Open data


Neural responses to naturalistic clips of behaving animals in two different task contexts

OpenNeuro ds000233  OpenNeuro DataLad DOI

In this public dataset, we collected functional MRI data while participants freely viewed brief naturalistic video clips of animals behaving in their natural environments. Participants (12 adults) performed a 1-back category repetition detection task requiring them to attend to either animal behavior or taxonomy. We implemented a factorial repeated measures design comprising five taxonomic categories, four behavioral categories, and two tasks. The five taxonomic categories were primates, ungulates, birds, reptiles, and insects. The four behavioral categories were eating, fighting, running, and swimming. Crossing the taxonomy and behavior factors yielded 20 total taxonomy–behavior conditions. Each of the 20 taxonomy–behavior conditions comprised two unique 2 s video clips, as well as horizontally flipped versions of each clip for 80 visually unique stimuli in total. Video clip stimuli were sampled from nature documentaries (Life, Life of Mammals, Microcosmos, Planet Earth) and high-resolution YouTube videos. We adopted a condition-rich ungrouped-events design: each trial consisted of a 2 s video clip presented without sound followed by a 2 s fixation period for a trial onset asynchrony of 4 s. Each of the 80 stimuli was presented once each run. The stimuli used in this experiment are provided alongside the data for non-profit, non-commercial scholarship and research under “fair use” or “fair dealing” provisions. This dataset is intended to provide a test bed for investigating object and action representation, as well as how task demands alter the neural representation of complex stimuli and their semantic qualities. See the following publications for more details:

Nastase, S. A., Halchenko, Y. O., Connolly, A. C., Gobbini, M. I., & Haxby, J. V. (2018). Neural responses to naturalistic clips of behaving animals in two different task contexts. Frontiers in Neuroscience, 12, 316. DOI PDF

Nastase, S. A., Connolly, A. C., Oosterhof, N. N., Halchenko, Y. O., Guntupalli, J. S., Visconti di Oleggio Castello, M., Gors, J., Gobbini, M. I., & Haxby, J. V. (2017). Attention selectively reshapes the geometry of distributed semantic representation. Cerebral Cortex, 27(8), 4277–4291. DOI PDF

This dataset has been re-analyzed in the following publications:

Coutanche, M. N., Akpan, E., & Buckser, R. R. (2020). Representational connectivity analysis: identifying networks of shared changes in representational strength through jackknife resampling. bioRxiv. DOI

Esteban, O., Markiewicz, C., Blair, R. W., Moodie, C., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S. S., Wright, J., Durnez, J., Poldrack, R., & Gorgolewski, K. J. (2019). fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature Methods, 16, 111–116. DOI

Wen, Z., Yu, T., Yang, X., & Li, Y. (2018). Goal-directed processing of naturalistic stimuli modulates large-scale functional connectivity. Frontiers in Neuroscience, 12, 1003. DOI

McClure, P., Rho, N., Lee, J. A., Kaczmarzyk, J. R., Zheng, C., Ghosh, S. S., Nielson, D., Thomas, A. G., Bandettini, P., & Pereira, F. (2018). Knowing what you know in brain segmentation using deep neural networks. Frontiers in Neuroinformatics, 13, 67. DOI