BACS: The Brussels Artificial Character Sets for studies in cognitive psychology and neuroscience

Article

Abstract

Written symbols such as letters have been used extensively in cognitive psychology, whether to understand their contributions to written word recognition or to examine the processes involved in other mental functions. Sometimes, however, researchers want to manipulate letters while removing their associated characteristics. A powerful solution to do so is to use new characters, devised to be highly similar to letters, but without the associated sound or name. Given the growing use of artificial characters in experimental paradigms, the aim of the present study was to make available the Brussels Artificial Character Sets (BACS): two full, strictly controlled, and portable sets of artificial characters for a broad range of experimental situations.

Keywords

Artificial characters Letters Uppercase/lowercase Similarity 

References

  1. Acuna, B. D., Sanes, J. N., & Donoghue, J. P. (2002). Cognitive mechanisms of transitive inference. Experimental Brain Research, 146, 1–10. doi:10.1007/s00221-002-1092-y CrossRefPubMedGoogle Scholar
  2. Awh, E., & Jonides, J. (2001). Overlapping mechanisms of attention and spatial working memory. Trends in Cognitive Sciences, 5, 119–126. doi:10.1016/S1364-6613(00)01593-X CrossRefPubMedGoogle Scholar
  3. Bagnara, S., Boles, D. B., Simion, F., & Umiltà, C. (1983). Symmetry and similarity effects in the comparison of visual patterns. Perception & Psychophysics, 34, 578–584. doi:10.3758/BF03205914 CrossRefGoogle Scholar
  4. Baron, J., & Hodge, J. (1978). Using spelling–sound correspondences without trying to learn them. Visible Language, 12, 55–70.Google Scholar
  5. Ben-Shachar, M., Dougherty, R. F., Deutsch, G. K., & Wandell, B. A. (2007). Differential sensitivity to words and shapes in ventral occipito-temporal cortex. Cerebral Cortex, 17, 1604–1611. doi:10.1093/cercor/bhl071 CrossRefPubMedGoogle Scholar
  6. Bishop, C. H. (1964). Transfer effects of word and letter training in reading. Journal of Verbal Learning and Verbal Behavior, 3, 215–221. doi:10.1016/S0022-5371(64)80044-X CrossRefGoogle Scholar
  7. Bitan, T., & Booth, J. R. (2012). Offline improvement in learning to read a novel orthography depends on Direct letter instruction. Cognitive Science, 36, 896e918. doi:10.1111/j.1551-6709.2012.01234.x CrossRefGoogle Scholar
  8. Bitan, T., & Karni, A. (2003). Alphabetical knowledge from whole words training: effects of explicit instruction and implicit experience on learning script segmentation. Cognitive Brain Research, 16, 323–337. doi:10.1016/S0926-6410(02)00301-4 CrossRefPubMedGoogle Scholar
  9. Bitan, T., & Karni, A. (2004). Procedural and declarative knowledge of word recognition and letter decoding in reading an artificial script. Cognitive Brain Research, 19, 229–243. doi:10.1016/j.cogbrainres.2004.01.001 CrossRefPubMedGoogle Scholar
  10. Bitan, T., Manor, D., Morocz, I. A., & Karni, A. (2005). Effects of alphabeticaly, practice and type of instruction on reading an artificial script: An fMRI study. Cognitive Brain Research, 25, 90–106. doi:10.1016/j.cogbrainres.2005.04.014 CrossRefPubMedGoogle Scholar
  11. Boles, D. B., & Clifford, J. E. (1989). An upper-and lowercase alphabetic similarity matrix, with derived generation similarity values. Behavior Research Methods, Instruments, & Computers, 21, 579–586. doi:10.3758/BF03210580 CrossRefGoogle Scholar
  12. Brainard, D. H. (1997). The Psychophysics Toolbox. Spatial Vision, 10, 433–436. doi:10.1163/156856897X00357 CrossRefPubMedGoogle Scholar
  13. Brooks, L. (1977). Visual pattern in fluent word identification. In A. S. Reber & D. L. Scarborough (Eds.), Toward a psychology of reading (pp. 143–181). Hillsdale, NJ: Erlbaum.Google Scholar
