Social Comparison without Explicit Inference of Others' Reward Values: A Constructive Approach Using a Probabilistic Generative Model

TL;DR

This study investigates whether macaques infer a partner's subjective reward valuation during social comparison or rely solely on observed objective rewards. It compares three probabilistic generative models—Internal Prediction Model, No Comparison Model, and External Comparison Model—using a multimodal Latent Dirichlet Allocation framework within the SERKET infrastructure on a long-term dataset of two monkeys facing six stimulus conditions. The External Comparison Model, which uses partner reward observations without inferring subjective states, shows the strongest alignment with behavioral data (Rand Index ≈ 0.88) and superior predictive and clustering performance, suggesting social comparison is driven by objective reward differences rather than inferred partner valuations. The findings have implications for understanding social valuation in primates and guide future neural and human studies to probe the generality and neural underpinnings of external versus inferred social information in valuation.

Abstract

Social comparison$\unicode{x2014}$the process of evaluating one's rewards relative to others$\unicode{x2014}$plays a fundamental role in primate social cognition. However, it remains unknown from a computational perspective how information about others' rewards affects the evaluation of one's own reward. With a constructive approach, this study examines whether monkeys merely recognize objective reward differences or, instead, infer others' subjective reward valuations. We developed three computational models with varying degrees of social information processing: an Internal Prediction Model (IPM), which infers the partner's subjective values; a No Comparison Model (NCM), which disregards partner information; and an External Comparison Model (ECM), which directly incorporates the partner's objective rewards. To test model performance, we used a multi-layered, multimodal latent Dirichlet allocation. We trained the models on a dataset containing the behavior of a pair of monkeys, their rewards, and the conditioned stimuli. Then, we evaluated the models' ability to classify subjective values across pre-defined experimental conditions. The ECM achieved the highest classification score in the Rand Index (0.88 vs. 0.79 for the IPM) under our settings, suggesting that social comparison relies on objective reward differences rather than inferences about subjective states.

Figures (7)

• Figure 1: A) Experimental setup: two face-to-face monkeys separated by a horizontally placed LCD monitor, which showed one of the six stimulus images; the water nozzles, equipped with sensors to measure anticipatory licking, were placed beside the monkeys. B) Experimental conditions: The self-variable blocks (left) with variable reward probabilities (25%, 50%, or 75%) for the self monkey and fixed 20% for the partner; the partner-variable blocks (right) with variable probabilities for the partner and fixed 20% for the self monkey. C) Results showing decreased licking when the partner received higher rewards (purple), despite the self monkey's fixed 20% reward probability, and increased licking as the self monkey's own reward probability increased (yellow). Center and error bars indicate mean $\pm$ s.e.m.
• Figure 2: Subjective Value Model: a building block for our more complex architectures. For simplicity, this figure shows only the observation and latent variables.
• Figure 3: Comparison of model architectures: A) IPM: incorporating inference of the partner's subjective values. B) NCM: processing only self-related information. C) ECM: including the partner's observed rewards but not their subjective valuation. For simplicity, the generative parameters $\theta^*$ and $\phi^*$ and hyperparameters $\alpha^*$ and $\beta^*$ are omitted, showing only the relationships between observed variables $w^*$ and latent variables $z^*$.
• Figure 4: Two-dimensional t-SNE projection of probability vectors from the six-dimensional subjective-value topic distributions ($\theta$ in terms of MLDA. See Appendix \ref{['app:mlda']}). Left column: plots categorized with actual experimental conditions. Right column: plots categorized with model-predicted clusters. Since five out of six categories were used across all models, the legend for 'Category 3' is omitted. A) IPM: displaying moderate clustering ability with notable confusion between Self-25% and Partner-25% conditions. B) NCM: showing poor differentiation, confusing the Partner-50% and Partner-75% conditions, among others. C) ECM: achieving the most refined separation of experimental conditions, with minimal overlap and only slight confusion between the Self-50% and Self-75% conditions. Note that the absolute values on the x- and y-axes in t-SNE are meaningless JMLR:v9:vandermaaten08a; only the distances between pairs of nearby points are essential for knowing the similarity of data points.
• Figure 5: Comparison of predicted licking frequencies for the self monkey across the six experimental conditions. The x-axis: three reward probability conditions; three self-variable conditions (Self-25%, Self-50%, and Self-75%) and partner-variable conditions (Partner-25%, Partner-50%, and Partner-75%). The y-axis: normalized licking frequency. Results for three models: IPM (left), NCM (center), and ECM (right), with ECM best preserving the experimentally observed pattern(in Figure \ref{['fig:experimental_settings']}C). Center and error bars indicate mean $\pm$ s.e.m.