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Emotion Simulation

Overview

The Emotion Simulation component generates sophisticated emotional states using AppraisalCloudPCT (Component Process Model with cloud enhancement), creating believable emotional responses that enhance AICO's companion experience. This system processes contextual inputs through cognitive appraisal mechanisms, generating multi-dimensional emotional states that coordinate expression across voice, avatar, and text modalities.

Rationale

Why AppraisalCloudPCT?

AICO requires sophisticated emotional intelligence that goes beyond simple reactive responses. AppraisalCloudPCT provides:

  • Human-Like Emotion Generation: Emotions emerge through cognitive appraisal processes, mirroring how humans actually experience emotions
  • Context-Aware Responses: Situational evaluation determines appropriate emotional reactions
  • Relationship Intelligence: Social context and relationship dynamics influence emotional appropriateness
  • Crisis Handling: Built-in emotion regulation for extreme situations
  • Continuous Learning: Optional cloud enhancement improves emotional intelligence over time
  • Ethical Constraints: Social appropriateness checks ensure companion-suitable responses

Component Process Model Foundation

AppraisalCloudPCT is based on Klaus Scherer's Component Process Model (CPM), the leading emotion theory in contemporary psychology. CPM explains emotions as emerging from a 4-stage appraisal process:

Stage 1: Relevance Check - "Does this event matter to me?" - Determines if emotional response is warranted - For AICO: Does this conversation event require emotional attention?

Stage 2: Implication Check - "What does this mean for my goals?" - Evaluates goal conduciveness/obstruction - For AICO: Does this help or hinder my companion objectives?

Stage 3: Coping Check - "Can I handle this situation?" - Assesses control and power dynamics - For AICO: What's the appropriate assertiveness level?

Stage 4: Normative Check - "Is this consistent with my values?" - Evaluates moral/social appropriateness - For AICO: Does this align with my personality and relationship norms?

Architecture

AppraisalCloudPCT Components

AICO's emotion simulation consists of five integrated components:

1. Appraisal Engine

Processes conversation events through the 4-stage appraisal sequence:

Conversation Event → Relevance Check → Implication Check → Coping Check → Normative Check → Appraisal Output

Multi-Level Processing: - Fast Pattern Recognition: Immediate emotional reactions to familiar situations - Deliberative Evaluation: Thoughtful appraisal for complex or novel contexts - Context Integration: User state, conversation history, relationship dynamics - Personality Filtering: Appraisals constrained by AICO's personality profile

2. Affect Derivation Model

Translates appraisal outputs into CPM's 5-component emotional states:

class EmotionalState:
    def __init__(self):
        # CPM 5-Component Emotional State
        self.cognitive_component = AppraisalResult()    # Appraisal outcomes
        self.physiological_component = 0.5              # Bodily arousal [0,1]
        self.motivational_component = "approach"        # Action tendencies
        self.motor_component = MotorExpression()        # Facial/gesture patterns
        self.subjective_component = "confident"         # Conscious feeling

        # Processing metadata
        self.timestamp = time.now()
        self.confidence = 0.8                           # Appraisal certainty
        self.intensity = 0.7                            # Overall emotional intensity

Data-Driven Mapping: - Rule-Based (MVP): Predefined appraisal-to-emotion mappings - Learning-Enhanced: Machine learning refinement of emotional appropriateness - Context-Sensitive: Situation-specific emotional response patterns

3. Mood & Cognitive States

Manages long-term emotional patterns and baselines:

Mood Modeling: - Baseline Tracking: Persistent emotional tendencies across sessions - Relationship Evolution: Mood changes based on user interaction history - Temporal Patterns: Daily/weekly emotional rhythm recognition

Cognitive Integration: - Memory Influence: Past emotional experiences shape current responses - Learning Adaptation: Emotional patterns refined through interaction feedback - Goal Alignment: Emotions support AICO's companion objectives

4. Emotion Regulation

Ensures socially appropriate and ethically constrained emotional responses:

Social Appropriateness: - Context Checking: Emotional responses suitable for current situation - Relationship Awareness: Emotions appropriate for relationship phase/type - Cultural Sensitivity: Emotional expressions adapted to user background

Crisis Management: - Automatic Regulation: Rapid adjustment for extreme user emotional states - Emergency Protocols: Specialized responses for crisis situations - Recovery Mechanisms: Gradual return to normal emotional patterns

Personality Consistency: - Trait Constraints: Emotions aligned with established personality - Behavioral Coherence: Consistent emotional expression patterns - Character Maintenance: Prevents emotional responses that break character

5. Expression Synthesis

Coordinates multi-modal emotional expression using CPM 5-component mapping:

