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

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.

Shared Protentions in Multi-Agent Active Inference.

In this paper, we unite concepts from Husserlian phenomenology, the active inference framework in theoretical biology, and category theory in mathematics to develop a comprehensive framework for understanding social action premised on shared goals. We begin with an overview of Husserlian phenomenology, focusing on aspects of inner time-consciousness, namely, retention, primal impression, and protention. We then review active inference as a formal approach to modeling agent behavior based on variational (approximate Bayesian) inference. Expanding upon Husserl's model of time consciousness, we consider collective goal-directed behavior, emphasizing shared protentions among agents and their connection to the shared generative models of active inference. This integrated framework aims to formalize shared goals in terms of shared protentions, and thereby shed light on the emergence of group intentionality. Building on this foundation, we incorporate mathematical tools from category theory, in particular, sheaf and topos theory, to furnish a mathematical image of individual and group interactions within a stochastic environment. Specifically, we employ morphisms between polynomial representations of individual agent models, allowing predictions not only of their own behaviors but also those of other agents and environmental responses. Sheaf and topos theory facilitates the construction of coherent agent worldviews and provides a way of representing consensus or shared understanding. We explore the emergence of shared protentions, bridging the phenomenology of temporal structure, multi-agent active inference systems, and category theory. Shared protentions are highlighted as pivotal for coordination and achieving common objectives. We conclude by acknowledging the intricacies stemming from stochastic systems and uncertainties in realizing shared goals.

Determinants of extinction in a streamed trial procedure.

The strength of an association between a cue and its outcome is influenced by both the probability of the outcome given the cue and the probability of the outcome in the absence of the cue. Once an association has been formed, extinction is the procedure for reducing responding indicative of the association by repeated presentation of the cue without the outcome. The present experiments tested whether cumulative frequency and/or cumulative duration of these events affects associative extinction in a streamed trial extinction procedure with human participants. Experiment 1 assessed the effects of parametric manipulations of the frequency and duration of either the cue by itself or cue-outcome co-absence. In Experiment 1, participants proved relatively insensitive to manipulation of the event's duration. In contrast, judgements of the association by participants decreased when the frequency of cue-alone events was increased, even when the durations of those events were decreased so that cumulative exposure to the cue was equated. No effect of either the duration or the frequency of cue-outcome co-absence was observed. Experiment 2 demonstrated that the effect of cue-alone (i.e., extinction trial) frequency generalises across a wide range of parameters for initial acquisition achieved by cue-outcome pairings. Experiment 3 tested for an interaction between event duration during initial learning and event duration during extinction. Collectively, these results indicate that the cumulative frequency, and not the cumulative duration, of extinction trials as well as the duration of the cue-outcome co-absences between extinction trials control the effectiveness of an extinction procedure.

Drift-Diffusion Model Reveals Impaired Reward-Based Perceptual Decision-Making Processes Associated with Depression in Late Childhood and Early Adolescent Girls.

Adolescent girls are a high-risk stratum for the emergence of depression. Previous research has established that depression is associated with blunted responses to rewards. Research using Drift Diffusion Model (DDM) has found that deficits in accumulating reward-based evidence characterize adult depression. However, little is known about how reduced reward sensitivity is reflected in the computational processes involved in reward-based decision-making in late childhood and early adolescent depression.One hundred and sixty-six 8- to 14-year-old girls completed a probabilistic reward-based decision-making task. Participants were instructed to identify which one of two similar visual stimuli were presented, and correct responses were rewarded with unequal probabilities. Analysis using hierarchical DDM quantified rate of evidence accumulation (i.e., drift rate) and starting point. Depression severity was measured using the Children's Depression Inventory.Across all participants, there was a higher drift rate, indicating faster evidence accumulation, for the more frequently rewarded than the less frequently rewarded decision. In addition, the starting point of the evidence accumulation was closer to the more frequently rewarded decision, indicating a starting point bias. Higher depression severity was associated with a slower drift rate for both types of decisions. Higher depression severity was associated with a smaller starting point bias towards the more frequently rewarded decision.The current study uses computational modeling to reveal that late childhood and early adolescent girls with greater depression demonstrate impairments in the reward-related evidence accumulation process.

Benefiting from trial spacing without the cost of prolonged training: Frequency, not duration, of trials with absent stimuli enhances perceived contingency.

The statistical relation between two events influences the perception of how one event relates to the presence or absence of another. Interestingly, the simultaneous absence of both events, just like their mutual occurrence, is relevant for describing their contingency. In three experiments, we explored the relevance of coabsent events by varying the duration and frequency of trials without stimuli. We used a rapid trial streaming procedure and found that the perceived association between events is enhanced with increasing frequency of coabsent events, unlike the duration of coabsent events, which had little effect. These findings suggest ways in which the benefits of trial spacing, during which both events are absent, could be obtained without increasing total training time. Centrally, this can be done by frequent repeating of shortened coabsent events, each marked by a trial contextual cue. We discuss four potential accounts of how coabsent experience might be processed contributing to this effect: (a) contingency sensitivity, (b) testing effect, (c) reduced associative interference by the context, and (d) reduced encoding interference. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Resilience and active inference.

In this article, we aim to conceptualize and formalize the construct of resilience using the tools of active inference, a new physics-based modeling approach apt for the description and analysis of complex adaptive systems. We intend this as a first step toward a computational model of resilient systems. We begin by offering a conceptual analysis of resilience, to clarify its meaning, as established in the literature. We examine an orthogonal, threefold distinction between meanings of the word "resilience": (i) inertia, or the ability to resist change (ii) elasticity, or the ability to bounce back from a perturbation, and (iii) plasticity, or the ability to flexibly expand the repertoire of adaptive states. We then situate all three senses of resilience within active inference. We map resilience as inertia onto high precision beliefs, resilience as elasticity onto relaxation back to characteristic (i.e., attracting) states, and resilience as plasticity onto functional redundancy and structural degeneracy.