Ryan Smith

Multiple measures of decision-making under uncertainty (e.g. jumping to conclusions (JTC), bias against disconfirmatory evidence (BADE), win-switch behavior, random exploration) have been associated with delusional thinking in independent studies. Yet, it is unknown whether these variables explain shared or unique variance in delusional thinking, and whether these relationships are specific to paranoia or delusional ideation more broadly. Additionally, the underlying computational mechanisms require further investigation. To investigate these questions, task and self-report data were collected in 88 individuals (46 healthy controls, 42 schizophrenia-spectrum) and included measures of cognitive biases and behavior on probabilistic reversal learning and explore/exploit tasks. Of those, only win-switch rate significantly differed between groups. In regression, reversal learning performance, random exploration, and poor evidence integration during BADE showed significant, independent associations with paranoia. Only self-reported JTC was associated with delusional ideation, controlling for paranoia. Computational parameters increased the proportion of variance explained in paranoia. Overall, decision-making influenced by strong volatility and variability is specifically associated with paranoia, whereas self-reported hasty decision-making is specifically associated with other themes of delusional ideation. These aspects of decision-making under uncertainty may therefore represent distinct cognitive processes that, together, have the potential to worsen delusional thinking across the psychosis spectrum.

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.

There is a persisting tension that exists between the block universe conception of time in modern physics and philosophy and the conception of time that stems naturally from experience, and entropic asymmetries have been proposed to explain this tension. This article argues that as biochemical processes in the brain depend upon spontaneous entropy increases in the forward-time direction, this should provide an entropic basis for the unidirectionality of psychological processes. As this view does not depend on considerations of abstract information processing or a past hypothesis, it provides advantages over previous entropy-based proposals attempting to explain asymmetries in temporal experience.

The purpose of the present article is to explore the relationship between consciousness and understanding. To do so, I first briefly review recent work on the nature of both understanding and consciousness within philosophy and psychology. Building off of this work, I then defend the thesis that if one is conscious of a given content then one also understands that content. I argue that this conclusion can be drawn from the fact that understanding is associated with rational intention formation and the fact that conscious access appears to involve the selective routing/broadcasting of representational content to neural systems that integrate information in order to select cognitive/behavioral intentions in conjunction with goals. Based on these premises I illustrate how a disruption to the rationality of a representation’s influence on intention formation would also remove any evidence that a person was conscious of the content of that representation...