Open AccessTraining-dependent transfer within a set of nested tasks
Author(s) -
Joseph P Rennie,
Jonathan Spencer Jones,
Duncan Astle
Publication year - 2021
Publication title -
the quarterly journal of experimental psychology/quarterly journal of experimental psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.249
H-Index - 76
eISSN - 1747-0226
pISSN - 1747-0218
DOI - 10.1177/1747021821993772
Subject(s) - set (abstract data type) , hierarchy , task (project management) , transfer of learning , negative transfer , computer science , feature (linguistics) , transfer (computing) , complement (music) , transfer of training , artificial intelligence , similarity (geometry) , cognitive psychology , cognition , psychology , machine learning , philosophy , image (mathematics) , linguistics , chemistry , biochemistry , management , parallel computing , market economy , programming language , neuroscience , complementation , first language , economics , gene , phenotype
Extended practice on a particular cognitive task can boost the performance of other tasks, even though they themselves have not been practised. This transfer of benefits appears to be specific, occurring most when tasks are very similar to those being trained. But what type of similarity is most important for predicting transfer? This question is addressed with a tightly controlled randomised design, with a relatively large sample ( N = 175) and an adaptive control group. We created a hierarchical set of nested assessment tasks. Participants then trained on two of the tasks: one was relatively "low" in the hierarchy requiring just simultaneous judgements of shapes' spikiness, whereas the other was relatively "high" requiring delayed judgements of shapes' spikiness or number of spikes in a switching paradigm. Using the full complement of nested tasks before and after training, we could then test whether and how these "low" and "high" training effects cascade through the hierarchy. For both training groups, relative to the control, whether or not an assessment task shared a single specific feature was the best predictor of transfer patterns. For the low-level training group, the overall proportion of feature overlap also significantly predicted transfer, but the same was not true for the high-level training group. Finally, pre-training between-task correlations were not predictive of the pattern of transfer for either group. Together these findings provide an experimental exploration of the specificity of transfer and establish the nature of task overlap that is crucial for the transfer of performance improvements.