Cognition· 2026Q1
Computational complexity improves recognition memory for reward-relevant attributes
- 0citations
- Q1SCImago
- 2026year
Short summary
Solving more complex 0-1 knapsack problem instances increased recognition memory for reward-relevant attributes by 44% compared to less complex instances (N=131).
AI-generated from the title and abstract; the full text is not read.
Key points
- Solving complex 0-1 knapsack problem instances led to a 44% increase in recognition memory for reward-relevant attributes.
- Increased processing time and item clicks were observed for more complex instances.
- The findings suggest that higher cognitive load during complex problem-solving can enhance memory encoding.
- The study used phase transition proximity and Sahni-k as objective complexity metrics.
AI-generated from the title and abstract; the full text is not read.
Abstract
Computational complexity in optimisation tasks is often seen as impairing optimal choice. We test whether it could have beneficial effects on memory. In a preregistered lab experiment ( N = 131) using the NP-hard 0–1 knapsack problem, we manipulated knapsack capacity to create instances differing in two objective complexity metrics: phase transition proximity and Sahni-k. As predicted, solving a more complex instance resulted in a 44% (d = 0.45) increase in above-chance recognition memory relative to a lower-complexity instance. Participants spent longer on the complex instance and clicked on items more often. A plausible interpretation of these findings is that the increased processing requirements of search algorithms under complexity may have resulted in enhanced encoding through frequent revisiting of reward-relevant information. These findings provide a formal framework to generate hypotheses about the memory consequences of complexity and the effects of cognitive offloading in real-world decisions.
The authors' abstract, as published at the source. Cognition, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
General Decision SciencesDecision Sciences