ATTENTIONAL SHIFTS IN MAINTENANCE REHEARSAL

Authors
Citation
Rh. Phaf et G. Wolters, ATTENTIONAL SHIFTS IN MAINTENANCE REHEARSAL, The American journal of psychology, 106(3), 1993, pp. 353-382
Citations number
53
Categorie Soggetti
Psychology
ISSN journal
00029556
Volume
106
Issue
3
Year of publication
1993
Pages
353 - 382
Database
ISI
SICI code
0002-9556(1993)106:3<353:ASIMR>2.0.ZU;2-J
Abstract
The distinction between maintenance and elaborative rehearsal was inve stigated in four experiments. Experiments 1-3 showed that long periods of maintenance rehearsal, induced in a distractor recall procedure, c onsistently produced small increases in recall performance. The increa ses were independent of manipulations that enhance overall performance , such as semantic relatedness and intentional learning, but seemed to depend mainly on the occurrence of rehearsal errors. Such errors pres umably have a dishabituating effect that draws attention to the words. Instead of qualitatively different maintenance and elaborative rehear sal processes, a single form of rehearsal with variable amounts of att entional processing is suggested. Attention results in active elaborat ion and the creation of new memory representations. Without attention, only passive activation and strengthening of already existing represe ntations occurs. Maintenance rehearsal is accompanied by relatively li ttle attentional processing, but the novelty of the initial presentati on and later rehearsal errors evoke some attentional processing which may lead to increases in recall. In Experiment 4, the effect of mainte nance rehearsal on an implicit test (word completion) was determined. Because implicit memory is supposed to depend mainly on the strengthen ing of old representations, no effect of rehearsal errors was expected , and none was found. The role of attention is discussed. It is argued that novelty-dependent attentional processes may play a major role in new learning and in ensuring stability of old representations both in the human system and in artificial neural-network models.