Publications
Active finger movements play a crucial role in natural haptic perception. For the perception of different haptic properties people use different well-chosen movement schemes (Lederman & Klatzky, 1987). The haptic property of softness is stereotypically judged by repeatedly pressing one’s finger against an objects’ surface, actively indenting the object. It has been shown that people adjust the peak indentation forces of their pressing movements to the expected stimulus’ softness in order to improve perception (Kaim & Drewing, 2011). Here, we aim to clarify the mechanisms underlying such adjustments. We disentangle how people modulate executed peak indentation forces depending on predictive versus sensory signals to softness, and investigate the influence of the participants’ motivational state on movement adjustments. In Experiment 1, participants performed a 2AFC softness discrimination task for stimulus pairs from one of four softness categories. We manipulated the predictability of the softness category. Either all stimuli of the same category were presented in a blocked fashion, which allowed predicting the softness category of the upcoming pair (predictive signals high), or stimuli from different categories were randomly intermixed, which made prediction impossible (predictive signals low). Sensory signals to softness category of the two stimuli in a pair are gathered during exploration. We contrasted the first indentation (sensory signals low) and last indentation (sensory signals high) in order to examine the effect of sensory signals. Lezkan, A., Metzger, A., & Drewing, K. (2018). Active haptic exploration of softness: Indentation force is systematically related to prediction, sensation and motivation. Frontiers in integrative neuroscience, 12, 59.
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. Lezkan, A., & Drewing, K. (2018). Interdependences between finger movement direction and haptic perception of oriented textures. Plos one, 13(12), e0208988.
When estimating the softness of an object by active touch, humans typically indent the object’s surface several times with their finger, applying higher peak indentation forces when they expect to explore harder as compared to softer stimuli [1]. Here, we compared how different types of prior knowledge differentially influence exploratory forces in softness discrimination. On each trial, participants successively explored two silicone rubber stimuli which were either both relatively soft or both relatively hard, and judged which of the two were softer. We measured peak forces of the first indentation. In the control condition, participants obtained no information about whether the upcoming stimulus pair would be from the hard or the soft category. In three test conditions, participants received implicit (pairs from the same category were blocked), semantic (the words soft and hard), or visual prior knowledge about the softness category. Visual information was provided by displaying the rendering of a compliant object deformed by a probe. Given implicit information, participants again used significantly more force in their first touch when exploring harder as compared to softer objects. Surprisingly, when given visual information, participants used significantly less force in the first touch when exploring harder objects. There was no effect when participants were given semantic information. We conclude that different types of prior knowledge influence the exploration behavior in very different ways. Thus, the mechanisms through which prior knowledge is integrated in the exploration process might be more complex than expected. Zöller, A. C., Lezkan, A., Paulun, V. C., Fleming, R. W., & Drewing, K. (2018, June). Influence of different types of prior knowledge on haptic exploration of soft objects. In International Conference on Human Haptic Sensing and Touch Enabled Computer Applications (pp. 413-424). Springer, Cham.
When judging the heaviness of two objects with equal mass, people perceive the smaller and denser of the two as being heavier. Despite the large number of theories, covering bottom-up and top-down approaches, none of them can fully account for all aspects of this size-weight illusion and thus for human heaviness perception. Here we propose a new maximum-likelihood estimation model which describes the illusion as the weighted average of two heaviness estimates with correlated noise: One estimate derived from the object’s mass, and the other from the object’s density, with estimates’ weights based on their relative reliabilities. While information about mass can directly be perceived, information about density will in some cases first have to be derived from mass and volume. However, according to our model at the crucial perceptual level, heaviness judgments will be biased by the objects’ density, not by its size. In two magnitude estimation experiments, we tested model predictions for the visual and the haptic size-weight illusion. Participants lifted objects which varied in mass and density. We additionally varied the reliability of the density estimate by varying the quality of either visual (Experiment 1) or haptic (Experiment 2) volume information. As predicted, with increasing quality of volume information, heaviness judgments were increasingly biased towards the object’s density: Objects of the same density were perceived as more similar and big objects were perceived as increasingly lighter than small (denser) objects of the same mass. This perceived difference increased with an increasing difference in density. Wolf, C., Bergmann Tiest, W. M., & Drewing, K. (2018). A mass-density model can account for the size-weight illusion. PloS one, 13(2), e0190624.
Redundant estimates of an environmental property derived simultaneously from different senses or cues are typically integrated according to the maximum likelihood estimation model (MLE): Sensory estimates are weighted according to their reliabilities, maximizing the percept’s reliability. Mechanisms underlying the integration of sequentially derived estimates from one sense are less clear. Here we investigate the integration of serially sampled redundant information in softness perception. We developed a method to manipulate haptically perceived softness of silicone rubber stimuli during bare-finger exploration. We then manipulated softness estimates derived from single movement segments (indentations) in a multisegmented exploration to assess their contributions to the overall percept. Participants explored two stimuli in sequence, using 2–5 indentations, and reported which stimulus felt softer. Metzger, A., Lezkan, A., & Drewing, K. (2018). Integration of serial sensory information in haptic perception of softness. Journal of Experimental Psychology: Human Perception and Performance, 44(4), 551.
