Modeling Valuation and Core Affect in a Cognitive Architecture: The Impact of Valence and Arousal on Memory and Decision-Making

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A novel approach to adding primitive evaluative capabilities to a cognitive architecture is proposed. Two sub-symbolic quantities called valuation and arousal are learned for each declarative memory element based on usage statistics and the reward it generates. As a result, each memory element can be characterized as positive or negative and having a certain degree of affective intensity. In turn, these characteristics affect the latency and probability of retrieval for that memory element. Two global accumulators called core-affect-valuation and core-affect-arousal are computed as weighted sums of all retrievable valuations and arousals, respectively. The weights reflect usage history, context relevance, and reward accrual for all retrievable memory elements. These accumulators describe the general disposition or mood of the system. Core affect dynamics are used as reward signals to learn valuation and arousal values for new objects or events. The new architectural mechanism is used to develop two models that demonstrate the impact of affective valence and arousal on memory and decision-making. The models are fit to datasets from the literature and make novel predictions. The value of including valuation and core affect mechanisms in a cognitive architecture is discussed.

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