Comparing Map Learning between Touchscreen-Based Visual and Haptic Displays: A Behavioral Evaluation with Blind and Sighted Users
Abstract
:1. Introduction
2. Related Work
3. Contributions
4. Materials and Methods
4.1. Experiment 1: Evaluation with BVI Users
4.1.1. Participants
4.1.2. Conditions
4.1.3. Stimulus and Apparatus
4.1.4. Procedure
4.2. Experiment 2: Evaluation with Sighted Users
4.2.1. Participants
4.2.2. Conditions, Stimulus, and Procedure
5. Results
5.1. Results from Experiment 1: BVI Users
5.1.1. Learning Time
5.1.2. Wayfinding Accuracy
5.1.3. Wayfinding Sequence
5.1.4. Relative Directional Accuracy
5.1.5. Reconstruction Accuracy
5.2. Results from Experiment 2: Blindfolded Sighted Users
5.3. Comparison between Participant Groups
6. Discussion and Future Work
- Evidence that incorporating our previously established perceptual parameters and design guidelines yield significant performance improvements in learning and spatial behaviors. For example, the pointing errors with the VAM were significantly less than the average ~18° pointing errors reported in an earlier study using a touchscreen-based haptic interface not optimized with the current parameters [13]. Although learning with the VAM took longer than learning with traditional hardcopy tactile maps, these temporal differences were narrowed in the current studies, where learning with the VAM was notably faster than has been found in previous research. For instance, average learning time was ~6.5 min in the current studies, whereas participants in previous work evaluating touchscreen-based vibration and auditory cues not optimized with the parameters took an average of ~15 min to learn maps of similar complexity [11,12,70,71]. Taken together, these findings suggest that the previously established perceptual parameters and design guidelines for use on touchscreen-based non-visual interfaces (e.g., our prototype vibro-audio map) have positively influenced user behavior, both in terms of temporal performance and spatial accuracy.
- Results provide compelling evidence for the similarity of spatio-behavioral performance across all test measures when using the VAM vs. traditional hardcopy tactile maps. This outcome not only supports the efficacy of the VAM (and touchscreen-based haptic feedback more generally) as a viable new solution for conveying graphical information, but it also suggests that it can be used as effectively as traditional non-visual maps. The similar (or better) behavioral performance observed across testing measures and experiments for the VAM suggests that the cognitive maps built up from VAM learning were at least as accurate as those formed by learning with the hardcopy tactile maps. Beyond supporting the VAM as a viable new interface, this lack of reliable difference is of theoretical interest because the similarity of performance between the two tactile (haptic) conditions speaks to the ability of both channels to support cognitive map development, despite employing information extraction and pick-up from different sensory receptors (pressure-activated mechanoreceptors versus vibration-sensitive Pacinian corpuscles) and feedback mechanisms (intrinsic perceptual feedback as opposed to extrinsic vibratory feedback).
- Results provide compelling evidence for the similarity of spatio-behavioral performance when using the VAM between BVI participants and blindfolded sighted participants during haptic map learning. The lack of reliable statistical differences observed between Experiments 1 and 2 suggest that non-visual map learning and subsequent spatio-behavioral task performance based on the ensuing cognitive map is not dependent on the presence or absence of vision. We interpret these functionally similar findings between sighted and BVI participants as: (1) Providing support against the conventional view that BVI spatial performance is impoverished with respect to their sighted peers (for reviews, see [36,42,69]). Indeed, the current findings are congruent with a growing body of evidence showing highly similar performance on spatial tasks between these groups when sufficient information is available through non-visual spatial supports [56,72,73]. (2) Showing that sighted users stand to greatly benefit from haptic-based interfaces and increased research interest, especially in eyes-free scenarios. (3) Demonstrating that valid data are possible from blindfolded sighted participants in non-visual studies when sufficient training is provided.
- The results provide compelling evidence that visual map learning and haptic map learning are functionally equivalent for developing accurate cognitive maps and supporting spatial behaviors when matched for information content. The statistically indistinguishable test performance observed here after haptic and visual map learning in Experiment 2 is consistent with the view that spatial learning from different sensory inputs, when matched for information content as we did here, leads to the development and use of sensory-independent, amodal representations of space in memory [55,74]. The similarity observed between blind and sighted participants across experiments, as discussed in the previous point, provides additional evidence for the notion of developing and accessing of a sensory-independent spatial representation that functions equivalently in the service of action. This interpretation is consistent with a growing corpus of data from other studies comparing performance by blindfolded sighted and BVI users on the same tasks after visual and tactile learning, e.g., of simple route maps [56], bar graphs and shapes [6], indoor floor maps [13], and spatial path patterns [65].