Participants manually explored 47 solid, fluid, and granular materials and rated them according to a list of sensory and affective attributes. In principal component analyses (PCA) of sensory ratings, we extracted six dimensions: Fluidity, Roughness, Deformability, Fibrousness, Heaviness, and Granularity. PCAs on affective ratings revealed Valence, Arousal, and Dominance. PCAs explained 87 percent of variance or more. We found sensory dimensions beyond the surface characteristics on which many previous studies had focused, and the affective dimension of Dominance which previously had not been reported-probably due to our wide range of materials. Experiment 1 investigated a single sample, Experiment 2 distinguished between participants with more versus less outdoor experience during childhood Drewing, K., Weyel, C., Celebi, H., & Kaya, D. (2018). Systematic relations between affective and sensory material dimensions in touch. IEEE Transactions on Haptics, 11(4), 611-622.
For different types of textures judged roughness has been shown to be an inverted U-shaped function of inter-element spacing when texture amplitude is low [1, 2]. This may be due to an interplay of two “components” that contribute to the skin’s spatial deformation, and thus to a spatial-intensive code to roughness [1, 3, 4]: (1) deformation increases with the depth of the finger’s intrusion between elements, which increases with inter-element spacing until the finger contacts the ground; and (2) skin deformation decreases with a decreasing number of inter-element gaps being simultaneously under the skin, i.e. with the texture’s spatial frequency (which is negatively correlated with inter-element spacing). The present study systematically tested these ideas. We presented participants different series of 3D-printed rectangular grating stimuli, in which the width of the grating’s grooves varied and the spatial frequency of grooves was constant, or vice versa. Participants touched the stimuli without lateral movement and judged roughness using magnitude estimation. As predicted and previously observed, judged roughness increased with groove width and groove frequency. However, the predicted increase with groove frequency, was only found for frequencies below about 0.5 mm−1. For larger frequencies, roughness decreased with increasing frequency. The decrease is at odds with findings from earlier studies that used aluminum rather than plastic gratings [5]. The results corroborate the assumption that the area of skin deformation plays a crucial role for roughness, but at the same time, point to the influence of subtle differences between materials that should be investigated in the future. Drewing, K. (2018, June). Judged roughness as a function of groove frequency and groove width in 3D-printed gratings. In International Conference on Human Haptic Sensing and Touch Enabled Computer Applications (pp. 258-269). Springer, Cham.
Demographic changes in most developed societies have fostered research on functional aging. While cognitive changes have been characterized elaborately, understanding of perceptual aging lacks behind. We investigated age effects on the mechanisms of how multiple sources of sensory information are merged into a common percept. We studied visuo-haptic integration in a length discrimination task. A total of 24 young (20–25 years) and 27 senior (69–77 years) adults compared standard stimuli to appropriate sets of comparison stimuli. Standard stimuli were explored under visual, haptic, or visuo-haptic conditions. The task procedure allowed introducing an intersensory conflict by anamorphic lenses. Comparison stimuli were exclusively explored haptically. We derived psychometric functions for each condition, determining points of subjective equality and discrimination thresholds. We notably evaluated visuo-haptic perception by different models of multisensory processing, i.e., the Maximum-Likelihood-Estimate model of optimal cue integration, a suboptimal integration model, and a cue switching model. Our results support robust visuo-haptic integration across the adult lifespan. We found suboptimal weighted averaging of sensory sources in young adults, however, senior adults exploited differential sensory reliabilities more efficiently to optimize thresholds. Indeed, evaluation of the MLE model indicates that young adults underweighted visual cues by more than 30%; in contrast, visual weights of senior adults deviated only by about 3% from predictions. We suggest that close to optimal multisensory integration might contribute to successful compensation for age-related sensory losses and provides a critical resource. Differentiation between multisensory integration during healthy aging and age-related pathological challenges on the sensory systems awaits further exploration. Billino, J., & Drewing, K. (2018). Age effects on visuo-haptic length discrimination: evidence for optimal integration of senses in senior adults. Multisensory research, 31(3-4), 273-300.
