Fornix and Uncinate Fasciculus Support Metacognition-Driven Cognitive Offloading

preprint OA: closed CC-BY-4.0

Abstract

People often use external tools to offload cognitive demands in remembering future intentions. While previous research has identified the causal role of metacognition in cognitive offloading, the neural mechanisms underlying this metacognitive control process remain unclear. To address this gap, we conducted a study with 34 participants using diffusion tensor imaging (DTI) to investigate how connections between brain regions support metacognition-driven cognitive offloading. Behaviorally, we confirmed that under-confidence in using internal memory to execute delayed intentions predicts a bias towards using external reminders. At the brain level we found that the fractional anisotropy (FA) of the fornix, a memory-related white matter tract connected to the hippocampus, positively correlated with the bias in setting up reminders. Additionally, the FA of the left uncinate fasciculus, which links the hippocampus to the prefrontal cortex and is involved in memory error monitoring, negatively correlated with deviations from optimal reminder use. Furthermore, the FA of the superior longitudinal fasciculus, a tract involved in metacognitive monitoring, moderated how confidence influenced the use of reminders. Taken together, our findings reveal a temporal-frontal neural circuit underlying metacognition-driven cognitive offloading, and provide new insights into the interaction between metacognitive monitoring and control.
Full text 621 characters · extracted from oa-doi-fallback · click to expand
There is a newer version available for this {{ publicationType }}. View latest version {{ publication.field_name }} {{ publication.subfield_name }} Copyright: © {{ publicationYear }} {{ publication.presentation_authors[0].full_name + (publication.presentation_authors.length > 1 ? ' et al' : '') }}. This is an open access publication distributed under the terms of the CC BY 4.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Check the {{ publicationType | capitalize }} Source for copyright and license information. Listen on

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2024) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00
unpaywall
last seen: 2026-05-30T02:00:01.510937+00:00
License: CC-BY-4.0