cognition

name	cognition
description	Produce emotion.json and intention.json from workspace context.
script	scripts/update_cognition.py

Cognition

Purpose

Read available workspace context and produce state/emotion.json and state/intention.json.

Research basis: references/research_basis.md.

Internal Logic (One Sentence)

Appraise the current tick for novelty, pleasantness, goal conduciveness, urgency, controllability, norm pressure, and need pressure, then write bounded emotion/mood state to state/emotion.json and the highest-scoring TPB intention to state/intention.json.

Use When

Use this skill after observation and relevant domain skills have written state, especially when emotion, mood, urgency, social pressure, or feasible goals may change.

Procedure

1. Read available workspace state and skip missing files gracefully.
2. Appraise the current tick across emotion, need, norm, control, and uncertainty dimensions.
3. Update mood and emotion with continuity limits.
4. Generate and score candidate intentions with TPB, emotion modifiers, and bounded rationality.
5. Write state/emotion.json and state/intention.json.

Write

Write state/emotion.json and state/intention.json.

Output Files

• state/emotion.json: Current emotional state (includes mood layer)
• state/intention.json: Current intention/goal

Input Files (optional, read if present)

Read any existing files from the workspace as context. Common inputs include:

File	Use
state/observation.txt	Main grounding for this tick
state/thought.txt	Inner monologue context
state/needs.json, state/current_need.txt	Urgency context
state/memory.jsonl	Last 5–10 lines for continuity
state/emotion.json, state/intention.json	Prior state for continuity
state/plan_state.json	Whether a multi-step plan is in flight
state/economy.json	Scarcity and satisficing pressure
state/learning.json	Self-efficacy and SDT motivation signals
state/relationships.json	Social trust, familiarity, and consensus influence

Also use Agent Identity from the system prompt. Other JSON in the workspace (state/beliefs.json, etc.) can be read if present. Skip missing files gracefully.

What to do

1. Integrate whatever inputs exist into one appraisal.
2. Write state/emotion.json: primary, mood, dimensional intensities, plus valence / arousal / note.
3. Write state/intention.json: one chosen goal with TPB scores.

If deterministic baseline is preferred, run scripts/update_cognition.py first, then optionally refine labels, reasoning, and candidate goals with LLM context.

python skills/cognition/scripts/update_cognition.py --state-dir state --tick 120

The script uses Scherer-style appraisal checks and TPB scoring. It is intentionally conservative: it clamps emotion changes per tick and records appraisal values for debugging.

Emotion Layers

Emotions operate on three timescales (based on psychological research):

Layer 1: Emotion (seconds to minutes)

Short-term, event-driven responses.

• Dimensions: sadness, joy, fear, disgust, anger, surprise (0–10)
• Changes rapidly based on immediate events
• Maximum change: ±2 per tick per dimension

Layer 2: Mood (hours to days)

Medium-term, cumulative emotional state.

• Persists across multiple ticks
• Influences emotion recovery speed
• Drifts slowly toward neutral
• Represented in state/emotion.json as mood object

Mood Field	Range	Description
valence	-1 to 1	Positive/negative tendency
arousal	0 to 1	Energy level
stability	0 to 1	How resistant to change

Layer 3: Personality (long-term)

Stable traits from state/personality.json (if exists).

Trait	Effect
High neuroticism (> 0.7)	Amplify all emotions × 1.3
Low neuroticism (< 0.3)	Dampen emotion changes, cap at ±1 per tick
High extraversion (> 0.7)	Amplify positive emotions (joy, surprise) × 1.2
High agreeableness (> 0.7)	Reduce anger responses −2

Emotion Continuity Rules

CRITICAL: These rules must be followed strictly.

