The DOI matches its Crossref record.
How trust and distrust shape perception and memory
Abstract
Trust is a key ingredient in decision making, as it allows us to rely on the information we receive. Although trust is usually viewed as a positive element of decision making, we suggest that its effects on memory are costly rather than beneficial. Across nine studies using three different manipulations of trust and distrust and three different memory paradigms, we find that trust reduces memory performance as compared with distrust. In Study 1, trust leads to higher acceptance rates of misinformation. Studies 2a and 2b demonstrate that participants in a distrust and a control condition perform better at a memory-recognition task than participants in a trust condition. Studies 3a and 3b show that trust also reduces free recall of memory content. Examining the underlying mechanism, we find that reduced memory performance in a state of trust is caused by an increased perception of similarities between items that are to be memorized. Following a causal chain design, Study 4 shows that trust increases the sensitivity to similarities as compared with distrust and a control condition, and Study 5 shows that a processing focus on similarities reduces memory accuracy. Studies 6 and 7 create circumstances that either leave the proposed mediator free to vary or interrupt it via the induction of a similarity-focus (Study 6) or a difference-focus (Study 7). The disadvantage of trust is only present if the mediating processing focus can freely operate. Overall, these studies show that tru…
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?
Distrust or suspicion led to an increase of memory performance.
“Moreover, a series of studies by Posten and Gino (2021) showed an increase of memory performance.”
From Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation (2025) · cites this paper as Posten and Gino (2021)
In one study, Posten and Gino observed a higher acceptance of misinformation in a trust condition.
“Interestingly, in one of their studies Posten and Gino (2021) observe a higher acceptance of misinformation in a trust condition, underlining the potential problem of the current trust focus in the (X)AI context and the danger of overtrust.”
From Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation (2025) · cites this paper as Posten and Gino (2021)
Distrust shifts the perception focus towards differences and increases memory performance.
“Distrust shifts the perception focus towards differences and, therefore, increases memory performance.”
From Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation (2025) · cites this paper as Posten and Gino (2021)
Trust triggers a perception focus on similarities that makes it harder to remember single entities.
“According to Posten and Gino (2021), trust triggers a perception focus on similarities that makes it harder to remember single entities.”
From Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation (2025) · cites this paper as Posten and Gino (2021)
Citations
Cited by
Sorted by citations
Visser, R., Peters, T. M., Scharlau, I., & Hammer, B. (2025) · Cognitive Systems Research · doi:10.1016/j.cogsys.2025.101357 · Cited by 58
Metadata
- Authors
- Posten, A.-C., & Gino, F.
- ORCID
- Posten, Ann-Christin
- Venue
- Journal of Personality and Social Psychology
- Published
- 2021-07
- Volume
- 121
- Issue
- 1
- Pages
- 43-58
- ISSN
- 1939-1315
- 0022-3514
- Cited by
- 12
- References
- 0
- Language
- en
- Funders
- German Science Foundation (PO 1850/1-1)
- Harvard University
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