Tom Froese

In August 2021, Froese et al. published survey data collected from 2,543 respondents on their subjective experiences living under imposed social distancing measures during COVID-19 (1). The questionnaire was issued to respondents in the UK, Japan, and Mexico. By combining the authors’ expertise in phenomenological philosophy, phenomenological psychopathology, and enactive cognitive science, the questions were carefully phrased to prompt reports that would be useful to phenomenological investigation and theorizing (2–4). These questions reflected the various author’s research interests (e.g., technology, grief, time). Between April 7th and July 31st, 2021, a second questionnaire with the same question set was issued to respondents of the original who had agreed to do a follow-up. This was intended to capture subjective reports of life under social distancing measures a year after the initial survey. By this time–depending on their country of residence and health status–respondents had potentially lived with repeated and prolonged lockdowns and a variety of other restrictions on their social lives. When taken together, Survey I and Survey II provide a cross-cultural and longitudinal dataset that allows for analysis of longer-term impacts of imposed social distancing measures on people’s experiences. For researchers working in diverse disciplines, this dataset offers a rich resource that reflects people’s reactions to the imposition of different social restrictions in different countries and over different time periods.

Biological agents can act in ways that express a sensitivity to context-dependent relevance. So far it has proven difficult to engineer this capacity for context-dependent sensitivity to relevance in artificial agents. We give this problem the label the “problem of meaning”. The problem of meaning could be circumvented if artificial intelligence researchers were to design agents based on the assumption of the continuity of life and mind. In this paper, we focus on the proposal made by enactive cognitive scientists to design artificial agents that possess sensorimotor autonomy—stable, self-sustaining patterns of sensorimotor interaction that can ground values, norms and goals necessary for encountering a meaningful environment. More specifically, we consider whether the Free Energy Principle (FEP) can provide formal tools for modeling sensorimotor autonomy. There is currently no consensus on how to understand the relationship between enactive cognitive science and the FEP. However, a number of recent papers have argued that the two frameworks are fundamentally incompatible. Some argue that biological systems exhibit historical path-dependent learning that is absent from systems that minimize free energy. Others have argued that a free energy minimizing system would fail to satisfy a key condition for sensorimotor agency referred to as “interactional asymmetry”. These critics question the claim we defend in this paper that the FEP can be used to formally model autonomy and adaptivity. We will argue it is too soon to conclude that the two frameworks are incompatible. There are undeniable conceptual differences between the two frameworks but in our view each has something important and necessary to offer. The FEP needs enactive cognitive science for the solution it provides to the problem of meaning. Enactive cognitive science needs the FEP to formally model the properties it argues to be constitutive of agency. Our conclusion will be that active inference models based on the FEP provides a way by which scientists can think about how to address the problems of engineering autonomy and adaptivity in artificial agents in formal terms. In the end engaging more closely with this formalism and its further developments will benefit those working within the enactive framework.