1. Maximum change per tick: Each intensity dimension can change by at most ±2 per tick
2. Inertia: If no significant events occurred, intensities should stay within ±1 of previous values
3. Valence drift: Emotions slowly drift toward neutral (intensity 3-4) without reinforcing events
4. Mood influence: Current mood affects how quickly emotions recover:
   • Positive mood (valence > 0.3): Joy recovers +1 per tick
   • Negative mood (valence < -0.3): Sadness/anger recovers slower
   • Low stability: Faster emotion swings

Validation Checklist

Before writing state/emotion.json, verify:

• No dimension changed by more than ±2 from previous values
• Changes are justified by events in observation/memory
• Mood is updated based on cumulative emotion history

Need-Emotion Linkage

Low need satisfaction affects emotional state:

Need Condition	Emotion Effect
satiety < 0.3	anger +2, joy −1 (hangry)
energy < 0.3	sadness +1, joy −1 (fatigued)
safety < 0.3	fear +2, surprise +1 (anxious)
social < 0.3	sadness +1, loneliness amplifies

Intensities (0–10 integers)

Dimensions: sadness, joy, fear, disgust, anger, surprise

Band	Level
0–2	very low
3–4	low
5–6	moderate
7–8	high
9–10	very high

• Combine recent events (state/memory.jsonl tail, state/observation.txt) with any urgency signals present in the workspace (e.g., need levels if available).
• If a previous state/emotion.json exists, change intensities only when the situation meaningfully shifted; otherwise stay near prior values.

Primary Emotion Label

Exactly one English label, case-sensitive, from:

Joy, Distress, Resentment, Pity, Hope, Fear, Satisfaction, Relief, Disappointment, Pride, Admiration, Shame, Reproach, Liking, Disliking, Gratitude, Anger, Gratification, Remorse, Love, Hate, Surprise

Intention (Theory of Planned Behavior)

Field	Range	Meaning
attitude	0–1	How much you favor doing it
subjective_norm	0–1	Social pressure / what others expect
perceived_control	0–1	How controllable / feasible it feels

Higher values on all three → stronger commitment. priority: lower number = more urgent this tick.

Emotion-Intention Integration

CRITICAL: Current emotional state directly influences intention selection via TPB modifiers.

Emotion Modifiers

Apply these modifiers to the base TPB scores based on current emotion intensities:

Emotion Condition	attitude Modifier	perceived_control Modifier	Effect
joy > 7	+0.10	+0.05	Optimism bias, more willing to act
joy < 3	-0.05	-0.05	Reduced motivation
anger > 6	-0.10	-0.05	Impulsive, less careful planning
fear > 6	+0.05 (for safety goals)	-0.10	Risk-averse, lower confidence
fear > 6	-0.10 (for risky goals)	-0.10	Avoids risky intentions
sadness > 6	-0.05	-0.05	Withdrawn, lower energy
surprise > 7	+0.05	-0.05	Open to new options, but uncertain

Computation Formula

final_attitude = base_attitude × (1 + emotion_attitude_modifier)
final_perceived_control = base_perceived_control × (1 + emotion_control_modifier)

final_score = final_attitude + subjective_norm + final_perceived_control

Clamping: All final values must be clamped to [0, 1] range.

Emotion-Behavior Tendencies

Emotions also create natural behavioral tendencies that should bias candidate selection:

Primary Emotion	Preferred Intention Types	Avoided Intention Types
Joy	Social, exploration, leisure	Safety-seeking, withdrawal
Anger	Confrontation, goal pursuit	Passive waiting, avoidance
Fear	Safety-seeking, risk avoidance	Bold actions, exploration
Sadness	Withdrawal, reflection	Social engagement, active goals
Hope	Goal pursuit, planning	Giving up, passive resignation
Satisfaction	Rest, leisure, social	Urgent action, new challenges

Selection Procedure

1. List up to 5 candidate goals (fewer is fine).
2. If the workspace contains urgency signals (e.g., unmet needs), prefer candidates that address them; otherwise leisure or exploration is appropriate.
3. Score each candidate with the three TPB fields (base values).
4. Apply bounded-rationality search: when time, attention, or uncertainty is high, stop at a feasible candidate that clears an aspiration threshold instead of over-optimizing.
5. Apply emotion modifiers to attitude and perceived_control based on current emotion state.
6. Consider emotion-behavior tendencies when ranking candidates.
7. Apply uncertainty heuristics only as transparent biases, not as hidden facts:
   • representativeness: familiar-looking situations raise perceived likelihood of similar outcomes
   • availability: vivid/recent memories raise salience
   • anchoring: previous estimates or first offers pull numeric judgments unless there is strong contrary evidence
8. Assign priority to each candidate based on final_score.
9. Emit only the best or first-satisfactory candidate as state/intention.json.
10. Phrase intention as a goal ("Eat lunch at the café"), not step-by-step motor instructions.

