Associative learning through acquired salience

Abstract

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Most associative learning studies describe the salience of stimuli as a fixed learning-rate parameter. Presumptive saliency signals, however, have also been linked to motivational and attentional processes. An interesting possibility, therefore, is that discriminative stimuli could also acquire salience as a consequence of becoming powerful predictors of outcomes. To explore this idea, we first characterized and extracted the learning curves from mice trained with discriminative images offering varying degrees of structural similarity. Next, we fitted a core model of associative learning coupled to a series of mathematical representations of visual salience. We found that the best prediction, from the set of tested models, was one in which the visual salience depended on stimulus similarity and a non-linear function of the associative strength. Our results support the idea that the net salience of a stimulus arises from the joint action of the items’ effective salience and the motivational state of the subject that learns about them. This compound salience model can explain why learning about a stimulus not only depends on the effective salience during acquisition but also on the specific learning trajectory that was used to reach this state. Our mathematical description could be instrumental for understanding aberrant salience acquisition under stressful situations and in neuropsychiatric disorders like schizophrenia, obsessive-compulsive disorder and addiction.

Keywords

• associative learning
• visual discrimination
• effective salience
• acquired salience
• acquired predictiveness