Analyzing
data
Open Analyzer. Here is a typical procedure for
analyzing data:
Step 1: Load MT files
Click the Load File button to load one individual
participant’s data from an MT file, or the Load Folder
button to load multiple participants’ data (all MT files from a
specified folder). Once loaded, the file(s) should appear in the
list on the left. It will prompt you: “Load LOG files (if any)?” LOG files
are optional files tied to MT files that contain information
about which trajectories in a participant’s data should be
manually excluded from analysis or should be recoded. These LOG files are generated
using the Exclude and Save buttons (detailed
later). Hit Yes here if you would like to load LOG files
(if they exist). This will exclude trajectories that have been
previously excluded and saved. Hit No if you would not
like to load LOG files (and thus, previous exclusions that were
saved will not be loaded).
In the file list are checkboxes. Files that are checked
will be used for analysis, and files that are unchecked will not
be used for analysis. By default, all files in a folder are
checked for use. To include or exclude a file for analysis,
simply check/uncheck it.
Step
2: Import mouse trajectories
To import mouse trajectories from these data files, click
the Import button. A screen will appear. There are four
tabs involved in the the Import process. At the bottom of the
screen are the Load and Save buttons.
You can save and load the import settings using these buttons,
which creates and loads .MTS (MouseTracker settings) files.
Conditions tab
Before the Import screen appears, the program first
searches through all the loaded MT files for all condition codes
that were used (as specified in their experiment .CSV file) and
displays them under the All used codes list. Here, you
need to define which condition codes are included in Condition 1
and Condition 2 for MouseTracker to analyze. To add a code into
Condition 1 or 2, simply click on the corresponding arrow
pointing right. To remove a code from the Condition 1 or 2
lists, simply click the corresponding arrow pointing left.
You can rename the conditions by typing in new text into
the Condition 1 and Condition 2 text boxes (in this example, I
kept the original “Condition 1” and “Condition 2” text, as seen
in image on the left). When finished, press Go.
Analyzing manually recoded trajectories
In some cases, you may have previously analyzed your data in
Analyzer and manually recoded some trajectories into a
specific condition number (e.g., condition 501, rather than 1 or
2). This recoding is available only if you loaded .LOG files
(see Step 1). Here, you might want to analyze trajectories
recoded into a unique condition number. For instance, you may
manually recoded some trajectories into unique condition number
500 and some other trajectories into unique condition number
501. You might want to analyze these side-by-side by slotting
them into main conditions 1 and 2. If you right click on the
All used codes list, a popup menu will appear and you will
be to select Add unique condition number. You will then
be prompted for the number. It will add the condition number to
the list, and you can then put it into Condition 1 or 2. For
instance, if you added the unique condition number 500 and put
it into the Condition 1 list, any trajectories recoded as 500 in
the .LOG files would be sent into Condition 1 for analysis.
Option: Correct/incorrect analysis
There is also another optional check box at the bottom to
perform a Correct/incorrect analysis. If you choose to perform a
correct/incorrect analysis, condition codes are defined only for
Condition 1. This is because trials involving a correct are
imported into Condition 1 and trials involving an incorrect
response imported into Condition 2. Rather than examine two
separate sets of condition codes, a correct/incorrect analysis
allows you to examine one single set of condition codes, but
separate out whether responses were correct versus incorrect.
This is most helpful in experiments in which there are no true
incorrect responses and participants are given no feedback on
errors. Because correct alternatives are always specified in the
experiment .CSV file, you could use this feature to separate out
trials based on whether the participant responded in a way that
the researcher expected (i.e., correct) versus did not expect
(i.e., incorrect). For instance, imagine an experiment where
participants are asked to decide whether they “like” or “do not
like” different foods. There are no correct or incorrect
responses here. However, when a chocolate cake is presented, we
can expect that participants should respond “like,” and we may
be interested to look at trials in which they respond
unexpectedly (“do not like”). When participants are deciding
whether they like versus do not like chocolate cake, and they
ultimate decide “do not like,” you might be interested to know
whether trajectories nevertheless show an attraction to the
“like” alternative before settling into “do not like.” A
correct/incorrect analysis would allow Condition 1 to comprise
the “like” trials and Condition 2 to comprise the “do not like”
trials. Thus, rather than examine two separate conditions, a
correct/incorrect analysis allows a user to examine the same
condition but separate out correct/expected responses versus
incorrect/unexpected ones.
