Nothing
R/data.R
In mousetrap: Process and Analyze Mouse-Tracking Data
#' Raw mouse-tracking dataset for demonstrations of the mousetrap package
#'
#' An exemplary mouse-tracking dataset collected
#' \href{https://osdoc.cogsci.nl/}{OpenSesame} using the
#' \href{https://github.com/pascalkieslich/mousetrap-os}{mousetrap plugin}
#' (Kieslich & Henninger, 2017). A preprocessed (as opposed to raw) version of
#' the same data can be found in \link{mt_example}.
#'
#' The data stem from a study based on experiment 1 by Dale et al. (2007). In
#' this experiment, participants have to assign exemplars (e.g., "shark") to one
#' of two categories (e.g., "fish" or "mammal") by clicking on the button
#' corresponding to the correct category. All exemplars and categories were
#' translated to and presented in German.
#'
#' Across the 19 trials of the experiment, participants categorized 13 exemplars
#' that were typical of their category and 6 atypical exemplars for which this
#' was not the case. For the atypical exemplars (e.g., "whale"), the competing
#' category ("fish") was selected to compete with the correct category
#' ("mammal"). The hypothesis under investigation is whether participants' mouse
#' trajectories deviate more towards the competing category for the atypical
#' exemplars, indicating increased conflict between the response options.
#'
#' Please note that \code{mt_example_raw} should only be used for exploring the
#' features of the mousetrap package and not for any substantive analysis.
#'
#' @references Kieslich, P. J., & Henninger, F. (2017). Mousetrap: An
#' integrated, open-source mouse-tracking package. \emph{Behavior Research
#' Methods, 49}(5), 1652-1667. \doi{10.3758/s13428-017-0900-z}
#'
#' Dale, R., Kehoe, C., & Spivey, M. J. (2007). Graded motor responses in the
#' time course of categorizing atypical exemplars. \emph{Memory & Cognition,
#' 35}(1), 15-28. \doi{10.3758/BF03195938}
#'
#' @format A \link{data.frame} with 38 rows and 19 variables. The data.frame is
#' based on the combined raw data that were created using
#' \link[readbulk]{read_opensesame} from the
#' \href{http://pascalkieslich.github.io/readbulk/}{readbulk} library. For
#' ease of use, unnecessary columns were excluded.
#'
#' The variables included relate to the item that was presented
#' (\code{Exemplar}), the answer categories (\code{Category1} and
#' \code{Category2}), the subject identifier (\code{subject_nr}) the subjects'
#' response (\code{response_get_response}), as well as the mouse-tracking
#' variables (\code{timestamps_get_response}, \code{xpos_get_response} and
#' \code{ypos_get_response}). Besides, a number of additional variables are
#' included, e.g., some variables relating to the general settings of the
#' experiment (e.g., the \code{width} and \code{height} of the screen in
#' pixels).
#'
#' Each mouse-tracking variable contains a list of values (separated by ', ')
#' - one entry for each recorded position of the mouse. The position
#' coordinates are given in pixels, such that values of zero for both
#' \code{xpos_get_response} and \code{ypos_get_response} indicate that the
#' cursor is located in the center of the screen. Both variables increase in
#' value as the mouse moves toward the bottom right. Timestamps are given in
#' milliseconds.
#'
"mt_example_raw"
#' A mousetrap data object.
#'
#' A data object of class "mousetrap" with example data created by importing
#' \link{mt_example_raw} and applying basic post-processing.
#'
#' The raw data set was imported using \link{mt_import_mousetrap}. Trajectories
#' were then remapped using \link{mt_remap_symmetric} so that all trajectories
#' end in the top-left corner and their starting point was aligned using
#' \link{mt_align_start} to a common value (0,0).
