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      Haptic discrimination of different types of soft materials

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      Author(s)
      Cavdan, Müge
      Doerschner, Katja
      Drewing, Knut
      Date
      2022-05-20
      Source Title
      Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
      Publisher
      Springer Science and Business Media Deutschland GmbH
      Volume
      13235
      Pages
      3 - 11
      Language
      English
      Type
      Conference Paper
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      Abstract
      We interact with different types of soft materials on a daily basis such as salt, hand cream, etc. Recently we have shown that soft materials can be described using four perceptual dimensions which are deformability, granularity, viscosity, and surface softness [1]. Here, we investigated whether humans can actually perceive systematic differences in materials that selectively vary along one of these four dimensions as well as how judgments on the different dimensions are correlated to softness judgments. We selected at least two material classes per dimension (e.g., hair gel and hand cream for viscosity) and varied the corresponding feature (e.g., the viscosity of hair gel). Participants ordered four to ten materials from each material class according to their corresponding main feature, and in addition, according to their softness. Rank orders of materials according to the main feature were consistent across participants and repetitions. Rank orders according to softness were correlated either positively or negatively with the judgments along the associated four perceptual dimensions. These findings support our notion of multiple softness dimensions and demonstrate that people can reliably discriminate materials which are artificially varied along each of these softness dimensions. © 2022, The Author(s).
      Keywords
      Granularity
      Haptics
      Material perception
      Softness perception
      Permalink
      http://hdl.handle.net/11693/112032
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      • National Magnetic Resonance Research Center (UMRAM) 301
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