## Codebook ### Raw data * **`workerID`:** String. An arbitrary number to replace the subject's Mechanical Turk Worker ID. * **`attended_color`:** String. Which color of display objects, `black` or `white,` the subject was instructed to attend to. * **`unex_window_size`:** Float. What proportion of the total display width the unexpected object crossed (either `.8`, `.4`, or `0` in this experiment). * **`unex_time_onscreen`:** Float. How long the unexpected object was visible on screen, in seconds (`5`, `2.66`, or `0`). * **`unex_velocity_x`:** Integer. `1` if the unexpected object was crossing left-to-right, `-1` if it was crossing right-to-left, or `0` for no unexpected object. * **`unex_crossing_direction`:** String. Indicates the horizontal direction of the unexpected object's motion, either left-to-right (`right`), right-to-left (`left`), or `none` for no unexpected object. * **`unex_onset_posx`:** Integer. The x coordinate at which the unexpected object appeared. * **`unex_offset_posx`:** Integer. The x coordinate at which the unexpected object offset. * **`unex_color:`** String. The color of the unexpected object. In this experiment, it is always `gray`. * **`unex_shape`:** String. The shape of the unexpected object. In this experiment, it is always a `cross`. * **`bounces_0`, `bounces_1`, `bounces_2`:** Integer. The total number of times the attended objects bounced on trial 1, 2, and 3 (the critical trial), respectively. * **`count_0`, `count_1`, `count_2`:** Integer. The subject's reported count of the bounces of the attended object on trial 1, 2, and 3 (the critical trial), respectively. * **`report_notice`:** Boolean. Whether the subject reported having noticed something new on the critical trial. * **`report_location_x`, `report_location_y`:** Integer. The x and y coordinates at which the subject placed the unexpected object on a .66 scale version of the display when asked where it was when they first noticed it. * **`report_shape`:** String. The shape the subject reported for the unexpected object. * **`report_color`:** String. The color the subject reported for the unexpected object. * **`age`:** Integer. * `0`: under 18 * `1`: 18 - 24 * `2`: 25 - 49 * `3`: 50 - 80 * `4`: over 80 * **`vision`:** Integer. * `0`: Normal vision * `1`: Corrected-to-normal vision, correction worn during experiment * `2`: Corrected-to-normal vision, correction not worn during the experiment * **`lagging`:** Boolean. Whether the animations lagged for the subject. * **`freezing`:** Boolean. Whether the animations froze completely for the subject. * **`other_issues`:** Boolean. Whether there were any other technical problems. * **`other_text`:** String. Explanation for the other technical problems. * **`prior`:** Boolean. Whether the subject had prior experience with an inattentional blindness task. * **`prior_text`:** String. Explanation of the prior experience with an inattentional blindness task. ### Additional variables created during analysis * **`noticed`:** Whether the subject met the criteria for having noticed the unexpected object. They had to have reported noticing something new *and* have correctly reported the unexpected object's color or location. * **`t0_err`, `t1_err`, `t2_err`:** The subject's unsigned percentage error on each trial, calculated as `actual bounces` - `reported bounces` / `actual bounces`. * **`rescale_report_x`, `rescale_report_y`:** The raw coordinates from the subject's location-clicking task (`report_location_x` and `report_location_y`), scaled back up to "actual size" by dividing by the scale factor of .66. ## Analysis Plan ### Exclusions Subjects will be excluded from analysis according to the following criteria: * Subjects reported being younger than 18 years old * Subjects miscounted the bounces of their assigned set of objects by more than 50% in either direction on two or more trials * Subjects reported needing vision correction but not wearing it during the experiment * Subjects reported any technical problems during the experiment * Subjects reported prior experience with inattentional blindness tasks ### Noticing For our analysis, a subject will be considered to have noticed the unexpected object if they were in a condition that actually had an unexpected object, report having seen something new on the critical trial, and correctly report that the new object was gray and/or that it was a cross. We will estimate the noticing rate for each condition, along with bootstrapped confidence intervals calculated via the percentile method. We will compare noticing rates between the two conditions as well by estimating the difference between them with an associated confidence interval. This will give us a coarse idea of the time course of noticing; if a much higher portion of subjects noticed the unexpected object in the 5s condition versus the 2.66s condition, it suggests that having extra