  14. Brooks, L. (1978). Non-analytic correspondences and pattern in word pronunciation. In J. Requin (Ed.), Attention and performance VII (pp. 163–177). Hillsdale, NJ: Erlbaum.Google Scholar
  15. Byrne, B. (1984). On teaching articulatory phonetics via an orthography. Memory & Cognition, 12, 181–189. doi:10.3758/BF03198432 CrossRefGoogle Scholar
  16. Byrne, B., & Carroll, M. (1989). Learning artificial orthographies: Further evidence of a non-analytic acquisition procedure. Memory & Cognition, 17, 311–317. doi:10.3758/BF03198469 CrossRefGoogle Scholar
  17. Callan, A. M., Callan, D. E., & Masaki, S. (2005). When meaningless symbols become letters: Neural activity change in learning new phonograms. NeuroImage, 28, 553–562. doi:10.1016/j.neuroimage.2005.06.031 CrossRefPubMedGoogle Scholar
  18. Chanceaux, M., Mathôt, S., & Grainger, J. (2014). Effects of number, complexity, and familiarity of flankers on crowded letter identification. Journal of Vision, 14(6), 7. doi:10.1167/14.6.7 CrossRefPubMedGoogle Scholar
  19. Changizi, M. A., & Shimojo, S. (2005). Character complexity and redundancy in writing systems over human history. Proceedings of the Royal Society B, 272, 267–275. doi:10.1098/rspb.2004.2942 CrossRefPubMedPubMedCentralGoogle Scholar
  20. Changizi, M. A., Zhang, Q., Ye, H., & Shimojo, S. (2006). The structures of letters and symbols throughout human history are selected to match those found in objects in natural scenes. American Naturalist, 167, 117–139. doi:10.1086/502806 CrossRefGoogle Scholar
  21. Chetail, F. (2015). Reconsidering the role of orthographic redundancy in visual word recognition. Frontiers in Psychological Science, 6, 645. doi:10.3389/fpsyg.2015.00645 Google Scholar
  22. Chisholm, D., & Knafle, J. D. (1975). Letter-name knowledge as a prerequisite to learning to read. Reading Improvement, 15(1), 2.Google Scholar
  23. Cosky, M. J. (1976). The role of letter recognition in word recognition. Memory & Cognition, 4, 207–214. doi:10.3758/BF03213165 CrossRefGoogle Scholar
  24. de Gardelle, V., Sackur, J., & Kouider, S. (2009). Perceptual illusions in brief visual presentations. Consciousness and Cognition, 18, 569–577. doi:10.1016/j.concog.2009.03.002 CrossRefPubMedGoogle Scholar
  25. Ehrich, J. F., & Meuter, R. F. (2009). Acquiring an artificial logographic orthography: The beneficial effects of a logographic l1 background and bilinguality. Journal of Cross-Cultural Psychology, 40, 711–745. doi:10.1177/0022022109338624 CrossRefGoogle Scholar
  26. Feldman, J. (1997). The structure of perceptual categories. Journal of Mathematical Psychology, 41, 145–170. doi:10.1006/jmps.1997.1154 CrossRefPubMedGoogle Scholar
  27. Fiset, D., Blais, C., Arguin, M., Tadros, K., Éthier-Majcher, C., Bub, D., & Gosselin, F. (2009). The spatio-temporal dynamics of visual letter recognition. Cognitive Neuropsychology, 26, 23–35. doi:10.1080/02643290802421160 CrossRefPubMedGoogle Scholar
  28. Fiset, D., Blais, C., Éthier-Majcher, C., Arguin, M., Bub, D., & Gosselin, F. (2008). Features for identification of uppercase and lowercase letters. Psychological Science, 19, 1161–1168. doi:10.1111/j.1467-9280.2008.02218.x CrossRefPubMedGoogle Scholar
  29. García-Orza, J., Perea, M., & Muñoz, S. (2010). Are transposition effects specific to letters? Quarterly Journal of Experimental Psychology, 63, 1603–1618. doi:10.1080/17470210903474278 CrossRefGoogle Scholar
  30. Gombert, J. E., & Peereman, R. (2001). Training children with artificial alphabet. Psychology, 8, 338–357.Google Scholar
  31. Grainger, J., Rey, A., & Dufau, S. (2008). Letter perception: From pixels to pandemonium. Trends in Cognitive Sciences, 12, 381–387.CrossRefPubMedGoogle Scholar
