A Computational Model of Human Emotion

This paper presents a novel computational model of human emotion that bridges cognitive and biological perspectives. The model proposes that emotions arise from the interaction between sophisticated, weighted, parallel pattern-matching processes (cognition) and dynamic state-change systems (biological substrate) triggered when these processes encounter significant disruptions. Specifically: emotional experience is the felt state that results when pattern-matching failures or novelties trigger state-change responses that temporarily alter processing parameters, creating feedback loops between cognitive disruption and state changes.