time contributes to noticing. However, if the difference is small, it suggests that most noticing happens early enough during the unexpected object's time on screen that extra time does not increase the likelihood of noticing. ### Location data One of the primary measures of interest in these experiments is the reliability of the self-reported location data. If this data is accurate to when a subject noticed the unexpected object, then we do not need any additional data or unexpected object conditions to determine the time course of noticing. We can simply convert the location data to a point in time rather than space, and draw conclusions about when a subject noticed the object based on where they reported first noticing it. In order to evaluate whether the location data is reliable, we will examine it in a variety of different ways. #### Proportion of points on onset versus offset side of fixation For each of the unexpected object conditions (no object, on screen for 2.67 s, and on screen for 5 s), possible motion directions (right-to-left and left-to-right), and noticing status, we will calculate the proportion of reported object locations falling between onset and fixation versus falling between fixation and offset. For the no-object and non-noticing cases, we expect the locations to be more or less random. As such, there should be approximately the same number of location reports falling on either side of fixation. For the cases in which an unexpected object appeared and was noticed, the points should cluster according to the timing of noticing. If subjects tend to notice the object early, we should see a greater proportion of location reports on the onset side of fixation, and the reverse if noticing tends to occur later. #### Consistent vertical position The unexpected object crosses the display at a fixed vertical distance. The reported locations from the noticers should have very little variance in the vertical dimension, particularly compared to the locations reported by the non-noticers. We will calculate the mean x and y position of the location points and the standard deviation of the x and y dimensions. We expect a mean near the vertical midpoint (300) of the display for noticers. We also expect a smaller standard deviation amongst the vertical positions for noticers than non-noticers. Additionally, we expect that the values for the non-noticers should be similar to those given by subjects who did not actually receive an unexpected object on the third trial. #### Average Euclidean distance to reference points For all conditions, we will calculate the average Euclidean distance and standard deviation to three points: fixation, onset, and offset. Regardless of condition, the non-noticing values should be similar to each other, and similarly close to fixation as the no-object locations. For the noticers, we should see shorter distances and less variance in the distance to the point that corresponds best to when noticing occurs. If noticing occurs early, the average Euclidean distance to the onset point should be the smallest of the three, with the smallest standard deviation; if it occurs late, then distance to offset should be minimized; and if it tends to occur around fixation, then that distance should be smallest. #### Cumulative distributions Finally, we will plot the cumulative distribution of the location reports for each condition. These should shift according to when noticing occurs, and we can compare the curves for the reports from subjects who noticed the unexpected object to those who did not, or who had no unexpected object. ## Executing the analysis script The analysis script and data files are provided with an accompanying R project. To run this script, at minimum one needs the following (versions under which the script was developed shown in parentheses): * R installed (3.5.3) * The following R packages installed: * dplyr (0.7.6) * purrr (0.2.5) * tidyr (0.8.1) * ggplot2 (3.0.0) * viridis (0.5.1) * RStudio (strongly encouraged) With RStudio installed, one can simply unzip the Analysis archive, open the .Rproj file, and hit `Source` to run the entire analysis from start to finish. ## Pilot Data We ran three small pilot studies over the course of developing this experiment, the data for which can be found in the `Data` folder along with the main experiment's data. We used only the 5s exposure condition, and the same general procedure with slight alterations to test our exclusion rate. Pilot 1 recruited with no requirements, using slightly faster objects that changed velocity more erratically. Pilot 2 added HIT approval requirements and minimums, with the same fast objects. Pilot 3 used HIT approval ratings and minimums, slowed the objects down, and smoothed out how they could change their velocity. Pilot 3's procedure matched that of the main experiment (without the 2.67s condition).