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An RT distribution analysis of relatedness proportion effects in lexical decision and semantic categorization reveals different mechanisms

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Abstract

The magnitude of the semantic priming effect is known to increase as the proportion of related prime–target pairs in an experiment increases. This relatedness proportion (RP) effect was studied in a lexical decision task at a short prime–target stimulus onset asynchrony (240 ms), which is widely assumed to preclude strategic prospective usage of the prime. The analysis of the reaction time (RT) distribution suggested that the observed RP effect reflected a modulation of a retrospective semantic matching process. The pattern of the RP effect on the RT distribution found here is contrasted to that reported in De Wit and Kinoshita’s (2014) semantic categorization study, and it is concluded that the RP effect is driven by different underlying mechanisms in lexical decision and semantic categorization.

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Notes

  1. It is relevant to note that the variation in nonword ratio that accompanies the manipulation of RP is less extreme (assuming an equal number of word and nonword trials). For example, the RP of .75 versus .25 corresponds to the nonword ratio of .80 versus .57, and the RP of .25 versus .50 corresponds to the nonword ratio of .57 versus .67. This may explain why an RP effect has not been found consistently in lexical decision with short SOAs (see Hutchison, 2007, Table 1).

  2. Another related measure is a vincentile, which is the average of each RT bin. Quantile plots and vincentile plots are generally very similar.

  3. It should be pointed out that in contrast to this pattern, at the longer SOAs and with clearly presented targets, Balota et al. (2008) found that the semantic priming manipulation consistently produced a shift in the RT distribution. This pattern will be discussed in more detail in the Discussion section.

  4. Balota et al. (2008) have made a similar point that lexical decision may involve both “the influence of the prime” and “the word recognition processes that drive lexical decisions for the target in the unrelated condition” (p. 507). However, their suggestion was that there is a “race” between these sources of information; in contrast, our view, as elaborated below, is that they are combined to form a “compound cue.”

References

  • Antos, S. J. (1979). Processing facilitation in a lexical decision task. Journal of Experimental Psychology, 5, 527–545.

    Google Scholar 

  • Balota, D. A., & Yap, M. J. (2011). Moving beyond the mean in studies of mental chronometry: The power of response time distributional analyses. Current Directions in Psychological Science, 20, 160–166.

    Article  Google Scholar 

  • Balota, D. A., Yap, M. J., Cortese, M. J., Hutchison, K. A., Kessler, B., Loftis, B., ... Treiman, R. (2007). The English Lexicon Project. Behavior Research Methods, 39, 445–459.

    Article  PubMed  Google Scholar 

  • Balota, D. A., Yap, M. J., Cortese, M. J., & Watson, J. M. (2008). Beyond mean response latency: Response time distributional analysis of semantic priming. Journal of Memory and Language, 59, 495–523.

    Article  Google Scholar 

  • Becker, C. A. (1980). Semantic context effects in visual word recognition: An analysis of semantic strategies. Memory & Cognition, 8, 493–512.

    Article  Google Scholar 

  • Blazely, A. M., Coltheart, M., & Casey, B. J. (2005). Semantic impairment with and without surface dyslexia: Implications for models of reading. Cognitive Neuropsychology, 22, 695–717.

    Article  PubMed  Google Scholar 

  • Bormann, T., & Weiller, C. (2012). “Are there lexicons”? A study of lexical and semantic processing in word-meaning deafness suggests “yes”. Cortex, 48, 294–307.

    Article  PubMed  Google Scholar 

  • Brysbaert, M., & New, B. (2009). Moving beyond Kuçera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods, 41, 977–990. doi:10.3758/brm.41.4.977

    Article  PubMed  Google Scholar 

  • Cousineau, D., Brown, S., & Heathcote, A. (2004). Fitting distributions using maximum likelihood: Methods and packages. Behavior Research Methods, Instruments, & Computers, 36, 742–756.

    Article  Google Scholar 

  • Davis, C. J. (2010). The spatial coding model of visual word identification. Psychological Review, 117, 713–758.

