The DOI matches its Crossref record.
Information without knowledge: the effects of Internet search on learning
Abstract
The Internet has radically shifted how people access information. Instead of storing information internally, increasingly, people outsource to the Internet and retrieve it when needed. While this is an efficient strategy in many ways, its downstream consequences remain largely unexplored. This research examines how accessing online information impacts how people remember information in a learning context. Across five experiments, participants studied for a quiz either by searching online to access relevant information or by directly receiving that same information without online search. Those who searched the Internet performed worse in the learning assessment, indicating that they stored less new knowledge in internal memory. However, participants who searched the Internet were as confident, or even more confident, that they had mastered the study material compared to those who did not search online. We argue that, by making information retrievability salient, Internet search reduces the likelihood of information being stored in memory. Further, these results suggest that searching online leads to the misattribution of online information to internal memory, thus masking the Internet-induced learning deficits.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Searching the Internet reduces the amount of information retained in memory compared to cases where participants read identical texts directly, bypassing the internet search stage.
“This leads to overconfidence in one's knowledge (Ward, 2021) and, at the same time, reduces the amount of information retained in memory compared to cases where participants read identical texts directly, bypassing the internet search stage (Fisher et al., 2022).”
From Overconfidence without Understanding: AI Explanations Increase the Illusion of Explanatory Depth (2025) · cites this paper as Fisher et al., 2022
Citations
Cited by
Sorted by citations
Overconfidence without Understanding: AI Explanations Increase the Illusion of Explanatory Depth
Aslanov, I., Felmer, P., & Guerra, E. (2025) · doi:10.31234/osf.io/8psgf_v1
Metadata
- Authors
- Fisher, M., Smiley, A. H., & Grillo, T. L. H.
- ORCID
- Fisher, Matthew
- Venue
- Memory Online
- Publisher
- Routledge
- Published
- 2022-12-30
- Pages
- 7-19
- Cited by
- 0
- References
- 0
- Language
- en
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review