How is the serial order of a verbal sequence coded? Some comparisons between models

Abstract

Current models of verbal short‐term memory (STM) propose various mechanisms for serial order. These include a gradient of activation over items, associations between items, and associations between items and their positions relative to the start or end of a sequence. We compared models using a variant of Hebb's procedure in which immediate serial recall of a sequence improves if the sequence is presented more than once. However, instead of repeating a complete sequence, we repeated different aspects of serial order information common to training lists and a subsequent test list. In Experiment 1, training lists repeated all the item–item pairings in the test list, with or without the position–item pairings in the test list. Substantial learning relative to a control condition was observed only when training lists repeated item–item pairs with position–item pairs, and position was defined relative to the start rather than end of a sequence. Experiment 2 attempted to analyse the basis of this learning effect further by repeating fragments of the test list during training, where fragments consisted of either isolated position–item pairings or clusters of both position–item and item–item pairings. Repetition of sequence fragments led to only weak learning effects. However, where learning was observed it was for specific position–item pairings. We conclude that positional cues play an important role in the coding of serial order in memory but that the information required to learn a sequence goes beyond position–item associations. We suggest that whereas STM for a novel sequence is based on positional cues, learning a sequence involves the development of some additional representation of the sequence as a whole.

Notes

Correspondence should be addressed to G. J. Hitch, Department of Psychology, University of York, York YO10 5DD, UK. Email: [email protected]

We are grateful to Lauren Broom for valuable assistance in pilot work.

Parts of this research were supported by the Medical Research Council and the Biotechnology and Biological Sciences Research Council of Great Britain.

We are grateful to Ben Murdock for suggesting that we look at conditional probabilities.