Feeling our place in the world: an active inference account of self-esteem

Abstract

Self-esteem, the evaluation of one's own worth or value, is a critical aspect of psychological well-being and mental health. In this paper, we propose an active inference account of self-esteem, casting it as a sociometer or an inferential capacity to interpret one's standing within a social group. This approach allows us to explore the interaction between an individual's self-perception and the expectations of their social environment.When there is a mismatch between these perceptions and expectations, the individual needs to adjust their actions or update their self-perception to better align with their current experiences. We also consider this hypothesis in relation with recent research on affective inference, suggesting that self-esteem enables the individual to track and respond to this discrepancy through affective states such as anxiety or positive affect. By acting as an inferential sociometer, self-esteem allows individuals to navigate and adapt to their social environment, ultimately impacting their psychological well-being and mental health.

Keywords: active inference; model; self-esteem; sociometer.

Figure 1.

Hierarchical message-passing architectures of self-esteem. The top equation illustrates this process, employing a mean field approximation to approximate posterior beliefs. This approximation involves the product of marginal distributions over hierarchical levels and points in time. The generative model represents the joint probability of observed outcomes and their latent causes. The Dⁱ arrays demonstrate the initial state , given the state of the level above, and they are referred to as the empirical priors. The probability of an outcome given hidden states is called the likelihood (Aⁱ). Next, we define matrix Bⁱ as the transition probabilities of the next state given the current state contingent on policies π. The preference vectors Cⁱ encode the degree of desirability of associated outcomes or preferences. The policy is sampled from a Gibbs distribution or softmax function of the path integral of expected free energy (G) multiplied by with inverse temperature or precision γ. The temperature parameter modulates the difference between the expected free energy under posterior and prior beliefs regarding policies.