  32. Hart, L., & Perfetti, C. A. (2008). Learning words in Zekkish: Implications for understanding lexical representations. In E. L. Grigorenko & A. J. Naples (Eds.), Single word reading: Behavioral and biological perspectives (pp. 107–128). New York, NY: Taylor & Francis.Google Scholar
  33. Hashimoto, R., & Sakai, K. L. (2004). Learning letters in adulthood: Direct visualization of cortical plasticity for forming a new link between orthography and phonology. Neuron, 42, 311–322. doi:10.1016/S0896-6273(04)00196-5 CrossRefPubMedGoogle Scholar
  34. Hirshorn, E., & Fiez, J. (2014). Using artificial orthographies for studying cross-linguistic differences in the cognitive and neural profiles of reading. Journal of Neurolinguistics, 31, 69–85. doi:10.1016/j.jneuroling.2014.06.006 CrossRefGoogle Scholar
  35. Jeffrey, W. E., & Samuels, S. J. (1967). Effect of method of reading training on intial learning and transfer. Journal of Verbal Learning and Verbal Behavior, 6, 354–358. doi:10.1016/S0022-5371(67)80124-5 CrossRefGoogle Scholar
  36. Jenkins, J. R., Bausell, R. B., & Jenkins, L. M. (1972). Comparisons of letter name and letter sound training as transfer variables. American Educational Research Journal, 75–86. doi:10.3102/00028312009001075Google Scholar
  37. Knafle, J. D., & Legenza, A. (1978). External generallzability of inquiry involving artificial orthography. American Educational Research Journal, 15, 331–347. doi:10.3102/00028312015002331 Google Scholar
  38. Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350, 1332–1338. doi:10.1126/science.aab3050 CrossRefPubMedGoogle Scholar
  39. Lanthier, S. N., Risko, E. F., Stolz, J. A., & Besner, D. (2009). Not all visual features are created equal: Early processing in letter and word recognition. Psychonomic Bulletin & Review, 16, 67–73. doi:10.3758/PBR.16.1.67 CrossRefGoogle Scholar
  40. Levin, I., Shatil-Carmon, S., & Asif-Rave, O. (2006). Learning of letter names and sounds and their contribution to word recognition. Journal of Experimental Child Psychology, 93, 139–165. doi:10.1016/j.jecp.2005.08.002 CrossRefPubMedGoogle Scholar
  41. Longcamp, M., Anton, J.-L., Roth, M., & Velay, J.-L. (2003). Visual presentation of single letters activates a premotor area involved in writing. NeuroImage, 19, 1492–1500. doi:10.1016/S1053-8119(03)00088-0 CrossRefPubMedGoogle Scholar
  42. Longcamp, M., Boucard, C., Gilhodes, J.-C., & Velay, J.-L. (2006). Remembering the orientation of newly learned characters depends on the associated writing knowledge: A comparison between handwriting and typing. Human Movement Science, 25, 646–656. doi:10.1016/j.humov.2006.07.007 CrossRefPubMedGoogle Scholar
  43. Maki, W. S., & Mebane, M. W. (2006). Attentional capture triggers an attentional blink. Psychonomic Bulletin & Review, 13, 125–131. doi:10.3758/BF03193823 CrossRefGoogle Scholar
  44. Marzouki, Y., Grainger, J., & Theeuwes, J. (2007). Exogenous spatial cueing modulates subliminal masked priming. Acta Psychologica, 126, 34–45. doi:10.1016/j.actpsy.2006.11.002 CrossRefPubMedGoogle Scholar
  45. Mason, M., & Katz, L. (1976). Visual processing of nonlinguistic strings: Redundancy effects and reading ability. Journal of Experimental Psychology: General, 105, 338–348. doi:10.1037/0096-3445.105.4.338 CrossRefGoogle Scholar
  46. Maurer, U., Blau, V. C., Yoncheva, Y. N., & McCandliss, B. D. (2010). Development of visual expertise for reading: rapid emergence of visual familiarity for an artificial script. Developmental Neuropsychology, 35, 404–422. doi:10.1080/87565641.2010.480916 CrossRefPubMedPubMedCentralGoogle Scholar
  47. McClelland, 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, 375–407. doi:10.1037/0033-295X.88.5.375 CrossRefGoogle Scholar