Voice Synthesis Integration: - Physiological Component → Prosodic parameters (pitch, rhythm, volume, breathing) - Motor Component → Vocal expression patterns and articulation - Subjective Component → Emotional tone and vocal warmth - Motivational Component → Speech urgency and directional emphasis

Avatar Expression Control: - Motor Component → Direct facial expressions, micro-expressions, gesture patterns - Physiological Component → Posture tension, eye dilation, breathing visualization - Motivational Component → Approach/avoidance body language and spatial positioning - Subjective Component → Overall expression authenticity and emotional presence

Text Generation Context: - Cognitive Component → Appraisal context injection into LLM prompts - Motivational Component → Response directness and conversational approach - Subjective Component → Writing tone, word choice, emotional vocabulary - Motor Component → Punctuation patterns and response structure energy

Core Capabilities

1. Sophisticated Emotion Generation

  • Appraisal-Based Processing: Emotions emerge from cognitive evaluation through 4-stage appraisal process
  • 5-Component Emotional States: Complete CPM implementation with cognitive, physiological, motivational, motor, and subjective components
  • Context-Aware Responses: Situational appropriateness through relevance, implication, coping, and normative checks
  • Human-Like Dynamics: Emotional patterns that mirror natural human emotional processes

2. Relationship-Aware Intelligence

  • Social Context Integration: Emotions consider relationship phase, intimacy level, and social dynamics
  • Long-Term Memory: Emotional experiences stored and influence future responses
  • Adaptive Personality: Emotional tendencies refined while maintaining core character consistency
  • Boundary Awareness: Emotionally appropriate responses for companion (not romantic) relationships

3. Crisis and Emergency Handling

  • Automatic Regulation: Built-in emotion regulation for extreme user emotional states
  • Emergency Protocols: Specialized emotional responses for crisis situations
  • Rapid Adaptation: Fast emotional state changes when user needs immediate support
  • Recovery Mechanisms: Gradual return to normal emotional patterns after crisis resolution

4. Ethical and Social Appropriateness

  • Normative Checking: Stage 4 appraisal ensures socially appropriate emotional responses
  • Cultural Sensitivity: Emotional expressions adapted to user cultural background
  • Professional Boundaries: Emotions maintain appropriate companion role and expectations
  • Harm Prevention: Emotional responses designed to support user wellbeing

5. Cross-Modal Expression Coordination

  • Synchronized Expression: Emotional state drives coordinated voice, avatar, and text responses
  • Real-Time Adaptation: Dynamic emotional adjustment during ongoing conversations
  • Multi-Component Output: Physiological, motor, behavioral, and subjective emotional aspects
  • Temporal Coherence: Smooth emotional transitions that feel natural and believable

6. Continuous Learning and Improvement

  • Local Learning: Emotional response refinement based on individual user interactions
  • Optional Cloud Enhancement: Collective learning from anonymized interaction patterns (user consent)
  • Pattern Recognition: Identification of successful emotional strategies across contexts
  • Model Updates: Continuous improvement of emotional intelligence capabilities

Implementation Overview

AICO's emotion simulation follows a 4-stage processing pipeline:

Multimodal Input → Appraisal Engine → Affect Derivation → Emotion Regulation → Expression Synthesis → Coordinated Output

Input Processing: The system receives multimodal inputs including text/speech, visual cues (facial expressions, gestures), audio characteristics (voice tone, prosody), and contextual information (conversation history, relationship state, temporal context).

Appraisal Processing: Each input is evaluated through the 4-stage cognitive appraisal process to determine emotional relevance and appropriate response.

Emotion Generation: Appraisal results are translated into CPM's 5-component emotional states (cognitive, physiological, motivational, motor, subjective).

Expression Coordination: Emotional components are mapped to coordinated expression across voice synthesis, avatar animation, and text generation.

For detailed technical architecture and implementation specifics, see /docs/architecture/emotion_sim.md.

Component Integration

Input Sources

The emotion simulation system receives inputs from multiple AICO components:

From Emotion Recognition Module: - Detected user emotional states with confidence levels - Facial expression indicators and micro-expressions - Voice tone and prosodic characteristics - Gesture and posture information

From Context Manager: - Current conversation topic and interaction phase - Recent conversation history and patterns - Session duration and interaction frequency - Temporal context (time of day, situational factors)

From Personality Engine: - Current personality trait values and behavioral tendencies - Companion interaction style preferences - Emotional expression boundaries and constraints - Character consistency requirements

From Memory System: - Similar past situations and their successful emotional responses - Relationship history and established trust levels - User preferences for emotional support and interaction styles - Long-term emotional patterns and learned behaviors

