levels beyond the retina [3]. Moreover, object shape and other global object features can be sampled in parallel by the visual system, while they generally have ...
A Comparison of Visual and Haptic Object Representations Based on Similarity Theresa Cooke, Christian Wallraven, Heinrich H. Bülthoff Max Planck Institute for Biological Cybernetics, Tübingen, Germany {firstname.lastname}@tuebingen.mpg.de Abstract Do we judge similarity between two objects to be the same using touch and vision? We investigated this using psychophysical experiments in which subjects rated the similarity between objects presented either visually or haptically. The stimuli were a family of novel, threedimensional objects whose microgeometry (“texture”) and macrogeometry (“shape”) were parametrically varied. Multidimensional scaling of the similarity data was used to reconstruct haptic and visual perceptual spaces. For both modalities, a two-dimensional perceptual space was found whose dimensions clearly corresponded to shape and texture. Interestingly, shape dominated in visual space, whereas both shape and texture were important in haptic space. Furthermore, stimuli clusters were observed in this space, suggesting the emergence of category structure based on similarity relationships. The same category boundaries were confirmed in a visual free sorting experiment. This study reveals differences in object processing across modality and demonstrates an approach for analyzing such differences in multisensory visualizations. Keywords--- similarity, categorization, shape, texture, touch/haptic, vision, MDS, psychophysics
1. Introduction Does touching an object give rise to a different percept than seeing it? Although the visual and haptic sensory systems are able to extract many of the same properties of an object, there are fundamental differences in the way these features are extracted by the two modalities. Touch and vision operate on different input dimensionalities: touch operates on input from tactile receptors in threedimensional (3D) space, while vision operates on twodimensional (2D) retinal input. The two modalities have different view preferences: the haptic system has a bias towards encoding the back of objects, whereas the visual
system prefers the front view of objects [1]. The haptic sensory system operates in much closer proximity to the motor system than the visual sensory system does, both in the central and in the peripheral nervous system [2]. Several of the differences between the two modalities are related to spatial scale and sampling properties. Visual perception has a large spatial extent and functions simultaneously at several scales, while haptic perception is limited to near-body space and is markedly affected by the scale at which a feature evolves (e.g., in curvature perception [3]). The haptic system is capable of directly extracting features at the level of the most peripheral receptors (e.g., for pressure or temperature), whereas visual object feature extraction requires processing at higher levels beyond the retina [3]. Moreover, object shape and other global object features can be sampled in parallel by the visual system, while they generally have to be sampled in serial by the haptic system due to its more limited effective field of view [4]. Although both modalities are capable of extracting geometric object properties and their spatial relationships, it is not surprising given the above differences that largescale or “global” spatial processing should be more accessible to the visual system, whereas properties of an object which can be “locally” extracted (i.e., by a single finger or small group of receptors) should be more easily accessible to the haptic system [3]. A fundamental question is how such preferences for scales and features affect object representation and behaviour. Not only is this question important for elucidating the basis of high-level cognitive competences (such as similarity judgments and categorization), but it is also critical for understanding how the brain integrates different modalities for the purpose of recognition. In this study, we chose to investigate this question by having subjects make similarity judgments between pairs of objects within an object family which varied both on a local scale (easily accessible to the haptic system) and on a global scale (easily accessible to the visual system). The objects were presented visually in a first experiment and haptically in a second experiment. Similarity ratings and perceptual maps derived from these ratings using MDS
(multidimensional scaling) were then compared across modality. Relationships between similarity and categorization were explored by comparing similarity maps with category boundaries determined in a visual free sorting task. These findings shed light on how local and global variations in object properties affect object descriptions, similarity relationships, and categorization, and how these effects vary as a function of perceptual modality. The methodology of our study also offers a new approach for comparing different visualizations and identifying important perceptual dimensions for effective multisensory visualization.
2. Methods In this section, we describe the stimuli used in the experiments, as well as the experimental design for the visual and haptic similarity ratings and free sorting experiments.
