An interdisciplinary overview of developmental indices and behavioral measures of the minimal self

Georgie, Yasmin Kim; Schillaci, Guido; Hafner, Verena V.

doi:10.1109/devlrn.2019.8850703

Cited by 26 publications

(20 citation statements)

References 49 publications

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“…As a result, experiencegrounded CGPMs will be effectively learned and exploited, while exploring and manipulating the encountered environment. This environment may be our actual world, which may be explored with a robot [37,67] or an agentive system, which could also interact with a simulated reality.…”

Section: Inductive Learning and Processing Biasesmentioning

confidence: 99%

Towards Strong AI

Butz

2021

Künstl Intell

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Strong AI—artificial intelligence that is in all respects at least as intelligent as humans—is still out of reach. Current AI lacks common sense, that is, it is not able to infer, understand, or explain the hidden processes, forces, and causes behind data. Main stream machine learning research on deep artificial neural networks (ANNs) may even be characterized as being behavioristic. In contrast, various sources of evidence from cognitive science suggest that human brains engage in the active development of compositional generative predictive models (CGPMs) from their self-generated sensorimotor experiences. Guided by evolutionarily-shaped inductive learning and information processing biases, they exhibit the tendency to organize the gathered experiences into event-predictive encodings. Meanwhile, they infer and optimize behavior and attention by means of both epistemic- and homeostasis-oriented drives. I argue that AI research should set a stronger focus on learning CGPMs of the hidden causes that lead to the registered observations. Endowed with suitable information-processing biases, AI may develop that will be able to explain the reality it is confronted with, reason about it, and find adaptive solutions, making it Strong AI. Seeing that such Strong AI can be equipped with a mental capacity and computational resources that exceed those of humans, the resulting system may have the potential to guide our knowledge, technology, and policies into sustainable directions. Clearly, though, Strong AI may also be used to manipulate us even more. Thus, it will be on us to put good, far-reaching and long-term, homeostasis-oriented purpose into these machines.

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Section: Inductive Learning and Processing Biasesmentioning

confidence: 99%

Towards Strong AI

Butz

2021

Künstl Intell

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“…To address these questions, and as our main contribution, we look into recent articles defining the prerequisites of an artificial self (Schillaci et al, 2016;Georgie et al, 2019;Hafner et al, 2020;Nguyen et al, 2021) and relate these prerequisites with verbal and non-verbal dialog methods for computational agents and reinforcement learning. In section 2, we survey the developmental processes of humans to ground language in embodied sensorimotor representations of the self and its surrounding world.…”

Section: Scientific Rationale and Contribution Of This Reviewmentioning

confidence: 99%

The Embodied Crossmodal Self Forms Language and Interaction: A Computational Cognitive Review

Röder¹,

Özdemir²,

Nguyen³

et al. 2021

Front. Psychol.

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Human language is inherently embodied and grounded in sensorimotor representations of the self and the world around it. This suggests that the body schema and ideomotor action-effect associations play an important role in language understanding, language generation, and verbal/physical interaction with others. There are computational models that focus purely on non-verbal interaction between humans and robots, and there are computational models for dialog systems that focus only on verbal interaction. However, there is a lack of research that integrates these approaches. We hypothesize that the development of computational models of the self is very appropriate for considering joint verbal and physical interaction. Therefore, they provide the substantial potential to foster the psychological and cognitive understanding of language grounding, and they have significant potential to improve human-robot interaction methods and applications. This review is a first step toward developing models of the self that integrate verbal and non-verbal communication. To this end, we first analyze the relevant findings and mechanisms for language grounding in the psychological and cognitive literature on ideomotor theory. Second, we identify the existing computational methods that implement physical decision-making and verbal interaction. As a result, we outline how the current computational methods can be used to create advanced computational interaction models that integrate language grounding with body schemas and self-representations.

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“…Pre-reflective stands for something that is experienced before rationally thinking about it, and mainly relates to the perception of our own body and the feeling of being in control of our own movements. This aspect of consciousness is perhaps the most easily accessible in terms of experimental exploration and quantification, and a number of measures and behavioral paradigms have been proposed in the literature (see Georgie et al, 2019 for a review). Empirical research supports the idea that such low-level subjective experiences rely on self-monitoring mechanisms and on predictive processes implemented by our brains.…”

Section: Gsmentioning

confidence: 99%

“…I would also look at more robust methods to quantify subjective experience. In a recent paper, we discussed different paradigms and measures used in cognitive and brain sciences, and reviewed related robotics studies (Georgie et al, 2019). What would constitute a successful demonstration of artificial consciousness (Spatola and Urbanska, 2018)?…”

Section: Gsmentioning

confidence: 99%

Why Build a Robot With Artificial Consciousness? How to Begin? A Cross-Disciplinary Dialogue on the Design and Implementation of a Synthetic Model of Consciousness

Smith

Schillaci

2021

Front. Psychol.

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Creativity is intrinsic to Humanities and STEM disciplines. In the activities of artists and engineers, for example, an attempt is made to bring something new into the world through counterfactual thinking. However, creativity in these disciplines is distinguished by differences in motivations and constraints. For example, engineers typically direct their creativity toward building solutions to practical problems, whereas the outcomes of artistic creativity, which are largely useless to practical purposes, aspire to enrich the world aesthetically and conceptually. In this essay, an artist (DHS) and a roboticist (GS) engage in a cross-disciplinary conceptual analysis of the creative problem of artificial consciousness in a robot, expressing the counterfactual thinking necessitated by the problem, as well as disciplinary differences in motivations, constraints, and applications. We especially deal with the question of why one would build an artificial consciousness and we consider how an illusionist theory of consciousness alters prominent ethical debates on synthetic consciousness. We discuss theories of consciousness and their applicability to synthetic consciousness. We discuss practical approaches to implementing artificial consciousness in a robot and conclude by considering the role of creativity in the project of developing an artificial consciousness.

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An interdisciplinary overview of developmental indices and behavioral measures of the minimal self

Cited by 26 publications

References 49 publications

Towards Strong AI

Towards Strong AI

The Embodied Crossmodal Self Forms Language and Interaction: A Computational Cognitive Review

Why Build a Robot With Artificial Consciousness? How to Begin? A Cross-Disciplinary Dialogue on the Design and Implementation of a Synthetic Model of Consciousness

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