Evidence map›Paper›PMID 31841421›Full record

ArticleIEEE transactions on haptics

Tactile Exploration Strategies With Natural Compliant Objects Elicit Virtual Stiffness Cues.

Chang Xu, Hankun He, Steven C Hauser, Gregory J Gerling

Abstract read
In one paragraph

Article in IEEE transactions on haptics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
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  10. Time-dependent Cues Encode the Minimum Exploration Time in Discriminating Naturalistic Compliances.IEEE Haptics Symposium : [proceedings]. IEEE Haptics Symposium · 2020
    Article
  11. Article
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.

Chang Xu
Hankun He
Steven C Hauser
Gregory J Gerling

Funding

CRCNS: Neural Representations of Object Compliance in the Periphery R01NS105241 · NINDS · UNIVERSITY OF VIRGINIA · PI GERLING, GREGORY JOHN, LUMPKIN, ELLEN A · 2017 to 2019
$990k
NINDS NIH HHS R01 NS105241
6 · The paper itself

Abstract

When interacting with deformable objects, tactile cues at the finger pad help inform our perception of material compliance. Nearly all prior studies have relied on highly homogenous, engineered materials such as silicone-elastomers and foams. In contrast, we employ soft plum fruit varying in ripeness; ecological substances associated with tasks of everyday life. In this article, we investigate volitional exploratory strategies and contact interactions, for comparison to engineered materials. New measurement techniques are introduced, including an ink-based method to capture finger pad to fruit contact interactions, and instrumented force and optical sensors to capture imposed force and displacement. Human-subjects experiments are conducted for both single finger touch and two finger grasp. The results indicate that terminal contact areas between soft and hard plums are indistinguishable, but the newly defined metric of virtual stiffness can differentiate between the fruits' ripeness, amidst their local variations in geometry, stiffness, and viscoelasticity. Moreover, it affords discrimination independent of one's touch force. This metric illustrates the tie between the deployment of active, exploratory strategies and the elicitation of optimal cues for perceptual discrimination. Compared to single finger touch, perceptual discrimination improves further in pinch grasp, which is indeed a more natural gesture for judging ripeness.

Indexed as

CuesExploratory BehaviorPhysical StimulationTouchTouch PerceptionAdultBiomechanical PhenomenaDiscrimination, PsychologicalFemaleFingersHand StrengthHumansMalePsychophysicsYoung Adult

Identifiers

PMID31841421
PMCPMC7147988

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Registered trials

None linked

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.