Source preference in which equally distributed risks may be valued differently has been receiving increasing attention. Using subjects recruited in Berkeley, Fox and Tversky demonstrate a familiarity bias in source preference—betting on a less than even-chance event based on San Francisco temperature is valued more than betting on a better than even-chance event based on Istanbul temperature. Neophobia is associated with the amygdala which is GABA-rich and is known to be modulated by benzodiazepines as anxiolytic agents that enhance the activity (...) of the GABAA receptor in processing anxiety and fear. This leads to our hypothesis that familiarity bias in decision making may be explained by polymorphic variations in this receptor mediated by anxiety regulation in the amygdala. In two companion studies involving Beijing-based subjects, we examine 10 single nucleotide polymorphisms of GABRB2 and find 7 SNPs each showing negative association between familiarity bias—preference for betting on parity of Beijing temperature over Tokyo temperature—and having at least one minor allele. In an imaging genetics study of a subsample of subjects based on the SNP with the most balanced allelic distribution, we find that subjects’ familiarity bias in terms of risk aversion towards bets on the parity of the temperature of 20 Chinese cities is negatively associated with their post-scanning familiarity ratings of the cities only for those with no minor allele in this SNP. Moreover, familiarity bias is positively associated with activation in the right amygdala along with the brain’s attention networks. Overall, our findings help discriminate between ambiguity aversion and familiarity bias in source preference and supports our gene–brain–behavior hypothesis of GABAergic modulation of amygdala activation in response to familiarity towards the source of uncertainty. (shrink)

In this paper I challenge the widely held assumption that loudness is the perceptual correlate of sound intensity. Drawing on psychological and neuroscientific evidence, I argue that loudness is best understood not as a representation of any feature of a sound wave, but rather as a reflection of the salience of a sound wave representation; loudness is determined by how much attention a sound receives. Loudness is what I call a quantitative character, a species of phenomenal character that is determined (...) by the amount of attention that an underlying perceptual representation commands. I distinguish quantitative from qualitative character; even qualitative characters that represent degrees of sensible magnitudes are phenomenally and functionally distinct from quantitative characters. A bifurcated account of phenomenal character emerges; the phenomenal is not exhausted by the qualitative. (shrink)

The mainstream science of consciousness offers a few predominate views of how the brain gives rise to awareness. Chief among these are the Higher-Order Thought Theory, Global Neuronal Workspace Theory, Integrated Information Theory, and hybrids thereof. In parallel, rapid development in predictive processing approaches have begun to outline concrete mechanisms by which interoceptive inference shapes selfhood, affect, and exteroceptive perception. Here, we consider these new approaches in terms of what they might offer our empirical, phenomenological, and philosophical understanding of consciousness (...) and its neurobiological roots. (shrink)

There is a steadily growing literature on the role of the immune system in psychiatric disorders. So far, these advances have largely taken the form of correlations between specific aspects of inflammation (e.g. blood plasma levels of inflammatory markers, genetic mutations in immune pathways, viral or bacterial infection) with the development of neuropsychiatric conditions such as autism, bipolar disorder, schizophrenia and depression. A fundamental question remains open: why are psychiatric disorders and immune responses intertwined? To address this would require a (...) step back from a historical mind–body dualism that has created such a dichotomy. We propose three contributions of active inference when addressing this question: translation, unification, and simulation. To illustrate these contributions, we consider the following questions. Is there an immunological analogue of sensory attenuation? Is there a common generative model that the brain and immune system jointly optimise? Can the immune response and psychiatric illness both be explained in terms of self-organising systems responding to threatening stimuli in their external environment, whether those stimuli happen to be pathogens, predators, or people? Does false inference at an immunological level alter the message passing at a psychological level (or vice versa) through a principled exchange between the two systems? (shrink)

