Asia Pacific Consciousness Network

Can we learn without conscious awareness? Numerous evidences in the research of implicit learning have indicated that people can learn the statistical structure of the stimuli but seemingly without any awareness of its underlying rules. However, it remains unclear what types of knowledge can be learned in implicit learning, what is the relationship between conscious and unconscious knowledge, and what are the neural substrates for the acquisition of conscious and unconscious knowledge. In this talk, I will discuss with you about these ongoing questions.

Visual perceptual learning (VPL) is defined as improved performance on a visual task due to visual experience. It was once argued that attention to a visual feature is necessary for VPL of the feature to occur. Contrary to this view, a phenomenon called task-irrelevant VPL demonstrated that VPL can occur due to exposure to a feature which is sub-threshold and task-irrelevant, and therefore, unattended. A series of findings based on task-irrelevant VPL has indicated the following two mechanisms. First, attention to a feature facilitates VPL of the feature while inhibiting VPL of unattended and supra-threshold features. Second, reward paired with a feature enables VPL of the feature irrespective of whether the feature is attended or not. However, we recently found an additional twist; VPL of a task-irrelevant and supra-threshold feature embedded in a natural scene is not subject to the inhibition of attention. This new finding suggests a need to revise the current view or add a new mechanism as to how VPL occurs.

In the first part of this talk, I will briefly present the unfolding argument by Doerig et al. (2019) and the various replies in the philosophical and neuroscientific literature. In the second part of the talk, I will explore the ramifications that this debate has for the science of consciousness and its philosophy, with particular focus on these questions: (i) which type of explanation should a theory of consciousness provide? (ii) what is the evidential basis for theories of consciousness?

I will build from Niccolo's discussion of the Blockhead argument to argue that having an FeedForward Network (FN) responding like an recurrent network (RN) in a consciousness experiment is not enough to convince us the two are the same with regards to the posession of mental states and conscious experience. I will then argue that a robust functional equivalence between FFN and RN is akso not supported by the mathematical work on the Universal Approximator theorem, and is also unlikely to hold, as a conjecture, given data in cognitive neuroscience; I will argue that an equivalence of RN and FFN may only apply to static functions between input/output layers and not to the temporal patterns or to the network's reactions to structural perturbations. Finally, I review data indicating that consciousness has functional characteristics, such as a flexible control of behavior, and that cognitive/brain dynamics reveal interacting top-down and bottom-up processes, which are necessary for the mediation of such control processes.

In this talk, I’ll introduce the formal concept of a (pre)sheaf as data attached to a topological space. Sheaves capture the notion of patching local sources of information to form a global whole, e.g., the binding of visual features such as colour and shape. The formal theory appears to be closely related to the foundational properties asserted by the Information Integration Theory (IIT) for phenomenology. A comparison is intended to engender discussion on ways that phenomenology may benefit from a sheaf theory, or (more generally) a category theory approach.

Neuroscientific methods successfully account for a system’s functional properties, but leave out the subjective properties of the accompanying experience. According to IIT, phenomenology can be studied scientifically by unfolding the cause-effect structure specified by a system. To illustrate how, in this talk I compare two systems (a grid and a map) to show that they can be functionally equivalent in performing fixation, but only one can specify a cause-effect structure that accounts for the extendedness of phenomenal space.

It is generally agreed upon that qualities of conscious experience instantiate structural properties, usually called relations. They furnish a representation of qualities (or qualia, in fact) in terms of a mathematical space Q (rather than a set), which is crucial to both modelling and measuring of conscious experience." "What is usually disregarded is that “only such structural properties generalize across individuals” (Austen Clark), but that qualities themselves as differentiated by stimulus specifications, behavior or reports do not. We show that this implies that only the part of Q which is invariant with respect to the automorphism group has a well-defined referent, while individual elements do not. This poses a prima facie limitation of any theory or experiment that aims to address individual qualities. We show how mathematical theories of consciousness can overcome this limitation via symmetry groups and group actions, making accessible to science what is properly called private language.

Category theory is a branch of mathematics which have been used to organize various regions of mathematics and related sciences from a radical “relation-first” point of view. Why consciousness researchers should care about category theory? " "There are (at least) 3 reasons:" "1 Everything is relational" "2 Everything is relation" "3 Relation is everything" "In this talk we explain the reasons above more concretely and introduce the ideas to utilize basic concepts in category theory for consciousness studies.

