Towards a computational theory of experience

Fekete, Tomer; Edelman, Shimon

doi:10.1016/j.concog.2011.02.010

Cited by 48 publications

(58 citation statements)

References 61 publications

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“…The phenomenological consequence of the HSP thesis is that the world looks the way it does "because evolution." This is fair enough, but in light of the notion that phenomenal awareness is first and foremost the awareness of distinctions and differences (Fekete and Edelman, 2011), and assuming that phenomenality is subject to evolution (Cleeremans, 2008;Ginsburg and Jablonka, 2010), we must hypothesize, in addition, that there are some things about the world that evolving agents get right.…”

Section: Discussionmentioning

confidence: 99%

Varieties of perceptual truth and their possible evolutionary roots

Edelman

2014

Psychon Bull Rev

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Hoffman, Singh, and Prakash (2014) observe that perception evolves to serve as an interface between the perceiver and the world and proceed to reason that percepts need not, or even cannot, resemble their objects. I accept their premise, but argue that there are interesting ways in which perception can be truthful, with regard not to "objects" but to relations, and that evolutionary pressure is expected to favor rather than rule out such veridicality.

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Section: Discussionmentioning

confidence: 99%

Varieties of perceptual truth and their possible evolutionary roots

Edelman

2014

Psychon Bull Rev

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“…However, others have provided evidences towards a contradictory view, where reductionism in symbolic tracker effectively degrades quality of consciousness model [25]. It is illustrated that, neurological phenomena of consciousness and experiences are not instantaneous neuronal activities and, the retention (memory) has a role into it [3]. Furthermore, the expression of experiences has a computational basis, which can be modeled by mathematical frameworks [17,19,20].…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

Distributed Computation using Evolutionary Consciousness : An Approach

Bagchi¹

2015

IJCIS

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The modeling of biological phenomena and its adaptations to distributed computing are promising research areas. The computational modeling of neurobiological phenomena, such as cognition and consciousness, has potential for applications into bio-inspired distributed computing. The functioning of neurological structures is inherently distributed in nature having a closer match to distributed computing. This paper proposes a mathematical model of state of consciousness by following the functional neurophysiology as well as elements of distributed computing. The scopes of evolution of consciousness and memory are incorporated into the model. The nodal classifications and formation of structural hierarchy in distributed computing nodes by incorporating elements of cognitive model are investigated. Evaluation of the model is made by numerical simulation considering different choice functions. The results illustrate that, model of consciousness can be adapted to bio-inspired distributed computing structures and the gradual evolution of consciousness is deterministic under fair excitations from environment.

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“…The term "trajectory" as used here refers to a temporal sequence of a neural system's activity states, much as is pictured in Figure 3, rather than to a single instantaneous activity state. Viewing a temporal sequence of activity patterns as a correlate of subjective experience overcomes several objections that exist for single activity states as correlates [35].…”

Section: Past Suggestions For Computational Correlates Of Consciousnessmentioning

confidence: 99%

“…This refers to the intrinsic properties of the neural representation of information used in cognitive states/processes. For example, a number of theoretical arguments and several early recurrent neural network models of consciousness were based on hypothesizing that neural activity attractor states are associated with or characterize consciousness [13,[35][36][37][38][39]. At any point in time, a recurrent neural network has an activity state, a pattern of active and inactive neurons that represents/encodes the contents of the network's current state ( Figure 3).…”

Section: Past Suggestions For Computational Correlates Of Consciousnessmentioning

confidence: 99%

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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Towards a computational theory of experience

Cited by 48 publications

References 61 publications

Varieties of perceptual truth and their possible evolutionary roots

Varieties of perceptual truth and their possible evolutionary roots

Distributed Computation using Evolutionary Consciousness : An Approach

Exploring the Computational Explanatory Gap

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