#'
#' @format A mousetrap data object is a \link{list} containing at least the
#' following objects:
#' \itemize{
#' \item{\code{data}: a \link{data.frame} containing the trial data (from
#' which the mouse-tracking data columns have been removed). More
#' information about the content of the trial data in \code{mt_example} can
#' be found in \link{mt_example_raw}. The \link{rownames} of \code{data}
#' correspond to the trial identifier. For convenience, the trial identifier
#' is also stored in an additional column called "mt_id".}
#' \item{\code{trajectories}: an \link{array} containing the raw
#' mouse-tracking trajectories. The first dimension represents the different
#' trials and the dimension names (which can be accessed using
#' \link{rownames}) correspond to the trial identifier (the same identifier
#' that is used as the \code{rownames} in \code{data}). The second dimension
#' corresponds to the samples taken over time which are included in
#' chronological order. The third dimension corresponds to the different
#' mouse-tracking variables (timestamps, x-positions, y-positions) which are
#' usually called \code{timestamps}, \code{xpos}, and \code{ypos}. }
#' }
#'
#' Some functions in this package (e.g., \link{mt_time_normalize} and
#' \link{mt_average}) add additional trajectory arrays (e.g.,
#' \code{tn_trajectories} and \code{av_trajectories}) to the mousetrap data
#' object. Other functions modify the existing arrays (e.g.,
#' \link{mt_derivatives} adds distance, velocity, and acceleration
#' to an existing dataset). Finally \link{mt_measures} adds an
#' additional data.frame with mouse-tracking measures to it.
#'
"mt_example"
#' Raw mouse-tracking dataset from Kieslich & Henninger (2017)
#'
#' Raw mouse-tracking dataset from Kieslich & Henninger (2017), an experiment
#' using the material and procedure of experiment 1 by Dale et al. (2007). A
#' preprocessed (as opposed to raw) version of the same data can be found in
#' \link{KH2017}.
#'
#' The data stem from a study by Kieslich & Henninger (2017) which used the
#' material and procedure of experiment 1 by Dale et al. (2007). In this
#' experiment, participants have to assign exemplars (e.g., "whale") to one of
#' two categories (e.g., "fish" or "mammal") by clicking on the button
#' corresponding to the correct category. All exemplars and categories from the
#' original study were translated to and presented in German.
#'
#' The data was collected in \href{https://osdoc.cogsci.nl/}{OpenSesame} using
#' the \href{https://github.com/pascalkieslich/mousetrap-os}{mousetrap plugin}
#' (Kieslich & Henninger, 2017).
#'
#' Across the 19 trials of the experiment, 60 participants categorized 13
#' exemplars that were typical of their category and 6 atypical exemplars for
#' which this was not the case. For the atypical exemplars (e.g., "whale"), the
#' competing category ("fish") was selected to compete with the correct category
#' ("mammal"). The hypothesis under investigation is whether participants' mouse
#' trajectories deviate more towards the competing category for the atypical
#' exemplars, indicating increased conflict between the response options.
#'
#' @references Kieslich, P. J., & Henninger, F. (2017). Mousetrap: An
#' integrated, open-source mouse-tracking package. \emph{Behavior Research
#' Methods, 49}(5), 1652-1667. \doi{10.3758/s13428-017-0900-z}
#'
#' Dale, R., Kehoe, C., & Spivey, M. J. (2007). Graded motor responses in the
#' time course of categorizing atypical exemplars. \emph{Memory & Cognition,
#' 35}(1), 15-28. \doi{10.3758/BF03195938}
#'
#' @format A \link{data.frame} with 1140 rows and 19 variables. The data.frame is
#' based on the combined raw data that were created using
#' \link[readbulk]{read_opensesame} from the
#' \href{http://pascalkieslich.github.io/readbulk/}{readbulk} library. For
#' ease of use, unnecessary columns were excluded.
#'
#' The variables included relate to the item that was presented
#' (\code{Exemplar}), the answer categories (\code{Category1} and
#' \code{Category2}), the subject identifier (\code{subject_nr}), the
#' subjects' response (\code{response}), the correctness of the response
#' (\code{response}) as well as the mouse-tracking variables
#' (\code{timestamps_get_response}, \code{xpos_get_response} and
#' \code{ypos_get_response})
#'
#' Each mouse-tracking variable contains a list of values (separated by ', ')
#' - one entry for each recorded position of the mouse. The position
#' coordinates are given in pixels, such that values of zero for both
#' \code{xpos_get_response} and \code{ypos_get_response} indicate that the
#' cursor is located in the center of the screen. Both variables increase in
#' value as the mouse moves toward the bottom right. Timestamps are given in
#' milliseconds.
