Study 2.3: Lexical decision

This online book is a reprint of:

Bernabeu, P. (2022). Language and sensorimotor simulation in conceptual processing: Multilevel analysis and statistical power. Lancaster University. https://doi.org/10.17635/lancaster/thesis/1795

Materials: https://osf.io/vyb8k

The core data set in this study was the lexical decision subset of the English Lexicon Project (ELP; Balota et al., 2007). As in Study 2.1, we limited our analysis to the lexical decision task because it was more relevant to a subsequent study that we were planning. The lexical decision task differs from semantic priming and semantic decision in two important aspects. First, lexical decision is likely to involve less semantic processing than the other paradigms (Balota & Lorch, 1986; Becker et al., 1997; de Wit & Kinoshita, 2015; Joordens & Becker, 1997; Muraki & Pexman, 2021; Ostarek & Huettig, 2017).

Second, it is more difficult in the lexical decision task to create word-to-word distance measures to capture language-based and vision-based information. The possibility of calculating the distance between words in consecutive trials is hindered by the need to skip trials, owing to the high prevalence of nonword trials throughout the lexical decision task. Therefore, the measures must be based on each word alone. Accordingly, vision-based information can be operationalised as the visual strength of each word. Language-based information could be operationalised as one of several lexical variables. In the present study, word frequency was chosen as it had the largest effect size out of five candidates—the other candidates being number of letters, number of syllables, orthographic Levenshtein distance and phonological Levenshtein distance (see Appendix A). It should also be noted that word frequency has been found to be more closely related to semantic variables than to lexical ones, such as word length, orthography and phonology (see Table 4 in Yap et al., 2012). Another noteworthy feature of word frequency how it relates to vocabulary size across different paradigms. In lexical decision, the effect of word frequency has been stronger in higher-vocabulary participants than in lower-vocabulary ones (Lim et al., 2020; Yap et al., 2012). In contrast, the opposite pattern has emerged in deeper semantic tasks, such as semantic priming (Yap et al., 2017) and semantic decision (Pexman & Yap, 2018).

Methods

Data set

Code


# Calculate some of the sample sizes to be reported in the paragraph below

# Number of words per participant.
# Save mean as integer and SD rounded while keeping trailing zeros
lexicaldecision_mean_words_per_participant = 
  lexicaldecision %>% group_by(Participant) %>% 
  summarise(length(unique(word))) %>% 
  select(2) %>% unlist %>% mean %>% round(0)

lexicaldecision_SD_words_per_participant = 
  lexicaldecision %>% group_by(Participant) %>% 
  summarise(length(unique(word))) %>% 
  select(2) %>% unlist %>% sd %>% sprintf('%.2f', .)

# Number of participants per word.
# Save mean as integer and SD rounded while keeping trailing zeros
lexicaldecision_mean_participants_per_word = 
  lexicaldecision %>% group_by(word) %>% 
  summarise(length(unique(Participant))) %>% 
  select(2) %>% unlist %>% mean %>% round(0)

lexicaldecision_SD_participants_per_word = 
  lexicaldecision %>% group_by(word) %>% 
  summarise(length(unique(Participant))) %>% 
  select(2) %>% unlist %>% sd %>% sprintf('%.2f', .)

The data set was trimmed by removing rows that lacked values on any variable, and by also removing RTs that were more than 3 standard deviations away from the mean. The standard deviation trimming was performed within participants, as done in the English Lexicon Project (Balota et al., 2007). The resulting data set contained 795 participants, 12,636 words and 19,828 RTs. On average, there were 25 words per participant (SD = 36.04), and conversely, 2 participants per word (SD = 0.86).

Figure 18 shows the correlations among the predictors and the dependent variable.