    Article  PubMed  Google Scholar 

  • De Groot, A. M. B. (1984). Primed lexical decision: Combined effects of the proportion of related prime-target pairs and the stimulus-onset asynchrony of prime and target. The Quarterly Journal of Experimental Psychology Section A, 36, 253–280. doi:10.1080/14640748408402158

    Article  Google Scholar 

  • De Wit, B., & Kinoshita, S. (2014). Relatedness proportion effects in semantic categorization: Reconsidering the automatic spreading activation process. Journal of Experimental Psychology: Learning, Memory, and Cognition. doi:10.1037/xlm0000004

    Google Scholar 

  • Forster, K. I., & Forster, J. C. (2003). DMDX: A windows display program with millisecond accuracy. Behavior Research Methods, Instruments and Computers, 35, 116–124.

    Article  Google Scholar 

  • Gomez, P., Perea, M., & Ratcliff, R. (2013). A diffusion model account of masked vs. unmasked priming: Are they qualitatively different? Journal of Experimental Psychology: Human Perception and Performance. doi:10.1037/a0032333

    PubMed  Google Scholar 

  • Grondin, R., Lupker, S. J., & McRae, K. (2009). Shared features dominate semantic richness effects for concrete concepts. Journal of Memory and Language, 60, 1019.

    Article  Google Scholar 

  • Heathcote, A., Popiel, S. J., & Mewhort, D. J. K. (1991). Analysis of response time distributions: An example using the Stroop task. Psychological Bulletin, 109, 340–347.

    Article  Google Scholar 

  • Hohle, R. H. (1965). Inferred components of reaction times as functions of foreperiod duration. Journal of Experimental Psychology, 69, 382–386.

    Article  PubMed  Google Scholar 

  • Huber, D. E., Shiffrin, R. M., Lyle, K. B., & Ruys, K. I. (2001). Perception and preference in short-term word priming. Psychological Review, 108, 149–182.

    Article  PubMed  Google Scholar 

  • Hutchison, K. A. (2007). Attentional control and the relatedness proportion effect in semantic priming. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 645–662.

    PubMed  Google Scholar 

  • Hutchison, K. A., Neely, J. H., & Johnson, J. D. (2001). With great expectations, can two “wrongs” prime a “right”? Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 1451–1463.

    PubMed  Google Scholar 

  • Hutchison, K. A., Heap, S. J., Neely, J. H., & Thomas, M. A. (2014). Attentional control and asymmetric associative priming. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40, 844–856. doi:10.1037/a0035781

  • Kahan, T. A., Neely, J. H., & Forsythe, W. (1999). Dissociated backward priming effects in lexical decision and pronunciation tasks. Psychonomic Bulletin & Review, 6, 105–110.

    Article  Google Scholar 

  • Landauer, T. K., McNamara, D. S., Dennis, S., & Kintsch, W. (2007). Handbook of Latent Semantic Analysis. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • McRae, K., & Boisvert, S. (1998). Automatic semantic similarity priming. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 558–572.

    Google Scholar 

  • McRae, K., Cree, G., Seidenberg, M., & McNorgan, C. (2005). Semantic feature production norms for a large set of living and nonliving things. Behavior Research Methods, 37, 547–559. doi:10.3758/bf03192726

    Article  PubMed  Google Scholar 

  • Neely, J. H. (1977). Semantic priming and retrieval from lexical memory: Roles of inhibitionless spreading activation and limited-capacity attention. Journal of Experimental Psychology: General, 106, 226–254.

    Article  Google Scholar 

  • Neely, J. H. (1991). Semantic priming effects in visual word recognition: A selective review of current findings and theories. In D. Besner & G. W. Humphreys (Eds.), Basic processes in reading: Visual word recognition (pp. 264–336). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Neely, J. H., & Keefe, D. E. (1989). Semantic context effects on visual word processing: A hybrid prospective/retrospective processing theory. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 24, pp. 207–248). New York, NY: Academic Press.

    Google Scholar 

  • Neely, J. H., Keefe, D. E., & Ross, K. L. (1989). Semantic priming in the lexical decision task: Roles of prospective prime-generated expectancies and retrospective semantic matching. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 1003–1019.

    PubMed  Google Scholar 

  • Norris, D. (1984). The mispriming effect: Evidence for an orthographic check in the lexical decision task. Memory & Cognition, 12, 470–476.

    Article  Google Scholar 

  • Norris, D. (2006). The Bayesian Reader: Explaining word recognition as an optimal Bayesian process. Psychological Review, 113, 327–357.