#'
"KH2017_raw"
#' Mouse-tracking dataset from Kieslich & Henninger (2017)
#'
#' A data object of class "mousetrap" with the imported and preprocessed
#' mouse-tracking data from Kieslich & Henninger (2017). More information about
#' the study and raw data can be found in \link{KH2017_raw}.
#'
#' The raw dataset (\link{KH2017_raw}) was filtered keeping only correctly
#' answered trials. The filtered dataset was imported using
#' \link{mt_import_mousetrap}. Trajectories were then remapped using
#' \link{mt_remap_symmetric} so that all trajectories end in the top-left corner
#' and their starting point was aligned to a common value (0,0) using
#' \link{mt_align_start}.
#'
#' @references Kieslich, P. J., & Henninger, F. (2017). Mousetrap: An
#' integrated, open-source mouse-tracking package. \emph{Behavior Research
#' Methods, 49}(5), 1652-1667. \doi{10.3758/s13428-017-0900-z}
#'
#' Dale, R., Kehoe, C., & Spivey, M. J. (2007). Graded motor responses in the
#' time course of categorizing atypical exemplars. \emph{Memory & Cognition,
#' 35}(1), 15-28. \doi{10.3758/BF03195938}
#'
#' @format A mousetrap data object is a \link{list} containing at least the
#' following objects:
#' \itemize{
#' \item{\code{data}: a \link{data.frame} containing the trial data (from
#' which the mouse-tracking data columns have been removed). More
#' information about the content of the trial data in \code{KH2017} can
#' be found in \link{KH2017_raw}. The \link{rownames} of \code{data}
#' correspond to the trial identifier. For convenience, the trial identifier
#' is also stored in an additional column called "mt_id".}
#' \item{\code{trajectories}: an \link{array} containing the raw
#' mouse-tracking trajectories. The first dimension represents the different
#' trials and the dimension names (which can be accessed using
#' \link{rownames}) correspond to the trial identifier (the same identifier
#' that is used as the \code{rownames} in \code{data}). The second dimension
#' corresponds to the samples taken over time which are included in
#' chronological order. The third dimension corresponds to the different
#' mouse-tracking variables (timestamps, x-positions, y-positions) which are
#' usually called \code{timestamps}, \code{xpos}, and \code{ypos}. }
#' }
#'
#' Some functions in this package (e.g., \link{mt_time_normalize} and
#' \link{mt_average}) add additional trajectory arrays (e.g.,
#' \code{tn_trajectories} and \code{av_trajectories}) to the mousetrap data
#' object. Other functions modify the existing arrays (e.g.,
#' \link{mt_derivatives} adds distance, velocity, and acceleration
#' to an existing dataset). Finally \link{mt_measures} adds an
#' additional data.frame with mouse-tracking measures to it.
#'
"KH2017"
#' Mouse trajectory prototypes.
#'
#' A core set of five mouse trajectory prototypes including the 'straight'
#' trajectory, the mildly curved trajectory, the continuous change-of-mind
#' trajectory, the discrete change-of-mind trajectory, and the double discrete
#' change-of-mind trajectory.
#'
#' Mouse- and hand-trajectories often occur in types. In such cases, movement
#' trajectory data should be analyzed in terms of discrete type assignments
#' (Wulff et al., 2023). To this end \link{mt_map} can be used to map mouse- or
#' hand-trajectory to the closest of several predefined prototypes.
#' \code{mt_prototypes} provides a core set of prototypes that has been shown to
#' represent well a large fraction of empirical movement trajectories (Wulff et
#' al., 2022).
#'
#' To tailor the set of prototypes to a given study, \code{mt_prototypes} can be
#' extended using \link{mt_add_trajectory}.
#'
#' @references Wulff, D. U., Kieslich, P. J., Henninger, F., Haslbeck, J. M.
#' B., & Schulte-Mecklenbeck, M. (2023). \emph{Movement tracking of psychological
#' processes: A tutorial using mousetrap.} PsyArXiv.
#' \doi{10.31234/osf.io/v685r}
#'
#' Wulff, D. U., Haslbeck, J. M. B., Schulte-Mecklenbeck, M. (2022).
#' \emph{Measuring the (dis-)continuous mind: What movement trajectories
#' reveal about cognition}. Manuscript in preparation.