It is easy to think of attention as a purely sensorimotor, exogenous mechanism divorced from the influence of an agent’s preferences and needs. However, according to the active inference framework, such a strict reduction cannot be straightforwardly invoked, since _all_ cognitive and behavioural processes can at least be described as maximising the evidence for a generative model entailed by the ongoing existence of that agent; that is, the minimisation of variational free energy. As such, active inference models could cast an (...) (embodied) cognitive mechanism like attention, described in this paper as a relevance filter, as constrained (or enslaved) by these prior preferences for which an agent must seek evidence, whether or not such priors are having direct, real-time neurocognitive effects on the sensorimotor loops that couple the attending agent and her surrounding environment. This duality with respect to the role of priors corresponds to a wider, ongoing debate in the active inference community regarding the framework’s explanatory power. More specifically, the debate centres on whether the notion of a generative model and the priors embedded ubiqitously therein act as a purely useful instrumental tool for scientists aiming to model the behaviours of self-organising entities, or, rather, the brain (and body) is genuinely constituted by a predictive hierarchy within which higher-order dynamics constrain and contextualise activity unfolding at lower levels. With a focus on the second (ontologically realist) construal of active inference presented here, this paper argues that in cognitive systems endowed with attentional schema, higher-order preferences do, indeed, impose a demonstrable and powerful modulating effect on the way attention unfolds. Furthermore, these preferences in question transcend the contingent, task-relevant goals that have already been shown to bias attention. Rather, attention is powerfully tuned by the most-deep rooted priors the agent possesses, such that, when sensory evidence against these priors is observed and free energy spikes, the agent attentionally prioritises the homeostatic restoration of these preferred states over their shorter-term desires. This suggests that, at its core, attention is a goal-driven process, which calls into question the putative dichotomy that exists between endogenous (goal-directed) attention and exogenous (stimulus-driven) attention. What emerges in its place is a symbiotic relationship between attention and preferences, whereby the fulfilment of the latter rests on successful application of the former, and the former derives its function from the organismic need to find evidence for the latter. (shrink)

This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as _computational phenomenology_ because it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents and evaluates (...) philosophical objections to that project and situates our version of computational phenomenology with respect to these projects. The third section reviews the generative modelling framework. The final section presents our approach in detail. We conclude by discussing how our approach differs from previous attempts to use generative modelling to help understand consciousness. In summary, we describe a version of computational phenomenology which uses generative modelling to construct a computational model of the inferential or interpretive processes that best explain this or that kind of lived experience. (shrink)

Recurrent, unvarying, and seemingly purposeless patterns of action and cognition are part of normal development, but also feature prominently in several neuropsychiatric conditions. Repetitive stereotyped behaviors can be viewed as exaggerated forms of learned habits and frequently correlate with alterations in motor, limbic, and associative basal ganglia circuits. However, it is still unclear how altered basal ganglia feedback signals actually relate to the phenomenological variability of RSBs. Why do behaviorally overlapping phenomena sometimes require different treatment approaches−for example, sensory shielding strategies (...) versus exposure therapy for autism and obsessive-compulsive disorder, respectively? Certain clues may be found in recent models of basal ganglia function that extend well beyond action selection and motivational control, and have implications for sensorimotor integration, prediction, learning under uncertainty, as well as aesthetic learning. In this paper, we systematically compare three exemplary conditions with basal ganglia involvement, obsessive-compulsive disorder, Parkinson’s disease, and autism spectrum conditions, to gain a new understanding of RSBs. We integrate clinical observations and neuroanatomical and neurophysiological alterations with accounts employing the predictive processing framework. Based on this review, we suggest that basal ganglia feedback plays a central role in preconditioning cortical networks to anticipate self-generated, movement-related perception. In this way, basal ganglia feedback appears ideally situated to adjust the salience of sensory signals through precision weighting of new sensory information, relative to the precision of predictions based on prior generated models. Accordingly, behavioral policies may preferentially rely on new data versus existing knowledge, in a spectrum spanning between novelty and stability. RSBs may then represent compensatory or reactive responses, respectively, at the opposite ends of this spectrum. This view places an important role of aesthetic learning on basal ganglia feedback, may account for observed changes in creativity and aesthetic experience in basal ganglia disorders, is empirically testable, and may inform creative art therapies in conditions characterized by stereotyped behaviors. (shrink)