Option: Between subjects analysis
There an another optional check box at the bottom to perform a
Between subjects analysis. If you select this, conditions
codes are defined only for Condition 1. This is because trials
corresponding with these codes will be loaded into Condition 1
if from one particular group of subjects and loaded into
Condition 2 if from another particular group of subjects. After
checking this and hitting “Go”, a window will pop up asking you
to select the two groups of subjects. Note that although
MouseTracker can visualize mean trajectories in a between
subjects analysis, it can only compute trial-by-trial
information (and does not compute mean trajectory information).
This can be done manually using the output .CSV file from a
between subjects analysis.
Restrictions tab
If you want to exclude incorrect responses from analysis, check
the Exclude incorrect responses checkbox. If you want to
restrict analysis to only trials whose response times are within
a certain range or within a min/max cutoff, check the
Restrict response times checkbox and define the Min
and Max response time values. If you want to restrict
analysis to only trials whose initiation times are within a
certain range or within a minimum/maximum cutoff, check the
Restrict initiation times checkbox and define the Min
and Max initiation time values.
Time tab
Here you choose whether to conduct a Normalized time
analysis or a Raw time analysis. If doing a
Normalized time analysis, you set how to many time steps to
time-normalize the trajectories into (default = 101). In a raw
time analysis, you set the raw time (ms) cutoff and how many
equal-sized raw time bins to create leading up to that cutoff.
See the About MouseTracker Data sections
on Time Normalization and Raw Time Analysis for more information
on what these entail. A Normalized time analysis will calculate
MD, AUC, x-flips, and y-flips, but not velocity, acceleration,
and angle profile. In a raw
time analysis, velocity, acceleration, and angle information is available, but
not
MD, AUC, x-flips, and y-flips.
Responses and Remapping tab
Here is where you decide which kind of responses you want to
include/exclude and also set up any remapping. It shows you a
visualization of the response buttons and "Start" button, much
like how is done in Designer. Each response button has a
checkbox. By default, all responses are checked. If a response
is unchecked, any trials where that response was selected will
be excluded from this analysis.
You set up remapping(s) one
response button at a
time. You set different response mapping(s) depending on what
response is eventually clicked on, in addition to what is listed
for a trial's default-compare response (see
Creating
experiments | Stimuli and response list for more information
on default-compare). Once all remappings are set up, MouseTracker
will perform each one, one at a time, in order. Remappings work
by performing one or many of the following:
-
Horizontal flip (invert along the x-axis, about the
Start button)
-
Vertical flip (invert along the y-axis, about the
Start button)
-
Rotate 90° clockwise
-
Rotate 90°
counter-clockwise
Note that remapping procedures are always
performed using the center point of the Start button and the
center points of the response buttons. Also note that, for
horizontal and vertical flips (but not necessarily for
rotations), you need to make sure that the responses you
want to remap are equidistant to the "Start"
button. This can be done in Designer when making an
experiment .CSV file.
Example 1: A simple 2-choice design
In a standard 2-choice design, such as that depicted in the
screenshot above, remapping is straightforward. To directly
compare the two conditions, we might want to remap all
trajectories that head to response #1 (on the left) over to
response #2 (on the right).
To begin, first click the Add Remap button. A message
will appear saying "Click the source response." Then, click
on response #1. It will turn green. (Or, rather than hitting the
Add Remap button, you could simply double-click on the
source response to begin the process).
A message will now appear saying
"Click the default-compare response." Then, click on
the response listed as default-compare (in the experiment .CSV
file) for the particular trials you're interested in remapping.
In a standard 2-choice design, this will always be the
opposite/incorrect response (response #2). Click response #2 and
it will turn pink.
A message will now appear saying "Click the destination
response." Then, click on the response
location where you'd like to remap trajectories to, which in
this case is response #2. Response #2 will now appear green,
indicating that trajectories will be moved to this response.
A message will now appear saying "Click another destination or
'End Remap' to set." If there were more than 2 response
alternatives, you might also want to do another remapping. For
instance, after horizontally inverting the trajectories from
response #1 to response #2, you might then want to rotate them
into a third response location. Because we do not need do that
here, we are done with this remapping and may press End Remap.
(Or, you could have simply double-clicked on response #2 when
selecting it as the destination to end the remapping).