Code


# Using the following variables...
lexicaldecision[, c('z_RT', 'z_vocabulary_age', 'z_word_frequency', 
                    'z_visual_rating', 'z_word_concreteness', 
                    'z_orthographic_Levenshtein_distance')] %>%
  
  # renamed for the sake of clarity
  rename('RT' = z_RT, 
         'Vocabulary age' = z_vocabulary_age, 
         'Word frequency' = z_word_frequency,
         'Visual strength' = z_visual_rating,
         'Word concreteness' = z_word_concreteness,
         'Orthographic Levenshtein distance' = z_orthographic_Levenshtein_distance) %>%
  
  # make correlation matrix (custom function from the 'R_functions' folder)
  correlation_matrix() + 
  theme(plot.margin = unit(c(0, 0, 0.1, -2), 'in'))

Figure 18: Zero-order correlations in the lexical decision study.

Variables

While the variables are outlined in the general introduction, a few further details are provided below regarding some of them.

Vocabulary age: the present study uses the name vocabulary age, as used in the study of Balota et al. (2007). It measures the same linguistic experience as vocabulary size.

A few details regarding the covariates follow.

General cognition covariate: unlike in the two previous studies, the present study did not include a general cognition covariate as such a variable was not available in the data set of Balota et al. (2007).
Lexical covariates (see preselection in Appendix A): orthographic Levenshtein distance (Balota et al., 2007).
Word concreteness (Brysbaert et al., 2014), used as a covariate of visual strength.

Diagnostics for the frequentist analysis

The model presented convergence warnings. To avoid removing important random slopes, which could increase the Type I error rate—i.e., false positives (Brauer & Curtin, 2018; Singmann & Kellen, 2019), we examined the model after refitting it using seven optimisation algorithms through the ‘allFit’ function of the ‘lme4’ package (Bates et al., 2021). The results showed that all optimisers produced virtually identical means for all effects, suggesting that the convergence warnings were not consequential (Bates et al., 2021; see Appendix B).

Code


# Calculate VIF for every predictor and return only the maximum VIF rounded up
maxVIF_lexicaldecision = car::vif(lexicaldecision_lmerTest) %>% max %>% ceiling

The residual errors were not normally distributed, and attempts to mitigate this deviation proved unsuccessful (see Appendix B). However, this is not likely to have posed a major problem, as mixed-effects models are fairly robust to deviations from normality (Knief & Forstmeier, 2021; Schielzeth et al., 2020). Last, the model did not present multicollinearity problems, with all VIFs below 2 (see Dormann et al., 2013; Harrison et al., 2018).

Diagnostics for the Bayesian analysis

Code


# Calculate number of post-warmup draws (as in 'brms' version 2.17.0).
# Informative prior model used but numbers are identical in the three models.
lexicaldecision_post_warmup_draws = 
  (lexicaldecision_summary_informativepriors_exgaussian$iter -
     lexicaldecision_summary_informativepriors_exgaussian$warmup) *
  lexicaldecision_summary_informativepriors_exgaussian$chains

# As a convergence diagnostic, find maximum R-hat value for the 
# fixed effects across the three models.
lexicaldecision_fixedeffects_max_Rhat = 
  max(lexicaldecision_summary_informativepriors_exgaussian$fixed$Rhat,
      lexicaldecision_summary_weaklyinformativepriors_exgaussian$fixed$Rhat,
      lexicaldecision_summary_diffusepriors_exgaussian$fixed$Rhat) %>% 
  # Round
  sprintf('%.2f', .)

# Next, find find maximum R-hat value for the random effects across the three models
lexicaldecision_randomeffects_max_Rhat = 
  max(lexicaldecision_summary_informativepriors_exgaussian$random[['Participant']]$Rhat,
      lexicaldecision_summary_weaklyinformativepriors_exgaussian$random[['Participant']]$Rhat,
      lexicaldecision_summary_diffusepriors_exgaussian$random[['Participant']]$Rhat,
      lexicaldecision_summary_informativepriors_exgaussian$random[['word']]$Rhat,
      lexicaldecision_summary_weaklyinformativepriors_exgaussian$random[['word']]$Rhat,
      lexicaldecision_summary_diffusepriors_exgaussian$random[['word']]$Rhat) %>% 
  # Round
  sprintf('%.2f', .)

