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Visual and haptic integration in the estimation of softness of deformable objects.

Cellini C, Kaim L, Drewing K - Iperception (2013)

Bottom Line: However, through everyday experiences we learn correspondences between felt softness and the visual effects of exploratory movements that are executed to feel softness.Bisensory judgments were less reliable than predicted from optimal integration.We conclude that the visuo-haptic integration of softness information is biased toward vision, rather than being optimal, and might even be guided by a fixed weighting scheme.

View Article: PubMed Central - PubMed

Affiliation: Department of General Psychology, Justus-Liebig-University of Giessen, Otto-Behaghel-Strasse 10F, 35394 Giessen, Germany; e-mail: Cristiano.Cellini@psychol.uni-giessen.de.

ABSTRACT
Softness perception intrinsically relies on haptic information. However, through everyday experiences we learn correspondences between felt softness and the visual effects of exploratory movements that are executed to feel softness. Here, we studied how visual and haptic information is integrated to assess the softness of deformable objects. Participants discriminated between the softness of two softer or two harder objects using only-visual, only-haptic or both visual and haptic information. We assessed the reliabilities of the softness judgments using the method of constant stimuli. In visuo-haptic trials, discrepancies between the two senses' information allowed us to measure the contribution of the individual senses to the judgments. Visual information (finger movement and object deformation) was simulated using computer graphics; input in visual trials was taken from previous visuo-haptic trials. Participants were able to infer softness from vision alone, and vision considerably contributed to bisensory judgments (∼35%). The visual contribution was higher than predicted from models of optimal integration (senses are weighted according to their reliabilities). Bisensory judgments were less reliable than predicted from optimal integration. We conclude that the visuo-haptic integration of softness information is biased toward vision, rather than being optimal, and might even be guided by a fixed weighting scheme.

No MeSH data available.


Related in: MedlinePlus

Individual visual weights, regression lines and line of identity (dotted): (a) individual weights for the soft stimulus set as a function of weights for the hard stimuli; (b) observed vs. predicted weights for both soft (darker triangles) and hard (lighter squares) set.
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Figure 7: Individual visual weights, regression lines and line of identity (dotted): (a) individual weights for the soft stimulus set as a function of weights for the hard stimuli; (b) observed vs. predicted weights for both soft (darker triangles) and hard (lighter squares) set.

Mentions: The averaged individual PSE values from the consistent visuo-haptic conditions were 0.194 mm/N for the hard range and 0.914 mm/N for the soft, and thus, as should be the case, close to the compliance of the standard stimuli (hard = 0.19 mm/N and soft = 0.92 mm/N). PSE values for the inconsistent visuo-haptic conditions were used to calculate empirical visual weights (Figure 6). The empirical weights were compared with the predicted optimal weights. Weights were entered into an ANOVA with two within-participant variables observation (observed vs. predicted) and compliance set (hard vs. soft). Both, the main effect of the compliance set F(1,7) = 6.03, p = 0.044 and the main effect of observation were significant, F(1,7) = 13.43, p = 0.008, as was the interaction between the two variables F(1,7) = 12.81, p = 0.009. Vision was weighted more than predicted from optimal integration. t-tests confirmed this effect both for the soft, t(7) = 2.45, p = 0.043, and the hard compliance set, t(7) = 4.40, p = 0.003. The effect was more pronounced for the hard as compared to the soft compliance set (difference between average predicted and observed weight: 32% vs. 16%, respectively). Also, individual data points in Figure 7b nicely show the larger deviation of the observed from the predicted weights for the hard as compared to the soft set (larger vertical distance of triangles from line of identity). Thereby, there was a significant difference between the soft and the hard stimuli for the predicted visual weights, t(7) = 5.92, p <0.001, but not for the observed weights, t(7) = 0.24, p = 0.81.


Visual and haptic integration in the estimation of softness of deformable objects.

Cellini C, Kaim L, Drewing K - Iperception (2013)

Individual visual weights, regression lines and line of identity (dotted): (a) individual weights for the soft stimulus set as a function of weights for the hard stimuli; (b) observed vs. predicted weights for both soft (darker triangles) and hard (lighter squares) set.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4129386&req=5

Figure 7: Individual visual weights, regression lines and line of identity (dotted): (a) individual weights for the soft stimulus set as a function of weights for the hard stimuli; (b) observed vs. predicted weights for both soft (darker triangles) and hard (lighter squares) set.
Mentions: The averaged individual PSE values from the consistent visuo-haptic conditions were 0.194 mm/N for the hard range and 0.914 mm/N for the soft, and thus, as should be the case, close to the compliance of the standard stimuli (hard = 0.19 mm/N and soft = 0.92 mm/N). PSE values for the inconsistent visuo-haptic conditions were used to calculate empirical visual weights (Figure 6). The empirical weights were compared with the predicted optimal weights. Weights were entered into an ANOVA with two within-participant variables observation (observed vs. predicted) and compliance set (hard vs. soft). Both, the main effect of the compliance set F(1,7) = 6.03, p = 0.044 and the main effect of observation were significant, F(1,7) = 13.43, p = 0.008, as was the interaction between the two variables F(1,7) = 12.81, p = 0.009. Vision was weighted more than predicted from optimal integration. t-tests confirmed this effect both for the soft, t(7) = 2.45, p = 0.043, and the hard compliance set, t(7) = 4.40, p = 0.003. The effect was more pronounced for the hard as compared to the soft compliance set (difference between average predicted and observed weight: 32% vs. 16%, respectively). Also, individual data points in Figure 7b nicely show the larger deviation of the observed from the predicted weights for the hard as compared to the soft set (larger vertical distance of triangles from line of identity). Thereby, there was a significant difference between the soft and the hard stimuli for the predicted visual weights, t(7) = 5.92, p <0.001, but not for the observed weights, t(7) = 0.24, p = 0.81.

Bottom Line: However, through everyday experiences we learn correspondences between felt softness and the visual effects of exploratory movements that are executed to feel softness.Bisensory judgments were less reliable than predicted from optimal integration.We conclude that the visuo-haptic integration of softness information is biased toward vision, rather than being optimal, and might even be guided by a fixed weighting scheme.

View Article: PubMed Central - PubMed

Affiliation: Department of General Psychology, Justus-Liebig-University of Giessen, Otto-Behaghel-Strasse 10F, 35394 Giessen, Germany; e-mail: Cristiano.Cellini@psychol.uni-giessen.de.

ABSTRACT
Softness perception intrinsically relies on haptic information. However, through everyday experiences we learn correspondences between felt softness and the visual effects of exploratory movements that are executed to feel softness. Here, we studied how visual and haptic information is integrated to assess the softness of deformable objects. Participants discriminated between the softness of two softer or two harder objects using only-visual, only-haptic or both visual and haptic information. We assessed the reliabilities of the softness judgments using the method of constant stimuli. In visuo-haptic trials, discrepancies between the two senses' information allowed us to measure the contribution of the individual senses to the judgments. Visual information (finger movement and object deformation) was simulated using computer graphics; input in visual trials was taken from previous visuo-haptic trials. Participants were able to infer softness from vision alone, and vision considerably contributed to bisensory judgments (∼35%). The visual contribution was higher than predicted from models of optimal integration (senses are weighted according to their reliabilities). Bisensory judgments were less reliable than predicted from optimal integration. We conclude that the visuo-haptic integration of softness information is biased toward vision, rather than being optimal, and might even be guided by a fixed weighting scheme.

No MeSH data available.


Related in: MedlinePlus