Incremental category learning without external information: An algorithm for category‐opening internal learning (COIL)

Over the past two decades the topic of category learning has received considerable attention. Category learning evolved from the earlier study of concept formation but differs from it in how concepts (or categories) are viewed. In concept formation, concepts were assumed to be strictly defined by necessary and sufficient characteristics whereas category learning extends consideration to more natural categories that are ‘fuzzy’ and ill‐defined. A number of category‐learning models have been proposed to explain how human participants categorize stimuli. Most of the models require external information in addition to the stimuli themselves in order to learn. This additional information may take the form of external feedback following decisions, information about the number of categories to use, or a ‘preview’ of a batch of category exemplars. In contrast, human participants appear to be able to learn category structures without these forms of additional information, although their success in doing so varies directly with the clarity of the classification to be learned. The article proposes mechanisms for category learning without external information which can be added to the majority of category‐learning models. The main mechanism is ‘self‐generated feedback’, where the learner provides feedback internally by assuming decisions to be correct. Using a model, self‐feedback was found to facilitate learning of two‐ and three‐category problems across varying levels of clarity of category structure. Conditions were used where the model was given (i) exemplars of each category with their category labels, (ii) the number of categories to use and (iii) neither. In all cases, learning occurred without external feedback. The degree of learning increased with increasing clarity of category structure. In this respect, as in most others, the results were similar to those reported for human participants.