A behavioral database for masked form priming

Abstract

Reading involves a process of matching an orthographic input with stored representations in lexical memory. The masked priming paradigm has become a standard tool for investigating this process. Use of existing results from this paradigm can be limited by the precision of the data and the need for cross-experiment comparisons that lack normal experimental controls. Here, we present a single, large, high-precision, multicondition experiment to address these problems. Over 1,000 participants from 14 sites responded to 840 trials involving 28 different types of orthographically related primes (e.g., castfe–CASTLE) in a lexical decision task, as well as completing measures of spelling and vocabulary. The data were indeed highly sensitive to differences between conditions: After correction for multiple comparisons, prime type condition differences of 2.90 ms and above reached significance at the 5% level. This article presents the method of data collection and preliminary findings from these data, which included replications of the most widely agreed-upon differences between prime types, further evidence for systematic individual differences in susceptibility to priming, and new evidence regarding lexical properties associated with a target word’s susceptibility to priming. These analyses will form a basis for the use of these data in quantitative model fitting and evaluation and for future exploration of these data that will inform and motivate new experiments.

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Notes

  1. 1.

    For this purpose, failure to respond before a 2,000-ms timeout, described in the Procedure section (0.47% of all trials), was counted as an error. Data are included in the downloadable database for all participants who were excluded or replaced for the analyses we present here.

  2. 2.

    The forms of analysis that could not be performed due to the computational memory requirements are those whose computation includes the calculation of the pseudoinverse of the design matrix. In modern software, such as SPSS, SAS, and R, this is routinely used as part of the fitting of linear models, including mixed effects models.

  3. 3.

    The Deming regression in those lines corrects for attenuation or regression dilution due to noise in the x-observations insofar as its ratio with that in the y-observations can be predicted from sample size.

  4. 4.

    With two positively correlated variables, the standard (equal-variance) principal components analysis gives the sum and difference of the z-scores, divided by \( \sqrt{2} \), so for the purposes of correlation these are equivalent.

  5. 5.

    On the other hand, there are reasons to suppose the wordlikeness of the prime might not contribute to the word–nonword decision directly. First, the pseudoword primes were just as nonword-like as the foils. Second, the foils were very wordlike, disfavoring a criterion based on wordlikeness rather than identification of a single word.

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Acknowledgments

James S. Adelman and Colin J. Davis are the organizers (senior authors) of this project. Other authors are listed in order of contribution. We thank Alexa Banculli, Peter Bowles, Josef Broder, Helen Brown, Leo Chong, Alison Crumpton, Laura Cunniffe, Louise Goddard, Laura Grima, Lydia Gunning, Anna Hall, Hyun Kyoung Jung, Hannah Jenkins, Julie Lee, Bree Loethen, Mayumi Kohiyama, Stuart Miller, Luke Mills, Nicole Newson, Ann-Marie Raphail, Emma Roscow, Jocelyn Schock, Annabel Snell, Allison Teevan, Wan Zhen Chua, Louise Warner, Melissa Yeo, and Jimmie Zhang for their assistance in data collection and Dave Balota for helpful comments. Data collection at Royal Holloway, University of London, was supported by a grant from the Experimental Psychology Society.

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Correspondence to James S. Adelman.

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Adelman, J.S., Johnson, R.L., McCormick, S.F. et al. A behavioral database for masked form priming. Behav Res 46, 1052–1067 (2014). https://doi.org/10.3758/s13428-013-0442-y

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Keywords

  • Visual word recognition
  • Lexical decision
  • Orthographic priming
  • Megastudies