Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 257

The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.

Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not

Open access
MacDorman, Karl F., Chattopadhyay, Debaleena·2016·Cognition

Abstract

Human replicas may elicit unintended cold, eerie feelings in viewers, an effect known as the uncanny valley. Masahiro Mori, who proposed the effect in 1970, attributed it to inconsistencies in the replica's realism with some of its features perceived as human and others as nonhuman. This study aims to determine whether reducing realism consistency in visual features increases the uncanny valley effect. In three rounds of experiments, 548 participants categorized and rated humans, animals, and objects that varied from computer animated to real. Two sets of features were manipulated to reduce realism consistency. (For humans, the sets were eyes-eyelashes-mouth and skin-nose-eyebrows.) Reducing realism consistency caused humans and animals, but not objects, to appear eerier and colder. However, the predictions of a competing theory, proposed by Ernst Jentsch in 1906, were not supported: The most ambiguous representations-those eliciting the greatest category uncertainty-were neither the eeriest nor the coldest.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.cognition.2015.09.019

Cites

67 of 67 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 67 references

Cited by

See all 257

Metadata

Authors
MacDorman, K. F., & Chattopadhyay, D.
Venue
Cognition
Publisher
Elsevier BV
Published
2016-01
Volume
146
Pages
190-205
ISSN
0010-0277 (ISSN-L)
OpenAlex
W1867827413
Cited by
257
References
67
FWCI
18.77
Type
journal article
Language
en
Open access
hybrid

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not. https://app.cochat.ai/papers/10.1016/j.cognition.2015.09.019
The paper
MacDorman, K. F., & Chattopadhyay, D. (2016). Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not. Cognition. https://doi.org/10.1016/j.cognition.2015.09.019

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.