Elevated decision uncertainty and reduced avoidance drives in depression, anxiety and substance use disorders during approach-avoidance conflict: a replication study

The neurobiology of aesthetic chills: How bodily sensations shape emotional experiences

The phenomenon of aesthetic chills-shivers and goosebumps associated with either rewarding or threatening stimuli-offers a unique window into the brain basis of conscious reward because of their universal nature and simultaneous subjective and physical counterparts. Elucidating the neural mechanisms underlying aesthetic chills can reveal fundamental insights about emotion, consciousness, and the embodied mind. What is the precise timing and mechanism of bodily feedback in emotional experience? How are conscious feelings and motivations generated from interoceptive predictions? What is the role of uncertainty and precision signaling in shaping emotions? How does the brain distinguish and balance processing of rewards versus threats? We review neuroimaging evidence and highlight key questions for understanding how bodily sensations shape conscious feelings. This research stands to advance models of brain-body interactions shaping affect and may lead to novel nonpharmacological interventions for disorders of motivation and pleasure.

An Introduction to Predictive Processing Models of Perception and Decision-Making

The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision-making, and motor control. This article provides an up-to-date introduction to the two most influential theories within this framework: predictive coding and active inference. The first half of the paper (Sections 2-5) reviews the evolution of predictive coding, from early ideas about efficient coding in the visual system to a more general model encompassing perception, cognition, and motor control. The theory is characterized in terms of the claims it makes at Marr's computational, algorithmic, and implementation levels of description, and the conceptual and mathematical connections between predictive coding, Bayesian inference, and variational free energy (a quantity jointly evaluating model accuracy and complexity) are explored. The second half of the paper (Sections 6-8) turns to recent theories of active inference. Like predictive coding, active inference models assume that perceptual and learning processes minimize variational free energy as a means of approximating Bayesian inference in a biologically plausible manner. However, these models focus primarily on planning and decision-making processes that predictive coding models were not developed to address. Under active inference, an agent evaluates potential plans (action sequences) based on their expected free energy (a quantity that combines anticipated reward and information gain). The agent is assumed to represent the world as a partially observable Markov decision process with discrete time and discrete states. Current research applications of active inference models are described, including a range of simulation work, as well as studies fitting models to empirical data. The paper concludes by considering future research directions that will be important for further development of both models.

Placing Approach-Avoidance Conflict Within the Framework of Multi-objective Reinforcement Learning

Many psychiatric disorders are marked by impaired decision-making during an approach-avoidance conflict. Current experiments elicit approach-avoidance conflicts in bandit tasks by pairing an individual's actions with consequences that are simultaneously desirable (reward) and undesirable (harm). We frame approach-avoidance conflict tasks as a multi-objective multi-armed bandit. By defining a general decision-maker as a limiting sequence of actions, we disentangle the decision process from learning. Each decision maker can then be identified as a multi-dimensional point representing its long-term average expected outcomes, while different decision making models can be associated by the geometry of their 'feasible region', the set of all possible long term performances on a fixed task. We introduce three example decision-makers based on popular reinforcement learning models and characterize their feasible regions, including whether they can be Pareto optimal. From this perspective, we find that existing tasks are unable to distinguish between the three examples of decision-makers. We show how to design new tasks whose geometric structure can be used to better distinguish between decision-makers. These findings are expected to guide the design of approach-avoidance conflict tasks and the modeling of resulting decision-making behavior.