Three Bayesian models were run that were respectively characterised by informative, weakly-informative and diffuse priors. In each model, 5 chains were used. In each chain, 2,000 warmup iterations were run, followed by 18,000 post-warmup iterations. Thus, a total of 90,000 post-warmup draws were produced over all the chains.

The maximum \(\widehat R\) value for the fixed effects across the three models was 1.00, suggesting that these effects hadconverged (Schoot et al., 2021; Vehtari et al., 2021). For the random effects, the maximum \(\widehat R\) value was 1.02, barely exceeding the 1.01 threshold (Vehtari et al., 2021).

The results of the posterior predictive checks were sound (see Appendix C), indicating that the posterior distributions were sufficiently consistent with the observed data. Furthermore, in the prior sensitivity analysis, the results were virtually identical with the three priors that were considered (refer to the priors in Figure 1 above; to view the results in detail, see Appendix E).

Results of Study 2.3

Code


# Calculate R^2. This coefficient must be interpreted with caution 
# (Nakagawa et al., 2017; https://doi.org/10.1098/rsif.2017.0213). 
# Also, transform coefficient to rounded percentage.

Nakagawa2017_fixedeffects_R2_lexicaldecision_lmerTest = 
  paste0(
    (MuMIn::r.squaredGLMM(lexicaldecision_lmerTest)[1, 'R2m'][[1]] * 100) %>% 
      sprintf('%.2f', .), '%'
  )

Nakagawa2017_randomeffects_R2_lexicaldecision_lmerTest = 
  paste0(
    (MuMIn::r.squaredGLMM(lexicaldecision_lmerTest)[1, 'R2c'][[1]] * 100) %>% 
      sprintf('%.2f', .), '%'
  )

Table 5 presents the results. The fixed effects explained 5.61% of the variance, and the random effects explained 10.25% (Nakagawa et al., 2017; for an explanation of this difference, see Results of Study 2.1). Word frequency produced a significant main effect, with higher values of variable facilitating participants’ performance, as reflected in shorter RTs. None of the other effects of interest were significant.

The effect size of word frequency was far larger than that of visual strength. Figure 19 displays the frequentist and the Bayesian estimates, which are broadly similar. The Bayesian estimates are from the weakly-informative prior model. The estimates of the two other models, based on informative and diffuse priors, were virtually identical to these (see Appendix E).

Code


# Rename effects in plain language and specify the random slopes
# (if any) for each effect, in the footnote. For this purpose, 
# superscripts are added to the names of the appropriate effects.
# 
# In the interactions below, word-level variables are presented 
# first for the sake of consistency (the order does not affect 
# the results in any way). Also in the interactions, double 
# colons are used to inform the 'frequentist_model_table' 
# function that the two terms in the interaction must be split 
# into two lines.

rownames(KR_summary_lexicaldecision_lmerTest$coefficients)[
  rownames(KR_summary_lexicaldecision_lmerTest$coefficients) == 
    'z_vocabulary_age'] = 'Vocabulary age <sup>a</sup>'

rownames(KR_summary_lexicaldecision_lmerTest$coefficients)[
  rownames(KR_summary_lexicaldecision_lmerTest$coefficients) == 
    'z_recoded_participant_gender'] = 'Gender <sup>a</sup>'

rownames(KR_summary_lexicaldecision_lmerTest$coefficients)[
  rownames(KR_summary_lexicaldecision_lmerTest$coefficients) == 
    'z_orthographic_Levenshtein_distance'] = 'Orthographic Levenshtein distance'

rownames(KR_summary_lexicaldecision_lmerTest$coefficients)[
  rownames(KR_summary_lexicaldecision_lmerTest$coefficients) == 
    'z_word_concreteness'] = 'Word concreteness'

