This data is from the paper "Capacity for movement is an organisational principle in object representations". This is the data of an online stimulus validation study (Experiment 0). The corresponding EEG data from Experiments 1 and 2 can be found on Open Neuro. The paper is now published in NeuroImage: https://doi.org/10.1016/j.neuroimage.2022.119517 Experiment 1: https://doi:10.18112/openneuro.ds003885.v1.0.0 Experiment 2: https://doi:10.18112/openneuro.ds003887.v1.0.0 ### Abstract: The ability to perceive moving objects is crucial for survival and threat identification. The association between the ability to move and being alive is learned early in childhood, yet not all moving objects are alive. Natural, non-agentive movement (e.g., clouds, fire) causes confusion in children and adults under time pressure. Recent neuroimaging evidence has shown that the visual system processes objects on a spectrum according to their ability to engage in self-propelled, goal-directed movement. Most prior work has used only moving stimuli that are also animate, so it is difficult to disentangle the effect of movement from aliveness or animacy in representational categorisation. In the current study, we investigated the relationship between movement and aliveness using both behavioural and neural measures. We examined electroencephalographic (EEG) data recorded while participants viewed static images of moving or non-moving objects that were either natural or artificial. Participants classified the images according to aliveness, or according to capacity for movement. Behavioural classification showed two key categorisation biases: moving natural things were often mistaken to be alive, and often classified as not moving. Movement explained significant variance in the neural data, during both a classification task and passive viewing. These results show that capacity for movement is an important dimension in the structure of human visual object representations. ## Description of dataset contents #### /code Contains scripts to clean data and find averages over subjects and over image/object/super-ordinate category (analyse_behavioural_mturk.m), experimental script (index.html, with fixation cross), and scripts with functions to call the BayesFactor package in R from MATLAB (bayesfactor_R_wrapper_anova.m, bayes_factor_R_wrapper.m). These Bayes Factors scripts were created by Tijl Grootswagers, and modified for use in this experiment. #### /results/pavlovia Summary of all data from pavlovia, cleaned to only include trials where participants were responding to the images (excluding instructions etc.), and condensed into a single file for all participants (all_results.csv). Also a cleaned version of the above document with the bad participants removed (all_results_cleaned.csv) #### /results/summary Output of the analyse_behavioural_mturk.m file - summary of aliveness responses (behavioural_q0.mat) and movement responses (behavioural_q1.mat), as well as a summary of means per category (behavioural_summary.mat) #### /stimuli 400 images, sorted according to 6 superordinate categories: animals (100), plants (100), moving artefacts (50), non-moving artefacts (50), moving natural things (50), and non-moving natural things (50). All are acquired with a CC0 license (for more details, see the associated paper) ### Author details If using this data, please cite the associated paper: Shatek, S. M., Robinson, A. K., Grootswagers, T., & Carlson, T. A. (2022). Capacity for movement is an organisational principle in object representations. NeuroImage, 261, 119517. https://doi.org/10.1016/j.neuroimage.2022.119517 Contact Sophia Shatek (sophia.shatek@sydney.edu.au) for additional information or queries. ### Task Participants were randomly allocated to answer one of three questions about each of the 400 stimuli: (1) “Is the thing in the image alive, or not alive?”, (2) “Can the thing in the image move, or is it still?”, or (3) “Is the thing in the image naturally occurring or man-made?” We gathered data from 50 participants for each question. Participants were shown one image at a time and instructed to press the ‘F’ and ‘J’ keys on their keyboard to indicate their response to the question for that image. The instructions stated that participants should “try to be fast and accurate.” Each image appeared after a 500ms fixation cross and remained on the screen until participants responded. Reaction times and reponses were used to measure classification behaviour. The main dataset contains all the data, without exclusions. For analysis, data were cleaned to remove ultra-fast responders, random responders, and extra long trials. ### Subjects Mechanical Turk (MTurk) workers were recruited from the U.S.A. and Canada to complete the 15-minute experiment in return for cash payment. Participants were randomly allocated to answer one of three questions about each of the 400 stimuli: (1) “Is the thing in the image alive, or not alive?”, (2) “Can the thing in the image move, or is it still?”, or (3) “Is the thing in the image naturally occurring or man-made?” We gathered data from 50 participants for each question. Participants were shown one image at a time and instructed to press the ‘F’ and ‘J’ keys on their keyboard to indicate their response to the question for that image. The instructions stated that participants should “try to be fast and accurate.” Each image appeared after a 500ms fixation cross and remained on the screen until participants responded (Figure 1B)