Metzger, A., Toscani, M., Akbarinia, A., Valsecchi, M. and Drewing, K., 2021. Deep neural network model of haptic saliency. Scientific Reports, 11, 1395.
Full text available as:
|
PDF (OPEN ACCESS ARTICLE)
metzger_2021.pdf - Published Version Available under License Creative Commons Attribution. 7MB | |
Copyright to original material in this document is with the original owner(s). Access to this content through BURO is granted on condition that you use it only for research, scholarly or other non-commercial purposes. If you wish to use it for any other purposes, you must contact BU via BURO@bournemouth.ac.uk. Any third party copyright material in this document remains the property of its respective owner(s). BU grants no licence for further use of that third party material. |
DOI: 0.1038/s41598-020-80675-6
Abstract
Haptic exploration usually involves stereotypical systematic movements that are adapted to the task. Here we tested whether exploration movements are also driven by physical stimulus features. We designed haptic stimuli, whose surface relief varied locally in spatial frequency, height, orientation, and anisotropy. In Experiment 1, participants subsequently explored two stimuli in order to decide whether they were same or different. We trained a variational autoencoder to predict the spatial distribution of touch duration from the surface relief of the haptic stimuli. The model successfully predicted where participants touched the stimuli. It could also predict participants’ touch distribution from the stimulus’ surface relief when tested with two new groups of participants, who performed a different task (Exp. 2) or explored different stimuli (Exp. 3). We further generated a large number of virtual surface reliefs (uniformly expressing a certain combination of features) and correlated the model’s responses with stimulus properties to understand the model’s preferences in order to infer which stimulus features were preferentially touched by participants. Our results indicate that haptic exploratory behavior is to some extent driven by the physical features of the stimuli, with e.g. edge-like structures, vertical and horizontal patterns, and rough regions being explored in more detail.
Item Type: | Article |
---|---|
ISSN: | 2045-2322 |
Group: | Faculty of Science & Technology |
ID Code: | 36688 |
Deposited By: | Symplectic RT2 |
Deposited On: | 01 Mar 2022 11:57 |
Last Modified: | 14 Mar 2022 14:33 |
Downloads
Downloads per month over past year
Repository Staff Only - |