rownames(KR_summary_lexicaldecision_lmerTest$coefficients)[
  rownames(KR_summary_lexicaldecision_lmerTest$coefficients) == 
    'z_word_frequency'] = 'Word frequency <sup>b</sup>'

rownames(KR_summary_lexicaldecision_lmerTest$coefficients)[
  rownames(KR_summary_lexicaldecision_lmerTest$coefficients) == 
    'z_visual_rating'] = 'Visual strength <sup>b</sup>'

rownames(KR_summary_lexicaldecision_lmerTest$coefficients)[
  rownames(KR_summary_lexicaldecision_lmerTest$coefficients) == 
    'z_word_concreteness:z_vocabulary_age'] = 
  'Word concreteness : Vocabulary age'

rownames(KR_summary_lexicaldecision_lmerTest$coefficients)[
  rownames(KR_summary_lexicaldecision_lmerTest$coefficients) == 
    'z_word_concreteness:z_recoded_participant_gender'] = 
  'Word concreteness : Gender'

rownames(KR_summary_lexicaldecision_lmerTest$coefficients)[
  rownames(KR_summary_lexicaldecision_lmerTest$coefficients) == 
    'z_vocabulary_age:z_word_frequency'] = 
  'Word frequency : Vocabulary age'

rownames(KR_summary_lexicaldecision_lmerTest$coefficients)[
  rownames(KR_summary_lexicaldecision_lmerTest$coefficients) == 
    'z_vocabulary_age:z_visual_rating'] = 
  'Visual strength : Vocabulary age'

rownames(KR_summary_lexicaldecision_lmerTest$coefficients)[
  rownames(KR_summary_lexicaldecision_lmerTest$coefficients) == 
    'z_recoded_participant_gender:z_word_frequency'] = 
  'Word frequency : Gender'

rownames(KR_summary_lexicaldecision_lmerTest$coefficients)[
  rownames(KR_summary_lexicaldecision_lmerTest$coefficients) == 
    'z_recoded_participant_gender:z_visual_rating'] = 
  'Visual strength : Gender'


# Next, change the names in the confidence intervals object

rownames(confint_lexicaldecision_lmerTest)[
  rownames(confint_lexicaldecision_lmerTest) == 
    'z_vocabulary_age'] = 'Vocabulary age <sup>a</sup>'

rownames(confint_lexicaldecision_lmerTest)[
  rownames(confint_lexicaldecision_lmerTest) == 
    'z_recoded_participant_gender'] = 'Gender <sup>a</sup>'

rownames(confint_lexicaldecision_lmerTest)[
  rownames(confint_lexicaldecision_lmerTest) == 
    'z_orthographic_Levenshtein_distance'] = 'Orthographic Levenshtein distance'

rownames(confint_lexicaldecision_lmerTest)[
  rownames(confint_lexicaldecision_lmerTest) == 
    'z_word_concreteness'] = 'Word concreteness'

rownames(confint_lexicaldecision_lmerTest)[
  rownames(confint_lexicaldecision_lmerTest) == 
    'z_word_frequency'] = 'Word frequency <sup>b</sup>'

rownames(confint_lexicaldecision_lmerTest)[
  rownames(confint_lexicaldecision_lmerTest) == 
    'z_visual_rating'] = 'Visual strength <sup>b</sup>'

rownames(confint_lexicaldecision_lmerTest)[
  rownames(confint_lexicaldecision_lmerTest) == 
    'z_word_concreteness:z_vocabulary_age'] = 
  'Word concreteness : Vocabulary age'

rownames(confint_lexicaldecision_lmerTest)[
  rownames(confint_lexicaldecision_lmerTest) == 
    'z_word_concreteness:z_recoded_participant_gender'] = 
  'Word concreteness : Gender'

rownames(confint_lexicaldecision_lmerTest)[
  rownames(confint_lexicaldecision_lmerTest) == 
    'z_vocabulary_age:z_word_frequency'] = 
  'Word frequency : Vocabulary age'

