Incorporating social knowledge structures into computational models

Abstract

To navigate social interactions successfully, humans need to continuously learn about the personality traits of other people (e.g., how helpful or aggressive is the other person?). However, formal models that capture the complexities of social learning processes are currently lacking. In this study, we specify and test potential strategies that humans can employ for learning about others. Standard Rescorla-Wagner (RW) learning models only capture parts of the learning process because they neglect inherent knowledge structures and omit previously acquired knowledge. We therefore formalize two social knowledge structures and implement them in hybrid RW models to test their usefulness across multiple social learning tasks. We name these concepts granularity (knowledge structures about personality traits that can be utilized at different levels of detail during learning) and reference points (previous knowledge formalized into representations of average people within a social group). In five behavioural experiments, results from model comparisons and statistical analyses indicate that participants efficiently combine the concepts of granularity and reference points—with the specific combinations in models depending on the people and traits that participants learned about. Overall, our experiments demonstrate that variants of RW algorithms, which incorporate social knowledge structures, describe crucial aspects of the dynamics at play when people interact with each other.

A Fluid Self-Concept: How the Brain Maintains Coherence and Positivity across an Interconnected Self-Concept While Incorporating Social Feedback

“…As one model of how people structure social knowledge, our trait network model suggests that people have directed, causal beliefs about the semantic dependencies between traits, which they use to maintain coherent (non-contradictory) beliefs when self-evaluating and incorporating social feedback. Our model is thus distinct from a recent model of learning from social knowledge structures (Frolichs et al, 2022), which is based on statistical associations between traits gathered from the Big 5 model of personality (Digman, 1997). Models of statistical association would not immediately predict our belief-based model's core findings of differences in how people evaluate and update traits with higher numbers of dependencies.…”

Strategic social decision making undergoes significant changes in typically developing and autistic early adolescents

“…Cognitive inefficiencies may be due to dramatic changes in brain regions that support decision making such as areas within the medial and lateral prefrontal cortices (Bramen et al, 2011;Casey et al, 2000;Nelson & Guyer, 2011;Raznahan et al, 2010;Toga et al, 2006) as well as increased connectivity between hippocampus and prefrontal regions (Euston et al, 2012). Connectivity between hippocampus and prefrontal regions has been shown to play an important role in episodic memory (Ghetti & Bunge, 2012) and accessing previous knowledge during learning (Preston & Eichenbaum, 2013), both prerequisites for social decision making (Frolichs et al, 2022).…”

Distinct neural computations scale the violation of expected reward and emotion in social transgressions

“…While numerous studies have employed computational models to determine the behavioral and neural dynamics of classic reward PEs during social learning, initial studies indicate that anticipated emotions affect decisions. Within a reinforcement learning (RL) framework, the Rescorla-Wagner RL model appears suitable to explain social learning mechanisms 11,12 . For instance, the learning rate at which people recalibrate their social expectations quantifies the extent to which PEs are integrated into the updating of reward values 13 .…”