2.1. Stimuli The stimuli consist of a family of novel, 3D objects (Figure 1), created in the graphics package 3D Studio Max 6.0. This package provides full control of object properties such as size, shape, and texture, allowing them to be varied in defined steps. The family begins with a “base object” (see Figure 1, object #5), which consists of three parts connected to a centre sphere and a texture map which is applied to the 3D mesh. The other family members are generated using two manipulations. The first manipulation alters the object’s microgeometry (or “texture”) by changing the amount of mesh displacement which the texture map is allowed to cause. The second manipulation alters the object’s macrogeometry (or “shape”) by moving mesh vertices towards a local average, essentially removing sharp angles in the global shape. Objects created using these variations can be plotted in a 2D creation parameter space whose dimension correspond to “microgeometry” and “macrogeometry”. The objects were then printed in 3D, layer-by-layer, by depositing filaments of heated plastic (Dimension 3D Printer, Stratasys, Minneapolis, USA). The printed objects are hard, white, and opaque, measuring 9.0 ± 0.1 cm wide, 8.3 ± 0.2 cm high, and 3.7 ± 0.1 cm deep and weighing approximately 40 g each.
2.2. Visual similarity ratings and free sorting Ten subjects with normal or corrected-to-normal vision were paid 8 EUR per hour to rate the similarities between photographs of the objects presented at 75 Hz on a Sony Trinitron 21” monitor with a resolution of 1024 x 768 pixels. None of the subjects had touched or seen the objects before. Photographs of the objects were displayed
Figure 1. Stimuli varied parametrically in terms of microgeometry (texture) and microgeometry (shape). using the Psychtoolbox extension for MATLAB [5, 6] on a Macintosh G4 computer. The image size was 7.6 x 7.6 degrees of visual angle (as if the object was held at arm’s length). Subjects were seated 60cm from the monitor in a dimly-lit room. A fixation cross appeared for 500ms and then each of the objects appeared for 500ms, separated by a 500ms interstimulus interval. At the end of each trial, subjects rated the similarity of the objects on a scale between 1 (low similarity) and 7 (high similarity). Response time was unlimited. There were six experimental blocks of 325 randomized trials (each object was compared once with itself and once with every other object, i.e., 25 + (25*24)/2 = 325). The total experiment lasted about two hours. At the end of the experiment, subjects filled out a debriefing questionnaire which asked them to describe the objects’ appearance, to explain how they had made their similarity judgments, and to explain how they would categorize the objects. In addition, subjects were given a randomized set of printouts of the images they had just seen (3 cm x 3 cm) and were asked to sort them into any number of categories (free sorting task).
2.3. Haptic similarity ratings Ten right-handed subjects were paid 8 EUR per hour to rate the similarities between the objects after exploring them haptically. None of the subjects had touched or seen the objects before. Subjects sat in front of a table, facing an opaque curtain. Behind the curtain, the experimenter presented two objects, one after the other. The objects were always presented in the same fixed position, face up on the table. Subjects were given up to 10 seconds to trace the contour of each object with their right hand. The contour-
Figure 2. Mean similarity ratings for objects explored visually (left) or haptically (right). The matrix is symmetric since ratings were averaged over all trials in which a given pair was shown, regardless of presentation order. Mean standard deviation was 1.3 for visual similarity ratings and 1.2 for haptic similarity ratings. consisted of three blocks of 325 randomized trials spread out over five two-hour sessions on consecutive days. At the end of the experiment, subjects filled out a debriefing questionnaire, asking them to describe how the objects felt, to explain how they had made their similarity judgments, and to explain how they would categorize the objects.
following procedure was chosen because it has been shown to allow for haptic extraction of a wide range of object properties, including both local texture and global shape [7]. Unimanual exploration was chosen over bimanual exploration for simplicity in this intial set of experiments. In the ten seconds provided, even untrained subjects had sufficient time to trace the object’s contour twice. Subjects rated the similarity between the objects on a seven point scale from 1 (low similarity) to 7 (high similarity). They could respond at any time after the second object was presented. They occasionally asked to repeat the trial and this was allowed. Subjects were instructed to keep their eyes closed during the experiment. The full experiment 0.4
visual haptic
0.35
3. Results and discussion 3.1. Visual similarity ratings and free sorting Mean visual similarity ratings for the twenty-five objects are shown in Figure 2. The most striking patterns
Shape dominant
Texture dominant
0.3 Stress
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stress threshold
haptic subjects visual subjects
0.15 0.1
-0.5
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2 3 4 Number of dimensions
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(a) Figure 3. MDS stress plot (using Young’s stress formula 1) (a) and shape/texture tradeoff values for individual subjects (b).