rownames(confint_lexicaldecision_lmerTest)[
  rownames(confint_lexicaldecision_lmerTest) == 
    'z_vocabulary_age:z_visual_rating'] = 
  'Visual strength : Vocabulary age'

rownames(confint_lexicaldecision_lmerTest)[
  rownames(confint_lexicaldecision_lmerTest) == 
    'z_recoded_participant_gender:z_word_frequency'] = 
  'Word frequency : Gender'

rownames(confint_lexicaldecision_lmerTest)[
  rownames(confint_lexicaldecision_lmerTest) == 
    'z_recoded_participant_gender:z_visual_rating'] = 
  'Visual strength : Gender'


# Create table (using custom function from the 'R_functions' folder)
frequentist_model_table(
  KR_summary_lexicaldecision_lmerTest, 
  confint_lexicaldecision_lmerTest,
  order_effects = c('(Intercept)',
                    'Vocabulary age <sup>a</sup>',
                    'Gender <sup>a</sup>',
                    'Orthographic Levenshtein distance',
                    'Word concreteness',
                    'Word frequency <sup>b</sup>',
                    'Visual strength <sup>b</sup>',
                    'Word concreteness : Vocabulary age',
                    'Word concreteness : Gender',
                    'Word frequency : Vocabulary age',
                    'Visual strength : Vocabulary age',
                    'Word frequency : Gender',
                    'Visual strength : Gender'),
  interaction_symbol_x = TRUE,
  caption = 'Frequentist model for the lexical decision study.') %>%
  
  # Group predictors under headings
  pack_rows('Individual differences', 2, 3) %>% 
  pack_rows('Lexicosemantic covariates', 4, 5) %>% 
  pack_rows('Semantic variables', 6, 7) %>% 
  pack_rows('Interactions', 8, 13) %>% 
  
  # Apply white background to override default shading in HTML output
  row_spec(1:13, background = 'white') %>%
  
  # Highlight covariates
  row_spec(c(4:5, 8:9), background = '#FFFFF1') %>%
  
  # Format
  kable_classic(full_width = FALSE, html_font = 'Cambria') %>%
  
  # Footnote describing abbreviations, random slopes, etc. 
  footnote(escape = FALSE, threeparttable = TRUE, 
           # The <p> below is used to enter a margin above the footnote 
           general_title = '<p style="margin-top: 10px;"></p>', 
           general = paste('*Note*. &beta; = Estimate based on $z$-scored predictors; *SE* = standard error;',
                           'CI = confidence interval. Yellow rows contain covariates. <br>', 
                           '<sup>a</sup> By-word random slopes were included for this effect.',
                           '<sup>b</sup> By-participant random slopes were included for this effect.'))

Table 5: Frequentist model for the lexical decision study.
	β	SE	95% CI	t	p
(Intercept)	0.00	0.01	[-0.01, 0.01]	-0.02	.983
Individual differences
Vocabulary age ^a	0.00	0.01	[-0.01, 0.01]	-0.06	.950
Gender ^a	0.00	0.01	[-0.01, 0.01]	0.01	.995
Lexicosemantic covariates
Orthographic Levenshtein distance	0.11	0.01	[0.09, 0.12]	13.41	<.001
Word concreteness	-0.02	0.01	[-0.04, -0.01]	-2.79	.005
Semantic variables
Word frequency ^b	-0.16	0.01	[-0.18, -0.14]	-13.01	<.001
Visual strength ^b	-0.01	0.01	[-0.03, 0.01]	-1.36	.175
Interactions
Word concreteness × Vocabulary age	0.01	0.01	[-0.01, 0.03]	1.16	.244
Word concreteness × Gender	0.00	0.01	[-0.02, 0.02]	0.16	.876
Word frequency × Vocabulary age	-0.02	0.01	[-0.04, 0.01]	-1.31	.191
Visual strength × Vocabulary age	0.00	0.01	[-0.02, 0.02]	0.05	.962
Word frequency × Gender	-0.02	0.01	[-0.04, 0.00]	-1.75	.080
Visual strength × Gender	-0.01	0.01	[-0.03, 0.01]	-0.86	.390

Note. β = Estimate based on \(z\)-scored predictors; SE = standard error; CI = confidence interval. Yellow rows contain covariates. ^a By-word random slopes were included for this effect. ^b By-participant random slopes were included for this effect.

