Recently, the mechanistic framework of active inference has been put forward as a principled foundation to develop an overarching theory of consciousness which would help address conceptual disparities in the field (Wiese 2018 ; Hohwy and Seth 2020 ). For that promise to bear out, we argue that current proposals resting on the active inference scheme need refinement to become a process theory of consciousness. One way of improving a theory in mechanistic terms is to use formalisms such as computational (...) models that implement, attune and validate the conceptual notions put forward. Here, we examine how computational modelling approaches have been used to refine the theoretical proposals linking active inference and consciousness, with a focus on the extent and success to which they have been developed to accommodate different facets of consciousness and experimental paradigms, as well as how simulations and empirical data have been used to test and improve these computational models. While current attempts using this approach have shown promising results, we argue they remain preliminary in nature. To refine their predictive and structural validity, testing those models against empirical data is needed i.e., new and unobserved neural data. A remaining challenge for active inference to become a theory of consciousness is to generalize the model to accommodate the broad range of consciousness explananda; and in particular to account for the phenomenological aspects of experience. Notwithstanding these gaps, this approach has proven to be a valuable avenue for theory advancement and holds great potential for future research. (shrink)

As interpersonal, racial, social, and international conflicts intensify in the world, it is important to safeguard the mental health of individuals affected by them. According to a Buddhist notion “if you want others to be happy, practice compassion; if you want to be happy, practice compassion,” compassion practice is an intervention to cultivate conflict-proof well-being. Here, compassion practice refers to a form of concentrated meditation wherein a practitioner attunes to friend, enemy, and someone in between, thinking, “I’m going to help (...) them.” The compassion meditation is based on Buddhist philosophy that mental suffering is rooted in conceptual thoughts that give rise to generic mental images of self and others and subsequent biases to preserve one’s egoism, blocking the ultimate nature of mind. To contextualize compassion meditation scientifically, we adopted a Bayesian active inference framework to incorporate relevant Buddhist concepts, including mind, compassion, aggregates, suffering, reification, conceptual thoughts, and superimposition. In this framework, a person is considered a Bayesian Engine that actively constructs phenomena based on the aggregates of forms, sensations, discriminations, actions, and consciousness. When the person embodies rigid beliefs about self and others’ identities and the resulting ego-preserving bias, the person’s Bayesian Engine malfunctions, failing to use prediction errors to update prior beliefs. To counter this problem, after recognizing the causes of sufferings, a practitioner of the compassion meditation aims to attune to all others equally, friends and enemies alike, suspend identity-based conceptual thoughts, and eventually let go of any identity-grasping belief and ego-preserving bias that obscure reality. We present a brain model for the Bayesian Engine of three components: Relation-Modeling, Reality-Checking, and Conflict-Alarming, which are subserved by the Default-Mode Network, Frontoparietal Network and Ventral Attention Network, and Salience Network, respectively. Upon perceiving conflicts, the strengthening or weakening of ego-preserving bias will critically depend on whether the SN up-regulates the DMN or FPN/VAN, respectively. We propose that compassion meditation can strengthen brain regions that are conducive for suspending prior beliefs and enhancing the attunements to the counterparts in conflicts. (shrink)

Deep sleep has traditionally been deemed as a period that lack consciousness. However, recent evidence challenges this assumption and highlights the possibility of experiencing awareness during deep sleep. By drawing from Indian philosophical traditions, this thesis defends a positive view of consciousness during dreamless sleep – the period of sleep where we are aware but not dreaming. Moreover, this thesis also challenges classic views on the nature of conscious states where consciousness is reduced to representational content. For that purpose, this (...) thesis develops a taxonomy of dreamless sleep that, contrary to other research in the area, considers the existence of different sorts of awareness with different degrees of content. Thus, a full spectrum of mental states during conscious dreamless sleep are considered: from awareness of substantial content to awareness devoid of content altogether. The aim of the thesis is to show how such a taxonomy can encourage a shift from a two-dimensional view of consciousness to a multidimensional one — from a view that only considers brain activation and reportable content to one that assesses conscious states on different level of analysis. This shift will result in an adequate conceptualisation of dreamless sleep as a period where we could have conscious states with content, but also, conscious states without content. Moreover, this shift on a multidimensional approach will allow us to undertake a proper comparison compare between different sleep phenomena. In order to develop this taxonomy of dreamless sleep, I explore definitions on awareness during dreamless sleep found in Indian philosophical traditions and Western analytical philosophy. From the definitions found in the literature, I argue that all the types of conscious states proposed in my taxonomy can be identified. Then, I test my taxonomy in a pilot study about awareness during deep sleep that I carried out. The results show that the different types of awareness can be recognised in the phenomenological descriptions gathered from the participants. Finally, I compare the phenomenon of dreamless sleep with other associated experiences, such as dreams and hypnagogia. My claim is that the differences among sleep phenomena are gradual and that in some cases there is not a strict cut-off line between them. Notwithstanding this assumption, I defend the development of future research were my proposed taxonomy can be improved by considering typical cases of dreamless sleep. (shrink)

We present a theoretical view of the cellular foundations for network-level processes involved in producing our conscious experience. Inputs to apical synapses in layer 1 of a large subset of neocortical cells are summed at an integration zone near the top of their apical trunk. These inputs come from diverse sources and provide a context within which the transmission of information abstracted from sensory input to their basal and perisomatic synapses can be amplified when relevant. We argue that apical amplification (...) enables conscious perceptual experience and makes it more flexible, and thus more adaptive, by being sensitive to context. Apical amplification provides a possible mechanism for recurrent processing theory that avoids strong loops. It makes the broadcasting hypothesized by global neuronal workspace theories feasible while preserving the distinct contributions of the individual cells receiving the broadcast. It also provides mechanisms that contribute to the holistic aspects of integrated information theory. As apical amplification is highly dependent on cholinergic, aminergic, and other neuromodulators, it relates the specific contents of conscious experience to global mental states and to fluctuations in arousal when awake. We conclude that apical dendrites provide a cellular mechanism for the context-sensitive selective amplification that is a cardinal prerequisite of conscious perception. (shrink)