References
- 1Acha, J., & Perea, M. (2008). The effect of neighborhood frequency in reading: Evidence with transposed-letter neighbors. Cognition, 108, 290–300. DOI: 10.1016/j.cognition.2008.02.006
- 2Baayen, R. H. (2008).
Analyzing linguistic data: A practical introduction to statistics . Cambridge: Cambridge University Press. DOI: 10.1017/CBO9780511801686 - 3Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of memory and language, 59, 390–412. DOI: 10.1016/j.jml.2007.12.005
- 4Balota, D. A., Yap, M. J., Cortese, M. J., Hutchison, K. A., Kessler, B., Loftis, B., Neely, J. H., Nelson, D. L., Simpson, G. B., & Treiman, R. (2007). The English Lexicon Project. Behavior Research Methods, 39, 445–459. DOI: 10.3758/BF03193014
- 5Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68, 255–278. DOI: 10.1016/j.jml.2012.11.001
- 6Bates, D., Kliegl, R., Vasishth, S., & Baayen, H. (2015). Parsimonious mixed models. arXiv preprint arXiv:1506.04967.
- 7Bruner, J. S., & O’Dowd, D. (1958). A note on the informativeness of parts of words. Language and Speech, 1, 98–101. DOI: 10.1177/002383095800100203
- 8Brysbaert, M., & Stevens, M. (2018). Power analysis and effect size in mixed effects models: A tutorial. Journal of Cognition, 1. DOI: 10.5334/joc.10
- 9Ferrand, L., New, B., Brysbaert, M., Keuleers, E., Bonin, P., Méot, A., Augustinova, M., & Pallier, C. (2010). The French Lexicon Project: Lexical decision data for 38,840 French words and 38,840 pseudowords. Behavior Research Methods, 42, 488–496. DOI: 10.3758/BRM.42.2.488
- 10Frankish, C., & Turner, E. (2007). SIHGT and SUNOD: The role of orthography and phonology in the perception of transposed letter anagrams. Journal of Memory and Language, 56, 189–211. DOI: 10.1016/j.jml.2006.11.002
- 11Grainger, J. (2008). Cracking the orthographic code: An introduction. Language and Cognitive Processes, 23, 1–35. DOI: 10.1080/01690960701578013
- 12Grainger, J. (2018). Orthographic processing: A “mid-level” vision of reading. Quarterly Journal of Experimental Psychology, 71, 335–359. DOI: 10.1080/17470218.2017.1314515
- 13Grainger, J., Kiyonaga, K., & Holcomb, P. J. (2006). The time-course of orthographic and phonological code activation. Psychological Science, 17, 1021–1026. DOI: 10.1111/j.1467-9280.2006.01821.x
- 14Grainger, J., & Whitney, C. (2004). Does the huamn mnid raed wrods as a wlohe? Trends in Cognitive Sciences, 8, 58–59. DOI: 10.1016/j.tics.2003.11.006
- 15Green, P., & MacLeod, C. J. (2016). SIMR: an R package for power analysis of generalized linear mixed models by simulation. Methods in Ecology and Evolution, 7, 493–498. DOI: 10.1111/2041-210X.12504
- 16Johnson, R. L. (2009). The quiet clam is quite calm: Transposed-letter neighborhood effects on eye movements during reading. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 943–969. DOI: 10.1037/a0015572
- 17Keuleers, E., Diependaele, K., & Brysbaert, M. (2010). Practice effects in large-scale visual word recognition studies: A lexical decision study on 14,000 Dutch mono-and disyllabic words and nonwords. Frontiers in Psychology, 1, 174. DOI: 10.3389/fpsyg.2010.00174
- 18Keuleers, E., Lacey, P., Rastle, K., et al. (2012). The British Lexicon Project: Lexical decision data for 28,730 monosyllabic and disyllabic English words. Behav Res, 44, 287–304. DOI: 10.3758/s13428-011-0118-4
- 19Li, X., Song, W., Xu, X., Zhang, J., Xia, L., & Shi, C. (2020). Experimental study on pedestrian contact force under different degrees of crowding. Safety Science, 127, 104713. DOI: 10.1016/j.ssci.2020.104713
- 20Marcet, A., Perea, M., Baciero, A., & Gomez, P. (2019). Can letter position encoding be modified by visual perceptual elements?. Quarterly Journal of Experimental Psychology, 72, 1344–1353. DOI: 10.1177/1747021818789876
- 21McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: I. An account of basic findings. Psychological Review, 88, 375. DOI: 10.1037/0033-295X.88.5.375
- 22Morton, J. (1969). Interaction of information in word recognition. Psychological Review, 76, 165–178. DOI: 10.1037/h0027366
- 23New, B., Pallier, C., Brysbaert, M., & Ferrand, L. (2004). Lexique 2: A new French lexical database. Behavior Research Methods, Instruments, & Computers, 36, 516–524. DOI: 10.3758/BF03195598
- 24Perea, M., Baciero, A., Rocabado, F., & Marcet, A. (2021). Does the cowl make the monk? Detecting counterfeits in brand names versus logos. Psychonomic Bulletin & Review, in press. DOI: 10.3758/s13423-020-01863-z
- 25Perea, M., & Lupker, S. J. (2003). Does jugde activate COURT? Transposed-letter similarity effects in masked associative priming. Memory & Cognition, 31, 829–841. DOI: 10.3758/BF03196438
- 26Perea, M., & Lupker, S. J. (2004). Can CANISO activate CASINO? Transposed-letter similarity effects with nonadjacent letter positions. Journal of Memory and Language, 51, 231–246. DOI: 10.1016/j.jml.2004.05.005
- 27Perea, M., Rosa, E., & Gomez, C. (2005). The frequency effect for pseudowords in the lexical decision task. Perception and Psychophysics, 67, 301–314. DOI: 10.3758/BF03206493
- 28Rayner, K., White, S. J., Johnson, R. L., & Liversedge, S. P. (2006). Raeding Wrods With Jubmled Lettres: There Is a Cost. Psychological Science, 17, 192–193. DOI: 10.1111/j.1467-9280.2006.01684.x
- 29R Core Team. (2018).
R: A language and environment for statistical computing . Vienna, Austria: R Foundation for Statistical Computing. Available online athttps://www.R-project.org/ . - 30van Heuven, W. J., Mandera, P., Keuleers, E., & Brysbaert, M. (2014). SUBTLEX-UK: A new and improved word frequency database for British English. Quarterly Journal of Experimental Psychology, 67, 1176–1190. DOI: 10.1080/17470218.2013.850521
