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Compute MultiMatch Metrics for Scanpath Similarity

Source: R/multimatch.R
multi_match.Rd

This function computes multiple similarity metrics between two scanpaths, including vector, direction, length, position, duration, and EMD-based position similarity.

Usage

multi_match(x, y, screensize)

Arguments

x

A data frame representing the first scanpath. Must contain at least three columns: x, y, and onset, and at least three rows.

y

A data frame representing the second scanpath. Must contain at least three columns: x, y, and onset, and at least three rows.

screensize

A numeric vector of length 2 indicating the width and height of the screen in pixels.

Value

A named numeric vector with the following elements:

mm_vector

Similarity based on the 2D vectors between fixations.

mm_direction

Similarity based on the direction (angle) of saccades between fixations.

mm_length

Similarity based on the length of saccades between fixations.

mm_position

Similarity based on the spatial position of fixations.

mm_duration

Similarity based on the duration of fixations.

mm_position_emd

Order-insensitive similarity based on the duration-weighted Earth Mover's Distance (EMD) between the spatial positions of all fixations.

Details

The function computes six different similarity metrics between the scanpaths x and y:

  • mm_vector: Similarity based on the 2D vectors between fixations.

  • mm_direction: Similarity based on the direction (angle) of saccades between fixations.

  • mm_length: Similarity based on the length of saccades between fixations.

  • mm_position: Similarity based on the spatial position of fixations.

  • mm_duration: Similarity based on the duration of fixations.

  • mm_position_emd: Order-insensitive similarity based on the Earth Mover's Distance (EMD) between the spatial positions of all fixations, weighted by duration, as 1 - EMD / sqrt(width^2 + height^2).

The function ensures that both scanpaths have strictly increasing onset times and contain at least three fixations. It also normalizes the similarity scores to lie between 0 and 1, with higher values indicating greater similarity.

References

Dewhurst, R., Nyström, M., Jarodzka, H., Foulsham, T., Johansson, R., & Holmqvist, K. (2012). It depends on how you look at it: Scanpath comparison in multiple dimensions with MultiMatch, a vector-based approach. Behavior research methods, 44, 1079-1100.

Examples

set.seed(1)
sp1 <- scanpath(fixation_group(
  x = cumsum(runif(5, -50, 50)) + 250,
  y = cumsum(runif(5, -50, 50)) + 250,
  onset = cumsum(runif(5, 100, 300)),
  duration = runif(5, 150, 350)
))
sp2 <- scanpath(fixation_group(
  x = cumsum(runif(5, -50, 50)) + 250,
  y = cumsum(runif(5, -50, 50)) + 250,
  onset = cumsum(runif(5, 100, 300)),
  duration = runif(5, 150, 350)
))
multi_match(sp1, sp2, screensize = c(500, 500))
#>       mm_vector    mm_direction       mm_length     mm_position     mm_duration 
#>       0.9608211       0.3854692       0.9858060       0.7861099       0.8232034 
#> mm_position_emd 
#>       0.8160965