Skip to main content
SpringerLink
Log in

Using the speeded word fragment completion task to examine semantic priming

  • Published:
Behavior Research Methods Aims and scope Submit manuscript

Abstract

The present research investigates semantic priming with an adapted version of the word fragment completion task. In this task, which we refer to as the speeded word fragment completion task, participants need to complete words such as lett_ce (lettuce), from which one letter was omitted, as quickly as possible. This paradigm has some interesting qualities in comparison with the traditionally used lexical decision task. That is, it requires no pseudowords, it is more engaging for participants, and most importantly, it allows for a more fine-grained investigation of semantic activation. In two studies, we found that words were completed faster when the preceding trial comprised a semantically related fragment such as tom_to (tomato) than when it comprised an unrelated fragment such as guit_r (guitar). A third experiment involved a lexical decision task, to compare both paradigms. The results showed that the magnitude of the priming effect was similar, but item-level priming effects were inconsistent over tasks. Crucially, the speeded word fragment completion task obtained strong priming effects for highly frequent, central words, such as work, money, and warm, whereas the lexical decision task did not. In a final experiment featuring only short, highly frequent words, the lexical decision task failed to find a priming effect, whereas the fragment completion task did obtain a robust effect. Taken together, these results suggest that the speeded word fragment completion task may prove a viable alternative for examining semantic priming.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. Contextual diversity is the log-transformed number of contexts in which a certain word occurs. This variable has been shown to be more informative than word frequency (Adelman et al., 2006; Brysbaert & New, 2009).

  2. Originally, the model also allowed the random intercepts and random slopes to be correlated. However, we obtained high correlations (i.e., 1.00), which indicate that the model is overparameterized (Baayen, Davidson, & Bates, 2008). We thus simplified the model by removing the correlation parameters, as suggested by Baayen and colleagues. Random effects for the control predictors were not included in the model because it would increase the number of parameters without being considered essential (Barr et al., 2013).

  3. Note that there were five different types of prime–target relations (i.e., coordinates, supraordinates, property relations, script relations, and synonyms). When repeating the analyses for every type separately, there was never evidence for a Relatedness × Experiment Version interaction (all ps > .15). However, we should point out that the number of items per type may have been too limited to discern differences between tasks in this respect.

  4. Because one cannot rely on frequentist statistics to quantify support for the null hypothesis, a default Bayesian hypothesis test for correlations was performed (Wetzels & Wagenmakers, 2012). The analysis yielded a Bayes factor of 0.096, which is, according to Jeffreys’ classification (1961), strong evidence for the null hypothesis (i.e., the correlation is zero).

  5. Even if we apply Spearman’s correction for attenuation formula (1904) to take measurement error into account, the correlation maximally increases to .36.

  6. The latter is not surprising, given the finding that priming in the lexical decision task decreases when word frequency increases (Becker, 1979).

  7. Both correlations increased to, respectively, .21 and –.22, and became marginally significant if Forward Association Strength was calculated considering only primary associates.

  8. There were 304 trials in Experiment 1 and 288 in Experiment 2, resulting in, respectively, 303 and 287 pairs because of its continuous nature. Thus, the relatedness proportion is only .125 (i.e., 38/303 and 36/287). Note that this number may be a little higher for some participants because of the random ordering of pairs (e.g., shower–chocolate followed by cake–vault).

  9. Note that the BA targets in the Thomas et al. study (2012) were significantly less frequent than the FA targets, with the SYM targets falling somewhere in between. Given that Scaltritti et al. (2013) found a significant Priming × Frequency × Stimulus quality (i.e., target degraded or not) interaction, it is unclear whether the pattern of results in Thomas et al. is (partly) a frequency effect in disguise. Indeed, Scaltritti and colleagues found a stronger Priming × Stimulus quality interaction for less-frequent target words.

  10. Note that the continuous lexical decision task has been argued to prevent semantic matching as well (McNamara & Altarriba, 1988). Nevertheless, the presence of a semantic relation in this task still predicts word 100 % of the time. Hence, the speeded word fragment completion task is more stringent.

  11. Throughout the three experiments with the speeded word fragment completion task, we always used vowels as the omitted letter (i.e., a, e, i, o, and u in Experiment 1; a and e in Experiments 2 and 4). The rationale was to use letters that are frequently used in everyday language while at the same time keeping the instructions straightforward and easy to remember. The latter is probably only an issue in the variant with five response options. That is, if we had picked five highly frequent consonants, it would arguably be more demanding to remember the response options. However, there is no a priori reason why the obtained results would not generalize to a paradigm that uses consonants; but that is something to examine in future research.

References

  • Adelman, J. S., Brown, G. D. A., & Quesada, J. F. (2006). Contextual diversity, not word frequency, determines word naming and lexical decision times. Psychological Science, 17, 814–823.

    Article  PubMed  Google Scholar 

  • Baayen, R. H. (2008). Analyzing linguistic data. A practical introduction to statistics using R. Cambridge, U.K.: Cambridge University Press.

