and comprehenders use covert imitation and forward modeling to make predictions at these levels of representation, (b) how they interweave production and comprehension processes, and (c) how they use these predictions to monitor the upcoming utterances. We show how these accounts explain a range of behavioral and neuroscientific data on language processing and discuss some of the implications of our proposal

Towards a complete multiple-mechanism account of predictive language processing [Commentary on Pickering & Garrod]

Although we agree with Pickering & Garrod (P&G) that prediction-by-simulation and prediction-by-association are important mechanisms of anticipatory language processing, this commentary suggests that they: (1) overlook other potential mechanisms that might underlie prediction in language processing, (2) overestimate the importance of prediction-by-association in early childhood, and (3) underestimate the complexity and significance of several factors that might mediate prediction during language processing

Frontiers in psychodynamic neuroscience

he term psychodynamics was introduced in 1874 by Ernst von Brücke, the renowned German physiologist and Freud’s research supervisor at the University of Vienna. Together with Helmholtz and others, Brücke proposed that all living organisms are energy systems, regulated by the same thermodynamic laws. Since Freud was a student of Brücke and a deep admirer of Helmholtz, he adopted this view, thus laying the foundations for his metapsychology. The discovery of the Default Network and the birth of Neuropsychoanalysis, twenty years ago, facilitated a deep return to this classical conception of the brain as an energy system, and therefore a return to Freud's early ambition to establish psychology as natural science. Our current investigations of neural networks and applications of the Free Energy Principle are equally ‘psychodynamic’ in Brücke’s original sense of the term. Some branches of contemporary neuroscience still eschew subjective data and therefore exclude the brain’s most remarkable property – its selfhood – from the field, and many neuroscientists remain skeptical about psychoanalytic methods, theories, and concepts. Likewise, some psychoanalysts continue to reject any consideration of the structure and functions of the brain from their conceptualization of the mind in health and disease. Both cases seem to perpetuate a Cartesian attitude in which the mind is linked to the brain in some equivocal relationship and an attitude that detaches the brain from the body -- rather than considering it an integral part of the complex and dynamic living organism as a whole. Evidence from psychodynamic neuroscience suggests that Freudian constructs can now be realized neurobiologically. For example, Freud’s notion of primary and secondary processes is consistent with the hierarchical organization of self-organized cortical and subcortical systems, and his description of the ego is consistent with the functions of the Default Network and its reciprocal exchanges with subordinate brain systems. Moreover, thanks to new methods of measuring brain entropy, we can now operationalize the primary and secondary processes and therefore test predictions arising from these Freudian constructs. All of this makes it possible to deepen the dialogue between neuroscience and psychoanalysis, in ways and to a degree that was unimaginable in Freud's time, and even compared to twenty years ago. Many psychoanalytical hypotheses are now well integrated with contemporary neuroscience. Other Freudian and post-Freudian hypotheses about the structure and function of the mind seem ripe for the detailed and sophisticated development that modern psychodynamic neuroscience can offer. This Research Topic aims to provide comprehensive coverage of the latest advances in psychodynamic neuroscience and neuropsychoanalysis. Potential authors are invited to submit papers (original research, case reports, review articles, commentaries) that deploy, review, compare or develop the methods and theories of psychodynamic neuroscience and neuropsychoanalysis. Potential authors include researchers, psychoanalysts, and neuroscientists

Seven Computations of the Social Brain

The social environment presents the human brain with the most complex of information processing demands. The computations that the brain must perform occur in parallel, combine social and nonsocial cues, produce verbal and non-verbal signals, and involve multiple cognitive systems; including memory, attention, emotion, learning. This occurs dynamically and at timescales ranging from milliseconds to years. Here, we propose that during social interactions, seven core operations interact to underwrite coherent social functioning; these operations accumulate evidence efficiently – from multiple modalities – when inferring what to do next. We deconstruct the social brain and outline the key components entailed for successful human social interaction. These include (1) social perception; (2) social inferences, such as mentalizing; (3) social learning; (4) social signaling through verbal and non-verbal cues; (5) social drives (e.g., how to increase one’s status); (6) determining the social identity of agents, including oneself; and (7) minimizing uncertainty within the current social context by integrating sensory signals and inferences. We argue that while it is important to examine these distinct aspects of social inference, to understand the true nature of the human social brain, we must also explain how the brain integrates information from the social world

Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception

Mechanistic insight is achieved only when experiments are employed to test formal or computational models. Furthermore, in analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying healthy auditory perception. With a special focus on tinnitus—as the prime example of auditory phantom perception—we review recent work at the intersection of artificial intelligence, psychology and neuroscience. In particular, we discuss why everyone with tinnitus suffers from (at least hidden) hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that intrinsic neural noise is generated and amplified along the auditory pathway as a compensatory mechanism to restore normal hearing based on adaptive stochastic resonance. The neural noise increase can then be misinterpreted as auditory input and perceived as tinnitus. This mechanism can be formalized in the Bayesian brain framework, where the percept (posterior) assimilates a prior prediction (brain’s expectations) and likelihood (bottom-up neural signal). A higher mean and lower variance (i.e. enhanced precision) of the likelihood shifts the posterior, evincing a misinterpretation of sensory evidence, which may be further confounded by plastic changes in the brain that underwrite prior predictions. Hence, two fundamental processing principles provide the most explanatory power for the emergence of auditory phantom perceptions: predictive coding as a top-down and adaptive stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles also play a crucial role in healthy auditory perception. Finally, in the context of neuroscience-inspired artificial intelligence, both processing principles may serve to improve contemporary machine learning techniques

Individual Differences in Speech Production and Perception

Inter-individual variation in speech is a topic of increasing interest both in human sciences and speech technology. It can yield important insights into biological, cognitive, communicative, and social aspects of language. Written by specialists in psycholinguistics, phonetics, speech development, speech perception and speech technology, this volume presents experimental and modeling studies that provide the reader with a deep understanding of interspeaker variability and its role in speech processing, speech development, and interspeaker interactions. It discusses how theoretical models take into account individual behavior, explains why interspeaker variability enriches speech communication, and summarizes the limitations of the use of speaker information in forensics

Embodied prediction

Versions of the “predictive brain” hypothesis rank among the most promising and the most conceptually challenging visions ever to emerge from computational and cognitive neuroscience. In this paper, I briefly introduce (section 1) the most radical and comprehensive of these visions —the account of “active inference”, or “action-oriented predictive processing” (Clark 2013a), developed by Karl Friston and colleagues. In section 2, I isolate and discuss four of the framework’s most provocative claims: (i) that the core flow of information is top-down, not bottom-up, with the forward flow of sensory information replaced by the forward flow of prediction error; (ii) that motor control is just more top-down sensory prediction; (iii) that efference copies, and distinct “controllers”, can be replaced by top-down predictions; and (iv) that cost functions can fruitfully be replaced by predictions. Working together, these four claims offer a tantalizing glimpse of a new, integrated framework for understanding perception, action, embodiment, and the nature of human experience. I end (section 3) by sketching what may be the most important aspect of the emerging view: its ability to embed the use of fast and frugal solutions (as highlighted by much work in robotics and embodied cognition) within an over-arching scheme that includes more structured, knowledge-intensive strategies, combining these fluently and continuously as task and context dictate

The interplay between movement and perception: how interaction can influence sensorimotor performance and neuromotor recovery

openMovement and perception interact continuously in daily activities. Motor output changes the outside world and affect perceptual representations. Similarly, perception has consequences on movement. Nevertheless, how movement and perception influence each other and share information is still an open question. Mappings from movement to perceptual outcome and vice versa change continuously throughout life. For example, a cerebrovascular accident (stroke) elicits in the nervous system a complex series of reorganization processes at various levels and with different temporal scales. Functional recovery after a stroke seems to be mediated by use-dependent reorganization of the preserved neural circuitry. The goal of this thesis is to discuss how interaction with the environment can influence the progress of both sensorimotor performance and neuromotor recovery. I investigate how individuals develop an implicit knowledge of the ways motor outputs regularly correlate with changes in sensory inputs, by interacting with the environment and experiencing the perceptual consequences of self-generated movements. Further, I applied this paradigm to model the exercise-based neurorehabilitation in stroke survivors, which aims at gradually improving both perceptual and motor performance through repeated exercise. The scientific findings of this thesis indicate that motor learning resolve visual perceptual uncertainty and contributes to persistent changes in visual and somatosensory perception. Moreover, computational neurorehabilitation may help to identify the underlying mechanisms of both motor and perceptual recovery, and may lead to more personalized therapies.openXXXII CICLO - BIOINGEGNERIA E ROBOTICA - BIOENGINEERING AND ROBOTICS - Bioengineering and bioelectronicsSedda, Giuli