The Human Affectome.

Over the last decades, theoretical perspectives in the interdisciplinary field of the affective sciences have proliferated rather than converged due to differing assumptions about what human affective phenomena are and how they work. These metaphysical and mechanistic assumptions, shaped by academic context and values, have dictated affective constructs and operationalizations. However, an assumption about the purpose of affective phenomena can guide us to a common set of metaphysical and mechanistic assumptions. In this capstone paper, we home in on a nested teleological principle for human affective phenomena in order to synthesize metaphysical and mechanistic assumptions. Under this framework, human affective phenomena can collectively be considered algorithms that either adjust based on the human comfort zone (affective concerns) or monitor those adaptive processes (affective features). This teleologically-grounded framework offers a principled agenda and launchpad for both organizing existing perspectives and generating new ones. Ultimately, we hope the Human Affectome brings us a step closer to not only an integrated understanding of human affective phenomena, but an integrated field for affective research.

Towards a neurocomputational account of social controllability: From models to mental health.

Controllability, or the influence one has over their surroundings, is crucial for decision-making and mental health. Traditionally, controllability is operationalized in sensorimotor terms as one's ability to exercise their actions to achieve an intended outcome (also termed "agency"). However, recent social neuroscience research suggests that humans also assess if and how they can exert influence over other people (i.e., their actions, outcomes, beliefs) to achieve desired outcomes ("social controllability"). In this review, we will synthesize empirical findings and neurocomputational frameworks related to social controllability. We first introduce the concepts of contextual and perceived controllability and their respective relevance for decision-making. Then, we outline neurocomputational frameworks that can be used to model social controllability, with a focus on behavioral economic paradigms and reinforcement learning approaches. Finally, we discuss the implications of social controllability for computational psychiatry research, using delusion and obsession-compulsion as examples. Taken together, we propose that social controllability could be a key area of investigation in future social neuroscience and computational psychiatry research.