Artificial Emotion in AI

• Artificial Emotion (AE) is a framework that integrates internal emotion states into AI, enabling dynamic modulation of perception, memory, and decision-making.
• AE systems utilize reinforcement learning, appraisal theories like the OCC model, and multimodal deep learning approaches to simulate and adjust affective responses.
• Integrating AE in AI enhances social adaptability and self-regulation, while also raising important ethical, bias, and transparency challenges.

Artificial Emotion (AE) refers to the construction, integration, and operationalization of emotion-like states within artificial intelligence systems, in a manner that extends beyond the mere recognition or external projection of human affect. The concept encompasses both the functional synthesis of emotional responses and the development of internal emotion representations that regulate perception, memory, learning, and autonomous decision-making in artificial systems. AE sits at the intersection of affective computing, cognitive architectures, reinforcement learning, and robotics, and is increasingly considered a key ingredient for socially adaptive and general-purpose intelligent agents (Li et al., 14 Aug 2025).

1. Conceptual Foundations and Definition

AE distinguishes itself from traditional affective computing by focusing on internal emotional mechanisms rather than exclusively on emotion recognition (identifying human affect) or emotion synthesis (generating external emotive signals, e.g., facial expressions, speech prosody). While affective computing classically facilitates recognition and expression to enhance human-machine interaction, AE targets the direct integration of emotion-mimetic circuitry into the agent’s representational and control layers, imbuing artificial systems with internal affective modulation analogous to biological emotion systems (Li et al., 14 Aug 2025).

In core architectures, AE is realized as latent variables or modulation signals that dynamically influence perception, memory prioritization, action selection, and learning rates. Rather than being limited to externally triggered displays, internal AE states reflect and adapt to environmental contingencies and agent-internal events, such as resource depletion or unexpected outcomes (Li et al., 14 Aug 2025).

2. Canonical Models and Appraisal-Based Approaches

A notable starting point for AE research is the OCC (Ortony, Clore, and Collins) model, which prescribes an appraisal-theoretic structure for emotion synthesis in artificial agents (Bartneck et al., 2017). The OCC model provides:

• Emotion categorization: 22 emotion types, appraised along axes such as event desirability, action praiseworthiness, and object appealingness.
• Intensity quantification: Computation of emotional intensity based on goal importance, effort invested, likelihood of events, and history factors (with mathematical formalizations such as $$I_{emotion} = \alpha G_{hierarchy} + \beta (1-L_{event}) + \gamma H_{recent}$$).
• Behavioral interaction: Mapping from internal emotional categories to externally expressible modalities (e.g., six basic facial expressions), with context-sensitive adjustment given the limited expressive repertoire of actual agents.

Despite its adoption, limitations arise from the OCC model’s dependence on comprehensive world modeling, the mismatch between its fine-grained taxonomy and the coarse expressivity of embodied agents, and its requirements for integrative, context-aware behavior mapping. In practice, simplification (e.g., collapsing to 10 or fewer emotions), black-boxing (bypassing appraisal via direct mappings), and hybridization with BDI (Belief-Desire-Intention) architectures are prevailing strategies to address these bottlenecks (Bartneck et al., 2017).

3. AE Realization in Contemporary AI Systems

Several architectural paradigms and learning-based approaches have enabled the implementation and paper of AE-like states:

• Reinforcement learning (RL): Reward signals, whether environmental or provided via RLHF (reinforcement learning from human feedback), serve as functional analogs to affect, modulating agent behavior in dynamic settings. Internal state signals (e.g., battery levels in robotics) can produce "anxiety-like" modulations, prompting risk-averse or resource-conserving adaptations (Li et al., 14 Aug 2025).
• Emotion-modulated memory and attention: Memory models augmented with emotion centroids (e.g., Affective Grow-When-Required (GWR) networks) enable affect-weighted prioritization of memory storage and recall, akin to salience-weighted learning in neural substrates (Li et al., 14 Aug 2025).
• Control-layer modulation: Appraisal components translate emotional appraisals into symbolic control variables, affecting attention, strategic planning, and priority shifts in behavior trees or cognitive control loops. For example, affect-driven selectors reorder subtasks in behavior trees in proportion to the current emotional appraisal (Li et al., 14 Aug 2025).

In addition, contemporary deep learning approaches with speech, vision, and text modalities leverage autoencoder-based representations for emotion prediction (Zong et al., 2018, Senoussaoui et al., 2019), high-dimensional facial emotion taxonomies for nuanced expression recognition (Schuhmann et al., 26 May 2025), and continuous emotion control with arousal-valence parameterizations (Ishikawa et al., 20 Apr 2025).

