Evidence map›Paper›PMID 41741562›Full record

ArticleScientific reports2026

Natural warning signals unexpectedly shape human metamemory ratings but not image recognition success.

Federico De Filippi, Olivier Penacchio, Akira R O'Connor, Julie M Harris

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Federico De FilippiSchool of Psychology & Neuroscience, University of St Andrews, St Andrews, KY16 9JP, UK. fdf1@st-andrews.ac.uk.
Olivier PenacchioBridging Research in AI and Neuroscience, Computer Vision Center, Cerdanyola del Vallès, 08193, Barcelona, Spain.
Akira R O'ConnorSchool of Psychology & Neuroscience, University of St Andrews, St Andrews, KY16 9JP, UK.
Julie M HarrisSchool of Psychology & Neuroscience, University of St Andrews, St Andrews, KY16 9JP, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although the bold warning signals of prey (known as aposematic) have been shown to facilitate predator learning through repeated encounters, it remains unclear to what extent their visual patterns and colours support memory retention. Here, we tested whether aposematic species appear more memorable to human observers, and whether they have an intrinsic advantage in being recognised from memory—even after a single exposure. Observers viewed images of aposematic and non-aposematic butterflies and moths, judged how likely they were to remember each one (metamemory rating), and later completed a test distinguishing previously seen species from novel ones (recognition memory). While aposematic patterns elicited higher metamemory ratings upon first sight, we found no evidence that they were more likely to be recognised when seen again. Despite this apparent metacognitive failure, for aposematic species the observers tend to remember and forget the same images as one another. This suggests that these images exhibit ‘memorability’, an intrinsic property of an image that allows one to predict how well images can be remembered. These findings raise the possibility that an effective visual warning may hinge less on recognition of a previously seen signal, and more on perceptual processes at play when it is first encountered.

Indexed as

MemoryPredatory BehaviorRecognition, PsychologyAnimalsButterfliesHumansMothsPattern Recognition, VisualPhotic StimulationAposematismMemorabilityMetamemoryReceiver psychologyVisionWarning signal

Identifiers

PMID41741562
PMCPMC13031780

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.