Multisensory Integration and Calibration in Children and Adults with and without Sensory and Motor Disabilities

in Multisensory Research
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During the first years of life, sensory modalities communicate with each other. This process is fundamental for the development of unisensory and multisensory skills. The absence of one sensory input impacts on the development of other modalities. Since 2008 we have studied these aspects and developed our cross-sensory calibration theory. This theory emerged from the observation that children start to integrate multisensory information (such as vision and touch) only after 8–10 years of age. Before this age the more accurate sense teaches (calibrates) the others; when one calibrating modality is missing, the other modalities result impaired. Children with visual disability have problems in understanding the haptic or auditory perception of space and children with motor disabilities have problems in understanding the visual dimension of objects. This review presents our recent studies on multisensory integration and cross-sensory calibration in children and adults with and without sensory and motor disabilities. The goal of this review is to show the importance of interaction between sensory systems during the early period of life in order to correct perceptual development to occur.

Multisensory Research

A Journal of Scientific Research on All Aspects of Multisensory Processing

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Figures

  • Development of cross-modal integration for size and orientation discrimination. (A, D) Illustration of the experimental setup for size (A) and orientation (D) discrimination. (B, C, E, F) Example psychometric functions for four children, with various degrees of cross-modal conflict. Size discriminations: child ‘SB’ age 10.2 (B); child ‘DV’ age 5.5 (C); Orientation discrimination: AR age 8.7 (E); GF age 5.7 (F). The lower colour-coded arrows show the MLE (Maximum Likelihood Estimation) predictions, calculated from threshold measurements. The black dashed horizontal lines show the 50% performance point, intersecting with the curves at their PSE (Point of Subjective Equality) indicated by short vertical bars. Colour-coded arrows above the plots indicate the size of the haptic standard for the size condition (B, C) and the orientation of the visual standard for the orientation condition (E, F). Older children generally followed the adult pattern, whereas in the five-year-olds, haptic information dominated in the size task, and visual information dominated in the orientation task. For the size judgment, the amount of conflict was 0 for the red symbols, +3 mm (where plus indicates that the visual stimulus was larger) for the blue symbols and −3 mm for the green symbols. For orientation, the same colours refer to 0 and ±4° respectively. Reprinted with permission from Gori et al. (2008).

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  • Development of precision thresholds and visuo-haptic weights. (A) Average thresholds (geometric means) for haptic (red symbols), visual (green) and visuo-haptic (dark blue) size and orientation discrimination, combined with the average MLE predictions (light blue), as a function of age. The predictions were calculated individually for each subject and then averaged. The tick labelled ‘blur’ indicates thresholds for visual stimuli blurred by a translucent screen 19 cm from the blocks. Error bars are ±1 SEM. (B) Haptic and visual weights for the size and orientation discrimination as predicted from thresholds (violet circles) and predicted from PSEs values (black squares) via the MLE model. Weights were calculated individually for each subject, then averaged. After the ages of 8–10 years the two estimates converge, suggesting that the system is integrating in a statistically optimal manner. Reprinted with permission from Gori et al. (2008).

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  • Accuracy and precision. Accuracy is defined as the closeness of a measurement to its true physical value (its veracity). Precision is the degree of reproducibility or repeatability between measurements, usually measured as the standard deviation of the distribution. The ‘target analogy’ shows high precision but poor accuracy on the left, and good average accuracy but poor precision on the right. The archer would correct his or her aim by calibrating the sights of the bow. Similarly, perceptual systems can correct for a bias by cross-calibration between senses. Reprinted with permission from Burr and Gori (2012).

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  • Development of precision thresholds and visual-audio weights. (A) Thresholds as a function of age for the temporal bisection task. Visual thresholds are reported in red, auditory in green, bimodal in blue, and predictions of the MLE model in grey. (B) Same colour code for the space bisection task. (C) Average weights as a function of age, predicted from thresholds plotted in purple and predicted from PSEs plotted in black via the MLE model. (D) Same colour scheme for the space bisection task. Reprinted with permission from Gori et al. (2012b).

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  • Comparison between motor and visual disability. (A) Individual visual size thresholds of children with motor disabilities normalized with respect to age-matched controls and plotted against normalized visual orientation thresholds. The thresholds were normalized by dividing each threshold value by the average from age-matched controls. Most points lie in the upper left quadrant, implying better orientation and poorer size visual discrimination. The red arrows refer to group averages, 0.96 ± 0.12 for orientation and 4.64 ± 1.22 for size. (B) Individual haptic size thresholds of children with visual disability normalized with respect to age-matched controls and plotted against normalized haptic orientation thresholds. Most points lie in the lower right quadrant, implying better size and poorer orientation discrimination. The green arrows refer to group averages, 2.2 ± 0.3 for orientation and 0.8 ± 0.06 for size. The blue star represents the one subject with an acquired loss of vision. Reprinted with permission from Gori et al. (2012d).

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  • Audio space disability in visually impaired individuals. Individual data plotting location bisection thresholds against minimal audible angle calculated from the width of individual psychometric functions. The arrows show the geometric means for each group and the shaded areas indicate the 95% confidence intervals. The dashed diagonal line represents the equality line: although the thresholds of the sighted subjects are scattered around this line, all but one non-sighted subject is above it. The only non-sighted subject, with bisection thresholds falling within the control range, had a threshold for minimal audible angle threshold six times lower than the mean of the controls, and this individual’s data point falls well above the bisection line. Reprinted with permission from Gori et al. (2014).

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  • Object constancy during development. (A) Error in perceived object size as a function of age for four comparison distances: light blue — 42.5 cm, dark blue — 60 cm, green — 85 cm, and red — 120 cm. (B) Error in perceived size at 42.5 m for the six- to ten-year-old children (N=26, average eight years) and for the 14–16-year-old children (N=8, average 15 years). The visual condition (V) is shown in green, the haptic condition (H) in blue, the bimodal condition (BIM) in orange and the MLE prediction (PRED) in grey. Reprinted with permission from Gori et al. (2012a).

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  • Setup and results for size judgment tasks. (A) Illustration of the verbal comparison task. (B) Illustration of the reproduction task. (C) Mean bias in size perception (13 subjects) as a function of sphere position for the verbal comparison task. The red line and dots refer to the average responses when only the sphere was presented (‘no-action’ condition). The blue line and dots refer to the average response when subjects observed a grasping action (‘grasp–observation’ condition). The vertical dashed line indicates the limit of the haptic workspace: The 42.5 and the 60 cm distances fall within the haptic workspace and the 85 and 120 cm distances fall beyond it. Error bars represent ±1 SEM of inter-subject variability. (D) Same as (C) for the reproduction task (10 subjects). Reprinted with permission from (Gori et al., 2011b).

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