A remapping has now been added to the list on the left side (see
image below). It says "Resp: 1" to indicate that it will be
remapping trajectories whose response was #1 (the source). It
says "DC: 2" to indicate that it will be remapping only
trajectories that headed to response #1 and whose
default-compare (DC) response (listed in experiment .CSV file)
was #2. Again, because this is a simple 2-choice design, all
trajectories that head to response #1 will always have
their default-compare response as the opposite/incorrect
response (#2). Finally, it says "--> 2" to indicate that
trajectories will be remapped into response #2 (the last
destination). Under this information is a list in order of all
remapping procedures. Here, there was only one: a horizontal
flip.
To continue, you would press Go! Or, you could save all
the settings and remappings into an .MTS file (so you may
re-load it easily later), using the Save button.
Example 2: A more complicated
4-choice design
The above screenshot shows a relatively complex 4-choice design. Let's pretend
that responses #4 and #2 (encompassing the horizontal axis) are
correct/incorrect targets, and responses #1 and #3 (encompassing
the vertical axis) are distractors. Thus, on some trials, we
might expect the mouse to click on #2, but show some attraction
to #3, and on other trials, to click on #2 and show attraction
to #1 (depending on what stimuli were presented on the trial).
We might want to take all trials in the entire experiment and
remap them such that they all head vertically to response #1 and
be rotated/flipped correctly such that the hypothetical distractor be located at response #2. Thus, in the example
above, regardless of whether we predicted response #1 or #3 to
attract participants' mouse movements en route to clicking on
response #2 on a given trial, trajectories for all trials would be remapped such that
they head to response #1 with the hypothetical distractor
located at response #2
(regardless of whether it was #1 vs. #3 for any given trial).
To begin, first click the Add Remap button. A message
will appear saying "Click the source response." Then, click
on one of the response buttons. It will turn green. A message will appear saying
"Click the default-compare response." Then, click on
the response listed as default-compare (in the experiment .CSV
file) for the particular trials you're interested in remapping.
It will turn pink. As seen below, response #2 is designated as
the source and response #4 as the default-compare response for
this remapping. Thus, this remapping will apply to any
trajectories that eventually clicked on response #2 and were
hypothetically distracted by response #4 (specifically, had "4"
listed under the default-compare column in the experiment .CSV
file).
A message will now appear saying "Click another destination or
'End Remap' to set." Click, response #3 to first remap the
trajectories from response #2 to #3 (a 90°
clockwise rotation). Now, response #3 will appear in green,
meaning that trajectories will now be remapped to that location.
Because we want trajectories to head toward response #1, with
the hypothetical distractor located at response #2, we have more
remappings to do.
Now click on response #4, which will turn green. We have
now rotated trajectories 90° clockwise
again. Finally, click on response #1, which will turn green (see
below). This conducts yet a third 90°
clockwise rotation. Trajectories will now be remapped to
response #1. Because we rotated trajectories clockwise and the
default-compare response was originally located at response #3,
the default-compare response is now located at response #2. To
finalize the remapping, hit the End Remap button (or
simply double-click response #1 when selecting it as a
destination).
Notice that a remapping has appeared in the list on the
left (see image below). It indicates that trajectories which
originally headed to response #2 and whose default-compare
response was #3 will now be remapped to response #1, and it will
do this by performing three 90°
clockwise rotations.
Now you would repeat this procedure for every relevant source
response and corresponding default-compare conditions so that
every single trajectory in the experiment is remapped to
response #1, with the default-compare response located at
response #2. For a complicated design such as this, you would
likely want to use the Save button to save the remappings
once finished. When done, press Go!
The sample data below in the screenshot of the main Analyzer
window is from Example 1 (the simpler 2-choice design).
Step 3:
Visualizing, exploring, and excluding trajectories
You can choose to visualize trajectories with their
coordinates marked or not marked—to visualize with marked
coordinates, check the Mark points checkbox. Hit the
Visualize button. All trajectories corresponding with
Condition 1 will be displayed in the left pane, appearing here
as purple trajectories. All trajectories corresponding with
Condition 2 will be displayed in the right pane, appearing here
as blue trajectories (see image below). These colors can
be changed using the Settings button. Mean trajectories
aggregated across all trials (and all participants if multiple
participants are included in the analysis) are displayed in the
bottom left pane.