Where textures are defined by repetitive small spatial structures, exploration covering a greater extent will lead to signal repetition. We investigated how sensory estimates derived from these signals are integrated. In Experiment 1, participants stroked with the index finger one to eight times across two virtual gratings. Half of the participants discriminated according to ridge amplitude, the other half according to ridge spatial period. In both tasks, just noticeable differences (JNDs) decreased with an increasing number of strokes. Those gains from additional exploration were more than three times smaller than predicted for optimal observers who have access to equally reliable, and therefore equally weighted, estimates for the entire exploration. We assume that the sequential nature of the exploration leads to memory decay of sensory estimates. Thus, participants compare an overall estimate of the first stimulus, which is affected by memory decay, to stroke-specific estimates during the exploration of the second stimulus. This was tested in Experiments 2 and 3. The spatial period of one stroke across either the first or second of two sequentially presented gratings was slightly discrepant from periods in all other strokes. This allowed calculating weights of stroke-specific estimates in the overall percept. As predicted, weights were approximately equal for all strokes in the first stimulus, while weights decreased during the exploration of the second stimulus. A quantitative Kalman filter model of our assumptions was consistent with the data. Hence, our results support an optimal integration model for sequential information given that memory decay affects comparison processes. Lezkan, A., & Drewing, K. (2018). Processing of haptic texture information over sequential exploration movements. Attention, Perception, & Psychophysics, 80(1), 177-192.
When small holes are felt with the tongue, they are perceived to be larger compared with when felt with the index finger. This oral illusion has not yet been consistently explained. From present action-specific accounts of perception, we derived a high-level sticking-action hypothesis to explain the oral illusion. In 5 experiments, we contrasted this hypothesis’ predictions with predictions from the low-level bending hypothesis, which states that felt hole size decreases with decreasing bending of the skin at the hole’s edges. Results from Experiments 1 to 3 showed that felt hole size decreases with the pliability of the exploring effector (tongue > index finger > big toe, big fingers > small fingers), which affects skin bending, and that size perception with the highly pliable tongue is more accurate than with the less pliable finger and toe. Experiment 4 showed that holes of intermediate size are perceived to be larger with the tongue’s tip than with its dorsum. Finally, exploration styles that lessen the skin’s bending (using low vs. high tongue forces in Experiment 5) decreased perceived hole size. Overall, the results favor the low-level bending hypothesis over the high-level sticking-action hypothesis.
When touching an object, we focus more on some of its parts rather than touching the whole object’s surface, i.e. some parts are more salient than others. Here we investigated how different physical properties of rigid, plastic, relieved textures determine haptic exploratory behavior. We produced haptic stimuli whose textures were locally defined by random distributions of four independent features: amplitude, spatial frequency, orientation and isotropy. Participants explored two stimuli one after the other and in order to promote exploration we asked them to judge their similarity. We used a linear regression model to relate the features and their gradients to the exploratory behavior (spatial distribution of touch duration). The model predicts human behavior significantly better than chance, suggesting that exploratory movements are to some extent driven by the low level features we investigated. Remarkably, the contribution of each predictor changed as a function of the spatial scale in which it was defined, showing that haptic exploration preferences are spatially tuned, i.e. specific features are most salient at different spatial scales. Metzger, A., Toscani, M., Valsecchi, M., & Drewing, K. (2018, June). Haptic saliency model for rigid textured surfaces. In International Conference on Human Haptic Sensing and Touch Enabled Computer Applications (pp. 389-400). Springer, Cham.
When a short flash occurs in spatial alignment with a moving object, the moving object is seen ahead the stationary one. Similar to this visual “flash-lag effect” (FLE) it has been recently observed for the haptic sense that participants judge a moving hand to be ahead a stationary hand when judged at the moment of a short vibration (“haptic flash”) that is applied when the two hands are spatially aligned. We further investigated the haptic FLE. First, we compared participants’ performance in two isosensory visual or haptic conditions, in which moving object and flash were presented only in a single modality (visual: sphere and short color change, haptic: hand and vibration), and two bisensory conditions, in which the moving object was presented in both modalities (hand aligned with visible sphere), but the flash was presented only visually or only haptically. The experiment aimed to disentangle contributions of the flash’s and the objects’ modalities to the FLEs in haptics versus vision. We observed a FLE when the flash was visually displayed, both when the moving object was visual and visuo-haptic. Because the position of a visual flash, but not of an analogue haptic flash, is misjudged relative to a same visuo-haptic moving object, the difference between visual and haptic conditions can be fully attributed to characteristics of the flash. The second experiment confirmed that a haptic FLE can be observed depending on flash characteristics: the FLE increases with decreasing intensity of the flash (slightly modulated by flash duration), which had been previously observed for vision. These findings underline the high relevance of flash characteristics in different senses, and thus fit well with the temporal-sampling framework, where the flash triggers a high-level, supra-modal process of position judgement, the time point of which further depends on the processing time of the flash. Drewing, K., Hitzel, E., & Scocchia, L. (2018). The haptic and the visual flash-lag effect and the role of flash characteristics. Plos one, 13(1), e0189291.