and analyzed using the ALSCAL algorithm in SPSS version 12.0.1 [8], while individual subject data was analyzed using the INDSCAL algorithm [9], which has the advantage of providing individual and mean weights for the output dimensions. The stress plot for the MDS analysis of mean visual similarity data is shown in Figure 3. Stress values below 0.2 are generally accepted as an indication that the dimensionality of the output space is sufficient to faithfully represent the input distance information [10]. Thus the stress values obtained here show that one perceptual dimension is sufficient to explain the similarity data (stress = 0.05 for one dimension). Plotting the two-dimensional stimulus configuration (Figure 4) confirmed that this first dimension corresponded to shape variation, while the second dimension corresponded to texture variation. Thus shape was the dominant perceptual dimension for visual similarity judgments, while texture played only a minor role. Note that the scaling and orientation of the map is not determined by MDS; in all cases, we fitted the maps obtained from MDS to a uniform grid with five shape and five texture levels, which we refer to as an “ordinal map.” When the perceptual map is overlaid on the ordinal map, the dominance of the shape dimension in the visual map is particularly striking. The dominant role of shape in visual judgments can also be seen from the relative shape/texture tradeoff values for individual subjects (Figure 3). These tradeoff values were derived from the individual dimension weights provided by INDSCAL. Almost all subjects in the visual
in the matrix are the large box patterns, which arise due to sharp changes in similarity ratings. A closer look reveals that this sharp change is directly related to shape changes in the stimuli. Stimulus 1 is perceptually very similar to 6 and 11 (mean ratings of 6.5 and 6.0, respectively), but suddenly much less similar to stimuli 16 and 21 (mean ratings of 3.2 and 2.8, respectively). Note that this pattern holds regardless of texture level. Texture-induced effects on similarity ratings are most visible in regions where the mean similarity is high, e.g., stimulus 1 is decreasingly similar to stimuli 2, 3, and 4, with mean ratings of 5.4, 5.1, 4.7, 4.2, respectively, or a total change of 1.2. In contrast, texture effects are muted in regions where the similarity is low due to large shape differences, e.g., there is little change in similarity between stimulus 1 versus 22, 23, 24, or 25, with mean ratings of 2.5, 2.4, 2.2, 2.2, respectively, or a total difference of 0.3. This pattern indicates that texture was used to differentiate stimuli when they closely resembled one another, but was used less when objects were perceived to be very different from one another. To see these effects more clearly, the similarity ratings were analyzed using multidimensional scaling (MDS). MDS takes a matrix of pair-wise distances as input and returns a stress plot from which the number of dimensions needed to represent the objects can be determined (similar to principle components), together with the coordinates of each object in the output space. MDS does not, however, provide an interpretation of the axes labels: these must be interpreted based on the output map. Similarity data was analyzed using two versions of MDS: mean similarity data across subjects were transformed to Euclidean distances visual map ordinal map
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Figure 4. Perceptual maps produced by ALSCAL MDS using visual similarity ratings (left) and haptic similarity ratings (right).
similarity experiment have a shape/texture tradeoff which is strongly biased towards shape. Two shape-based category groupings were evident in the visual map (the three bottom shape groups vs. the two top shape groups), suggesting the emergence of natural categories and an intriguing relationship between similarity judgments and categorization. Although these groupings could already be seen in the similarity matrix, applying MDS and plotting the stimuli in perceptual space makes the grouping much easier to visualize. In order to test whether these groupings could indeed be considered “natural categories”, we compared the similarity map against the category boundaries created by subjects in the free sorting task (Figure 5). The category boundaries shown were calculated as follows: for each adjacency in the stimulus map (e.g., the link between stimulus 13 and 18), we counted the number of subjects who identified it as a category boundary; then, for each of the 5 shape and 5 texture levels, we calculated the average number of subjects who had identified adjacencies along that line as belonging to their category boundaries; finally, shape and texture levels for which this average exceeded 30% were plotted as category boundaries.1 Interestingly, category boundaries fell between clusters of stimuli in the space derived from similarity ratings, suggesting that similarity relationships could be used to predict category structure (see General Discussion). The results of the post-experiment debriefing
Figure 5. Category boundaries chosen by visual free sorting, overlaid on visual stimulus map. Percentages refer to average proportion of subjects who selected this boundary.
