## 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 `.2` in this experiment). * **`unex_time_onscreen`:** Float. How long the unexpected object was visible on screen, in seconds (`5`, `2.66`, or `1.5`). * **`unex_velocity_x`:** Integer. `1` if the unexpected object was crossing left-to-right, `-1` if it was crossing right-to-left. * **`unex_crossing_direction`:** String. Indicates the horizontal direction of the unexpected object's motion, either left-to-right (`right`), right-to-left (`left`). * **`unex_onset_posx`:** Integer. The x coordinate of the edge of the invisible occluder behind which the unexpected object emerged. `0` corresponds to the leftmost edge of the display, and `700` is the rightmost edge. * **`unex_offset_posx`:** Integer. The x coordinate of the edge of the invisible occluder behind which the unexpected object disappeared. `0` corresponds to the leftmost edge of the display, and `700` is the rightmost edge. * **`onset_type`:** String. Whether the unexpected object appeared from the edge of the display and offset in the middle of the display (`edge`) or onset in the middle of the display and offset at one of the edges (`floating`). * **`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. * **`time_by_onset`:** String. A combined version of the `unex_time_onscreen` and `unex_onset_type` variables, to make certain analyses more straightforward. ## 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 derive point estimates and 95% bootstrapped confidence intervals for the following data: * Noticing for each exposure time and onset type, collapsed across crossing direction * Overall noticing for the 5s, 2.67s, and 1.5s conditions, collapsed across crossing direction and onset type * Overall noticing for floating versus edge onsets, collapsed across crossing direction and exposure time We will calculate difference estimates and 95% bootstrapped confidence intervals for the following: * Pairwise differences in noticing between each of the exposure time conditions (5 vs 2.67, 2.67 vs 1.5, 5 vs 1.5) * Overall differences in noticing between floating and edge onsets * Differences in noticing between floating and edge onsets within each exposure time condition ### Location data In Experiment 2, we want to further verify that subjects can accurately localize the unexpected object when they notice it. #### Proportion of points on onset versus offset side of fixation Based on the results from Experiment 1, we expect that non-noticing subjects will place the unexpected object in a random cloud around fixation. For these subjects, there ought to be about 50% of loctation reports falling to the left of fixation and 50% falling to the right, irrespective of condition. The data should be dramatically different for the noticing subjects, particularly in conditions where the unexpected object never passes through fixation. In those cases (1.5s and 2.67s), almost 100% of the location reports should fall on the side of fixation corresponding to where the unexpected object onset and offset. In the 5s condition, we still expect a higher proportion of location reports to fall on the onset side of fixation, but it may not be as extreme as in the other two conditions. #### Consistent vertical position As before, 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 in all conditions. We also expect a smaller standard deviation amongst the vertical positions for noticers than non-noticers. If noticing subjects are localizing accurately and only reporting the unexpected object in places in actually appeared, then the standard deviation of the horizontal positions should increase as exposure time does, being smallest in the 1.5s condition and largest in the 5s condition. #### 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. Based on the patterns of location reports amongst non-noticers in the previous experiment, the points should be closest to fixation on average across the three conditions. 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. Due to the offset of the unexpected object's onset and offset point, there should be large distances to fixation in the shorter exposure times. #### 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. These curves should also demonstrate a dramatic offset by condition in the noticing subjects, due to the offset of the unexpected object's onset and offset point. ## 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.