Human generalization of internal representations through prototype learning with goal-directed attention

The world is overabundant with feature-rich information obscuring the latent causes of experience. How do people approximate the complexities of the external world with simplified internal representations that generalize to novel examples or situations? Theories suggest that internal representations could be determined by decision boundaries that discriminate between alternatives, or by distance measurements against prototypes and individual exemplars. Each provide advantages and drawbacks for generalization. We therefore developed theoretical models that leverage both discriminative and distance components to form internal representations via action-reward feedback. We then developed three latent-state learning tasks to test how humans use goal-oriented discrimination attention and prototypes/exemplar representations. The majority of participants attended to both goal-relevant discriminative features and the covariance of features within a prototype. A minority of participants relied only on the discriminative feature. Behaviour of all participants could be captured by parameterizing a model combining prototype representations with goal-oriented discriminative attention. The authors use three latent-state learning tasks to test how people approximate the complexities of the external world with simplified internal representations that generalize to novel examples or situations. They show that behaviour can be captured by a model combining prototype representations with goal-oriented discriminative attention.

Automated summary

The study uses three learning tasks to examine how people approximate the complexities of the external world with simplified internal representations that generalize to novel examples or situations. The findings reveal that most participants attend to goal-relevant discriminative features and the covariance of features within a prototype, while a minority rely solely on discriminative features. The behavior of all participants can be captured by a model combining prototype representations with goal-oriented discriminative attention.