priorization criteria:

• emotion when horizons seconds-days, stakes personal-social, contexts embodied and survival-relevant
• cognition when horizons months-decades, stakes systemic-abstract, contexts delayed or probabilistic

any issue can be reframed emotionally by linking to self or similar others, but this reframing introduces distortions:

• temporal discounting: undervaluing distant risks relative to immediate ones
• salience bias: overweighting vivid or personal stories relative to systemic dynamics
• binary collapse: compressing complex trade-offs into good/bad poles
• misprioritization: allocating urgency to symbolically salient but low-probability threats
• neglect of scale: privileging individual-level affect over aggregate or statistical outcomes

any issue can also be reframed cognitively by abstracting from direct affective relevance, but this reframing shifts emphasis:

• depersonalization: stripping urgency from personally critical issues

• over-abstraction: privileging general models over embodied signals

• delayed calibration: acting too slowly where immediate response is adaptive

• probability fixation: overweighting statistical reasoning at cost of situational salience

• underweighting of motivation: accurate models without corresponding drive to act

approaches:

• associative learning models of fear conditioning
• reinforcement learning with value functions and prediction errors
• computational appraisal models (cpm-like)

ai applications:

• deep learning for sensory trigger detection
• reinforcement learning for affect-like value assignment
• large language models for appraisal-like text evaluation without grounding
• hybrid architectures combining recognition with appraisal evaluators

chatbot design:

• multimodal affect embeddings stored in an emotion vector space

• valence-arousal and appraisal dimensions as indices

• sequential appraisal pipeline applied to embeddings

• reasoning and retrieval weighted by affect vectors alongside semantics