Freeing Capacity in Working Memory (WM) Through the Use of Long-Term Memory (LTM) Representations

Previous research indicates that long-term memory (LTM) may contribute to performance in working memory (WM) tasks. Across 3 experiments, we investigated the extent to which active maintenance in WM can be replaced by relying on information stored in episodic LTM, thereby freeing capacity for additional information in WM. First, participants encoded word pairs into LTM, and then completed a WM task, also involving word pairs. Crucially, the pairs presented in each WM trial comprised varying numbers of new pairs and the previously learned LTM pairs. Experiment 1 showed that recall performance in the WM task was unaffected when memory set size increased through the addition of LTM pairs, but that it deteriorated when set size increased through adding new pairs. In Experiment 2, we investigated the robustness of this effect, orthogonally manipulating the number of new and LTM pairs used in the WM task. When WM load was low, performance declined with the addition of LTM pairs but remained superior to performance with the matched set size comprising only new pairs. By contrast, when WM load was higher, adding LTM pairs did not affect performance. In Experiment 3, we found that the benefit of LTM representations arises from retrieving these during the WM test, leading them to suffer from typical interference effects. We conclude that individuals can outsource workload to LTM to optimize performance, and that the WM system negotiates the exchange of information between WM and LTM depending on the current memory load.