Code
Fast implementation of the PSI method
MATLAB code for three implementations of the adaptive PSI method for psychophysical data collection which generalize well to higher dimensional stimulus spaces. This code is free to the scientific community for research purposes. If you use this resource, please cite the following publication:
DiMattina, C. (2015). Fast adaptive estimation of multi-dimensional psychometric functions. Journal of Vision 15(9): 1-20. download
Virtual vocalization stimuli
MATLAB code for synthesizing highly accurate species-specific vocalizations of the common marmoset. This code is free to the scientific community for research purposes. If you use this resource, please cite the following publication:
DiMattina, C., & Wang, X. (2006). Virtual vocalizations stimuli for investigating neural representations of species-specific vocalizations. J. Neurophysiol. 95: 1244-62. download
Datasets
Case Occlusion Boundary Database
Six subjects at Case Western Reserve University hand-labeled all of the occlusion boundaries in 100 images taken from the McGill Calibrated Colour Image Database. This database is free to the vision science community for research purposes. If you use this resource, please cite the following two papers:
Olmos, A., Kingdom, F. A. A. (2004). A biologically inspired algorithm for the recovery of shading and reflectance images. Perception 33: 1463 - 1473. DiMattina, C., Fox, S.A., Lewicki, M.S. (2012). Detecting natural occlusions using local cues. Journal of Vision 12(13):1-21.
downloadTrypophobic Images
A set of 31 images of repetitive patterns of holes and bumps. If you use this resource, please cite the following paper:
Pipitone, R.N., DiMattina, C. (2020). Object clusters or spectral energy? Assessing the relative contributions of image phase and amplitude spectra to Trypophobia. Frontiers In Psychology (Perception Science) 11:1847 .
download Modeling second-order boundary perception
Data from psychophysical experiments described in the the following paper. If you use this data, please cite:
DiMattina, C., & Baker, C.L., Jr. (2019). Modeling second-order boundary perception: A machine learning approach. PLoS Computational Biology 15(3): e1006829.
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