| Literature DB >> 35399356 |
Aviv Mokady1, Niv Reggev1,2.
Abstract
The predictive processing framework posits that people continuously use predictive principles when interacting with, learning from, and interpreting their surroundings. Here, we suggest that the same framework may help explain how people process self-relevant knowledge and maintain a stable and positive self-concept. Specifically, we recast two prominent self-relevant motivations, self-verification and self-enhancement, in predictive processing (PP) terms. We suggest that these self-relevant motivations interact with the self-concept (i.e., priors) to create strong predictions. These predictions, in turn, influence how people interpret information about themselves. In particular, we argue that these strong self-relevant predictions dictate how prediction error, the deviation from the original prediction, is processed. In contrast to many implementations of the PP framework, we suggest that predictions and priors emanating from stable constructs (such as the self-concept) cultivate belief-maintaining, rather than belief-updating, dynamics. Based on recent findings, we also postulate that evidence supporting a predicted model of the self (or interpreted as such) triggers subjective reward responses, potentially reinforcing existing beliefs. Characterizing the role of rewards in self-belief maintenance and reframing self-relevant motivations and rewards in predictive processing terms offers novel insights into how the self is maintained in neurotypical adults, as well as in pathological populations, potentially pointing to therapeutic implications.Entities:
Keywords: belief maintenance; motivations; predictive processing; reward; self-concept; self-enhancement; self-verification
Year: 2022 PMID: 35399356 PMCID: PMC8987106 DOI: 10.3389/fnhum.2022.824085
Source DB: PubMed Journal: Front Hum Neurosci ISSN: 1662-5161 Impact factor: 3.169
Figure 1Illustration of the proposed model. Self-Knowledge serves as an inner model of the self (i.e., prior) from which predictions are derived. The (context-dependent) dominant self-relevant motivation, be it self-verification, self-enhancement, or another form of motivation, mediates these predictions. These predictions are compared with inputs from the world, leading to a match or a mismatch. The outcomes of these comparisons lead to one of several possible outcomes. The less likely outcomes (indicated by broken lines in the figure) include model updating (in the unlikely case of belief-updating following prediction-inconsistent information) and input-dismissal (when receiving information that seems extremely irrelevant to the self). More likely outcomes (indicated by solid lines) include self-knowledge maintenance via active inference or prediction-consistent input. The more likely outcomes (grouped by the broken lined box) also generate a reward response (money bag), thus further increasing their likelihood to be used in the future.