questionnaire are shown in Table 1. For each of the questions we asked (to describe the object’s appearance/how it felt, to describe how similarity judgments were made, and to describe object categories), we calculated the percentage of subjects who: 1) mentioned either the word shape explicitly or shape-related words (e.g., “round”, “leg”, “sphere”); 2) mentioned the word “texture” or texture-related words (e.g., “bumpiness”, “surface structure”); 3) mentioned other object properties (e.g., colour, weight, size, material). Both texture and shape were mentioned by a majority of subjects in the visual similarity experiment when describing the objects, explaining how they judged similarity, and describing object categories. Other properties were not mentioned at all for similarity and categorization, though other properties were mentioned for object descriptions. Shape was mentioned more often than texture when explaining similarity judgments, a result which correlates well with the dominance of shape found in the MDS analysis of similarity ratings. Interestingly, texture was mentioned almost as often as shape in category descriptions; this finding agrees with the existence of both shape-based and texture-based category boundaries revealed by the free sorting experiment.
3.2. Haptic similarity ratings Mean haptic similarity ratings for the twenty-five objects are shown in Figure 2. Clear effects of both texture and shape variation in the stimuli can be seen. The pattern of fading diagonals in the matrix is related to shape variation (e.g., the shape of stimulus 1 is decreasingly similar to the shapes of stimuli 6, 11, 16, and 21, with mean similarity ratings of 6.4, 5.5, 4.6, and 3.8, respectively) whereas the fading box-like pattern is related to the texture variation (e.g., the texture of stimulus 1 is decreasingly similar to the textures of stimuli 2, 3, 4, and 5, with mean ratings of 6.0, 4.8, 3.8 and 3.5, respectively). The stress plot obtained by running MDS on mean haptic similarity ratings is shown in Figure 3. Stress drops sharply below the threshold of 0.2 only once the stimuli are embedded in a two-dimensional space. Plotting the twodimensional output configuration (Figure 4) enabled us to interpret these perceptual dimensions as texture (as the first output dimension) and shape (as the second output dimension). Thus texture alone was insufficient to explain the similarity data; instead both texture and shape were important perceptual dimensions for haptic similarity judgments. This finding also serves as a demonstration of subjects’ non-trivial ability to extract these two features from a high-dimensional haptic measurement space. 1
Although this procedure necessarily results in uni-dimensional categorization rules, most of the subjects’ category boundaries could be approximated by uni-dimensional rules up to one or two adjacencies.
Table 1. Responses to debriefing questionnaire (% subjects mentioning a given object property) Answers after visual similarity ratings Object Similarity Category description judgment description
Answers after haptic similarity ratings Object Similarity Category description judgment description
Mention shape or shaperelated property
90%
90%
90%
70%
100%
100%
Mention texture or texturerelated property
80%
60%
80%
100%
100%
100%
Mention other object properties (e.g., colour)
40%
0%
0%
30%
10%
10%
The importance of both shape and texture for haptic judgments can also be seen from the relative shape/texture tradeoff values for individual subjects, shown in Figure 3. Although texture was clearly the dominant dimension for some subjects, the mean tradeoff value for haptic judgments is close to zero, meaning that both shape and texture were, on average, important dimensions. The perceptual map obtained by plotting the stimulus coordinates in the two-dimensional haptic MDS space is shown in Figure 4. The haptic perceptual map is remarkably regular and bears a strong resemblance to the ordinal map: both the ordinal configuration of the stimuli is preserved and the relative dimension weights are the same (i.e., shape and texture are weighted equally in both cases). One notable difference is the irregular spacing between shape and texture levels. This spacing suggests two texture-based groupings (three leftmost rows and two rightmost rows) and two shape-based groupings (bottom three rows and top two rows). The results of the post-experiment debriefing questionnaire are shown in Table 1. Both shape and texture were mentioned by all subjects when asked about similarity judgments and object categories, while texture was mentioned slightly more often than shape for object descriptions. This equal proportion of references to shape and texture in verbal reports agrees with the mean tradeoff value derived from the individual subject weights, confirming that shape and texture both played important roles in haptic similarity judgments. The fact that other object properties were seldom mentioned correlates with the sharp drop in MDS stress for a two-dimensional solution and indicates that shape and texture were both sufficient and necessary perceptual dimensions in subjects’ similarity haptic judgments.
