When Working Memory May Be Just Working, Not Memory

Andre Beukers, Maia Hamin, Kenneth A. Norman, Jonathan D. Cohen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The N-back task is often considered to be a canonical example of a task that relies on working memory (WM), requiring both maintenance of representations of previously presented stimuli and also processing of these representations. In particular, the set-size effect in this task (e.g., poorer performance on three-back than two-back judgments), as in others, is often interpreted as indicating that the task relies on retention and processing of information in a limited-capacity WM system. Here, we consider an alternative possibility: that retention in episodic memory (EM) rather than WM can account for both set-size and lure effects in the N-back task. Accordingly, performance in the N-back task may reflect engagement of the processing (“working”) function of WM but not necessarily limits in either that processing ability nor in retention (“memory”). To demonstrate this point, we constructed a neural network model that was augmented with an EM component, but lacked any capacity to retain information across trials in WM, and trained it to perform the N-back task. We show that this model can account for the set-size and lure effects obtained in an N-back study by M. J. Kane et al. (2007), and that it does so as a result of the well-understood effects of temporal distinctiveness on EM retrieval, and the processing of this information in WM. These findings help illuminate the ways in which WM may interact with EM in the service of cognitive function and add to a growing body of evidence that tasks commonly assumed to rely on WM may alternatively (or additionally) rely on EM.

Original languageEnglish (US)
Pages (from-to)563-577
Number of pages15
JournalPsychological Review
Issue number2
StatePublished - Nov 13 2023

All Science Journal Classification (ASJC) codes

  • General Psychology


  • episodic memory
  • n-back task
  • neural network models
  • temporal context model
  • working memory


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