#'
#'
"mt_prototypes"
NULL
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#' Raw mouse-tracking dataset for demonstrations of the mousetrap package
#'
#' An exemplary mouse-tracking dataset collected
#' \href{https://osdoc.cogsci.nl/}{OpenSesame} using the
#' \href{https://github.com/pascalkieslich/mousetrap-os}{mousetrap plugin}
#' (Kieslich & Henninger, 2017). A preprocessed (as opposed to raw) version of
#' the same data can be found in \link{mt_example}.
#'
#' The data stem from a study based on experiment 1 by Dale et al. (2007). In
#' this experiment, participants have to assign exemplars (e.g., "shark") to one
#' of two categories (e.g., "fish" or "mammal") by clicking on the button
#' corresponding to the correct category. All exemplars and categories were
#' translated to and presented in German.
#'
#' Across the 19 trials of the experiment, participants categorized 13 exemplars
#' that were typical of their category and 6 atypical exemplars for which this
#' was not the case. For the atypical exemplars (e.g., "whale"), the competing
#' category ("fish") was selected to compete with the correct category
#' ("mammal"). The hypothesis under investigation is whether participants' mouse
#' trajectories deviate more towards the competing category for the atypical
#' exemplars, indicating increased conflict between the response options.
#'
#' Please note that \code{mt_example_raw} should only be used for exploring the
#' features of the mousetrap package and not for any substantive analysis.
#'
#' @references Kieslich, P. J., & Henninger, F. (2017). Mousetrap: An
#' integrated, open-source mouse-tracking package. \emph{Behavior Research
#' Methods, 49}(5), 1652-1667. \doi{10.3758/s13428-017-0900-z}
#'
#' Dale, R., Kehoe, C., & Spivey, M. J. (2007). Graded motor responses in the
#' time course of categorizing atypical exemplars. \emph{Memory & Cognition,
#' 35}(1), 15-28. \doi{10.3758/BF03195938}
#'
#' @format A \link{data.frame} with 38 rows and 19 variables. The data.frame is
#' based on the combined raw data that were created using
#' \link[readbulk]{read_opensesame} from the
#' \href{http://pascalkieslich.github.io/readbulk/}{readbulk} library. For
#' ease of use, unnecessary columns were excluded.
#'
#' The variables included relate to the item that was presented
#' (\code{Exemplar}), the answer categories (\code{Category1} and
#' \code{Category2}), the subject identifier (\code{subject_nr}) the subjects'
#' response (\code{response_get_response}), as well as the mouse-tracking
#' variables (\code{timestamps_get_response}, \code{xpos_get_response} and
#' \code{ypos_get_response}). Besides, a number of additional variables are
#' included, e.g., some variables relating to the general settings of the
#' experiment (e.g., the \code{width} and \code{height} of the screen in
#' pixels).
#'
#' Each mouse-tracking variable contains a list of values (separated by ', ')
#' - one entry for each recorded position of the mouse. The position
#' coordinates are given in pixels, such that values of zero for both
#' \code{xpos_get_response} and \code{ypos_get_response} indicate that the
#' cursor is located in the center of the screen. Both variables increase in
#' value as the mouse moves toward the bottom right. Timestamps are given in
#' milliseconds.
#'
"mt_example_raw"
#' A mousetrap data object.
#'
#' A data object of class "mousetrap" with example data created by importing
#' \link{mt_example_raw} and applying basic post-processing.
#'
#' The raw data set was imported using \link{mt_import_mousetrap}. Trajectories
#' were then remapped using \link{mt_remap_symmetric} so that all trajectories
#' end in the top-left corner and their starting point was aligned using
#' \link{mt_align_start} to a common value (0,0).
