Revising the limits of learning in Absolute Identification

Abstract

Miller’s (1956) review of a series of absolute identification (AI) experiments, as well as a multitude of subsequent absolute identification research, suggests a fundamental limit to human information processing capacity. This limit is thought to be highly resistant to practice, independent of stimulus modality, and has been universally accepted as a fundamental constraint on human information processing capacity. Generally it is expected that people improve their performance slightly in absolute identification tasks, but quickly reach an asymptote after which they fail to improve any more. Recently however, we have replicated an experiment that demonstrates significant improvement in AI performance with only moderate practice. We conclude that there are several factors that are essential to the ability to learn unidimensional AI stimuli. Motivation is essential for improvement in performance, as is an initial performance level that greatly exceeds what would be expected by chance – this also constrains the type of stimuli that can be learned. In addition, in contrast to Miller’s conclusion that the asymptote in performance is independent of set size, we suggest that indeed set size does affect the asymptote in performance, namely that a larger set size (around n=30), allows a higher asymptote in performance


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