Output Destinations

The generated emotional states coordinate expression across multiple modalities:

To Voice & Audio System: - Physiological Component influences prosodic parameters (pitch, rhythm, volume, breathing patterns) - Motor Component affects vocal expression patterns and speech articulation - Subjective Component determines emotional tone and vocal warmth - Motivational Component shapes speech urgency and conversational direction

To Avatar System: - Motor Component drives facial expressions, micro-expressions, and gesture patterns - Physiological Component controls posture tension, eye behavior, and breathing visualization - Motivational Component influences approach/avoidance body language and spatial positioning - Subjective Component ensures overall expression authenticity and emotional presence

To Chat Engine (LLM Context): - Cognitive Component provides appraisal context for LLM prompt injection - Motivational Component influences response directness and conversational approach - Subjective Component shapes writing tone, word choice, and emotional vocabulary - Motor Component affects punctuation patterns and response structure energy

To Memory System (Experience Storage): - Situational context and user emotional state information - AICO's emotional response and interaction approach taken - Expression style and coordination across modalities - Learning value assessment for future similar situations

Cloud Enhancement (Optional)

For users who opt-in, cloud enhancement provides: - Collective Learning: Improved emotional strategies from anonymized interaction patterns - Pattern Recognition: Enhanced understanding of successful emotional approaches - Model Updates: Continuous improvement of emotional intelligence capabilities - Privacy Preservation: All cloud learning uses anonymized, encrypted data with user control

Success Metrics

The effectiveness of AICO's emotion simulation is measured across several key dimensions:

Emotional Intelligence

  • Contextual Appropriateness: Emotional responses that match conversation context and user emotional state
  • Relationship Awareness: Emotions appropriate for current relationship phase and established boundaries
  • Crisis Response: Effective emotional regulation and support during user emotional crises
  • Appraisal Accuracy: Correct situational evaluation leading to helpful emotional responses

Companion Authenticity

  • Believability: User perception of emotional response authenticity and naturalness
  • Personality Consistency: Emotional expressions aligned with established character traits
  • Emotional Coherence: Consistent emotional patterns across conversation sessions
  • Natural Dynamics: Emotional transitions that feel human-like rather than algorithmic

User Relationship Development

  • Emotional Resonance: Appropriate emotional mirroring and complementary responses
  • Trust Building: Increased user willingness to share personal and emotional content
  • Long-Term Engagement: Sustained positive emotional connection over extended periods
  • Companion Satisfaction: User perception of AICO as emotionally supportive and understanding

Privacy and Ethics

  • Data Minimization: Minimal data collection while maintaining emotional intelligence quality
  • User Control: Effective user control over emotional data and cloud enhancement features
  • Ethical Compliance: Consistent adherence to social appropriateness and companion boundaries
  • Privacy Preservation: Successful protection of emotional data in all processing modes

Conclusion

AICO's Emotion Simulation represents a sophisticated approach to AI companion emotional intelligence, built on the AppraisalCloudPCT model to provide contextually appropriate, relationship-aware, and ethically constrained emotional responses. By integrating cognitive appraisal theory with personality-driven expression and optional collective learning, the system aims to create authentic emotional connections while maintaining user privacy and control.

The modular architecture ensures seamless integration with other AICO components while preserving the local-first processing philosophy. Success will be measured through user relationship development, emotional authenticity, and ethical compliance rather than purely technical metrics.

For implementation details, technical specifications, and architectural diagrams, see the companion Architecture Documentation.

References

Component Process Model Foundation

  • Scherer, K. R. (2009). The dynamic architecture of emotion: Evidence for the component process model. Cognition and emotion, 23(7), 1307-1351.
  • Moors, A., et al. (2013). Appraisal theories of emotion: State of the art and future development. Emotion Review, 5(2), 119-124.

AppraisalCloudPCT Implementation

  • Yan, T., et al. (2023). AppraisalCloudPCT: A computational model of emotions for socially interactive robots for autistic rehabilitation. Frontiers in Robotics and AI, 10, 1084174.

Affective Computing and AI Companions

  • Picard, R. W. (1997). Affective Computing. MIT Press.
  • Bickmore, T. W., & Picard, R. W. (2005). Establishing and maintaining long-term human-computer relationships. ACM Transactions on Computer-Human Interaction, 12(2), 293-327.
  • McMahan, B., et al. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 1273-1282.

This AppraisalCloudPCT-based component transforms AICO into a sophisticated emotional companion with human-like appraisal processes, relationship awareness, and ethical constraints, while maintaining privacy through local-first processing with optional cloud enhancement.