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Figure 1.

The nine stimuli used in the study, with one of the weighted ABS handles used to hold the stimuli during the experiment.

From left to right: polystyrene (PS), rough polystyrene (R PS), stainless steel, copper, rough copper, birch, balsa, glass and silicone.

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Figure 1 Expand

Figure 2.

A scree plot showing the reduction normalised raw stress with an increase in dimensionality.

A pronounced elbow at 2 dimensions suggests that the data may be most simply explained using a two dimensional MDS plot.

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Figure 2 Expand

Figure 3.

The MDS solution plotted in two-dimensions.

The data positions show that the participants perceived similarities between the metals, and between the woods and rough polystyrene. The spacing between the glass, polystyrene and silicone suggest these were perceived as being dissimilar to any other of the materials. The isolation of silicone suggests it was perceived as being significantly different to all other materials.

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Figure 3 Expand

Figure 4.

The two-dimensional MDS solutions plotted, with the subjective tactile ratings regressed over the MDS coordinates and plotted as vectors.

Two tight groupings (metals in the bottom left, woods and rough polystyrene in the bottom right) are seen. Metals sit in a tight group between the hard and the cold vectors, whilst the woods and rough polystyrene sit between the rough and the warm vectors, revealing that the metals were perceived as ‘cold and hard’ and the woods and rough polystyrene perceived as ‘warm and rough’.

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Figure 4 Expand

Figure 5.

The three-dimensional MDS solutions plotted in paired dimensions, with the subjective taste ratings regressed over the MDS coordinates and plotted as vectors.

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Table 1.

A summary of the responses in Experiment 2, where the participants were given freedom to describe the sensations experienced when sampling the materials.

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Table 1 Expand

Figure 6.

Log plots of the perceived warmth with thermal effusivity (e), perceived hardness with elastic modulus (E), and perceived roughness with surface roughness (Ra), with the linear regression lines shown on each.

A close correlation can be seen in all cases.

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Figure 6 Expand