Abstract
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 they become 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 linear model of associative learning coupled to a series of mathematical representations for stimulus 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. Therefore, these analytic results support the idea that the net salience of a stimulus depends both on the items' effective salience and the motivational state of the subject that learns about it. Moreover, this dual 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.
Highlights
In nature, visual stimuli are organized in complex combinations
We adapted a mathematical model in order to predict the choice records from nine groups of mice trained with heterogeneous visual stimuli (Treviño et al, 2013)
We compared the predictive power of a simple associative learning rule coupled to six different saliency descriptions with the idea of gaining insight into the salience mechanisms involved in the learning process
Summary
Visual stimuli are organized in complex combinations. Animals must focus their visual system on salient objects from visual scenes to extract relevant information for guiding their behavior. Their structure or intensity) contribute to establishing how salient or conspicuous they are (Itti and Koch, 2001; Pearce and Bouton, 2001) Such effective salience specifies the relative capacity of a stimulus to stand out among other items in the visual scene. Salient stimuli attract attention and increase the rate of learning about them as well as other similar visual objects (Rescorla and Wagner, 1972; Mackintosh, 1975; Le Pelley, 2004; Treviño et al, 2013). From both behavioral and neurobiological perspectives, salience is a fundamental stimulus-specific learning rate parameter
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