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Research ArticleResearch Article: New Research, Cognition and Behavior

Visual and Tactile Sensory Systems Share Common Features in Object Recognition

Sepideh Tabrik, Mehdi Behroozi, Lara Schlaffke, Stefanie Heba, Melanie Lenz, Silke Lissek, Onur Güntürkün, Hubert R. Dinse and Martin Tegenthoff
eNeuro 20 September 2021, 8 (5) ENEURO.0101-21.2021; https://doi.org/10.1523/ENEURO.0101-21.2021
Sepideh Tabrik
1Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
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  • ORCID record for Sepideh Tabrik
Mehdi Behroozi
2Institute of Cognitive Neuroscience, Department of Biopsychology, Faculty of Psychology, Ruhr University Bochum, 44780 Bochum, Germany
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Lara Schlaffke
1Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
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Stefanie Heba
1Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
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Melanie Lenz
1Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
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Silke Lissek
1Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
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Onur Güntürkün
2Institute of Cognitive Neuroscience, Department of Biopsychology, Faculty of Psychology, Ruhr University Bochum, 44780 Bochum, Germany
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Hubert R. Dinse
1Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
3Neural Plasticity Laboratory, Institute for Neuroinformatics, Ruhr University Bochum, 44780 Bochum, Germany
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Martin Tegenthoff
1Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr University Bochum, 44789 Bochum, Germany
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Article Figures & Data

Figures

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  • Extended Data
  • Figure 1.
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    Figure 1.

    Stimuli generation and task designs. A, Generating object categories using a virtual phylogenesis algorithm starting from an icosahedron. At each generation Gn, selected embryos procreate, leading to generation Gn+1. Simulated embryonic development processes were applied to a given parent object from G2 (circles) to generate two classes of novel objects in G3: eight G3 siblings from one parent formed a distinct object category. In total, two object categories from the third generation served as stimuli for the current study, with siblings 1–8 numbered by the experimenter accordingly within each category. The subjects were unaware of how the digital embryos were generated and/or categorized. B, The virtual office was furnished with a desk, which was located in front of the participants. If the participants looked toward their left, bookshelves, a printer, some books, and a monitor on a study table were visible; toward their right, there was a window with a view of the outside. C, Visual similarity task using virtual reality technology. D, Tactile similarity experiment using 3D tangible objects generated by a 3D printer. The objects were printed out with two different colors to be more recognizable for the experimenter. Since participants were unable to see the objects, this color difference did not affect the experimental results.

  • Figure 2.
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    Figure 2.

    Similarity matrices. A, Average group similarity matrix for visual similarity judgment. B, Average group similarity matrix for tactile similarity judgment. The color codes for the similarity ratings corresponded to the numbers, ranging from 1 (dissimilar, dark blue) to 7 (identical, dark red). Numbers on the x- and y-axes refer to the digital embryos in each category (eight objects per category) according to Figure 1A.

  • Figure 3.
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    Figure 3.

    Two-dimensional visual and tactile perceptual spaces. A, The stress values for both modalities were calculated for 1–10 dimensions. The elbow indicates that two data dimensions are sufficient to explain the visual and the tactile perceptual space. B, Two-dimensional visual perceptual space (Extended Data Fig. 3-1A,C, one- and three-dimensional visual perceptual spaces). C, Two-dimensional tactile perceptual space (Extended Data Fig. 3-1B,D, one- and three-dimensional tactile perceptual spaces). The numbers refer to the object numbers in each category according to Figure 1A. Contrast level codes for different categories; black, category 1; gray, category 2.

  • Figure 4.
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    Figure 4.

    Euclidian distance. The average distance between pairs of objects within a category (black bars) is significantly smaller than the distance between pairs of objects from different categories (gray bars) for both modalities. Error bars represent the SEM. **p < 0.0001.

  • Figure 5.
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    Figure 5.

    Questionnaires. At the end of each similarity judgment test, participants were asked to rate the importance of features to determine which features played the main role in their similarity judgments. In addition to the weight, color, material, global patterning, and texture, we listed further details describing the shape of digital embryos: branch size, branch pattern, number of branches, global shape, and curvature. The results for both modalities indicated that the shape features played a major role. Features such as weight, size, and the pattern of branch distributions were significantly more important for the tactile similarity judgment experiment than for the visual. Bars represent the mean ratings across all participants over the visual (gray) and tactile (black) modalities (0 means no importance; 6 means very important). Error bars represent the SEM. *p < 0.01.

  • Figure 6.
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    Figure 6.

    Mean d values (Procrustes fit error). A, Fits between computational measures of a single feature with visual and tactile maps. B, Fits between computational measures of a combination of features with visual and tactile maps. F1–F17 indicate the number of single features. N1–N17 present a combination of features that are listed in Table 2. Asterisks demonstrate that a reconstructed map of a feature or combination of features is significantly different from perception in human behavior (p < 0.01). MD indicates cross-fitting error.

  • Figure 7.
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    Figure 7.

    The average distance for five different sets. The average distances between pairs of objects within and between pairs of objects for five different sets of objects. Within category, black bars; between categories, gray bars. Error bars represent the SEM. **p < 0.0001.

