Information Generation as a Functional Basis of Consciousness

Abstract

What is the biological advantage of having consciousness? Functions of consciousness have been elusive due to the subjective nature of consciousness and ample empirical evidence showing the presence of many nonconscious cognitive performances in the human brain. Drawing upon empirical literature, here, we propose that a core function of consciousness be the ability to internally generate representations of events possibly detached from the current sensory input. Such representations are constructed by generative models learned through sensory-motor interactions with the environment. We argue that the ability to generate information underlies a variety of cognitive functions associated with consciousness such as intention, imagination, planning, short-term memory, attention, curiosity, and creativity, all of which contribute to non-reflexive behaviour. According to this view, consciousness emerged in evolution when organisms gained the ability to perform internal simulations using internal models, which endowed them with flexible intelligent behaviour. To illustrate the notion of information generation, we take variational autoencoders (VAEs) as an analogy and show that information generation corresponds the decoding (or decompression) part of VAEs. In biological brains, we propose that information generation corresponds to top-down predictions in the predictive coding framework. This is compatible with empirical observations that recurrent feedback activations are linked with consciousness whereas feedforward processing alone seems to occur without evoking conscious experience. Taken together, the information generation hypothesis captures many aspects of existing ideas about potential functions of consciousness and provides new perspectives on the functional roles of consciousness.

Information Closure Theory of Consciousness

“…Note that many of the claims above are compatible with several theories of consciousness which highlight the connection between consciousness and internal simulation, predictive mechanism, or generative models inside a system (e.g., world simulation metaphor Revonsuo, 2006, predictive processing and Bayesian brain Clark, 2013;Hohwy, 2013;Seth, 2014, generative model and information generation Kanai et al, 2019). Instead of relating functional or mechanistic aspects of a system to consciousness, ICT captures common informational properties underlying those cognitive functions which are associated with consciousness.…”

Predictive processing as a systematic basis for identifying the neural correlates of consciousness

“…There has been a significant amount of research mapping a large set of properties of PP to many different aspects of conscious phenomenology (Hohwy 2013, Clark 2016, Seth 2019, Hohwy 2020. Theories of consciousness are being interpreted in the light of PP, such as Heterophenomenology (Dołęga and Dewhurst 2020), Global neuronal workspace theory (Hohwy 2013, Whyte 2019, Whyte and Smith 2020, versions of Higher-order thought theory and Metacognitive theories (Hohwy 2015, Sandved Smith, Hesp et al 2020, Attention-based theories (Marchi and Hohwy 2020), and Integrated information theory (for related discussion, see (Kanai, Chang et al 2019)).…”

The science of consciousness does not need another theory, it needs a minimal unifying model

“…For instance, it is reminiscent of Gerald Edelman’s notion of a “remembered present” ( Edelman 1989 ). Although Kanai et al (2019) do not provide a formal definition of information generation, it also resonates with formal approaches to consciousness, such as Tononi et al ’s (2016) integrated information theory (Tononi), Thagard’s and Stewart’s (2014) semantic pointer competition theory, Ruffini’s (2017) Kolmogorov theory (KT), or van Hateren’s (2019) inversed-fitness-estimate theory.…”

“…In their article “Information generation as a functional basis of consciousness,” Kanai et al (2019) suggest that information generation might constitute the functional basis of consciousness. Information generation, as defined in the article, roughly corresponds to the ability to (i) encode information in a format that allows the system to transiently keep the information in memory, in compressed form, and (ii) decompress that information to construct detailed representations (for online or offline processing).…”