Filling the gaps: A speeded word fragment completion megastudy

Abstract

In the speeded word fragment completion task, participants have to complete fragments such as tom_to as quickly and accurately as possible. Previous work has shown that this paradigm can successfully capture subtle priming effects (Heyman, De Deyne, Hutchison, & Storms Behavior Research Methods, 47, 580-606, 2015). In addition, it has several advantages over the widely used lexical decision task. That is, the speeded word fragment completion task is more efficient, more engaging, and easier. Given its potential, we conducted a study to gather speeded word fragment completion norms. The goal of this megastudy was twofold. On the one hand, it provides a rich database of over 8,000 stimuli, which can, for instance, be used in future research to equate stimuli on baseline response times. On the other hand, the aim was to gain insight into the underlying processes of the speeded word fragment completion task. To this end, item-level regression and mixed-effects analyses were performed on the response latencies using 23 predictor variables. Since all items were selected from the Dutch Lexicon Project (Keuleers, Diependaele, & Brysbaert Frontiers in Psychology, 1, 174, 2010), we ran the same analyses on lexical decision latencies to compare the two tasks. Overall, the results revealed many similarities, but also some remarkable differences, which are discussed. We propose that both tasks are complementary when examining visual word recognition. The article ends with a discussion of potential process models of the speeded word fragment completion task.Keywords Speeded word fragment completion task . Lexical decision task . Visual word recognitionIn the last decade, the field of visual word recognition has seen a surge in so-called megastudies (see Balota, Yap, Hutchison, & Cortese, 2012, for an overview). Generally speaking, a typical megastudy comprises several thousand items for which lexical decision, naming, and/or word identification responses are collected. The rationale behind megastudies is that they complement (traditional) factorial studies in which stimuli are selected on the basis of specific lexical or semantic characteristics. That is, factorial studies require one to experimentally control for a number of variables that could potentially obscure the effect(s) of interest. Megastudies, on the other hand, aim to gather data for as many stimuli as possible, without many constraints. The idea is that one can then statistically control for confounding variables by conducting a multiple regression analysis. In addition, continuous variables such as word frequency need not be divided into distinct categories (i.e., high-frequency vs. low-frequency words). This is a critical advantage of the megastudy approach, because artificially dichotomizing continuous variables has been shown to reduce power and increase the probability of Type I errors (Maxwell & Delaney, 1993).In the present study we sought to build on this work, and we describe a megastudy involving the speeded word Electronic supplementary ma...

Lexicosemantic, affective, and distributional norms for 1,000 Dutch adjectives

“…Our norms add a substantive number of adjectives to the growing set of Dutch experiential and distributional norms, and can easily be connected to the behavioral norm data that are amassing for Dutch, pertaining to lexical decision (Brysbaert, Stevens, Mandera, & Keuleers, 2016;Keuleers, Diependaele, & Brysbaert, 2010b), word prevalence (Keuleers et al, 2015), text reading (Cop, Dirix, Drieghe, & Duyck, 2017), and word fragment completion (Heyman, Van Akeren, Hutchison, & Storms, 2016). We believe the norms would benefit both research that studies adjectives proper (e.g., to establish a typology; Dixon, 1982;Raskin & Nirenburg, 1998) or in which adjectives constitute the preferred stimulus material such as vagueness (Hampton, 2011;Kennedy, 2007;Van Rooij, 2011;Verheyen & Egré, 2018), spatial cognition (Bianchi, Savardi, & Burro, 2011a;Bianchi, Savardi, & Kubovy, 2011b), affective word processing (Bernat, Bunce, & Shevrin, 2001;Herbert, Kissler, Junghofer, Peyk, & Rockstroh, 2006), and inference (Gotzner, Solt, & Benz, 2018;Ruytenbeek, Verheyen, & Spector, 2017).…”

The (un)reliability of item-level semantic priming effects

“…First of all, one could add a bunch of covariates that have been shown to predict word recognition times. The stumbling block is that no set of variables (yet) can explain all the systematic variability in baseline response times (e.g., Adelman, Marquis, Sabatos-DeVito, & Estes, 2013;Heyman, Van Akeren, Hutchison, & Storms, 2016b). Thus, it is simply not possible to rule out that the relatedness variable(s) merely predict target response times instead of semantic priming, even when controlling for word frequency, length, orthographic/phonological neighborhood density, age of acquisition, and the like.…”

Recognition Times for 54 Thousand Dutch Words: Data from the Dutch Crowdsourcing Project

“…Now, 30 thousand words were tested, mainly lemmas (i.e., uninflected, base words) without a length restriction. The fourth study was run by Heyman, Van Akeren, Hutchison, and Storms (2016) and used a speeded fragment completion task. Participants were shown letter strings (e.g., f_lm) and had to decide as fast as possible whether the missing letter was i or o .…”