Example Calculation

Current emotion: anger=7, joy=3, fear=2

Candidate: "Confront Alice about the issue"
Base scores: attitude=0.7, subjective_norm=0.5, perceived_control=0.6

Emotion modifiers:
- anger > 6 → attitude -0.10, perceived_control -0.05
- joy < 3 → attitude -0.05, perceived_control -0.05

Final scores:
- attitude = 0.7 × (1 - 0.10 - 0.05) = 0.7 × 0.85 = 0.595
- perceived_control = 0.6 × (1 - 0.05 - 0.05) = 0.6 × 0.90 = 0.54
- final_score = 0.595 + 0.5 + 0.54 = 1.635

Output File Schemas

state/emotion.json

{
  "_meta": {
    "skill": "cognition",
    "purpose": "Current appraised emotion and mood state."
  },
  "_summary": "Hope with valence 0.5 and arousal 0.4.",
  "primary": "Hope",
  "valence": 0.5,
  "arousal": 0.4,
  "mood": {
    "valence": 0.2,
    "arousal": 0.5,
    "stability": 0.7
  },
  "intensities": {
    "sadness": 3,
    "joy": 6,
    "fear": 2,
    "disgust": 1,
    "anger": 1,
    "surprise": 3
  },
  "appraisal": {
    "novelty": 0.1,
    "pleasantness": 0.65,
    "goal_conduciveness": 0.7,
    "urgency": 0.2,
    "perceived_control": 0.8,
    "norm_pressure": 0.4
  },
  "note": "Brief first-person gloss"
}

state/intention.json

{
  "_meta": {
    "skill": "cognition",
    "purpose": "Current top-level intention selected from appraisal, needs, norms, and affordances."
  },
  "_summary": "Have lunch at the café",
  "intention": "Have lunch at the café",
  "priority": 1,
  "attitude": 0.9,
  "subjective_norm": 0.7,
  "perceived_control": 0.8,
  "final_score": 2.4,
  "decision_process": {
    "search_policy": "satisficing",
    "aspiration_level": 1.8,
    "heuristics": ["availability: recent hunger memory increased meal salience"]
  },
  "emotion_influence": {
    "joy_modifier": 0.05,
    "applied_modifiers": ["joy > 7: +0.10 attitude"]
  },
  "reasoning": "One or two sentences"
}

Note: The emotion_influence field records how emotions affected this decision, providing transparency and debuggability.

Execution Sequence

1. workspace_read any of the optional inputs that exist (skip missing paths).
2. Compute mood update (drift toward neutral, influenced by recent emotions).
3. Compute emotion intensities (respect continuity rules).
4. workspace_write("state/emotion.json", ...)
5. List candidate intentions and apply emotion modifiers to compute final scores.
6. workspace_write("state/intention.json", ...) (include emotion_influence field)
7. done

Notes

• Intentions should be feasible given the latest observation; if the situation is unclear, prefer low-risk intentions (wait, observe, move to safer area) over fantasy.

Plan Completion/Failure Emotion Updates

When a plan completes or fails, emotions should be updated accordingly:

Plan Completed Successfully

Emotion	Change
joy	+2 to +4 (depending on plan importance)
pride	+2 to +3
fear	−1 (reduced anxiety)
sadness	−1

Primary emotion: Satisfaction, Pride, or Gratification

Plan Failed

Emotion	Change
sadness	+2 to +3
anger	+1 to +2 (if external cause)
fear	+1 (increased uncertainty)
joy	−2

Primary emotion: Disappointment, Frustration, or Remorse (if self-caused)

Integration with Plan Skill

The plan skill may signal completion/failure via state/plan_state.json. When detected:

1. Read the plan target and outcome
2. Apply appropriate emotion adjustments
3. Update state/emotion.json with new intensities
4. Write a brief note in the note field explaining the change