Time-courses of the mean x-position and mean y-position
(across every time-step) appear in the bottom middle of the
screen. To view the actual mean x-coordinate and y-coordinate
data over time, simply click on the small 'Data' button over the
time-course panels or click on the mean trajectory or
time-course panels. A small window will appear with the data.
Every trajectory displayed in the Condition 1 and Condition 2
panes is able to be individually selected for more detailed
information or to exclude from analysis. To select one or
multiple trajectories, hover over the Condition 1 or Condition 2
pane. Notice that the mouse cursor turns into a crosshair. Hold
down the mouse and drag, and
notice the black rectangular selection window (as shown above).
Once the mouse is released, any trajectories captured within
this selection window are listed in the Selected tracks
list box in the bottom right of the screen. To select a
trajectory, click on the item in the
list; the trajectory will be redrawn in a new color (here,
black). The color of selected trajectories and the selection
window can be changed using the Settings button. To
obtain more detailed information about the trajectory,
double-click on the trajectory item in the list and a new window
will appear with details (see image on right).
You can also right click on items in the Selected tracks
list, which will give you a series of options. By
right-clicking, you can get detailed information on the
trajectory, you can exclude a single trajectory or multiple
trajectories, and you can also manually recode a single
trajectory or multiple trajectories into a unique condition
number (e.g., 500 or 501). This manual recoding will make
trajectories disappear from the current analysis (since the
analysis compares condition 1 and 2). However, the trajectories
will be identified with their unique condition number and all
their information will be given in the output .CSV file. If you
hit the Save button, the manual recoding will be saved
into the LOG file(s). Once saved in the LOG file(s), you can
load the data again and go to the Import screen. You
could then import trajectories from a unique manual condition
code (e.g., 500 or 501) into the main Condition 1 and 2 so you
can focus your analysis on these manually recoded trajectories
(see Step 2 for more information on this).
Note that the MD, AUC, x-flip, and y-flip values are only computed after hitting the
Compute button (detailed in Step 4). At this stage, they would read: “<Hit Compute>”. When first loading, it
will display a time-course of the x-coordinates of the
trajectory. To display a time-course of the y-coordinates,
click the Y button in the bottom left of the plot. Also
when first loading, it loads all the response alternatives
information into the dropdown list under Alternatives. It
automatically starts with the response alternative specified in
that trial's default-compare column (from the experiment
.CSV file). This is the default comparison unselected response.
The MD and AUC values in reference to this response alternative
for this trial are the ones that get aggregated into
MouseTracker's average of MD and AUC in the two conditions.
To exclude the selected trajectory item(s) from analysis,
hit the Exclude button. This will re-code the trajectory
item(s) into condition 888 (rather than condition 1 or 2). To
save these exclusion changes, hit the Save button. This
will create a corresponding LOG file in the same folder as the
MT file, which stores information about which trajectories have
been converted into the manual-exclusion condition 888 (and if
the option is set, which trajectories have been converted into
systematic-exclusion condition 666). When loading the MT file(s)
later, it will ask you whether you would like to load LOG files
as well. When a LOG file has been loaded with an MT file, it is
indicated by a “[*]” appended to the end of the MT file in the
file list on the left side of the window.
If you right click
on the Condition 1 pane or Condition 2 pane, you can export all
the trajectories in that pane into a .CSV file for immediate
reading in Excel. This exporting comes in a vertical format that
makes it extremely easy to draw an Excel scatterplot of every
single trajectory in the condition.
After hitting Exclude, a message box will then
prompt you to re-visualize the trajectories. Indicating yes will
re-visualize trajectories and the selected trajectory item(s)
that you excluded should now disappear from the Condition 1 and
2 panes.
Step 4:
Computing and outlier screening
Hit the Compute button. MouseTracker computes two
measures of spatial attraction toward the opposite response
alternative: maximum deviation (MD) and area-under-the-curve (AUC).
See About
MouseTracker Data | Measuring Attraction. you press this button, the mean MD and AUC values for
Condition 1 and Condition 2 will appear above the mean
trajectory pane. Although MD and AUC is calculated for every
unselected response alternative for every trial, the mean values
reflect the default comparison response alternative that was
specified in the default-compare column of the experiment
.CSV file. If this is not useful for you, all the MD and AUC
values for each trial (if there are more than 2 response
alternatives) is provided in the export .CSV file. Also included
in the export .CSV file is an MD_time measure. This is
simply the time in milliseconds at which point a trajectory
maximally deviates toward another response alternative (i.e.,
MD).