The tight relationship between attention and conscious perception has been extensively researched in the past decades. However, whether attentional modulation extended to unconscious processes remained largely unknown, particularly when it came to abstract and high-level processing. I will talk about a recent study where we utilized the Stroop paradigm to show that task load gates unconscious semantic processing. In a series of psychophysical experiments, the unconscious word semantics influenced conscious task performance only under the low task load condition, but not the high task load condition. Intriguingly, with enough practice in the high task load condition, the unconscious effect reemerged. These findings suggest a competition of attentional resources between unconscious and conscious processes, challenging the automaticity account of unconscious processing.

In this talk, I shall not argue for any single thesis or theory in the realm of the (un)consciousness and (in)attention. Instead I will discuss specific examples where philosophers and psychologists can have genuine collaborations in this area. Since issues concerning phenomenological overflow is already too familiar for this audience, I will briefly discuss it only, and focus on other issues that have not been overworked. The exact contents are to be determined, but I will perhaps focus on recent controversies over “sustained representation of perspectival shape” (Morales, Bax, and Firestone, 2020, 2021).

Tom Froese is Assistant Professor at the Okinawa Institute of Science and Technology Graduate University (OIST), where he heads the Embodied Cognitive Science Unit. He is a cognitive scientist with a background in phenomenological philosophy, human-computer interaction, and complex systems theory. His interdisciplinary research centers on the role of agent-environment interaction in shaping cognition and consciousness, specifically when the interaction process involves sociality and technology. In this talk he will present current work in progress on “irruption theory”, a new theory of consciousness that integrates an embodied-enactive account of basic mind with radical formulations of the freedom and efficacy of intentional agency.

The brain is a prediction machine. Yet the world is never entirely predictable, for any animal. Unexpected events are surprising and this typically evokes prediction error signatures in animal brains. In humans such mismatched expectations are often associated with an emotional response as well. Appropriate emotional responses are understood to be important for memory consolidation, suggesting that valence cues more generally constitute an ancient mechanism designed to potently refine and generalize internal models of the world and thereby minimize prediction errors. On the other hand, abolishing error detection and surprise entirely is probably also maladaptive, as this might undermine the very mechanism that brains use to become better prediction machines. This paradoxical view of brain functions as an ongoing tug-of-war between prediction and surprise suggests a compelling new way to study and understand the evolution of consciousness in animals. I will present approaches to studying attention and prediction in the tiny brain of the fruit fly, Drosophila melanogaster. I will discuss how an ‘active’ sleep stage (termed rapid eye movement – REM – sleep in mammals) may have evolved in the first animal brains as a mechanism for optimizing prediction in motile creatures confronted with constantly changing environments. A role for REM sleep in emotional regulation could thus be better understood as an ancient sleep function that evolved alongside selective attention to maintain an adaptive balance between prediction and surprise. This view of active sleep has some interesting implications for the evolution of subjective awareness and consciousness.

Agent-causal libertarian free will asserts that the conscious agent is the ultimate cause of her own voluntary behavior. A major reason to reject libertarian free will is that it seems incompatible with our current knowledge of physics. In this talk I will argue how quantum processes, specifically non-random collapses of the wavefunction in the human cortex, may enable libertarian free will. I will discuss how this account can be empirically tested.

Recent years have seen a considerable surge of research on interest-based engagement, examining how and why people are engaged in activities without relying on extrinsic rewards. However, the field of inquiry has been somewhat segregated into three different research traditions which have been developed relatively independently -- research on curiosity, interest, and trait curiosity/interest. The current talk sets out an integrative perspective; the reward-learning framework of knowledge acquisition. This conceptual framework takes on the basic premise of existing reward-learning models of information seeking: that knowledge acquisition serves as an inherent reward, which reinforces people’s information-seeking behavior through a reward-learning process. However, the framework reveals how the knowledge-acquisition process is sustained and boosted over a long period of time in real-life settings, allowing us to integrate the different research traditions within reward-learning models. The framework also characterizes the knowledge-acquisition process with four distinct features that are not present in the reward-learning process with extrinsic rewards -- (1) cumulativeness, (2) selectivity, (3) vulnerability, and (4) under-appreciation. The talk describes some evidence from our lab supporting these claims.