Code


# Run plot through source() rather than directly in this R Markdown document
# to preserve the format.

source('lexicaldecision/frequentist_bayesian_plots/lexicaldecision_frequentist_bayesian_plots.R',
       local = TRUE)

include_graphics(
  paste0(
    getwd(),  # Circumvent illegal characters in file path
    '/lexicaldecision/frequentist_bayesian_plots/plots/lexicaldecision_frequentist_bayesian_plot_weaklyinformativepriors_exgaussian.pdf'
  ))

Figure 19: Estimates for the lexical decision study. The frequentist means (represented by red points) are flanked by 95% confidence intervals. The Bayesian means (represented by blue vertical lines) are flanked by 95% credible intervals in light blue.

Figure 20 presents the interactions of vocabulary age with word frequency and with visual strength, both non-significant. Figure 21 shows the interactions with gender, both non-significant too.¹⁷

Code


# Run plot through source() rather than directly in this R Markdown document 
# to preserve the italicised text.

source('lexicaldecision/frequentist_analysis/lexicaldecision-interactions-with-vocabulary-age.R', 
       local = TRUE)

include_graphics(
  paste0(
    getwd(),  # Circumvent illegal characters in file path
    '/lexicaldecision/frequentist_analysis/plots/lexicaldecision-interactions-with-vocabulary-age.pdf'
  ))

Figure 20: Interactions of vocabulary age with word frequency (panel a) and with visual strength (panel b). Vocabulary age is constrained to sextiles (6 sections) in this plot, whereas in the statistical analysis it contained more values within the current range. Sextiles were used because there was not enough data for deciles nor for octiles. nnumber of participants contained between sextiles.

Code


# Run plot through source() rather than directly in this R Markdown document 
# to preserve the italicised text.

source('lexicaldecision/frequentist_analysis/lexicaldecision-interactions-with-gender.R', 
       local = TRUE)

include_graphics(
  paste0(
    getwd(),  # Circumvent illegal characters in file path
    '/lexicaldecision/frequentist_analysis/plots/lexicaldecision-interactions-with-gender.pdf'
  ))

Figure 21: Interactions of gender with word frequency (panel a) and with visual strength (panel b) in the lexical decision study. Gender was analysed using z-scores, but for clarity, the basic labels are used in the legend.

Statistical power analysis

Figures 22 and 23 show the estimated power for some main effects and interactions of interest as a function of the number of participants. To plan the sample size for future studies, these results must be considered under the assumptions that the future study would apply a statistical method similar to ours—namely, a mixed-effects model with random intercepts and slopes—, and that the analysis would encompass at least as many words as the current study, namely, 12,636 (distributed in various blocks across participants, not all being presented to every participant). Furthermore, it is necessary to consider each figure in detail. Here, we provide a summary. First, detecting the main effect of word frequency would require 100 participants. Second, detecting the interactions of word frequency and visual strength with vocabulary size would require 1,500 participants. Third, detecting the other effects would require more than 2,000 participants.

Code


# Run plot through source() rather than directly in this R Markdown document 
# to preserve the italicised text.
source('lexicaldecision/power_analysis/lexicaldecision_all_powercurves.R', 
       local = TRUE)

include_graphics(
  paste0(
    getwd(),  # Circumvent illegal characters in file path
    '/lexicaldecision/power_analysis/plots/lexicaldecision_powercurve_plots_1_2_3.pdf'
  ))

Figure 22: Power curves for some main effects in the lexical decision study.