The Compute button also generates a measure of motor
complexity: x-flips (XF) and y-flips (YF),
which are the amount of reversals along the x-axis and
y-axis, respectively. They reflect a trajectory’s
fluctuation between response alternatives. You can set the
minimum/maximum size of a change along the x-axis and
y-axis that qualifies as an x-flip or y-flip,
respectively, using the Settings button.
After hitting the
Compute button, MouseTracker will
prompt you about whether you would like to conduct outlier
screening. If you hit Yes, the following screen will appear:
First, you select which measure(s) you would like to use to
define outliers. Options include: MD, AUC, XF,
YF, IT, and RT (see left). You can select
only one measure or you can select multiple ones.
You also need to specify how many SDs away from the mean
constitutes an outlier, and whether you would like to define
outliers as larger than the SD threshold in the positive
direction (Greater than) or smaller than the SD threshold
in the negative direction (Less than), or both. If the
outlier threshold were 3 SD, then selecting Greater than
would screen out trajectories whose data is greater than M + 3
SD and selecting Less than would screen out trajectories
whose data is less than M - 3 SD.
Lastly, you need to choose whether to Screen across all
subjects (M and SD are averaged across all subjects)
or to Screen separately for each subject (M and SD are
averaged separately for each subject). The M and SD are always
averaged across both Condition 1 and 2.
As an example, given the options selected on the screen on
the left, MouseTracker would compute means across all subjects,
and then screen based on MD, AUC, and RT. If a trial's MD, AUC,
or RT exceeded 3 SD above the mean of the respective measure
(the mean averaged across all subjects), then it would be
screened out.
Once you hit Go, all trajectories screened
out will be re-coded to condition 666 (rather than 1 or 2).
MouseTracker will then
prompt you about re-visualizing. Hit Yes to re-visualize
trajectories; if trajectories were extreme enough to exceed your
threshold, then they should disappear from the Condition 1 and 2
panes upon re-visualization.
Whether this systematic screening
is saved to .LOG files (just like manual exclusions are) is optional (set
using the Settings button).
Hitting the Compute button
also generates two z-normalized values of the MD and AUC
of all trajectories in Condition 1 and 2, which are useful for
distributional analyses. One z-normalized value comes
from pooling across both Condition 1 and 2 (“together”)
and another z-normalized value comes from pooling
separately within Condition 1 and within Condition 2 (“separate”).
These z-normalized MD and AUC values appear in the exported .CSV data sheet. Again, this is only done
for MD and AUC values coming from the default-compare
response alternative.
By right-clicking on the Condition 1 or Condition 2 pane, you
can also run a Distributional Analysis. A screenshot of
such an analysis is below. A histogram is depicted, showing the
z-normalized MD or AUC values of trajectories in
Condition 1 or Condition 2 (using the "together"
normalization, see above). To modify the histogram
properties, change the # Bins slider (to change the
number of bins in the histogram) and the Z range slider
(to change the max/min z values in the histogram). For
instance, if the total number of bins is set to 40 and the z
range is set to 3 (as done below), 40 bins ranging from -3 to 3
will be included in the histogram. To analyze MD, select the
MD option, and to analyze AUC, select the AUC option.
Computed values for the distribution's Skewness,
Kurtosis, N, and Bimodality are included on the
right. For more information about distributional analyses and
the bimodality coefficient (b), see the
About MouseTracker
Data | Distributional Analysis section.
If you opted to conduct a raw time analysis, MD, AUC, x-flips,
y-flips are not computed. Rather, velocity, acceleration,
and angle profiles are generated and these are displayed in a
new window after clicking the Compute button. It will first
prompt you whether you would like to compute
velocity/acceleration based on x,y-coordinates, just
x-coordinates, or just y-coordinates. This information also appears in the output
.CSV file.
Step 5:
Exporting data to .CSV file
Finally, hit the
Export to .CSV button. This exports all of the data to a
.CSV file readable in Microsoft Excel. It will prompt you for
what path and name you would like to save the .CSV file as
saves. It will then automatically load this file in your default
application associated with .CSV files (probably Microsoft
Excel) for you to inspect it immediately.