    Article  PubMed  Google Scholar 

  • Norris, D., & Kinoshita, S. (2008). Perception as evidence accumulation and Bayesian inference: Insights from masked priming. Journal of Experimental Psychology: General, 137, 434–455.

    Article  Google Scholar 

  • Norris, D., & Kinoshita, S. (2012). Reading through a noisy channel: Why there’s nothing special about the perception of orthography. Psychological Review, 119, 517–545.

    Article  PubMed  Google Scholar 

  • O’Connor, R. E., & Forster, K. I. (1981). Criterion bias and search sequence bias in word recognition. Memory & Cognition, 9, 78–92.

    Article  Google Scholar 

  • Plaut, D. C. (1997). Structure and function in the lexical system: Insights from distributed models of word reading and lexical decision. Language and Cognitive Processes, 12, 765–805.

    Article  Google Scholar 

  • Posner, M. I., & Snyder, C. R. R. (1975). Attention and cognitive control. In R. L. Solso (Ed.), Information processing and cognition: The Loyala symposium (pp. 55–85). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

    Google Scholar 

  • Pratte, M. S., Rouder, J. N., Morey, R. D., & Feng, C. (2010). Exploring the differences in distributional properties between Stroop and Simon effects using delta plots. Attention, Perception, & Psychophysics, 72, 2013–2025.

    Article  Google Scholar 

  • Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 88, 552–572.

    Article  Google Scholar 

  • Ratcliff, R., & McKoon, G. (1988). A retrieval theory of priming in memory. Psychological Review, 95, 385–408.

    Article  PubMed  Google Scholar 

  • Seidenberg, M. S., Waters, G. S., Sanders, M., & Langer, P. (1984). Pre- and postlexical loci of contextual effects on word recognition. Memory & Cognition, 12, 315–328.

    Article  Google Scholar 

  • Smith, E. E., Shoben, E. J., & Rips, L. J. (1974). Structure and process in semantic memory: A featural model of semantic decisions. Psychological Review, 81, 214–241.

    Article  Google Scholar 

  • Thomas, M. A., Neely, J. H., & O’Connor, P. (2012). When word identification gets tough, retrospective processing comes to the rescue. Journal of Memory and Language, 66, 623–643.

    Article  Google Scholar 

  • Tweedy, J., Lapinski, R., & Schvaneveldt, R. (1977). Semantic-context effects on word recognition: Influence of varying the proportion of items presented in an appropriate context. Memory & Cognition, 5, 84–89. doi:10.3758/bf03209197

    Article  Google Scholar 

  • Yap, M. J., Tse, C.-S., & Balota, D. A. (2009). Individual differences in the joint effects of semantic priming and word frequency revealed by RT distributional analyses: The role of lexical integrity. Journal of Memory and Language, 61, 303–325.

    Article  PubMed Central  PubMed  Google Scholar 

  • Yap, M. J., Balota, D. A., & Tan, S. E. (2013). Isolating lexical and decision processes in the lexical decision task. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 140–158.

    PubMed  Google Scholar 

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Acknowledgments

This research was supported by the International Macquarie University Research Excellence Scholarship (2011043) awarded to Bianca de Wit. We thank the reviewers—Jim Neely and two anonymous—who provided valuable comments on an earlier version of the manuscript.

Author note

Bianca de Wit, ARC Centre of Excellence in Cognition and its Disorders (CCD) and Department of Cognitive Science, Macquarie University. Sachiko Kinoshita, ARC Centre of Excellence in Cognition and its Disorders (CCD) and Department of Psychology, Macquarie University.

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Authors and Affiliations

  1. ARC Centre of Excellence in Cognition and its Disorders (CCD) and Department of Cognitive Science, Macquarie University, Sydney, Australia

    Bianca de Wit

  2. ARC Centre of Excellence in Cognition and its Disorders (CCD) and Department of Psychology, Macquarie University, Sydney, Australia

    Sachiko Kinoshita

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  1. Bianca de Wit
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  2. Sachiko Kinoshita
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Correspondence to Bianca de Wit.

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de Wit, B., Kinoshita, S. An RT distribution analysis of relatedness proportion effects in lexical decision and semantic categorization reveals different mechanisms. Mem Cogn 43, 99–110 (2015). https://doi.org/10.3758/s13421-014-0446-6

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