Phonographic neighbors, not orthographic neighbors, determine word naming latencies

Adelman, James S.; Brown, Gordon D.

doi:10.3758/bf03194088

Cited by 44 publications

(57 citation statements)

References 31 publications

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“…Thus, they argued that the extent to which orthographic neighborhood size could accelerate phonological computation is dependent on the similarity between the phonological codes of neighboring words and the target word. This finding is corroborated by the results reported in a multiple regression study on four large English naming datasets (Adelman & Brown, 2007), where the number of phonographic neighbors was a stronger predictor than the conventional neighborhood size in accounting for naming data.…”

Section: Introductionsupporting

confidence: 79%

“…The phonological computation account of the neighborhood size effect is supported by most current theories of reading (Adelman & Brown, 2007). According to the dual-route cascade (DRC) models (Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001;Perry, Ziegler, & Zorzi, 2007), the facilitatory effect in naming is expected because the models allow the processing to activate orthographic neighbors of word stimuli in the orthographic lexicon, which in turn activates phonological entries and phonemes.…”

Section: Introductionmentioning

confidence: 88%

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Exploring orthographic neighborhood size effects in a computational model of Chinese character naming

Chang

Welbourne

Lee

2016

Cognitive Psychology

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Orthographic neighborhood (N) size effects have been extensively studied in English consistently producing a facilitatory effect in word naming tasks. In contrast, several recent studies on Chinese character naming have demonstrated an inhibitory effect of neighborhood size. Response latencies tend to be inhibited by inconsistent characters with large neighborhoods relative to small neighborhoods. These differences in neighborhood effects between languages may depend on the characteristics (depth) of the mapping between orthography and phonology. To explore this, we first conducted a behavioral experiment to investigate the relationship between neighborhood size, consistency and reading response.The results showed an inhibitory effect of neighborhood size for inconsistent characters but a faciliatory effect for consistent characters. We then developed two computational models based on parallel distributed processing principles to try and capture the nature of the processing that leads to these results in Chinese character naming. Simulations using models based on the triangle model of reading indicated that consistency and neighborhood size interact with the division of labor between semantics and phonology to produce these effects.

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Section: Introductionsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 88%

Exploring orthographic neighborhood size effects in a computational model of Chinese character naming

Chang

Welbourne

Lee

2016

Cognitive Psychology

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“…subtitle (Brysbaert & New, 2009) frequency, SUB-CD = log. subtitle contextual diversity (Adelman et al, 2006); Orth N = orthographic neighborhood size (Coltheart, Davelaar, Jonasson, & Besner, 1977); Phon N = phonological neighborhood size; PhGr N = phonographic neighborhood size (Adelman & Brown, 2007;Peereman & Content, 1997); BG Freq = mean bigram frequency; OLD20, PLD20 = orthographic/phonological Levenshtein distance, average of smallest 20 (Yarkoni et al, 2008); Len Phon = length in phonemes; Len Syll = length in syllables; Homophones = total number of entries with same pronunciation. Note -*** = p < .001; ** = p < .01; * = p < .05; † = p < .1.…”

Section: Resultsmentioning

confidence: 99%

“…Rather, mega-studies have been used to assess and compare models (e.g., Adelman & Brown, 2008b;Spieler & Balota, 1997), to consider the role of individual differences , and to investigate new (continuous) predictors or measures (e.g., Adelman & Brown, 2007;Adelman, Brown, & Quesada, 2006;Yarkoni et al, 2008). We discuss our preliminary findings along these lines below, and we envisage that many uses of these new data will be analogous to those with other mega-studies, with the expectation that predictors of interest will interact with type of prime.…”

Section: Uses Of the Databasementioning

confidence: 99%

A Behavioral Database for Masked Form Priming

Johnson¹,

McCormick²,

McKague³

et al. 2013

PsycEXTRA Dataset

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Reading involves a process of matching an orthographic input with stored representations in lexical memory. The masked priming paradigm has become a standard tool for investigating this process. Use of existing results from this paradigm can be limited by the precision of the data and the need for cross-experiment comparisons that lack normal experimental controls. Here, we present a single, large, high-precision, multi-condition experiment to address these problems. Over 1000 participants from 14 sites responded to 840 trials involving 28 different types of orthographically related primes (e.g., castfe-CASTLE) in a lexical decision task, as well as completing measures of spelling and vocabulary. The data 1.4.1 were indeed highly sensitive to differences between conditions: After correction for multiple comparisons, prime type condition differences of 2.90 ms and above reached significance at the 5% level. This paper presents the method of data collection and preliminary findings from these data, which included replications of the most widely agreed-upon differences between prime types, further evidence for systematic individual differences in susceptibility to priming, and new evidence regarding lexical properties associated with a target word's susceptibility to priming. These analyses will form a basis for the use of these data in quantitative model fitting and evaluation, and future exploration of these data that will inform and motivate new experiments.FORM PRIMING PROJECT 3

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“…Moreover, Adelman and Brown (2007) have found evidence of the same effect from four mega-studies in English (including the one on which these analyses are based). Mulatti, Reynolds, and Besner (2006) have argued in contrast that the (whole) phonological neighborhood drives neighborhood effects rather than specifically the phonographic neighbors.…”

Section: Neighborhood Size (Revisited)mentioning

confidence: 96%

Methods of Testing and Diagnosing Model Error: Dual and Single Route Cascaded Models of Reading Aloud

Adelman¹,

Brown²

2007

PsycEXTRA Dataset

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Phonographic neighbors, not orthographic neighbors, determine word naming latencies

Cited by 44 publications

References 31 publications

Exploring orthographic neighborhood size effects in a computational model of Chinese character naming

Exploring orthographic neighborhood size effects in a computational model of Chinese character naming

A Behavioral Database for Masked Form Priming

Methods of Testing and Diagnosing Model Error: Dual and Single Route Cascaded Models of Reading Aloud

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