Interdependences between finger movement direction and haptic perception of oriented textures

Abstract

Although the natural haptic perception of textures includes active finger movements, it is unclear how closely perception and movements are linked. Here we investigated this question using oriented textures. Textures that are composed of periodically repeating grooves have a clear orientation defined by the grooves. The direction of finger movement relative to texture orientation determines the availability of temporal cues to the spatial period of the texture. These cues are absent during movements directed in line with texture orientation, whereas movements orthogonal to texture orientation maximize the temporal frequency of stimulation. This may optimize temporal cues. In Experiment 1 we tested whether texture perception gets more precise the more orthogonal the movement direction is to the texture. We systematically varied the movement direction within a 2IFC spatial period discrimination task. As expected, perception was more precise (lower discrimination thresholds) when finger movements were directed closer towards the texture orthogonal as compared to in parallel to the texture. In Experiment 2 we investigated whether people adjust movement directions to the texture orthogonal in free exploration. We recorded movement directions during free exploration of standard and comparison gratings. The standard gratings were clearly oriented. The comparison gratings did not have a clear orientation defined by grooves. Participants adjusted movement directions to the texture orthogonal only for clearly oriented textures (standards). The adjustment to texture orthogonal was present in the final movement but not in the first movement. This suggests that movement adjustment is based on sensory signals for texture orientation that were gathered over the course of exploration. In Experiment 3 we assessed whether the perception of texture orientation and movement adjustments are based on shared sensory signals. We determined perceptual thresholds for orientation discrimination and computed ‘movometric’ thresholds from the stroke-by-stroke adjustment of movement direction. Perception and movements were influenced by a common factor, the spatial period, suggesting that the same sensory signals for texture orientation contribute to both. We conclude that people optimize texture perception by adjusting their movements in directions that maximize temporal cue frequency. Adjustments are performed on the basis of sensory signals that are also used for perception.

Masking interferes with haptic texture perception from sequential exploratory movements

“…We did so under relatively well-defined conditions (e.g., of exploration). This perspective had been proven fruitful in previous studies (Lezkan & Drewing, 2018a , 2018b ; Metzger et al, 2018 ). It also allowed us to simplify the description of the exploratory movement and information gathering by its discretization into successive stroke units.…”

“…A haptic sensory memory would be an appropriate mechanism to preserve and integrate redundant haptic information gathered during the exploration of a stimulus. Sensory memories are involved in information integration in the range of a few seconds, which covers observed durations of the haptic exploration of single stimuli (Lezkan et al, 2018 ; Lezkan & Drewing, 2018a ). One characteristic of sensory memories is that masking interferes with its integration capabilities, erasing or perturbing the content of the sensory memory (Averbach & Sperling, 1961 ; Gegenfurtner & Sperling, 1993 ).…”

Active palpation underlying shape perception is shaped by physiological thresholds and experience

“…With adaptive active sensing it is expected that scanning velocities are tuned to optimize receptor activation. It has been shown that primates prefer hand movement patterns that preserve certain temporal cues (Cascio & Sathian, 2001;Drewing, 2012;Gamzu & Ahissar, 2001;Hollins & Risner, 2000;Lezkan & Drewing, 2018), cues whose temporal frequencies best fit one class of mechanoreceptors in the fingertip (Abraira & Ginty, 2013;Ahissar, 1998;Johansson et al, 1982;Talbot et al, 1968) .Hence, adaptive active sensing predicts that variations in hand speeds across participants should correspond to variations in the spatial spacing of their receptors. In this case, the temporal frequencies of activation will be preserved in the preferred working range, as the temporal frequency generated on the skin when scanning a single edge is determined by the multiplication of the finger speed and the spatial frequency of receptors across the skin (Darian- Smith & Oke, 1980).…”

“…CF variants were further analyzed in consequent studies (Klatzky & Balakrishnan, 1991;Lederman & Balakrishnan, 1991;reviewed in Nelinger et al, 2015). Quantitative studies of planar (2D) objects revealed that humans adapt their movement patterns to the spatial characteristics of the scanned surfaces; speed, for example, is adapted to spatial frequency (Gamzu & Ahissar, 2001) and direction -to the spatial orientation of the surface's texture (Drewing, 2012;Lezkan & Drewing, 2018). Such adaptations evolve through development (Withagen et al, 2013), can be acquired via consequent exposures to specific features (Lezkan & Drewing, 2018;Withagen et al, 2013) and can be directly taught (Gamzu & Ahissar, 2001).…”

“…Quantitative studies of planar (2D) objects revealed that humans adapt their movement patterns to the spatial characteristics of the scanned surfaces; speed, for example, is adapted to spatial frequency (Gamzu & Ahissar, 2001) and direction -to the spatial orientation of the surface's texture (Drewing, 2012;Lezkan & Drewing, 2018). Such adaptations evolve through development (Withagen et al, 2013), can be acquired via consequent exposures to specific features (Lezkan & Drewing, 2018;Withagen et al, 2013) and can be directly taught (Gamzu & Ahissar, 2001). In these studies, the adaptations resulted in maintaining specific sensory cues in a given "working range", likely optimal for sensation, consistent with principles of closed-loop control (Ahissar & Assa, 2016;Ahissar & Vaadia, 1990;Buckley & Toyoizumi, 2018a;Marken, 2009).…”