  48. Mei, L., Xue, G., Lu, Z.-L., He, Q., Zhang, M., Xue, F., & Dong, Q. (2013). Orthographic transparency modulates the functional asymmetry in the fusiform cortex: An artificial language training study. Brain and Language, 125, 165–172. doi:10.1016/j.bandl.2012.01.006 CrossRefPubMedGoogle Scholar
  49. Meuter, R. F. I., & Ehrich, J. F. (2012). The acquisition of an artificial logographic script and bilingual working memory: Evidence for L1-specific orthographic processing skills transfer in Chinese–English bilinguals. Writing Systems Research, 4(1), 8–29. doi:10.1080/17586801.2012.665011 CrossRefGoogle Scholar
  50. Moore, M. W., Brendel, P. C., & Fiez, J. A. (2014). Reading faces: Investigating the use of a novel face-based orthography in acquired alexia. Brain and Language, 129, 7–13. doi:10.1016/j.bandl.2013.11.005 CrossRefPubMedPubMedCentralGoogle Scholar
  51. Mueller, S. T., & Weidemann, C. T. (2012). Alphabetic letter identification: Effects of perceivability, similarity, and bias. Acta Psychologica, 139, 19–37. doi:10.1016/j.actpsy.2011.09.014 CrossRefPubMedGoogle Scholar
  52. Navon, D. (1977). Forest before trees: The precedence of global features in visual perception. Cognitive Psychology, 9, 353–383. doi:10.1016/0010-0285(77)90012-3 CrossRefGoogle Scholar
  53. New, B., & Grainger, J. (2011). On letter frequency effects. Acta Psychologica, 138, 322–328. doi:10.1016/j.actpsy.2011.07.001 CrossRefPubMedGoogle Scholar
  54. Park, J., Chiang, C., Brannon, E. M., & Woldorff, M. G. (2014). Experience-dependent hemispheric specialization of letters and numbers is revealed in early visual processing. Journal of Cognitive Neuroscience, 26, 2239–2249. doi:10.1162/jocn_a_00621 CrossRefPubMedPubMedCentralGoogle Scholar
  55. Peirce, J. W. (2007). PsychoPy—Psychophysics software in Python. Journal of Neuroscience Methods, 162, 8–13. doi:10.1016/j.jneumeth.2006.11.017 CrossRefPubMedPubMedCentralGoogle Scholar
  56. Petersen, S. E., Fox, P. T., Snyder, A. Z., & Raichle, M. E. (1990). Activation of extrastriate and frontal cortical areas by visual words and word-like stimuli. Science, 249, 1041–1044.CrossRefPubMedGoogle Scholar
  57. Petit, J. P., & Grainger, J. (2002). Masked partial priming of letter perception. Visual Cognition, 9, 337–354. doi:10.1080/13506280042000207 CrossRefGoogle Scholar
  58. Podgorny, P., & Garner, W. R. (1979). Reaction time as a measure of inter- and intraobject visual similarity: Letters of the alphabet. Perception & Psychophysics, 26, 37–52. doi:10.3758/bf03199860 CrossRefGoogle Scholar
  59. Pollack, I. (1953). Assimilation of sequentially encoded information. American Journal of Psychology, 66, 421–435. doi:10.2307/1418237 CrossRefPubMedGoogle Scholar
  60. R Development Core Team. (2015). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from www.R-project.org.Google Scholar
  61. Raymond, J. E., Shapiro, K. L., & Arnell, K. M. (1992). Temporary suppression of visual processing in an RSVP task: an attentional blink? Journal of Experimental Psychology Human Perception and Performance, 18, 849–860.CrossRefPubMedGoogle Scholar
  62. Rosa, E., Perea, M., & Enneson, P. (2016). The role of letter features in visual-word recognition: Evidence from a delayed segment technique. Acta Psychologica, 169, 133–142. doi:10.1016/j.actpsy.2016.05.016 CrossRefPubMedGoogle Scholar
  63. Samara, A., & Caravolas, M. (2014). Statistical learning of novel graphotactic constraints in children and adults. Journal of Experimental Child Psychology, 121, 137–155. doi:10.1016/j.jecp.2013.11.009 CrossRefPubMedGoogle Scholar
  64. Samuels, S. J. (1972). The effect of letter-name knowledge on learning to read. American Educational Research Journal, 9, 65–74. doi:10.3102/00028312009001065 CrossRefGoogle Scholar
