Active inference models do not contradict folk psychology

Abstract

Active inference offers a unified theory of perception, learning, and decision-making at computational and neural levels of description. In this article, we address the worry that active inference may be in tension with the belief–desire–intention (BDI) model within folk psychology because it does not include terms for desires (or other conative constructs) at the mathematical level of description. To resolve this concern, we first provide a brief review of the historical progression from predictive coding to active inference, enabling us to distinguish between active inference formulations of motor control (which need not have desires under folk psychology) and active inference formulations of decision processes (which do have desires within folk psychology). We then show that, despite a superficial tension when viewed at the mathematical level of description, the active inference formalism contains terms that are readily identifiable as encoding both the objects of desire and the strength of desire at the psychological level of description. We demonstrate this with simple simulations of an active inference agent motivated to leave a dark room for different reasons. Despite their consistency, we further show how active inference may increase the granularity of folk-psychological descriptions by highlighting distinctions between drives to seek information versus reward—and how it may also offer more precise, quantitative folk-psychological predictions. Finally, we consider how the implicitly conative components of active inference may have partial analogues (i.e., “as if” desires) in other systems describable by the broader free energy principle to which it conforms.

Restoring the missing person to personalized medicine and precision psychiatry

“…The implication for research is that we need to study the interplay of self-understanding and interpersonal interactions with biomarkers, symptoms, and the course of illness and treatment response. Although computational modeling is already starting to advance a formal model of the descriptions of lived experience (Ramstead et al, 2022), the social dimensions of illness and their link to the causal processes that contribute to health and illness are not included in most studies (Dumas et al, 2020). Incorporating measures of patients' self-understanding can improve the generalizability of findings and promote more effective clinical knowledge translation and intervention.…”

“…In some cases, correlational studies are essentially redescriptions of plausible information-processing mechanisms based on an intuitive understanding of everyday functioning. Of course, neuroscientific research and modeling can lead to refinements in our everyday concepts of mental function ( Genon et al, 2018 ; Bielczyk et al, 2019 ; Smith et al, 2022 ), but the relationship between these folk concepts and underlying brain mechanisms may be complex, context-dependent and many-to-many, defying any simple isomorphic mapping ( Shulman, 2013 ; Passingham and Rowe, 2016 ).…”

Why can’t we say what cognition is (at least for the time being)

“…Closer to us, the "dark room" argument against predictive processing views of cognition is based on the intuitive idea that, if all our brain tries to do is to predict the incoming inputs as accurately as possible, our brains (and thus, we) should crave very predictable and boring environments. But we clearly don't crave them, so predictive processing must be wrong (Sims, 2017;Smith et al, 2022).…”

Entropy, prediction and the cultural ecosystem of human cognition

“…In active inference formulations of PP, it is expected free energy that is minimised by proximate mechanisms. Thus, the proximate mechanisms are temporally extended, considering future free energy that is a consequence of different action policies (Smith et al, 2022). Nevertheless, cultural practices are supposed to be a key ingredient in this large process of entropy reduction.…”