Consciousness and Robot Sentience

Haikonen, Pentti O

doi:10.1142/8486

Cited by 36 publications

(16 citation statements)

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“…In other words, it is the collective, cooperative attending to a single topic that represents a computational correlate of consciousness. Recently implemented in a robot [64], this idea suggests that a machine can only be conscious of one topic at a time, and like global workspace theory (from which it differs significantly), this hypothesis is consistent with empirical evidence that conscious brain states are associated with widespread communication between cerebral cortex regions.…”

Section: Past Suggestions For Computational Correlates Of Consciousnesssupporting

confidence: 68%

“…Our view is that top-down gating fits best in the class of computational correlates of consciousness involving cognitive control. This latter class has previously focused primarily on aspects of attention mechanisms, such as the collective simultaneous attending of multiple modules to a single topic [63,64] or the generation of an "efference copy" of attention control signals [76]. Our proposed correlate of top-down gating of working memory differs from these, but appears to us to be complementary to these attention mechanisms rather than an alternative.…”

Section: Top-down Gating As a Computational Correlate Of Consciousnessmentioning

confidence: 97%

“…In other words, it is the collective, cooperative attending to a single topic that represents a computational correlate of consciousness. Recently implemented in a robot [64], this idea suggests A fifth class of computational correlates of consciousness is based on cognitive control, the executive processes that manage other cognitive processes [59]. Examples of cognitive control include directing attention, maintaining working memory, goal setting, and inhibiting irrelevant signals.…”

Section: Past Suggestions For Computational Correlates Of Consciousnessmentioning

confidence: 99%

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Exploring the Computational Explanatory Gap

2017

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While substantial progress has been made in the field known as artificial consciousness, at the present time there is no generally accepted phenomenally conscious machine, nor even a clear route to how one might be produced should we decide to try. Here, we take the position that, from our computer science perspective, a major reason for this is a computational explanatory gap: our inability to understand/explain the implementation of high-level cognitive algorithms in terms of neurocomputational processing. We explain how addressing the computational explanatory gap can identify computational correlates of consciousness. We suggest that bridging this gap is not only critical to further progress in the area of machine consciousness, but would also inform the search for neurobiological correlates of consciousness and would, with high probability, contribute to demystifying the "hard problem" of understanding the mind-brain relationship. We compile a listing of previously proposed computational correlates of consciousness and, based on the results of recent computational modeling, suggest that the gating mechanisms associated with top-down cognitive control of working memory should be added to this list. We conclude that developing neurocognitive architectures that contribute to bridging the computational explanatory gap provides a credible and achievable roadmap to understanding the ultimate prospects for a conscious machine, and to a better understanding of the mind-brain problem in general.

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Section: Past Suggestions For Computational Correlates Of Consciousnesssupporting

confidence: 68%

Section: Top-down Gating As a Computational Correlate Of Consciousnessmentioning

confidence: 97%

“…In other words, it is the collective, cooperative attending to a single topic that represents a computational correlate of consciousness. Recently implemented in a robot [64], this idea suggests A fifth class of computational correlates of consciousness is based on cognitive control, the executive processes that manage other cognitive processes [59]. Examples of cognitive control include directing attention, maintaining working memory, goal setting, and inhibiting irrelevant signals.…”

Section: Past Suggestions For Computational Correlates Of Consciousnessmentioning

confidence: 99%

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Exploring the Computational Explanatory Gap

2017

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“…Consciousness is a necessary function for the brain to perform at the full potential as a central control system of an animal. Our proposed system is close to Haikonen's robot and Franklin's system (Haikonen, 2012;Franklin et al, 2014), and to proceed in the future, it is necessary to incorporate the various functions proposed in these systems. However, our proposed system is different from their methods in the core design regarding action decision and consciousness.…”

Section: Consciousness As Awarenessmentioning

confidence: 99%

A Basic Architecture of an Autonomous Adaptive System With Conscious-Like Function for a Humanoid Robot

Kinouchi

Mackin

2018

Front. Robot. AI

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In developing a humanoid robot, there are two major objectives. One is developing a physical robot having body, hands, and feet resembling those of human beings and being able to similarly control them. The other is to develop a control system that works similarly to our brain, to feel, think, act, and learn like ours. In this article, an architecture of a control system with a brain-oriented logical structure for the second objective is proposed. The proposed system autonomously adapts to the environment and implements a clearly defined "consciousness" function, through which both habitual behavior and goaldirected behavior are realized. Consciousness is regarded as a function for effective adaptation at the system-level, based on matching and organizing the individual results of the underlying parallel-processing units. This consciousness is assumed to correspond to how our mind is "aware" when making our moment to moment decisions in our daily life. The binding problem and the basic causes of delay in Libet's experiment are also explained by capturing awareness in this manner. The goal is set as an image in the system, and efficient actions toward achieving this goal are selected in the goaldirected behavior process. The system is designed as an artificial neural network and aims at achieving consistent and efficient system behavior, through the interaction of highly independent neural nodes. The proposed architecture is based on a two-level design. The first level, which we call the "basic-system," is an artificial neural network system that realizes consciousness, habitual behavior and explains the binding problem. The second level, which we call the "extended-system," is an artificial neural network system that realizes goal-directed behavior.

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“…Haikonen has asserted that, when implemented with sufficient complexity, HCA will develop consciousness (Haikonen, 2012).…”

mentioning

confidence: 99%