Revising the limits of learning in Absolute Identification

Revising the limits of learning in Absolute Identification Pennie Dodds (Pennie.Dodds@newcastle.edu.au) School of Psychology, University Drive Callaghan, NSW, Australia Chris Donkin (Chris.Donkin@newcastle.edu.au) School of Psychology, University Drive Callaghan, NSW, Australia Scott Brown (Scott.Brown@newcastle.edu.au) School of Psychology, University Drive Callaghan, NSW, Australia Andrew Heathcote (Andrew.Heathcote@newcastle.edu.au) School of Psychology, University Drive Callaghan, NSW, Australia 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. Keywords: Absolute Identification; Modality; Set Size In a typical AI task, stimuli vary on just one dimension, for example, line length or tone intensity. These stimuli (called unidimensional stimuli) are first presented to the participant labelled with a unique marker. The smallest is labelled # 1, the next # 2, and so on. In the test phase, the participant is presented with one item at a time, and asked to label it with its unique referent. An incorrect response is followed by the display of the correct stimulus label. Using this seemingly simple task, researchers have examined the capacity of human information processing by calculating the amount of information transferred from stimuli to responses, where an increase in information transfer is analogous to an increase in memory for stimulus items. Information transfer is measured in units of bits, taken from information theory (Attneave, 1959). AI tasks provide a test of recognition for a set of unidimensional stimuli. Given the apparent simplicity of the task and the generally unlimited capacity of long term memory for stimuli such as faces, one would expect that a participant in an AI task would be able to reach perfect performance (in the least, if given enough practice). Unidimensional stimuli however, are an exception to the usually unlimited capacity of long term memory. Performance in tasks using unidimensional stimuli is generally very poor, and fifty years of research have consistently shown that even extensive practice does not significantly increase this limit in memory (Garner, 1953; Weber, Green & Luce, 1977; Shiffrin & Nosofsky, 1994). This limitation in performance has become a truism of AI research: people cannot learn to improve their performance. Even significant practice has little effect on performance in AI tasks. For example, Pollack (1952) used absolute identification of tones varying in pitch, and found that after several days of practice, information transfer remained at only 2.3 bits, equivalent to perfect identification of only approximately 5 tones. In a similar experiment, Hartman (1954) found that during an eight week testing period, performance increased very slowly and never approached perfect levels. Garner (1953) concentrated on absolute identification of tones varying in loudness rather than pitch, and found even when using 12,000 trials, information transmission was still low (1.62 bits). Similar to Garner and Weber, Green and Luce (1977), also used 12,000 trials, and compared the performance on the initial and last 2,000 trials to calculate change in accuracy. Even with monetary incentives and significant practice, performance was only shown to improve 8.5%. Weber et al. only used six stimuli, which is fewer than the generally accepted short-term memory limit of 7±2. Even so, practice failed to significantly improve performance, and no ceiling effects were found. More recently, Rouder, Morey, Cowan and Pfaltz (2004)