Distributional analyses in auditory lexical decision: Neighborhood density and word-frequency effects

Goh, Winston D.; Suárez, Lidia; Yap, Melvin J.; Tan, Siew Li

doi:10.3758/pbr.16.5.882

Cited by 35 publications

(43 citation statements)

References 14 publications

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“…Specifically, Goh et al (2009) found larger density effects for high-(68 ms) than for low-(30 ms) frequency words, a pattern also reported by Luce and Pisoni (1998), who found effects of 21 versus 6 ms, respectively. If one assumes that PLD20 effects are analogous to density effects, why are density effects stronger for high-frequency words?…”

Section: Discussionsupporting

confidence: 60%

“…We predicted main effects of PLD20 and word frequency: Recognition should be slower for close-than for distant-PLD20 words, and for low-than for high-frequency words. Density effects have been observed to be stronger for highthan for low-frequency monosyllabic words (Goh et al, 2009;Luce & Pisoni, 1998), suggesting that PLD20 effects should be stronger for high-than for low-frequency words. However, Yarkoni et al (2008) found orthographic Levenshtein distance (OLD20) effects to be stronger for low-than for high-frequency words in visual word recognition.…”

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Observing neighborhood effects without neighbors

et al. 2011

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With a new metric called phonological Levenshtein distance (PLD20), the present study explores the effects of phonological similarity and word frequency on spoken word recognition, using polysyllabic words that have neither phonological nor orthographic neighbors, as defined by neighborhood density (the N-metric). Inhibitory effects of PLD20 were observed for these lexical hermits: Close-PLD20 words were recognized more slowly than distant PLD20 words, indicating lexical competition. Importantly, these inhibitory effects were found only for low-(not high-) frequency words, in line with previous findings that phonetically related primes inhibit recognition of low-frequency words. These results indicate that the properties of PLD20-a continuous measure of word-form similarity-make it a promising new metric for quantifying phonological distinctiveness in spoken word recognition research.

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Section: Discussionsupporting

confidence: 60%

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Observing neighborhood effects without neighbors

et al. 2011

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“…We determined that a gesture had a common interpretation if its meaning was agreed upon by 70% of the participants (see also Goh, Suárez, Yap, & Tan, 2009). Our findings showed that 40 of the gestures had consistency rates of 70% or above.…”

Section: Participantsmentioning

confidence: 99%

Iconic Gestures Prime Words

Yap

et al. 2010

Cognitive Science

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Using a cross-modal semantic priming paradigm, both experiments of the present study investigated the link between the mental representations of iconic gestures and words. Two groups of the participants performed a primed lexical decision task where they had to discriminate between visually presented words and nonwords (e.g., flirp). Word targets (e.g., bird) were preceded by video clips depicting either semantically related (e.g., pair of hands flapping) or semantically unrelated (e.g., drawing a square with both hands) gestures. The duration of gestures was on average 3,500 ms in Experiment 1 but only 1,000 ms in Experiment 2. Significant priming effects were observed in both experiments, with faster response latencies for related gesture-word pairs than unrelated pairs. These results are consistent with the idea of interactions between the gestural and lexical representational systems, such that mere exposure to iconic gestures facilitates the recognition of semantically related words.

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“…The seven measures of lexical competition were entered in a stepwise multiple regression, which followed a forward selection approach but also evaluated whether the removal of a predictor improved the model at each step (Field, 2009). No previously selected variables were removed in any of our analyses, so the results were identical to a forward-selection approach (see Tables 3 and 4).…”

Section: Links With Lexical Variablesmentioning

confidence: 99%

“…No previously selected variables were removed in any of our analyses, so the results were identical to a forward-selection approach (see Tables 3 and 4). Given the finding that lexical competition effects may be moderated by frequency (Goh, Suárez, Yap, & Tan, 2009;Luce & Pisoni, 1998), we also included a term for the Neighborhood Size × Frequency interaction, but this failed to account for significant unique variance in either the lab data or the AMT data.…”

Section: Links With Lexical Variablesmentioning

confidence: 99%

Conducting spoken word recognition research online: Validation and a new timing method

Slote

Strand

2015

Behav Res

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Models of spoken word recognition typically make predictions that are then tested in the laboratory against the word recognition scores of human subjects (e.g., Luce & Pisoni Ear and Hearing, 19, 1-36, 1998). Unfortunately, laboratory collection of large sets of word recognition data can be costly and time-consuming. Due to the numerous advantages of online research in speed, cost, and participant diversity, some labs have begun to explore the use of online platforms such as Amazon's Mechanical Turk (AMT) to source participation and collect data (Buhrmester, Kwang, & Gosling Perspectives on Psychological Science, 6, 3-5, 2011). Many classic findings in cognitive psychology have been successfully replicated online, including the Stroop effect, task-switching costs, and Simon and flanker interference (Crump, McDonnell, & Gureckis PLoS ONE, 8, e57410, 2013). However, tasks requiring auditory stimulus delivery have not typically made use of AMT. In the present study, we evaluated the use of AMT for collecting spoken word identification and auditory lexical decision data. Although online users were faster and less accurate than participants in the lab, the results revealed strong correlations between the online and laboratory measures for both word identification accuracy and lexical decision speed. In addition, the scores obtained in the lab and online were equivalently correlated with factors that have been well established to predict word recognition, including word frequency and phonological neighborhood density. We also present and analyze a method for precise auditory reaction timing that is novel to behavioral research. Taken together, these findings suggest that AMT can be a viable alternative to the traditional laboratory setting as a source of participation for some spoken word recognition research.

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Distributional analyses in auditory lexical decision: Neighborhood density and word-frequency effects

Cited by 35 publications

References 14 publications

Observing neighborhood effects without neighbors

Observing neighborhood effects without neighbors

Iconic Gestures Prime Words

Conducting spoken word recognition research online: Validation and a new timing method

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