The effect of abstract inter-chunk relationships on serial-order control

Cognition. 2023 Oct:239:105578. doi: 10.1016/j.cognition.2023.105578. Epub 2023 Aug 2.

Abstract

Hierarchical control is often thought to dissect a complex task space into isolated subspaces in order to eliminate interference. Yet, there is also evidence from serial-order control tasks that our cognitive system can make use of abstract relationships between different parts (chunks) of a sequence. Past evidence in this regard was limited to situations with ordered stimuli (e.g., numbers or positions) that may have aided the detection of relationships and allowed gradual learning and hypothesis testing. Therefore, we used a modified task-span paradigm (with no ordered elements between tasks) in which participants performed memorized sequences of tasks that were encoded in terms of separate chunks of three tasks each. To allow examination of learning effects, each sequence was "cycled" through repeatedly. Importantly, we compared sequences whose chunks were governed by a common, abstract grammar with sequences whose chunks were governed by different grammars. Experiment 1 examined the effect of relationships between shared-element chunks (e.g., ABB-BAA vs. ABB-BAB), Experiment 2 and 3 between different-element chunks (e.g., ABA-CDC vs. ABA-CCD), and Experiment 4 examined second-order relationships (e.g., ABA-ABB--CDC-CDD vs. ABA-ABB--CDC-CCD). Robust evidence in favor of beneficial effects of abstract inter-chunk relationships was obtained across all four experiments. Importantly, these effects were at least as strong in initial cycles of performing a given sequence as during later cycles, suggesting that the cognitive system operates with an "expectation of abstract relationships," rather than benefitting from them through gradual learning. We discuss the implications of these results for models of hierarchical control.

Keywords: Hierarchical control; Sequential representations; Serial-order control.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Humans
  • Learning*
  • Memory*