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Consistent with research in the visual domain [3] we examined whether Probability Summation or Additive Summation provided a better fit to our data. Despite having a large dataset, we found no evidence that one model fit better than the other (19/40 better fits for PS and 21/40 better fits for AS). ∆AIC 1 2 3 4 5 6 7 8 9 10 5% 1.05 -0.56 -10.82 -0.91 -3.78 -1.94 -0.14 -2.71 -11.83 -0.37 10% -3.38 1.25 -7.96 0.91 5.98 -0.23 4.13 2.93 7.16 -5.07 15% 14.34 1.63 -5.20 7.85 3.43 5.58 -6.17 -5.66 -2.73 8.53 20% 1.49 10.62 -5.79 13.85 -7.63 6.90 1.67 17.23 -2.44 -0.12 Table: Table of ∆AIC values per participant over noise levels, where negative values, coloured red, indicate that Additive Summation is a better fit, while positive values, coloured blue, indicate that Probability Summation is a better fit.

Multisensory Detection: Using Vision and Haptics to detect hidden objects.