4. General discussion 4.1. Shape and texture as critical perceptual dimensions In both visual and haptic modalities, subjects were able to extract the two kinds of parametric variation which were used to create the stimuli. This is a non-trivial ability
given the high-dimensionality of the visual and haptic measurement spaces. These two stimulus variations, which we initially referred to as changes in “macrogeometry” and “microgeometry” were perceived by the subjects as changes in “shape” and “texture”. MDS analysis showed, and verbal report confirmed, that shape was a necessary and sufficient perceptual dimension for representing similarity relationships between the stimuli when they were presented visually. In contrast, both shape and texture constituted the necessary and sufficient perceptual dimensions when stimuli were presented haptically. In their verbal reports, subjects also made reference to shape and texture when describing these objects and categorizing them. Although subjects mentioned other object properties when describing the objects, they rarely mentioned any other property when describing similarities or categories. Thus shape and texture may already have become so-called diagnostic dimensions for the subjects, i.e., features which take on perceptual importance due to experience and task demands [11, 12]. Although one could argue that diagnosticity was induced by the categorization task in the visual experiment, there was no such task involved in the haptic experiment. This raises the possibility that stimulus dimensions take on category diagnosticity even in the absence of an explicit categorization task and that performing a similarity judgment task may be sufficient to invoke mechanisms of category diagnosticity.
4.2. Differences in critical perceptual dimensions for vision and touch In visual similarity judgments, shape was both a sufficient and necessary perceptual dimension; texture was not needed to account for the similarity data. This finding agrees with the idea advanced by Edelman that shape plays a crucial role in determining similarity relationships between objects [17]. This finding is also consistent with the notion that the extraction of global form is one of the visual system’s areas of expertise [3]. In a recent computational study [13], however, we found good correlation between 1) the similarity matrix derived from
simple pixel-wise differences between images of the objects and 2) the similarity matrix measured in the present study, including the dominance of shape over texture. This result raises the possibility that texture played a lesser role in similarity judgments because texture-related image differences occur on a smaller spatial scale and/or have lower local contrast than shape-related image differences. Another explanation is that texture changes require more time to build up perceptual weight in the visual modality than shape changes do; systematic manipulation of stimulus presentation times would be required in order to test this hypothesis. In haptic similarity judgments, both shape and texture were important perceptual dimensions. Given that local material properties are known to be more easily accessible to the haptic system than global geometric properties [3], it is not surprising that texture played a more important role in the haptic similarity judgments than in visual judgments. However, the finding that shape was an equally important perceptual dimension for the haptic task was somewhat surprising. For example, one study had subjects perform haptic free sorting of 3D objects based on their similarity and found that material properties such as texture were more salient than shape [16]. In addition, the fact that exploration was unimanual should also have biased the results towards texture, due to the greater difficulty of integrating shape information relative to bimanual exploration [18]. Two task parameters may explain the importance of shape in this experiment: first, subjects may have been biased towards extracting shape by the contour-following procedure. Klatzky & Lederman [7] rated the relative ability of this procedure to extract “exact shape” with a score of 3, “global shape” with a score of 1, and “texture” with a score of 1. Had we chosen another procedure, such as lateral motion (scores of 0 for both shape properties and 2 for texture), texture may have played a more dominant role in haptic similarity judgments. However, another study involving the same task found that contour-following was not associated with a differential emphasis on local versus global features [14]; the authors suggested that this was instead because objects were explored for a long period of time and that global shape properties tend to become more salient over time. Exploration time may also have played a role in our finding that shape was as important as texture for haptic similarity judgments. The study mentioned above systematically manipulated exploration time and found that “local shape” had the same effect as “global shape” on haptic similarity judgments for a 1s exploration time, but that the effect of local shape differences decreased as exploration time was increased up to 16s, up to about a 15% difference in ratings (amongst objects differing in local shape vs. amongst objects differing in global shape) [14]. Thus, the 10s exploration time in our haptic