#'
#' @format A mousetrap data object is a \link{list} containing at least the
#' following objects:
#' \itemize{
#' \item{\code{data}: a \link{data.frame} containing the trial data (from
#' which the mouse-tracking data columns have been removed). More
#' information about the content of the trial data in \code{mt_example} can
#' be found in \link{mt_example_raw}. The \link{rownames} of \code{data}
#' correspond to the trial identifier. For convenience, the trial identifier
#' is also stored in an additional column called "mt_id".}
#' \item{\code{trajectories}: an \link{array} containing the raw
#' mouse-tracking trajectories. The first dimension represents the different
#' trials and the dimension names (which can be accessed using
#' \link{rownames}) correspond to the trial identifier (the same identifier
#' that is used as the \code{rownames} in \code{data}). The second dimension
#' corresponds to the samples taken over time which are included in
#' chronological order. The third dimension corresponds to the different
#' mouse-tracking variables (timestamps, x-positions, y-positions) which are
#' usually called \code{timestamps}, \code{xpos}, and \code{ypos}. }
#' }
#'
#' Some functions in this package (e.g., \link{mt_time_normalize} and
#' \link{mt_average}) add additional trajectory arrays (e.g.,
#' \code{tn_trajectories} and \code{av_trajectories}) to the mousetrap data
#' object. Other functions modify the existing arrays (e.g.,
#' \link{mt_derivatives} adds distance, velocity, and acceleration
#' to an existing dataset). Finally \link{mt_measures} adds an
#' additional data.frame with mouse-tracking measures to it.
#'
"mt_example"
#' Raw mouse-tracking dataset from Kieslich & Henninger (2017)
#'
#' Raw mouse-tracking dataset from Kieslich & Henninger (2017), an experiment
#' using the material and procedure of experiment 1 by Dale et al. (2007). A
#' preprocessed (as opposed to raw) version of the same data can be found in
#' \link{KH2017}.
#'
#' The data stem from a study by Kieslich & Henninger (2017) which used the
#' material and procedure of experiment 1 by Dale et al. (2007). In this
#' experiment, participants have to assign exemplars (e.g., "whale") to one of
#' two categories (e.g., "fish" or "mammal") by clicking on the button
#' corresponding to the correct category. All exemplars and categories from the
#' original study were translated to and presented in German.
#'
#' The data was collected in \href{https://osdoc.cogsci.nl/}{OpenSesame} using
#' the \href{https://github.com/pascalkieslich/mousetrap-os}{mousetrap plugin}
#' (Kieslich & Henninger, 2017).
#'
#' Across the 19 trials of the experiment, 60 participants categorized 13
#' exemplars that were typical of their category and 6 atypical exemplars for
#' which this was not the case. For the atypical exemplars (e.g., "whale"), the
#' competing category ("fish") was selected to compete with the correct category
#' ("mammal"). The hypothesis under investigation is whether participants' mouse
#' trajectories deviate more towards the competing category for the atypical
#' exemplars, indicating increased conflict between the response options.
#'
#' @references Kieslich, P. J., & Henninger, F. (2017). Mousetrap: An
#' integrated, open-source mouse-tracking package. \emph{Behavior Research
#' Methods, 49}(5), 1652-1667. \doi{10.3758/s13428-017-0900-z}
#'
#' Dale, R., Kehoe, C., & Spivey, M. J. (2007). Graded motor responses in the
#' time course of categorizing atypical exemplars. \emph{Memory & Cognition,
#' 35}(1), 15-28. \doi{10.3758/BF03195938}
#'
#' @format A \link{data.frame} with 1140 rows and 19 variables. The data.frame is
#' based on the combined raw data that were created using
#' \link[readbulk]{read_opensesame} from the
#' \href{http://pascalkieslich.github.io/readbulk/}{readbulk} library. For
#' ease of use, unnecessary columns were excluded.
#'
#' The variables included relate to the item that was presented
#' (\code{Exemplar}), the answer categories (\code{Category1} and
#' \code{Category2}), the subject identifier (\code{subject_nr}), the
#' subjects' response (\code{response}), the correctness of the response
#' (\code{response}) as well as the mouse-tracking variables
#' (\code{timestamps_get_response}, \code{xpos_get_response} and
#' \code{ypos_get_response})
#'
#' Each mouse-tracking variable contains a list of values (separated by ', ')
#' - one entry for each recorded position of the mouse. The position
#' coordinates are given in pixels, such that values of zero for both
#' \code{xpos_get_response} and \code{ypos_get_response} indicate that the
#' cursor is located in the center of the screen. Both variables increase in
#' value as the mouse moves toward the bottom right. Timestamps are given in
#' milliseconds.
#'
"KH2017_raw"
#' Mouse-tracking dataset from Kieslich & Henninger (2017)
#'
#' A data object of class "mousetrap" with the imported and preprocessed
#' mouse-tracking data from Kieslich & Henninger (2017). More information about
#' the study and raw data can be found in \link{KH2017_raw}.
