Emotional perception for updating agents' beliefs

Abstract

The relative influence of perception and situation in emotional judgments has been extensively debated in psychology. A main issue in this debate concerns how these sources of information are integrated. This work proposes a method able to make probabilistic predictions of appraisals of other agents, using mental models of those agents. From these appraisal predictions, predictions about another agent's expressions are made, integrated with observations of the other agent's ambiguous emotional expressions using Bayesian techniques, resulting in updates to the agent's mental models. Our method is inspired by psychological work on human interpretation of emotional expressions. We demonstrate how these appraisals of others' emotions and observations of their expressions can be an integral part of an agent capable of Theory of Mind reasoning.

Emotion prediction as computation over a generative theory of mind

“…Rather than relying on observers to generate appraisals from context, it would be possible to define appraisals based directly on the event context without using inverse planning, but this would not address how emotion predictions depend on a player’s intentions and motivations [ 4 ]. Finally, it would be possible to align a model’s appraisal computations with variables prescribed by Appraisal Theories in order to predict emotions based on the emotion concepts defined in terms of those appraisal variables [ 24 ]. However, this would limit the model to emotions that have been previously defined, and require one to hand code functions that transform Theory of Mind representations into prescribed appraisal variables, such as motivational relevance, goal congruence, controllability, probability and novelty [ 75 ].…”

“…Some computer science work favours building highly structured causal models with cognitive latent spaces. This approach forgoes learning transformations between latent variables and emotions, opting to hand-code computations for appraisal variables that are prescribed by Appraisal Theories [12,23,24], see [18,74] for reviews. Appraisal variables are then translated into emotions according to prescribed definitions [22,[75][76][77].…”

“…the same information given to observers). There are many approaches to building situation-computable models of emotion prediction [ 18 ], including behavioural economic decision models [ 19 , 20 ], rule-based emotion schemas [ 21 , 22 ], multi-agent computer simulations [ 12 , 23 , 24 ] and large language models [ 25 , 26 ]. These methodologically and philosophically diverse approaches share a view that emotion prediction depends on abstracting Theory of Mind representations from contextual information about a situation.…”

“…Whereas reverse appraisals reflect an inverse inference (appraisals are inferred from their effects), computed appraisals reflect a forward inference (appraisals are computed from their causes). A related approach is seen in multi-agent partially observable Markov decision processes (POMDPs), where simulated agents compute appraisals based on their action planning policies and belief updates, and can infer appraisals of other agents by representing models of their states [ 12 , 23 , 24 ]. At present, these computer simulations have not been tested against human judgements.…”

Agents with Affective Traits for Decision-Making in Complex Environments

“…An interesting line of research would be to study how to use GenIA 3 's affective (and non-affective) reasoning "machinery" as a service. This will allow to anticipate other agents' affective state [Alfonso et al, 2015a].…”