  65. Simpson, I. C., Mousikou, P., Montoya, J. M., & Defior, S. (2013). A letter visual-similarity matrix for Latin-based alphabets. Behavior Research Methods, 45, 431–439. doi:10.3758/s13428-012-0271-4 CrossRefPubMedGoogle Scholar
  66. Singer, M. H. (1980). The primacy of visual information inthe analysis of letter strings. Attention, Perception, & Psychophysics, 27, 153–162. doi:10.3758/BF03204304 CrossRefGoogle Scholar
  67. Stevens, C., McIlraith, A., Rusk, N., Niermeyer, M., & Waller, H. (2013). Relative laterality of the N170 to single letter stimuli is predicted by a concurrent neural index of implicit processing of letternames. Neuropsychologia, 51, 667–674. doi:10.1016/j.neuropsychologia.2012.12.009 CrossRefPubMedGoogle Scholar
  68. Szwed, M., Cohen, L., Qiao, E., & Dehaene, S. (2009). The role of invariant line junctions in object and visual word recognition. Vision Research, 49, 718–725. doi:10.1016/j.visres.2009.01.003 CrossRefPubMedGoogle Scholar
  69. Szwed, M., Dehaene, S., Eger, E., Kleinschmidt, A., Valabregue, R., Amadon, A., & Cohen, L. (2011). Specialization for written words over objects in the visual cortex. NeuroImage, 56, 330–344. doi:10.1016/j.neuroimage.2011.01.073 CrossRefPubMedGoogle Scholar
  70. Taylor, J. S. H., Plunkett, K., & Nation, K. (2011). The influence of consistency, frequency, and semantics on learning to read: An artificial orthography paradigm. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 60–76. doi:10.1037/a0020126 PubMedGoogle Scholar
  71. Turkeltaub, P. E., Gareau, L., Flowers, D. L., Zeffiro, T. A., & Eden, G. F. (2003). Development of neural mechanisms for reading. Nature Neuroscience, 6, 767–773. doi:10.1038/nn1065 CrossRefPubMedGoogle Scholar
  72. Valentine, C. W. (1913). Expermiments on the method of teaching reading. Journal of Experimental Pedagogy, 2, 99–112.Google Scholar
  73. Van Opstal, F., Verguts, T., Orban, G. A., & Fias, W. (2008). A hippocampal–parietal network for learning an ordered sequence. NeuroImage, 40, 333–341. doi:10.1016/j.neuroimage.2007.11.027 CrossRefPubMedGoogle Scholar
  74. Vinckier, F., Dehaene, S., Jobert, A., Dubus, J., Sigman, M., & Cohen, L. (2007). Hierarchical coding of letter strings in the ventral stream: Dissecting the inner organization of the visual word-form system. Neuron, 55, 143–156. doi:10.1016/j.neuron.2007.05.031 CrossRefPubMedGoogle Scholar
  75. Williams, J. P. (1969). Training kindergarten children to discriminate letter-like forms. American Educational Research Journal, 6, 501–514. doi:10.3102/00028312006004501 CrossRefGoogle Scholar
  76. Xue, G., Chen, C., Jin, Z., & Dong, Q. (2006). Cerebral asymmetry in the fusiform areas predicted the efficiency of learning a new writing system. Journal of Cognitive Neuroscience, 18, 923–931. doi:10.1162/jocn.2006.18.6.923 CrossRefPubMedGoogle Scholar
  77. Yoncheva, Y. N., Blau, V. C., Maurer, U., & McCandliss, B. D. (2010). Attentional focus during learning impacts N170 ERP responses to an artificial script. Developmental Neuropsychology, 35, 423–445. doi:10.1080/87565641.2010.480918 CrossRefPubMedPubMedCentralGoogle Scholar
  78. Yoncheva, Y. N., Wise, J., & McCandliss, B. (2015). Hemispheric specialization for visual words is shaped by attention to sublexical units during initial learning. Brain and Language, 145, 23–33. doi:10.1016/j.bandl.2015.04.001 CrossRefPubMedGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2017

Authors and Affiliations

  • Camille Vidal
    • 1
  • Alain Content
    • 1
  • Fabienne Chetail
    • 1
  1. 1.Laboratoire Cognition Langage, Développement (LCLD), Centre de Recherche Cognition et Neurosciences (CRCN)Université Libre de Bruxelles (ULB)BrusselsBelgium

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