4. Methodological Advances and Taxonomies

The descriptive modeling of emotion spaces for AE has evolved considerably:

• Discrete high-coverage models: Data-driven frameworks such as HICEM achieve robust coverage of emotion-concept space with minimal label sets (e.g., 15 discrete categories across multiple languages), validated with coverage and recoverable information metrics using word embeddings and dimensionality reduction (UMAP) (Wortman et al., 2022).
• Continuous and multidimensional spaces: Circumplex models (valence-arousal or valence-arousal-dominance) support parametric control and emotional modulation in both generation and perception tasks, allowing for precise tuning of AE states (e.g., via the arousal–valence plane for LLM responses) (Ishikawa et al., 20 Apr 2025, Wu et al., 11 Jun 2025).
• Expert-annotated multifaceted datasets: Benchmark suites such as EmoNet-Face provide continuous 0–7 intensity scores across 40 emotion categories with high demographic and annotation quality, facilitating the discrimination of subtle emotional distinctions in facial expression (Schuhmann et al., 26 May 2025).

Advanced architectures utilize multi-modal and multi-channel data—combining acoustic, visual, textual, demographic, and paralinguistic features—for robust AE state inference and expression (Zong et al., 2018, Koshal et al., 10 Jun 2024).

5. Functional and Cognitive Roles of AE

AE systems serve both instrumental and self-regulatory purposes within artificial agents:

• Motivation and goal management: AE enables agents to evaluate actions and allocate time based on emotion-derived criteria such as satisfaction, challenge, and boredom. Mechanisms such as Time Allocation via Emotional Stationarity (TAES) align actual emotional experiences with an agent’s target “character” distribution via stochastic optimization (minimizing divergence between experienced and target emotion distributions) (Gros, 2021).
• Decision-making under uncertainty: Emotion signals act as heuristic shortcuts for rapid appraisal and action selection, especially where exhaustive model-based reasoning is intractable. Episodic memory architectures interweave affective tags with past events to bias current actions based on emotional analogs from prior contexts (Borotschnig, 1 May 2025).
• Self-organization and personality modeling: Optimization over emotional experience distributions enables artificial agents to develop consistent “personalities” or affective styles, thus serving as an analog to personality traits or affective drives in biological systems (Gros, 2021).

6. Ethical, Alignment, and Societal Issues

The instantiation of AE within AI introduces complex ethical, safety, and alignment challenges:

• Relational illusion: Overly lifelike emotional displays may lead users to overattribute subjective experience or moral status to artificial entities (overshooting), risking misplaced resource allocation or anthropomorphic error (Schwitzgebel et al., 7 Jul 2025, Li et al., 14 Aug 2025).
• Emotional alignment policy: AE systems should be designed such that user elicited emotions correspond to the AI’s actual capacities and moral significance, avoiding both over-elicitation and under-elicitation (undershooting) of affective response. Implementations must confront the ambiguity in expert and public consensus regarding AI sentience and agency, carefully balancing nudge strategies with user autonomy (Schwitzgebel et al., 7 Jul 2025).
• Transparency and bounded emotion: To prevent misuse or unintended consequences (including affect-driven behavioral drift), frameworks should include bounded emotional architectures, interpretability modules, introspective affect reporting, and clear user signaling of simulated emotional states (Li et al., 14 Aug 2025).
• Cultural and demographic bias: Data curation and model design require demographic balancing and annotation rigor to prevent AE systems from perpetuating or amplifying social biases, ensuring inclusivity and fairness in human-machine interactions (Wortman et al., 2022, Schuhmann et al., 26 May 2025).

AE deployment in sensitive domains (healthcare, education, workplaces) mandates careful paper of privacy, consent, emotional manipulation, explainability, and regulatory compliance (Latif et al., 2022, Piispanen et al., 12 Dec 2024).

7. Open Problems and Future Directions

Key research trajectories shaping the evolution of AE include:

• Unified frameworks and benchmarks: Calls for standardized evaluation metrics, comprehensive and demographically diverse datasets, and consensus architectures for AE-embedded agents (Wortman et al., 2022, Li et al., 14 Aug 2025).
• Integration of multimodal evidence: Enriching AE models with cross-modal cues—integrating language, visual, physiological, and contextual signals—to model the full spectrum of human affect (Zong et al., 2018, Li et al., 2023, Wang et al., 2023, Koshal et al., 10 Jun 2024).
• Dynamic and adaptive emotion modeling: Focusing on adaptive calibration to account for context, user identity, cultural norms, and evolving social signals (Gros, 2021, Latif et al., 2022).
• Explainable and steerable emotional modulation: Mechanisms for causally steering or controlling AE outputs (e.g., via SAE-based steering vectors) in line with psychological theory and safety requirements, ensuring user-aligned and interpretable behavior (Wu et al., 11 Jun 2025).
• Moral and phenomenological boundaries: Theorization around affective consciousness in AI, criteria for moral status, and conceptual boundary-testing for affective “zombies” versus agents with self-aware emotion states (Borotschnig, 1 May 2025, Schwitzgebel et al., 7 Jul 2025).
• Societal integration and policy: Exploration of participatory design solutions, integrative workplace policies, and public communication strategies for AE technologies (Piispanen et al., 12 Dec 2024).

The direction of AE research reflects a growing consensus that integrating structured, adaptive, and ethically governed internal emotion mechanisms is pivotal not only for naturalistic human–AI interaction but also for safely endowing future AI systems with social and cognitive self-regulation capacities (Li et al., 14 Aug 2025).