Here you can find information and the data sets for the Massive Auditory Lexical Decision Project. Currently, we have made available the data from MALD1v1, this version is the version of the data reported in our Behavior Research Methods paper. For each of the data files, you can download either a tab-delimited text file or an R data file. The audio files with force-aligned segmentation can also be downloaded (the TextGrid files and audio can be opened using Praat).
Check out the latest release of the MALD dataset. This new dataset increases the total number of participants to 440, providing more responses per item. We have also updated the item data set to include a calculation of the phonotactic probability of all words and pseudowords and the temporal uniqueness point of the words and pseudowords. When you use this data please cite the Behavior Research Methods paper and note that you are using MALD1.1. You can access the updated files (All Data, Items, Response, Subjects in TXT and R Data) here: All Data
We have added the moving average Response Latency (with alpha set to 0.1, ten Bosch et al., 2018) and the previous trial Response Latency to the All Data file. You can access the updated files (TXT and R Data) here: All Data
All data: TXT RData
Response data: TXT RData
Item data: TXT RData
Subject data: TXT RData
Audio Files: Words Pseudowords
TextGrids: Words Pseudowords
This research was funded by Social Sciences and Humanities Research Council Grant #435-2014-0678 and by a University of Alberta Killam Research Grant.
Recent Papers and Talks:
Nenadić, F. & Tucker, B. V. (2020). Computational modelling of an auditory lexical decision experiment using jTRACE and TISK. Language, Cognition and Neuroscience.
Nenadic, F., Kelley, M. C., Podlubny, R. G., & Tucker, B. V. (2019). Speech Perception and The Role of Semantic Richness in Processing. Canadian Acoustics, 47(3), 96-97.
Morphological Processing Conference 2019 (Investigating morphological processing using the MALD database: A megastudy of auditory lexical decision)
Ford, C., Nenadić, F., Brenner, D., & Tucker, B. V. (2019). Shorter phone duration facilitates isolated spoken word recognition. Proceedings of The 11th International Conference on the Mental Lexicon, 1, e059.
Lorentzen, P., Nenadić, F., Kelley, M. C., & Tucker, B. V. (2019). Massive auditory lexical decision: Investigating performance in noisy environments. Proceedings of The 11th International Conference on the Mental Lexicon, 1, e127.
Nenadić, F. & Tucker, B. V. (2019). Computational modeling of an auditory lexical decision task using jTRACE. Proceedings of The 11th International Conference on the Mental Lexicon, 1, e123.
Tucker, B.V., Brenner, D., Danielson, D.K., Kelley, M.C., Nenadić, F., Sims, M.N. (2019). The Massive Auditory Lexical Decision Database. Behavior Research Methods. 51(3), 1187–1204.
Schmidtke, D., Gagné, C. L., Kuperman, V., Spalding, T. L., & Tucker, B. V. (2018). Conceptual relations compete during auditory and visual compound word recognition. Language, Cognition and Neuroscience.