#'
#' The raw dataset (\link{KH2017_raw}) was filtered keeping only correctly
#' answered trials. The filtered dataset was imported using
#' \link{mt_import_mousetrap}. Trajectories were then remapped using
#' \link{mt_remap_symmetric} so that all trajectories end in the top-left corner
#' and their starting point was aligned to a common value (0,0) using
#' \link{mt_align_start}.
#'
#' @references Kieslich, P. J., & Henninger, F. (2017). Mousetrap: An
#' integrated, open-source mouse-tracking package. \emph{Behavior Research
#' Methods, 49}(5), 1652-1667. \doi{10.3758/s13428-017-0900-z}
#'
#' Dale, R., Kehoe, C., & Spivey, M. J. (2007). Graded motor responses in the
#' time course of categorizing atypical exemplars. \emph{Memory & Cognition,
#' 35}(1), 15-28. \doi{10.3758/BF03195938}
#'
#' @format A mousetrap data object is a \link{list} containing at least the
#' following objects:
#' \itemize{
#' \item{\code{data}: a \link{data.frame} containing the trial data (from
#' which the mouse-tracking data columns have been removed). More
#' information about the content of the trial data in \code{KH2017} can
#' be found in \link{KH2017_raw}. The \link{rownames} of \code{data}
#' correspond to the trial identifier. For convenience, the trial identifier
#' is also stored in an additional column called "mt_id".}
#' \item{\code{trajectories}: an \link{array} containing the raw
#' mouse-tracking trajectories. The first dimension represents the different
#' trials and the dimension names (which can be accessed using
#' \link{rownames}) correspond to the trial identifier (the same identifier
#' that is used as the \code{rownames} in \code{data}). The second dimension
#' corresponds to the samples taken over time which are included in
#' chronological order. The third dimension corresponds to the different
#' mouse-tracking variables (timestamps, x-positions, y-positions) which are
#' usually called \code{timestamps}, \code{xpos}, and \code{ypos}. }
#' }
#'
#' Some functions in this package (e.g., \link{mt_time_normalize} and
#' \link{mt_average}) add additional trajectory arrays (e.g.,
#' \code{tn_trajectories} and \code{av_trajectories}) to the mousetrap data
#' object. Other functions modify the existing arrays (e.g.,
#' \link{mt_derivatives} adds distance, velocity, and acceleration
#' to an existing dataset). Finally \link{mt_measures} adds an
#' additional data.frame with mouse-tracking measures to it.
#'
"KH2017"
#' Mouse trajectory prototypes.
#'
#' A core set of five mouse trajectory prototypes including the 'straight'
#' trajectory, the mildly curved trajectory, the continuous change-of-mind
#' trajectory, the discrete change-of-mind trajectory, and the double discrete
#' change-of-mind trajectory.
#'
#' Mouse- and hand-trajectories often occur in types. In such cases, movement
#' trajectory data should be analyzed in terms of discrete type assignments
#' (Wulff et al., 2023). To this end \link{mt_map} can be used to map mouse- or
#' hand-trajectory to the closest of several predefined prototypes.
#' \code{mt_prototypes} provides a core set of prototypes that has been shown to
#' represent well a large fraction of empirical movement trajectories (Wulff et
#' al., 2022).
#'
#' To tailor the set of prototypes to a given study, \code{mt_prototypes} can be
#' extended using \link{mt_add_trajectory}.
#'
#' @references Wulff, D. U., Kieslich, P. J., Henninger, F., Haslbeck, J. M.
#' B., & Schulte-Mecklenbeck, M. (2023). \emph{Movement tracking of psychological
#' processes: A tutorial using mousetrap.} PsyArXiv.
#' \doi{10.31234/osf.io/v685r}
#'
#' Wulff, D. U., Haslbeck, J. M. B., Schulte-Mecklenbeck, M. (2022).
#' \emph{Measuring the (dis-)continuous mind: What movement trajectories
#' reveal about cognition}. Manuscript in preparation.
#'
#'
"mt_prototypes"
NULL
Try the mousetrap package in your browser
Any scripts or data that you put into this service are public.
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