    Book  Google Scholar 

  • Baayen, 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.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Barr, 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.

    Article  Google Scholar 

  • Bassili, J. N., Smith, M. C., & MacLeod, C. M. (1989). Auditory and visual word-stem completion: Separating data-driven and conceptually driven processes. Quarterly Journal of Experimental Psychology, 41A, 439–453.

    Article  Google Scholar 

  • Bates, D. M., & Sarkar, D. (2007). lme4: Linear mixed-effects models using S4 classes. R package version 0.999375-42.

  • Becker, C. A. (1979). Semantic context and word frequency effects in visual word recognition. Journal of Experimental Psychology: Human Perception and Performance, 5, 252–259.

    PubMed  Google Scholar 

  • Besner, D., Stolz, J. A., & Boutilier, C. (1997). The Stroop effect and the myth of automaticity. Psychonomic Bulletin & Review, 4, 221–225.

    Article  Google Scholar 

  • Brown, M. S., Roberts, M. A., & Besner, D. (2001). Semantic processing in visual word recognition: Activation blocking and domain specificity. Psychonomic Bulletin & Review, 8, 778–784.

    Article  Google Scholar 

  • Brysbaert, M., & New, B. (2009). Moving beyond Kucera 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.

    Article  PubMed  Google Scholar 

  • Challis, B. H., & Brodbeck, D. R. (1992). Level of processing affects priming in word fragment completion. Journal of Experimental Psychology: Learning, Memory & Cognition, 18, 595–607.

    Google Scholar 

  • De Deyne, S., Navarro, D., & Storms, G. (2013). Better explanations of lexical and semantic cognition using networks derived from continued rather than single word associations. Behavior Research Methods, 45, 480–498.

    Article  PubMed  Google Scholar 

  • De Deyne, S., Verheyen, S., Ameel, E., Vanpaemel, W., Dry, M., Voorspoels, W., & Storms, G. (2008). Exemplar by feature applicability matrices and other Dutch normative data for semantic concepts. Behavior Research Methods, 40, 1030–1048.

    Article  PubMed  Google Scholar 

  • de Groot, A. M. B. (1990). The locus of the associative-priming effects in the mental lexicon. In D. A. Balota, G. B. Flores d’Arcais, & K. Rayner (Eds.), Comprehension processes in reading (pp. 101–123). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum.

    Book  Google Scholar 

  • Griffiths, T. L., Steyvers, M., & Firl, A. (2007). Google and the mind: Predicting fluency with PageRank. Psychological Science, 18, 1069–1076.

    Article  PubMed  Google Scholar 

  • Hutchison, K. A. (2003). Is semantic priming due to association strength or feature overlap? A microanalytic review. Psychonomic Bulletin & Review, 10, 785–813.

    Article  Google Scholar 

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

    Google Scholar 

  • Hutchison, K. A., Balota, D. A., Cortese, M. J., & Watson, J. M. (2008). Predicting semantic priming at the item level. The Quarterly Journal of Experimental Psychology, 61, 1036–1066.

    Article  PubMed  Google Scholar 

  • Jeffreys, H. (1961). Theory of probability. Oxford: Oxford University Press.

    Google Scholar 

  • Jones, L. L. (2010). Pure mediated priming: A retrospective semantic matching model. Journal of Experimental Psychology: Learning, Memory & Cognition, 36, 135–146.

    Google Scholar 

  • Joordens, S., & Becker, S. (1997). The long and short of semantic priming effects in lexical decision. Journal of Experimental Psychology: Learning, Memory & Cognition, 23, 1083–1105.

    Google Scholar 

  • Kessler, B., Treiman, R., & Mullennix, J. (2002). Phonetic biases in voice key response time measurements. Journal of Memory and Language, 47, 145–171.

    Article  Google Scholar 

  • Keuleers, E. (2011). Vwr: Useful functions for visual word recognition research, version 0.1.

  • Keuleers, E., & Brysbaert, M. (2010). Wuggy: A multilingual pseudoword generator. Behavior Research Methods, 42, 627–633.

    Article  PubMed  Google Scholar 

  • Keuleers, E., Brysbaert, M., & New, B. (2010a). SUBTLEX-NL: A new measure for Dutch word frequency based on film subtitles. Behavior Research Methods, 42, 643–650.

    Article  PubMed  Google Scholar 

  • Keuleers, E., Diependaele, K., & Brysbaert, M. (2010b). 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.

    Article  PubMed Central  PubMed  Google Scholar 

  • Kliegl, R., Grabner, E., Rolfs, M., & Engbert, R. (2004). Length, frequency, and predictability effects of words on eye movements in reading. European Journal of Cognitive Psychology, 16, 262–284.

    Article  Google Scholar 

  • Lucas, M. (2000). Semantic priming without association: A meta-analytic review. Psychonomic Bulletin & Review, 7, 618–630.

    Article  Google Scholar 

  • McDermott, K. B. (1997). Priming on perceptual implicit memory tests can be achieved through presentation of associates. Psychonomic Bulletin & Review, 4, 582–586.

