Toward physics of the mind: Concepts, emotions, consciousness, and symbols

Perlovsky, Leonid

doi:10.1016/j.plrev.2005.11.003

Cited by 216 publications

(254 citation statements)

References 52 publications

(69 reference statements)

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“…Therefore humans and higher animals have an inborn drive to fit top-down and bottom-up signals. We call this mechanism the knowledge instinct, KI [13,23-25]. This mechanism is similar to other instincts [13, 26]) in that our mind has a sensor-like mechanism that measures a similarity between top-down and bottom-up signals, between concept-models and sensory percepts.…”

Section: The Knowledge Instinctmentioning

confidence: 99%

“…We call this mechanism the knowledge instinct, KI [13,23-25]. This mechanism is similar to other instincts [13, 26]) in that our mind has a sensor-like mechanism that measures a similarity between top-down and bottom-up signals, between concept-models and sensory percepts. Brain areas participating in the knowledge instinct were discussed in [27].…”

Section: The Knowledge Instinctmentioning

confidence: 99%

“…Mathematically, CC is related to formal logic, which turned out to be used by most mathematical procedures, even by those specifically designed to overcome logic limitations, such as neural networks and fuzzy logic [25,29-31,64]. The mathematics capable of overcoming CC, dynamic logic (DL), which models the process of satisfaction of KI, while overcoming CC has been developed in [13,23-25,32-34]. In several cases it was mathematically proved that DL achieves the best possible performance [35-38].…”

Section: The Knowledge Instinctmentioning

confidence: 99%

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Neurally and Mathematically Motivated Architecture for Language and Thought

Perlovsky

Ilin²

2010

TONIJ

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Neural structures of interaction between thinking and language are unknown. This paper suggests a possible architecture motivated by neural and mathematical considerations. A mathematical requirement of computability imposes significant constraints on possible architectures consistent with brain neural structure and with a wealth of psychological knowledge. How language interacts with cognition. Do we think with words, or is thinking independent from language with words being just labels for decisions? Why is language learned by the age of 5 or 7, but acquisition of knowledge represented by learning to use this language knowledge takes a lifetime? This paper discusses hierarchical aspects of language and thought and argues that high level abstract thinking is impossible without language. We discuss a mathematical technique that can model the joint language-thought architecture, while overcoming previously encountered difficulties of computability. This architecture explains a contradiction between human ability for rational thoughtful decisions and irrationality of human thinking revealed by Tversky and Kahneman; a crucial role in this contradiction might be played by language. The proposed model resolves long-standing issues: how the brain learns correct words-object associations; why animals do not talk and think like people. We propose the role played by language emotionality in its interaction with thought. We relate the mathematical model to Humboldt’s “firmness” of languages; and discuss possible influence of language grammar on its emotionality. Psychological and brain imaging experiments related to the proposed model are discussed. Future theoretical and experimental research is outlined.

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Section: The Knowledge Instinctmentioning

confidence: 99%

Section: The Knowledge Instinctmentioning

confidence: 99%

Section: The Knowledge Instinctmentioning

confidence: 99%

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Neurally and Mathematically Motivated Architecture for Language and Thought

Perlovsky

Ilin²

2010

TONIJ

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“…Not only does culture accumulate over time, but it adapts, diversifies, becomes increasingly complex, and exhibits phenomena observed in biological evolution such as niches, drift, epistasis, and punctuated equilibrium [5,22,29,32]. Processes of both biological and cultural evolution tend to gravitate toward a balance between differentiation (or divergence) and synthesis (or convergence) of different forms [98,99]. Moreover, like biological evolution, culture is open-ended; there is no limit to the variety of new forms it can give rise to.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary framework for cultural change: Selectionism versus communal exchange