Code


include_graphics(
  paste0(
    getwd(),  # Circumvent illegal characters in file path
    '/lexicaldecision/power_analysis/plots/lexicaldecision_powercurve_plots_4_5_6_7.pdf'
  ))

Figure 23: Power curves for some interactions in the lexical decision study.

Discussion of Study 2.3

In the present study, we have delved into a task that is likely to elicit a shallower level of semantic processing than the tasks from the previous studies. Furthermore, the data set used in this study was considerably smaller (19,828 RTs, compared to 345,666 RTs in Study 2.1 and 246,432 in Study 2.2). The relatively small size of the data set of Study 2.3 was due to the small number of words per participant (M = 25) and participants per word (M = 2 participants per word). In this regard, the English Lexicon Project (Balota et al., 2007) prioritised the total number of words included in their archive.

While the covariates presented large effects, none of the effects of interest turned out to be significant or noteworthy. Furthermore, he comparison with the two previous tasks is hindered by the major difference in the size of the data sets. Therefore, while it is reasonable to find smaller semantic effects in the lexical decision task than in the other two, we cannot reliably attribute this difference to the nature of the task.

As a minor suggestion, future studies could operationalise language using a measure of orthographic neighbourhood size (e.g., orthographic Levenshtein distance), instead of using word frequency as in the present study. While we used word frequency guided by a data-driven selection (see Appendix A), neighbourhood size is a measure created for the purpose of indexing word co-occurrence where only one word is directly available to the researcher (Suárez et al., 2011; Yap & Balota, 2009).

Statistical power analysis

We analysed the statistical power associated with several effects of interest, across various sample sizes. The results of this power analysis can help determine the number of participants required to reliably examine each of these effects in a future study. Importantly, the results assume two conditions. First, the future study would apply a statistical method similar to ours—namely, a mixed-effects model with random intercepts and slopes. Second, the analysis of the future study would encompass at least 12,636 stimulus words (distributed in various blocks across participants, not all being presented to every participant).

The results revealed that detecting the main effect of word frequency would require 100 participants. In contrast, detecting the other effects would require more than 2,000 participants.

References

Balota, D. A., & Lorch, R. F. (1986). Depth of automatic spreading activation: Mediated priming effects in pronunciation but not in lexical decision. Journal of Experimental Psychology: Learning, Memory, and Cognition, 12(3), 336–345. https://doi.org/10.1037/0278-7393.12.3.336

Balota, D. A., Yap, M. J., Hutchison, K. A., Cortese, M. J., 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. https://doi.org/10.3758/BF03193014

Bates, D., Maechler, M., Bolker, B., Walker, S., Christensen, R. H. B., Singmann, H., Dai, B., Scheipl, F., Grothendieck, G., Green, P., Fox, J., Brauer, A., & Krivitsky, P. N. (2021). Package ’lme4’. CRAN. https://cran.r-project.org/web/packages/lme4/lme4.pdf

Becker, S., Moscovitch, M., Behrmann, M., & Joordens, S. (1997). Long-term semantic priming: A computational account and empirical evidence. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23(5), 1059–1082. https://doi.org/10.1037/0278-7393.23.5.1059

Brauer, M., & Curtin, J. J. (2018). Linear mixed-effects models and the analysis of nonindependent data: A unified framework to analyze categorical and continuous independent variables that vary within-subjects and/or within-items. Psychological Methods, 23(3), 389–411. https://doi.org/10.1037/met0000159

Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46, 904–911. https://doi.org/10.3758/s13428-013-0403-5

de Wit, B., & Kinoshita, S. (2015). The masked semantic priming effect is task dependent: Reconsidering the automatic spreading activation process. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41(4), 1062–1075. https://doi.org/10.1037/xlm0000074

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Further interaction plots available in Appendix D.↩︎

Pablo Bernabeu, 2022. Licence: CC BY 4.0.
Thesis: https://doi.org/10.17635/lancaster/thesis/1795.

Online book created using the R package bookdown.