Memory influences haptic perception of softness

Abstract

The memory of an object’s property (e.g. its typical colour) can affect its visual perception. We investigated whether memory of the softness of every-day objects influences their haptic perception. We produced bipartite silicone rubber stimuli: one half of the stimuli was covered with a layer of an object (sponge, wood, tennis ball, foam ball); the other half was uncovered silicone. Participants were not aware of the partition. They first used their bare finger to stroke laterally over the covering layer to recognize the well-known object and then indented the other half of the stimulus with a probe to compare its softness to that of an uncovered silicone stimulus. Across four experiments with different methods we showed that silicon stimuli covered with a layer of rather hard objects (tennis ball and wood) were perceived harder than the same silicon stimuli when being covered with a layer of rather soft objects (sponge and foam ball), indicating that haptic perception of softness is affected by memory.

Visual and Tactile Sensory Systems Share Common Features in Object Recognition

“…While much is known about visual shape processing ( Haushofer et al, 2008 ; Peelen et al, 2014 ), less information is available regarding tactile shape processing ( Klatzky et al, 1985 ; Hernández-Pérez et al, 2017 ; Metzger et al, 2019 ). A series of studies comparing visual and tactile perceptual spaces with familiar objects have revealed that the human perception of familiar objects is not solely determined by the physical features of objects but is influenced by high-level cognitive abilities, including memory ( Amedi et al, 2002 ; Norman et al, 2008 ; Haag, 2011 ; Metzger and Drewing, 2019 ) and prior knowledge of objects for integrating sensory systems ( Ernst and Bülthoff, 2004 ). While other studies have used parametric shape models, such as shell-shaped 3D objects ( Gaißert et al, 2008 , 2010a , 2011 ; Gaißert and Wallraven, 2012 ), it is difficult to capture the shape complexity of natural objects with parametric approaches and avoid possible confounds or special cases in object shapes ( Haushofer et al, 2008 ; Lee Masson et al, 2016 ).…”

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

“…Up to now, deep learning has proven to be a powerful tool for generating multimodal data suitable for robotics and autonomous systems [146]. These systems involve, for example, the interaction of sophisticated perception/vision and haptic sensors (e.g., monocular cameras, stereo cameras, and so on) [147], the merging of depth and color information from RGB-D cameras [148], and so on. Figure 15 shows an autonomous vehicle with several on-board sensors, including a camera and several radars and LiDARs.…”

Materials in action: The look and feel of soft

“…It is possible sand or salt are materials that most observers are very familiar with, and when identifying the materials, memories of interacting with the material might become activated enabling participants to make these judgments. For example, found that perceived softness in haptic experiments is influenced by memory (Metzger & Drewing, 2019), and haptic experiences (Kangur et al, 2019). Conversely, it is possible that, when judging the granularity of lentils, pebbles, or cranberries, such a prior experience is not available and therefore, participants are left with visual information 'only', which might lead to different perceptions.…”