Virtual experiments in megastudies: A case study of language and emotion

A recent dramatic increase in the number and scope of chronometric and norming lexical megastudies offers the ability to conduct virtual experiments—that is, to draw samples of items with properties that vary in critical linguistic dimensions. This paper introduces a bootstrapping approach, which enables testing of research hypotheses against a range of samples selected in a uniform, principled manner and evaluates how likely a theoretically motivated pattern is in a broad distribution of possible outcome patterns. We apply this approach to conflicting theoretical and empirical accounts of the relationship between the psychological valence (positivity) of a word and its speed of recognition. To this end, we conduct three sets of multiple virtual experiments with a factorial and a regression design, drawing data from two lexical decision megastudies. We discuss the influence that criteria for stimuli selection, statistical power, collinearity, and the choice of dataset have on the efficacy and outcomes of the bootstrapping procedure.     

Megastudies,crowdsourcing, and large datasets in psycholinguistics: An overview of recent developments

This paper introduces and summarizes the special issue on megastudies, crowdsourcing, and large datasets in psycholinguistics. We provide a brief historical overview and show how the papers in this issue have extended the field by compiling new databases and making important theoretical contributions. In addition, we discuss several studies that use text corpora to build distributional semantic models to tackle various interesting problems in psycholinguistics. Finally, as is the case across the papers, we highlight some methodological issues that are brought forth via the analyses of such datasets.     

Long-term memory for performed and observed actions: Retrieval awareness and source monitoring

The self-performed task (SPT) paradigm was employed to investigate incidental memory for action events, focusing on states of awareness at retrieval (Remembering–Knowing) and source monitoring in performed and observed actions, at delays of 1 and 2 weeks. Recognition was better for performed than observed actions, but this effect was visible only after 1 week of retention. Conversely, observed actions were associated with better source memory at both delays. When the source was correctly identified, performed and observed actions were accompanied by equal proportions of Remember judgements. However, when source monitoring was incorrect, performed actions were more frequently associated with a feeling of re-experiencing the original event, thus suggesting an enactment-related recollective superiority, although secondary to the availability of reliable contextual information.     

Semantic transparency in free stems: The effect of Orthography-Semantics Consistency on word recognition

A largely overlooked side effect in most studies of morphological priming is a consistent main effect of semantic transparency across priming conditions. That is, participants are faster at recognizing stems from transparent sets (e.g., farm) in comparison to stems from opaque sets (e.g., fruit), regardless of the preceding primes. This suggests that semantic transparency may also be consistently associated with some property of the stem word. We propose that this property might be traced back to the consistency, throughout the lexicon, between the orthographic form of a word and its meaning, here named Orthography-Semantics Consistency (OSC), and that an imbalance in OSC scores might explain the “stem transparency” effect. We exploited distributional semantic models to quantitatively characterize OSC, and tested its effect on visual word identification relying on large-scale data taken from the British Lexicon Project (BLP). Results indicated that (a) the “stem transparency” effect is solid and reliable, insofar as it holds in BLP lexical decision times (Experiment 1); (b) an imbalance in terms of OSC can account for it (Experiment 2); and (c) more generally, OSC explains variance in a large item sample from the BLP, proving to be an effective predictor in visual word access (Experiment 3).     

What can megastudies tell us about the orthographic structure of English words?

Although the majority of research in visual word recognition has targeted single-syllable words, most words are polysyllabic. These words engender special challenges, one of which concerns their analysis into smaller units. According to a recent hypothesis, the organization of letters into groups of successive consonants (C) and vowels (V) constrains the orthographic structure of printed words. So far, evidence has been reported only in French with factorial studies of relatively small sets of items. In the present study, we performed regression analyses on corpora of megastudies (English and British Lexicon Project databases) to examine the influence of the CV pattern in English. We compared hiatus words, which present a mismatch between the number of syllables and the number of groups of adjacent vowel letters (e.g., client), to other words, controlling for standard lexical variables. In speeded pronunciation, hiatus words were processed more slowly than control words, and the effect was stronger in low-frequency words. In the lexical decision task, the interference effect of hiatus in low-frequency words was balanced by a facilitatory effect in high-frequency words. Taken together, the results support the hypothesis that the configuration of consonant and vowel letters influences the processing of polysyllabic words in English.