Gabora

2013

Physics of Life Reviews

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Dawkins' replicator-based conception of evolution has led to widespread misapplication of selectionism across the social sciences because it does not address the paradox that inspired the theory of natural selection in the first place: how do organisms accumulate change when traits acquired over their lifetime are obliterated? This is addressed by von Neumann's concept of a self-replicating automaton (SRA). A SRA consists of a self-assembly code that is used in two distinct ways: (1) actively deciphered during development to construct a self-similar replicant, and (2) passively copied to the replicant to ensure that it can reproduce. Information that is acquired over a lifetime is not transmitted to offspring, whereas information that is inherited during copying is transmitted. In cultural evolution there is no mechanism for discarding acquired change. Acquired change can accumulate orders of magnitude faster than, and quickly overwhelm, inherited change due to differential replication of variants in response to selection. This prohibits a selectionist but not an evolutionary framework for culture and the creative processes that fuel it. The importance non-Darwinian processes in biological evolution is increasingly recognized. Recent work on the origin of life suggests that early life evolved through a non-Darwinian process referred to as communal exchange that does not involve a self-assembly code, and that natural selection emerged from this more haphazard, ancestral evolutionary process. It is proposed that communal exchange provides an evolutionary framework for culture that enables specification of cognitive features necessary for a (real or artificial) society to evolve culture. This is supported by a computational model of cultural evolution and a conceptual network based program for documenting material cultural history, and it is consistent with high levels of human cooperation. Evolutionary Framework Culture 3Keywords: creativity, cultural evolution, meme, natural selection, replicator, selfreplicating automaton Evolutionary Framework Culture 4 IntroductionLike organisms, elements of culture exhibit descent with modification; new ideas and artifacts build adaptively on previous ones. If it were possible to root the social and behavioral sciences in an evolutionary framework, they might achieve a unification comparable with Darwin's unification of the life sciences. Thus it is unsurprising that, dating back to Herbert Spencer's introduction of the notion of social Darwinism a few years after Darwin's Origin of Species, Darwinian thinking has been applied to a range of phenomena outside of biology, including creativity [14,111], neural copying and pruning [11,12,13,23,26], law [55], cosmology [118], computer-mediated communication [74], and perhaps most extensively, cultural and economic change [7,8,15,71,86,87,107,108]. Elements of culture build on one another cumulatively, as demonstrated even in laboratory settings [10]. Not only does culture accumulate over time, but it adapts, diversifies,...

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“…In MFT, at every level in the hierarchy, the afferent signals are represented by the input signal field X, and the efferent signals are represented by the modeling field signals M h ; resonances correspond to high similarity measures l(n|h) for some subsets of {n} that are "recognized" as concepts (or objects) h. This mechanism, leading to the resonances eqs. (3)(4)(5)(6), is a thought-process. In this process, subsets of signals corresponding to objects or situations are understood as concepts, signals acquire meanings and become accessible by consciousness.…”

Section: Consciousness and Sapiencementioning

confidence: 99%

Sapience, Consciousness, and the Knowledge Instinct (Prolegomena to a Physical Theory)

Perlovsky

Toward Artificial Sapience

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The chapter describes a mathematical theory of sapience and consciousness: higher mental abilities including abilities for concepts, emotions, instincts, understanding, imagination, intuition, beautiful, and sublime. The knowledge instinct drives our understanding of the world. Aesthetic emotions, our needs for beautiful and sublime are related to the knowledge instinct. I briefly discuss neurobiological grounds as well as difficulties encountered by previous attempts at mathematical modeling of the mind encountered since the 1950s. Dynamic logic, the mathematics of the knowledge instinct, is related to cognitive and philosophical discussions about the mind and sapience.

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Toward physics of the mind: Concepts, emotions, consciousness, and symbols

Cited by 216 publications

References 52 publications

Neurally and Mathematically Motivated Architecture for Language and Thought

Neurally and Mathematically Motivated Architecture for Language and Thought

An evolutionary framework for cultural change: Selectionism versus communal exchange

Sapience, Consciousness, and the Knowledge Instinct (Prolegomena to a Physical Theory)

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