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter for | Guideline |
---|---|
Vibrotactile Line Detection | On-screen lines must be rendered at a minimum width of 1 mm for supporting accurate detection via haptic feedback |
Vibrotactile Gap Detection | An interline gap width of 4 mm bounded by lines rendered at a width of 4 mm is recommended for discriminating parallel lines. |
Discriminating Oriented Vibrotactile Lines | A minimum angular separation (i.e., cord length) of 4 mm is recommended for supporting discrimination of oriented lines. Angular elements should be schematized by calculating the minimum perceivable angle (using the formula: θ = 2 arcsin (cord length/2r)). |
Vibrotactile Line Tracing and Orientation Judgments | A minimum line width of 4 mm is necessary for supporting tasks that require line tracing (path following), judging line orientation, and learning of complex spatial path patterns. |
Building Mental Representations from Spatial Patterns | When rendered at a width of 4 mm, users can accurately judge vibrotactile line orientation to an angular interval of 7°. |
Feedback Mechanism for Vibrotactile Perception | Users prefer vibrotactile feedback as a guiding cue (i.e., used to identify/follow lines) as opposed to a warning cue. This interaction style also leads to better performance. |
Sex | Etiology of Blindness | Residual Vision | Age | Onset | Years (Stable) |
---|---|---|---|---|---|
M | Retinopathy of Prematurity | Light/dark perception | 44 | Birth | 44 |
M | Retinopathy of Prematurity | None | 28 | Birth | 28 |
M | Leber’s Congenital Amaurosis | Light perception | 40 | Birth | 40 |
F | Retinitis Pigmentosa | Light/dark perception | 63 | Age 11 | 52 |
F | Retinitis Pigmentosa | Light/dark perception | 38 | Birth | 38 |
F | Unknown | Light/dark perception | 33 | Age 17 | 16 |
M | Retinitis Pigmentosa | Light/dark perception | 48 | Age 25 | 13 |
F | Retinitis Pigmentosa | Light/dark perception | 61 | Age 11 | 50 |
M | Retinal Detachment | None | 61 | Birth | 61 |
F | Retinopathy of Prematurity | None | 57 | Age 20 | 37 |
F | Retinopathy of Prematurity | Light perception | 43 | Birth | 43 |
M | Retinopathy of Prematurity | None | 48 | Birth | 48 |
Measures | VAM | Hardcopy | ||
---|---|---|---|---|
Mean | SD | Mean | SD | |
Learning time (in seconds) | 426.75 | 186.05 | 130 | 43.22 |
Wayfinding accuracy (in percent) | 91 | 28.3 | 95 | 21.5 |
Wayfinding sequence (in percent) | 72 | 45.1 | 54 | 50.1 |
Relative directional error (in angle) | 6.5 | 8.84 | 9.78 | 11.95 |
Reconstruction accuracy (in percent) | 83 | 38.9 | 83 | 38.9 |
Measures | df | f | Sig. | |
---|---|---|---|---|
Hypothesis | Error | |||
Learning time | 1 | 22 | 28.96 | <0.001 |
Wayfinding accuracy | 1 | 94 | 1.09 | >0.05 |
Wayfinding sequence accuracy | 1 | 94 | 3.14 | >0.05 |
Relative directional accuracy | 1 | 94 | 4.50 | <0.05 |
Reconstruction accuracy | 1 | 22 | 0.00 | >0.05 |
Measures | VAM | Hardcopy | Visual | |||
---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | |
Learning time (in seconds) | 360.00 | 93.64 | 178.93 | 49.07 | 97.86 | 23.35 |
Wayfinding accuracy (in percent) | 95.00 | 22.70 | 96.00 | 18.70 | 98.00 | 13.40 |
Wayfinding sequence (in percent) | 68.00 | 47.00 | 48.00 | 50.00 | 66.00 | 47.00 |
Relative directional error (in angle) | 5.89 | 8.37 | 7.77 | 10.74 | 5.80 | 8.20 |
Reconstruction accuracy (in percent) | 71.00 | 46.90 | 86.00 | 36.30 | 86.00 | 36.30 |
Scale (in percent) | 88.06 | 9.47 | 86.70 | 8.15 | 90.33 | 8.73 |
Theta (in degree) | 3.01 | 5.48 | 0.68 | 3.10 | 1.06 | 2.77 |
Distortion Index | 14.95 | 1.41 | 14.74 | 1.63 | 15.25 | 1.57 |
Measures | df | f | Sig. | |
---|---|---|---|---|
Hypothesis | Error | |||
Learning time | 2 | 39 | 64.53 | <0.001 |
Wayfinding accuracy | 2 | 165 | 0.512 | >0.05 |
Wayfinding sequence accuracy | 2 | 165 | 2.813 | >0.05 |
Relative directional accuracy | 2 | 165 | 0.816 | >0.05 |
Reconstruction accuracy | 2 | 39 | 0.591 | >0.05 |
Scale | 2 | 39 | 0.608 | >0.05 |
Theta | 2 | 39 | 1.387 | >0.05 |
Distortion Index | 2 | 39 | 0.381 | >0.05 |
Measures | df | VAM | Hardcopy | |||
---|---|---|---|---|---|---|
Hypothesis | Error | f | Sig. | f | Sig. | |
Learning time | 1 | 24 | 1.39 | >0.05 | 7.1 | <0.05 |
Wayfinding accuracy | 1 | 102 | 1.66 | >0.05 | 0.39 | >0.05 |
Wayfinding sequence accuracy | 1 | 102 | 1.08 | >0.05 | 1.542 | >0.05 |
Relative directional accuracy | 1 | 102 | 0.57 | >0.05 | 3.516 | >0.05 |
Reconstruction accuracy | 1 | 24 | 0.48 | >0.05 | 0.026 | >0.05 |
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Palani, H.P.; Fink, P.D.S.; Giudice, N.A. Comparing Map Learning between Touchscreen-Based Visual and Haptic Displays: A Behavioral Evaluation with Blind and Sighted Users. Multimodal Technol. Interact. 2022, 6, 1. https://doi.org/10.3390/mti6010001
Palani HP, Fink PDS, Giudice NA. Comparing Map Learning between Touchscreen-Based Visual and Haptic Displays: A Behavioral Evaluation with Blind and Sighted Users. Multimodal Technologies and Interaction. 2022; 6(1):1. https://doi.org/10.3390/mti6010001
Chicago/Turabian StylePalani, Hari Prasath, Paul D. S. Fink, and Nicholas A. Giudice. 2022. "Comparing Map Learning between Touchscreen-Based Visual and Haptic Displays: A Behavioral Evaluation with Blind and Sighted Users" Multimodal Technologies and Interaction 6, no. 1: 1. https://doi.org/10.3390/mti6010001