Emotions as computations

A cognitive-computational account of mood swings in adolescence

Teenagers have a reputation for being fickle, in both their choices and their moods. This variability may help adolescents as they begin to independently navigate novel environments. Recently, however, adolescent moodiness has also been linked to psychopathology. Here, we consider adolescents' mood swings from a novel computational perspective, grounded in reinforcement learning (RL). This model proposes that mood is determined by surprises about outcomes in the environment, and how much we learn from these surprises. It additionally suggests that mood biases learning and choice in a bidirectional manner. Integrating independent lines of research, we sketch a cognitive-computational account of how adolescents' mood, learning, and choice dynamics influence each other, with implications for normative and psychopathological development.

The Human Affectome

Over the last decades, theoretical perspectives in the interdisciplinary field of the affective sciences have proliferated rather than converged due to differing assumptions about what human affective phenomena are and how they work. These metaphysical and mechanistic assumptions, shaped by academic context and values, have dictated affective constructs and operationalizations. However, an assumption about the purpose of affective phenomena can guide us to a common set of metaphysical and mechanistic assumptions. In this capstone paper, we home in on a nested teleological principle for human affective phenomena in order to synthesize metaphysical and mechanistic assumptions. Under this framework, human affective phenomena can collectively be considered algorithms that either adjust based on the human comfort zone (affective concerns) or monitor those adaptive processes (affective features). This teleologically-grounded framework offers a principled agenda and launchpad for both organizing existing perspectives and generating new ones. Ultimately, we hope the Human Affectome brings us a step closer to not only an integrated understanding of human affective phenomena, but an integrated field for affective research.

Affective valence does not reflect progress prediction errors in perceptual decisions

Affective valence and intensity form the core of our emotional experiences. It has been proposed that affect reflects the prediction error between expected and actual states, such that better/worse-than-expected discrepancies result in positive/negative affect. However, whether the same principle applies to progress prediction errors remains unclear. We empirically and computationally evaluate the hypothesis that affect reflects the difference between expected and actual progress in forming a perceptual decision. We model affect within an evidence accumulation framework where actual progress is mapped onto the drift-rate parameter and expected progress onto an expected drift-rate parameter. Affect is computed as the difference between the expected and actual amount of accumulated evidence. We find that expected and actual progress both influence affect, but in an additive manner that does not align with a prediction error account. Our computational model reproduces both task behavior and affective ratings, suggesting that sequential sampling models provide a promising framework to model progress appraisals. These results show that although affect is sensitive to both expected and actual progress, it does not reflect the computation of a progress prediction error.

Decoding the language of fear: Unveiling objective and subjective indicators in rodent models through a systematic review and meta-analysis

While rodent models are vital for studying mental disorders, the underestimation of construct validity of fear indicators has led to limitations in translating to effective clinical treatments. Addressing this gap, we systematically reviewed 5054 articles from the 1960 s, understanding underlying theoretical advancement, and selected 68 articles with at least two fear indicators for a three-level meta-analysis. We hypothesized correlations between different indicators would elucidate similar functions, while magnitude differences could reveal distinct neural or behavioral mechanisms. Our findings reveal a shift towards using freezing behavior as the primary fear indicator in rodent models, and strong, moderate, and weak correlations between freezing and conditioned suppression ratios, 22-kHz ultrasonic vocalizations, and autonomic nervous system responses, respectively. Using freezing as a reference, moderator analysis shows treatment types and fear stages significantly influenced differences in magnitudes between two indicators. Our analysis supports a two-system model of fear in rodents, where objective and subjective fears could operate on a threshold-based mechanism.

The computational psychopathology of emotion

Mood and anxiety disorders involve recurring, maladaptive patterns of distinct emotions and moods. Here, we argue that understanding these maladaptive patterns first requires understanding how emotions and moods guide adaptive behavior. We thus review recent progress in computational accounts of emotion that aims to explain the adaptive role of distinct emotions and mood. We then highlight how this emerging approach could be used to explain maladaptive emotions in various psychopathologies. In particular, we identify three computational factors that may be responsible for excessive emotions and moods of different types: self-intensifying affective biases, misestimations of predictability, and misestimations of controllability. Finally, we outline how the psychopathological roles of these factors can be tested, and how they may be used to improve psychotherapeutic and psychopharmacological interventions.

Mood fluctuations shift cost-benefit tradeoffs in economic decisions

Mood effects on economic choice seem blatantly irrational, but might rise from mechanisms adapted to natural environments. We have proposed a theory in which mood helps adapting the behaviour to statistical dependencies in the environment, by biasing the expected value of foraging actions (which involve taking risk, spending time and making effort to get more reward). Here, we tested the existence of this mechanism, using an established mood induction paradigm combined with independent economic choices that opposed small but uncostly rewards to larger but costly rewards (involving either risk, delay or effort). To maximise the sensitivity to mood fluctuations, we developed an algorithm ensuring that choice options were continuously adjusted to subjective indifference points. In 102 participants tested twice, we found that during episodes of positive mood (relative to negative mood), choices were biased towards better rewarded but costly options, irrespective of the cost type. Computational modelling confirmed that the incidental mood effect was best explained by a bias added to the expected value of costly options, prior to decision making. This bias is therefore automatically applied even in artificial environments where it is not adaptive, allowing mood to spill over many sorts of decisions and generate irrational behaviours.

Emotion prediction as computation over a generative theory of mind

From sparse descriptions of events, observers can make systematic and nuanced predictions of what emotions the people involved will experience. We propose a formal model of emotion prediction in the context of a public high-stakes social dilemma. This model uses inverse planning to infer a person's beliefs and preferences, including social preferences for equity and for maintaining a good reputation. The model then combines these inferred mental contents with the event to compute 'appraisals': whether the situation conformed to the expectations and fulfilled the preferences. We learn functions mapping computed appraisals to emotion labels, allowing the model to match human observers' quantitative predictions of 20 emotions, including joy, relief, guilt and envy. Model comparison indicates that inferred monetary preferences are not sufficient to explain observers' emotion predictions; inferred social preferences are factored into predictions for nearly every emotion. Human observers and the model both use minimal individualizing information to adjust predictions of how different people will respond to the same event. Thus, our framework integrates inverse planning, event appraisals and emotion concepts in a single computational model to reverse-engineer people's intuitive theory of emotions. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.