    Article  Google Scholar 

  • McNamara, T. P., & Altarriba, J. (1988). Depth of spreading activation revisited: Semantic mediated priming occurs in lexical decisions. Journal of Memory and Language, 27, 545–559.

    Article  Google Scholar 

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

    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. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Neely, J. H., & Keefe, D. E. (1989). Semantic context effects in 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. New York: Academic Press.

    Google Scholar 

  • Nelson, D. L., McEvoy, C. L., & Schreiber, T. A. (2004). The University of South Florida word association, rhyme, and word fragment norms. Behavior Research Methods, Instruments, & Computers, 36, 402–407.

    Article  Google Scholar 

  • Peirce, J. (2007). PsychoPy—psychophysics software in Python. Journal of Neuroscience Methods, 162, 8–13.

    Article  PubMed Central  PubMed  Google Scholar 

  • R Development Core Team (2011). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing, http://www.R-project.org

  • Robidoux, S., Stolz, J. A., & Besner, D. (2010). Visual word recognition: Evidence for global and local control over semantic feedback. Journal of Experimental Psychology: Human Perception and Performance, 36, 689–703.

    PubMed  Google Scholar 

  • Roediger, H. L., III, & Challis, B. H. (1992). Effects of exact repetition and conceptual repetition on free recall and primed word-fragment completion. Journal of Experimental Psychology: Learning, Memory & Cognition, 18, 3–14.

    Google Scholar 

  • Rogers, T. T., Lambon Ralph, M. A., Hodges, J. R., & Patterson, K. (2004). Natural selection: The impact of semantic impairment on lexical and object decision. Cognitive Neuropsychology, 21, 331–352.

    Article  PubMed  Google Scholar 

  • Scaltritti, M., Balota, D. A., & Peressotti, F. (2013). Exploring the additive effects of stimulus quality and word frequency: The influence of local and list-wide prime relatedness. Quarterly Journal of Experimental Psychology, 66, 91–107.

    Article  Google Scholar 

  • Shelton, J. R., & Martin, R. C. (1992). How semantic is automatic semantic priming? Journal of Experimental Psychology: Learning, Memory & Cognition, 18, 1191–1210.

    Google Scholar 

  • Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15, 72–101.

    Article  Google Scholar 

  • Stolz, J. A., & Neely, J. H. (1995). When target degradation does and does not enhance semantic context effects in word recognition. Journal of Experimental Psychology: Learning, Memory & Cognition, 21, 596–611.

    Google Scholar 

  • Stone, G. O., & Van Orden, G. C. (1993). Strategic control of processing in word recognition. Journal of Experimental Psychology: Human Perception and Performance, 19, 744–774.

    PubMed  Google Scholar 

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

    Article  Google Scholar 

  • Weldon, M. S. (1993). The time course of perceptual and conceptual contributions to word fragment completion priming. Journal of Experimental Psychology: Learning, Memory & Cognition, 19, 1010–1023.

    Google Scholar 

  • Wetzels, R., & Wagenmakers, E.-J. (2012). A default Bayesian hypothesis test for correlations and partial correlations. Psychonomic Bulletin & Review, 19, 1057–1064.

    Article  Google Scholar 

  • Yap, M. J., & Balota, D. A. (2009). Visual word recognition of multisyllabic words. Journal of Memory and Language, 60, 502–529.

    Article  Google Scholar 

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

    Google Scholar 

Download references

Author note

T. H. is a research assistant of the Research Foundation–Flanders (FWO–Vlaanderen). This research was partly sponsored by Grant G.0436.13 of the Research Foundation–Flanders (FWO–Vlaanderen), awarded to G. S.

Author information

Authors and Affiliations

  1. University of Leuven, Leuven, Belgium

    Tom Heyman, Simon De Deyne & Gert Storms

  2. Montana State University, Bozeman, MT, 59717, USA

    Keith A. Hutchison

  3. Department of Psychology, University of Leuven, Tiensestraat 102, 3000, Leuven, Belgium

    Tom Heyman

Authors
  1. Tom Heyman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Simon De Deyne
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Keith A. Hutchison
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gert Storms
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tom Heyman.

Appendixes

Appendixes

Appendix A

Table 3 Prime–target pairs from Experiment 1. The first and second columns give the English translations; the third and fourth columns show the Dutch word fragments, with the correct completions in parentheses
Full size table

Appendix B

Table 4 Prime–target pairs from Experiments 2 and 3. The first and second columns give the English translations; the third and fourth columns show the Dutch word fragments, with the correct completions in parentheses
Full size table

Appendix C

Table 5 Prime–target pairs from Experiment 4. The first and second columns give the English translations; the third and fourth columns show the Dutch word fragments, with the correct completions in parentheses
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Heyman, T., De Deyne, S., Hutchison, K.A. et al. Using the speeded word fragment completion task to examine semantic priming. Behav Res 47, 580–606 (2015). https://doi.org/10.3758/s13428-014-0496-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3758/s13428-014-0496-5

Keywords

Search

Navigation