experiment may have resulted in less importance being accorded to “local shape”, which one might interpret as texture/microgeometry in our case. However, our subjects only used the full 10s for the first 2-4 hours of the experiment; after this point, they reduced their exploration time, sometimes by up to 50%. Thus, given that exploration time in our experiment effectively ranged between 5 and 10s and that the size of the effect reported in [14] was 5-10% for exploration times of 5-10s, it seems unlikely that exploration time alone could explain the lower-than-expected texture weight. A final possibility is that haptic feature extraction may have been biased toward shape by the similarity judgment task itself. If indeed shape plays a critical role in determining object similarity, the task may have triggered an additional amount of haptic spatial processing, which could in fact be carried out within the exploration time available to subjects. The task effect could also have been augmented by the fact that the stimuli were novel 3D objects with relatively complex shape, which may trigger more spatial processing than familiar objects or objects with a simpler geometrical structure. Further studies are needed to disentangle the effects of task, stimulus complexity, and stimulus familiarity on modality-specific feature weightings.
4.3. From similarity to categorization Stimulus clusters in the perceptual map derived from visual similarity ratings clearly suggested the emergence of natural categories. Furthermore, these clusters correlated with the category boundaries chosen by subjects in the free sorting task (Figure 5). In addition, subjects reported that the same features (shape and texture) were important both for making similarity judgments and for assigning category membership. These results provide a striking example of the close relationship between similarity and categorization, a topic of great interest to categorization researchers (e.g., [15]). It may even be possible to actually predict natural category boundaries from the clusters in similarity space, a task we are currently undertaking. Category boundaries could also be inferred from the haptic similarity-based maps, though they are not as obvious as in the visual case. One possibility is that categorization of such stimuli is a more automatic process for the visual system, e.g., if categorization is indeed tightly coupled to spatial processing of global form (which the visual system can accomplish much more effectively than the haptic system). A second possibility is that the similarity ratings scale, which was limited to 7 numbers, simply does not provide enough information capacity or resolution to store variance along two perceptual dimensions and, in addition, variance due to category boundaries. Further studies are required in order to
elucidate how category structure develops in the haptic modality.
[4]
4.4. Summary and outlook
[5]
We have shown that the same 3D objects are represented differently in the human brain depending on the sensory modality used to perceive them (vision or touch). The main discrepancy was the differential weighting of perceptual dimensions: while shape was the sole critical perceptual feature for vision, both shape and texture were critical features for touch. Intrinsic biases in feature extraction and differences in the task or in the types of processing triggered by the task may explain the differences in representations. In future work, we will investigate the specific role of biases in feature extraction by computationally extracting 2D and 3D features and exploring how such features can be combined to simulate human results. We found that the features which were critical for similarity-based representations were also diagnostic for category membership and demonstrated that similaritybased representations correlated with categorization behaviour in the visual domain. Future studies will examine whether categories can indeed be predicted by similarities and address how category structures differ in vision and touch. Finally, the methodology presented here demonstrates a unique approach for comparing different kinds of shape rendering. Similarity judgments are a powerful tool to create perceptual maps of the visualizations and determine the most relevant perceptual dimensions. Our approach can help to explain why one visualization may be more effective than another in a given modality, thus providing design guidelines for effective multisensory visualizations.
[6] [7] [8] [9]
[10]
[11] [12] [13]
[14] [15]
Acknowledgements
[16]
We thank Quoc Vuong and Martin Breidt for help with stimulus design, and anonymous reviewers for their comments.
[17]
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[18]
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