Criteria for consciousness in humans and other mammals

Seth, Anil K.; Baars, Bernard J.; Edelman, David B.

doi:10.1016/j.concog.2004.08.006

Cited by 223 publications

(158 citation statements)

References 94 publications

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“…At the neural level, no single measure could adequately describe the complexity of the underlying brain system (this would be akin, for example, to claiming that the complex state of the economy could be described by the gross domestic product alone). At the phenomenal level, conscious scenes have many diverse features (1,14), several of which do not appear to be quantifiable by a single measure (see Table 1). These features include subjectivity, the attribution of conscious experience to a self, and intentionality, which reflects the observation that consciousness is largely about events and objects.…”

mentioning

confidence: 99%

“…Application of quantitative measures may also contribute to comparative studies of consciousness. The attribution of conscious states to non-human animals is made difficult by their inability verbally to report the contents of their putative con-sciousness (14). A quantitative measure of relevant complexity might provide one criterion for assessing the relative degree of consciousness in such non-human animals (15).…”

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confidence: 99%

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Theories and measures of consciousness: An extended framework

Seth

Izhikevich

Reeke

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

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257

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A recent theoretical emphasis on complex interactions within neural systems underlying consciousness has been accompanied by proposals for the quantitative characterization of these interactions. In this article, we distinguish key aspects of consciousness that are amenable to quantitative measurement from those that are not. We carry out a formal analysis of the strengths and limitations of three quantitative measures of dynamical complexity in the neural systems underlying consciousness: neural complexity, information integration, and causal density. We find that no single measure fully captures the multidimensional complexity of these systems, and all of these measures have practical limitations. Our analysis suggests guidelines for the specification of alternative measures which, in combination, may improve the quantitative characterization of conscious neural systems. Given that some aspects of consciousness are likely to resist quantification altogether, we conclude that a satisfactory theory is likely to be one that combines both qualitative and quantitative elements.complexity ͉ differentiation ͉ dynamic core ͉ integration

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mentioning

confidence: 99%

mentioning

confidence: 99%

Theories and measures of consciousness: An extended framework

Seth

Izhikevich

Reeke

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

257

214

View full text Add to dashboard Cite

show abstract

“…This conclusion is hardly surprising, since even in situations where people are not conscious the existence of feature binding is expected. Currently there is an intense debate about the possibility that some animals may have consciousness (Baars, 2005;Boly et al, 2013;Droege & Braithwaite, 2015;Fabbro et al, 2015;Seth et al, 2005). Although this discussion is still in progress, there is no doubt that many animals are capable of forming one integral image of objects observed in the environment.…”

Section: Relation With Consciousnessmentioning

confidence: 99%

Neural Binding, Consciousness, and Mental Disorders: Complexity as a Common Element

Assis

2015

Act Nerv Super

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“…A prominent theory of global brain function that has been applied to consciousness, the TNGS, proposes that consciousness is entailed by complex interactions among neural populations in the thalamocortical system, the socalled dynamic core (Edelman 2003;Edelman and Tononi 2000;Tononi and Edelman 1998;Seth and Baars 2005). This proposal raises the question: How can these complex interactions best be characterized quantitatively?…”

Section: Causal Density and Consciousnessmentioning

confidence: 99%

“…Conscious scenes have many diverse features, several of which do not appear to be readily quantifiable (Edelman 2003;Seth et al 2005Seth et al , 2006. These features include subjectivity, the attribution of conscious experience to a self, and intentionality, which reflects the observation that consciousness is largely about events and objects.…”

Section: Causal Densitymentioning

confidence: 99%

Causal networks in simulated neural systems

Seth

2007

Cogn Neurodyn

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Neurons engage in causal interactions with one another and with the surrounding body and environment. Neural systems can therefore be analyzed in terms of causal networks, without assumptions about information processing, neural coding, and the like. Here, we review a series of studies analyzing causal networks in simulated neural systems using a combination of Granger causality analysis and graph theory. Analysis of a simple targetfixation model shows that causal networks provide intuitive representations of neural dynamics during behavior which can be validated by lesion experiments. Extension of the approach to a neurorobotic model of the hippocampus and surrounding areas identifies shifting causal pathways during learning of a spatial navigation task. Analysis of causal interactions at the population level in the model shows that behavioral learning is accompanied by selection of specific causal pathways-''causal cores''-from among large and variable repertoires of neuronal interactions. Finally, we argue that a causal network perspective may be useful for characterizing the complex neural dynamics underlying consciousness.

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Criteria for consciousness in humans and other mammals

Cited by 223 publications

References 94 publications

Theories and measures of consciousness: An extended framework

Theories and measures of consciousness: An extended framework

Neural Binding, Consciousness, and Mental Disorders: Complexity as a Common Element

Causal networks in simulated neural systems

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