Tables

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    Table 1

    Summary of the extracted features

    FeatureDefinitionVisual modalityTactile modality
    F1Gaussian curvatures of objects✓×
    F2The distances from all vertices to the left of objects✓✓
    F3The surface area of objects××
    F4The volume of objects✓✓
    F5The area of the projection of the object to x–y-plane (top/back view)✓✓
    F6The area of the projection of the object to x–y-plane (lateral view)××
    F7The area of the projection of the object to x–y-plane (frontal view)✓×
    F8The distances of the left from edges on the x–y-projection✓×
    F9The distances of the left from edges on the x–y-projection××
    F10The distances of the left from edges on the x–y-projection××
    F11Geometric measure: bounding box size✓✓
    F12Geometric measure: bounding box diagonal××
    F13Geometric measure: inertia tensor✓✓
    F14Geometric measure: principal axes✓×
    F15Geometric measure: axis momenta✓✓
    F16The left of objects××
    F17Topological measure: the number of faces that constructed objects×✓
    • The first and second columns illustrate 17 extracted shape features. The third column represents the selected feature that demonstrates the lowest d value between the physical and the visual perceptual spaces. The fourth column shows the selected features that lead to a minimum d value between physical and the tactile perceptual space.

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    Table 2

    The goodness of fit

    NMinimum d values for N combination of features (N = 1–17)
    Fit quality between physical and visual perceptual spacesFit quality between physical and tactile perceptual spaces
    1d = 0.158 (F13)d = 0.266 (F13)
    2d = 0.120 (F2, F13)d = 0.215 (F2, F9)
    3d = 0.098 (F5, F13, F14)d = 0.158 (F2, F4, F13)
    4d = 0.072 (F2, F11, F13, F14)d = 0.135 (F2, F4, F11, F16)
    5d = 0.061 (F2, F5, F11, F13, F14)d = 0.126 (F2, F4, F9, F15, F17)
    6d = 0.062 (F2, F3, F6, F12, F14, F15)d = 0.118 (F2, F4, F5, F10, F11, F17)
    7d = 0.053 (F2, F5, F6, F11, F13, F14, F15)d = 0.101 (F2, F4, F5, F11, F13, F15, F17)
    8d = 0.052 (F2, F3, F4, F8, F11, F13, F14, F15)d = 0.110 (F2, F4, F5, F6, F9, F10, F11, F15)
    9d = 0.050 (F2, F4, F5, F6, F7, F11, F13, F14, F15)d = 0.116 (F2, F4, F7, F10, F11, F12, F13, F15, F16)
    10d = 0.048 (F1, F2, F4, F5, F7, F8, F11, F13, F14, F15)d = 0.120 (F2, F4, F5, F7, F9, F10, F11, F14, F15, F17)
    11d = 0.050 (F2, F4, F5, F6, F7, F10, F11, F12, F13, F14, F15)d = 0.132 (F2, F3, F4, F7, F9, F10, F11, F12, F14, F15, F16)
    12d = 0.060 (F1, F2, F4, F5, F7, F8, F10, F11, F12, F13, F14, F15)d = 0.145 (F2, F3, F4, F6, F7, F9, F10, F11, F12, F14, F15, F17)
    13d = 0.078; all features were selected except F6, F9, F12, F17d = 0.150; all features were selected except F1, F6, F8, F13
    14d = 0.110; all features were selected except F6, F16, F17d = 0.171; all features were selected except F5, F8, F13
    15d = 0.167; all features were selected except F10, F14d = 0.271; all features were selected except F10, F14
    16d = 0.168; all features were selected except F14d = 0.272; all features were selected except F14
    17d = 0.168; all features were selectedd = 0.272; all features were selected
    • The d values in the columns represent the minimum d values between the physical and the visual/tactile perceptual space for a different combination of features. The best fit quality between physical and visual perceptual spaces occurred when the ten features F1, F2, F4, F5, F7, F8, F11, F13, F14, F15 were selected. On the other hand, the combination of the seven features F2, F4, F5, F11, F13, F15, F17 lead to the best fit quality between physical and tactile perceptual spaces. These two modalities share the features F2, F4, F5, F11, F13, F15. (Extended Data Table 2-1).

Extended Data

  • Figures
  • Tables
  • Table 2-1

    The d values in Table 2 show that visual and tactile d values lead to U-shaped curves. A single feature or a combination of a few features led to high d values, and when the number of involving features rose, the d values again increased. It supports the notion that humans do not necessarily need to use all given features to reconstruct the perceptual spaces. Download Table 2-1, TIF file.

  • Figure 3-1

    One- and three-dimensional visual and tactile perceptual spaces. A, One-dimensional visual perceptual space. B, One-dimensional tactile perceptual space. C, Three-dimensional visual perceptual space. D, Three-dimensional tactile perceptual space. The numbers refer to the object. Color codes two different categories. Download Figure 3-1, TIF file.

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Visual and Tactile Sensory Systems Share Common Features in Object Recognition
Sepideh Tabrik, Mehdi Behroozi, Lara Schlaffke, Stefanie Heba, Melanie Lenz, Silke Lissek, Onur Güntürkün, Hubert R. Dinse, Martin Tegenthoff
eNeuro 20 September 2021, 8 (5) ENEURO.0101-21.2021; DOI: 10.1523/ENEURO.0101-21.2021

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Visual and Tactile Sensory Systems Share Common Features in Object Recognition
Sepideh Tabrik, Mehdi Behroozi, Lara Schlaffke, Stefanie Heba, Melanie Lenz, Silke Lissek, Onur Güntürkün, Hubert R. Dinse, Martin Tegenthoff
eNeuro 20 September 2021, 8 (5) ENEURO.0101-21.2021; DOI: 10.1523/ENEURO.0101-21.2021
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Keywords

  • shape features
  • shape perception
  • similarity judgment
  • virtual reality
  • visual and tactile perception

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