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). Below you also find other versions of the MALD datasets that may be of use or of interest.
Single-MALD
Single-MALD is a large-scale auditory lexical decision study in English with a fully crossed design. SingleMALD is freely available and includes over two million trials in which forty native speakers of English responded to over twenty-six thousand different words and over nine thousand different pseudowords, each in sixty-seven balanced sessions. SingleMALD features a large number of responses per stimulus, but a smaller number of participants, thus complementing the Massive Auditory Lexical Decision (MALD) dataset which features many listeners, but fewer responses per stimulus. You can find a write up of the data in Behavior Research Methods paper. The data files and analysis files (All Data, Analysis and other supporting files) here: All Data
TWOSE-MALD
TWOSE-MALD uses a subset of words from the Massive Auditory Lexical Decision (MALD) database and runs a series of shorter auditory lexical decision experiments at a busy science museum, the Telus World of Science Edmonton, as part of a science outreach program. You can find a write up of the paper here. The data files and analysis files (All Data, Analysis and other supporting files) here: All Data
MALD1.1
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
MALD1.01
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
MALD1
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., Bujandric, K., Kelley, M. C., & Tucker, B. V. (accepted). SingleMALD: Investigating practice effects in auditory lexical decision. Behavior Research Methods.
Tucker, B.V., Mukai, Y., Perry, S.J., Kelley, M.C., Nenadić, F. (2024). Massive Auditory Lexical Decision. In Hilary Nesi and Petar Milin(Ed.). International Encyclopedia of Language and Linguistics, 3rd Edition. Elsevier.
Nenadić, F., Podlubny, R.G., Schmidtke, D., Kelley, M.C., & Tucker, B.V. (2024). Semantic richness effects in isolated spoken word recognition: Evidence from massive auditory lexical decision. Journal of Experimental Psychology: Learning, Memory, and Cognition, 50(4), 650–673.
Nenadić, F., ten Bosch, L. & Tucker, B.V. (2023). Computational modelling of an auditory lexical decision experiment using DIANA. Language and Speech. 66:3, 564-605.
Kelley, M.C. & Tucker, B.V. (2022). The recognition of spoken pseudowords. Language, Cognition and Neuroscience. 39(9), 1169-1190.
Kelley, M. C., & Tucker, B. V. (2022). Using acoustic distance and acoustic absement to quantify lexical competition. The Journal of the Acoustical Society of America, 151(2), 1367–1379.
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.