Aligning human psychomotor characteristics with robots, exoskeletons and augmented reality

2021

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Psychomotor experience can be based on what people predict they will experience, rather than on sensory inputs. It has been argued that disconnects between human experience and sensory inputs can be addressed better through further development of predictive processing theory. In this paper, the scope of predictive processing theory is extended through three developments. First, by going beyond previous studies that have encompassed embodied cognition but have not addressed some fundamental aspects of psychomotor functioning. Second, by proposing a scientific basis for explaining predictive processing that spans objective neuroscience and subjective experience. Third, by providing an explanation of predictive processing that can be incorporated into the planning and operation of systems involving robots and other new technologies. This is necessary because such systems are becoming increasingly common and move us farther away from the hunter-gatherer lifestyles within which our psychomotor functioning evolved. For example, beliefs that workplace robots are threatening can generate anxiety, while wearing hardware, such as augmented reality headsets and exoskeletons, can impede the natural functioning of psychomotor systems. The primary contribution of the paper is the introduction of a new formulation of hierarchical predictive processing that is focused on psychomotor functioning.

Section: Need For Predictive Processing Formulation Focused On Psychomotor Functioningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Psychomotor Predictive Processing

2021

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“…Where there is zero relative entropy, there is zero prediction error and zero unwanted surprise from what is produced. For example, if production contributors expect that a production process will conform to specification 99 times in 100, and the predicted process capability is also 99 times per 100, then there will be zero relative entropy [ 37 , 38 ]. Relative entropy is applicable to groups and to individuals.…”

Section: Relative Entropy In Production Distributionsmentioning

confidence: 99%

An Information-Theoretic Analysis of Flexible Efficient Cognition for Persistent Sustainable Production

Kotelba

2020

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Amidst certainty, efficiency can improve sustainability by reducing resource consumption. However, flexibility is needed to be able to survive when uncertainty increases. Apropos, sustainable production cannot persist in the long-term without having both flexibility and efficiency. Referring to cognitive science to inform the development of production systems is well established. However, recent research in cognitive science encompassing flexibility and efficiency in brain functioning have not been considered previously. In particular, research by others that encompasses information (I), information entropy (H), relative entropy (D), transfer entropy (TE), and brain entropy. By contrast, in this paper, flexibility and efficiency for persistent sustainable production is analyzed in relation to these information theory applications in cognitive science and is quantified in terms of information. Thus, this paper is consistent with the established practice of referring to cognitive science to inform the development of production systems. However, it is novel in addressing the need to combine flexibility and efficiency for persistent sustainability in terms of cognitive functioning as modelled with information theory.

“…The paper is intended to contribute to bridging the gap between theoretical papers in active inference theory, which have been reported to be too opaque to be understood widely [11,12], and potential application opportunities in multi-intelligence social organization-that being social organization that employs human intelligence and different types of artificial intelligence [13]. Throughout the paper, theory-based practical examples are discussed that can provide common descriptions for scientists researching active inference and engineers who are interested in implementing active inference research in multi-intelligence social organization.…”

Section: Introductionmentioning

confidence: 99%

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

2021

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Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.