Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 278

The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.

Application of Psychophysical Techniques to Haptic Research

Jones, Lynette A., Tan, Hong Z.·2013·IEEE Transactions on Haptics

Abstract

Various psychophysical methods have been used to study human haptic perception, although the selection of a particular method is often based on convention, rather than an analysis of which technique is optimal for the question being addressed. In this review, classical psychophysical techniques used to measure sensory thresholds are described as well as more modern methods such as adaptive procedures and those associated with signal detection theory. Details are provided as to how these techniques should be implemented to measure absolute and difference thresholds and factors that influence subjects' responses are noted. In addition to the methods used to measure sensory thresholds, the techniques available for measuring the perception of suprathreshold stimuli are presented. These scaling methods are reviewed in the context of the various stimulus and response biases that influence how subjects respond to stimuli. The importance of understanding the factors that influence perceptual processing is highlighted throughout the review with reference to experimental studies of haptic perception.

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 · 10.1109/toh.2012.74

Cites

89 of 89 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 89 references

Cited by

See all 278

Metadata

Authors
Jones, L. A., & Tan, H. Z.
Venue
IEEE Transactions on Haptics
Published
2013-07
Volume
6
Issue
3
Pages
268-284
ISSN
1939-1412 (ISSN-L)
OpenAlex
W1978703524
Cited by
278
References
89
FWCI
2.76
Type
journal article
Language
en
Open access
closed

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: Application of Psychophysical Techniques to Haptic Research. https://app.cochat.ai/papers/10.1109/toh.2012.74
The paper
Jones, L. A., & Tan, H. Z. (2013). Application of Psychophysical Techniques to Haptic Research. IEEE Transactions on Haptics. https://doi.org/10.1109/toh.2012.74

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.