Individual Differences in Visual Word Recognition

Studies of word recognition traditionally have concentrated on the role of word variables (e.g. word frequency or neighborhood size) in the recognition process, taking the discrepancies between participants’ responses as statistical deviation. One pitfall of the

7

traditional studies is that spurious or conflicting results might be obtained. An example can be seen in the paper of Usworth and Pexman (2003).

Usworth and Pexman (2003) provided compelling evidence that a controversy over regularity effects in the literature were attributable to lacking control over participants’ IDs of reading skills. Regularity denotes that the extent to which the spelling-to-sound correspondence in words are invariant. The effects of regularity are that a response is made slower to less ‘regular’ words (e.g. pint) than to ‘regular’ words (e.g. name). Some researchers failed to find the effects in the visual lexical decision tasks (Coltheart, Davelaar, Jonasson, & Besner, 1979; Jared, McRae, & Seidenberg, 1990), whereas others reported significant regularity effects did exist (Parkin, 1982; Parkin & Ellingham, 1983; Parkin &

Underwood, 1983; Stanovich & Bauer, 1978). Usworth and Pexman (2003) showed that although phonological processing was involved when people responded in visual lexical decision, the regularity effects were found in the responses of low-skilled readers, but not in high-skilled readers. This finding suggested the necessity of controlling IDs in word recognition studies. Especially, note that the participants in Usworth and Pexman (2003)’s study all came from the same university. Even if they were at the same education level, their IDs sufficiently resulted in distinct performance in word recognition.

The findings in other papers also supported that the IDs among people with homogeneous education background would lead to significant differences in the process of

8

word recognition. Chateau and Jared (2000) conducted a homophone choice task, a lexical decision task, a pseudoword naming task, and a form priming tasks, where participants were undergraduates at the University of Western Ontario. The results demonstrated that during the word recognition, the orthographic and phonological representations of words were activated more efficiently for students with high exposure-to-print than those with low exposure-to-print. Similarly, Sears et al. (2008) reported that the word recognition of high-print-exposure people was less affected by the differences between high- and low-frequency words, or between large- and small-neighborhood-size words, compared with that of low-print-exposure people. The participants in Sears et al. (2008)’s research were all undergraduates at University of Calgary. Another example is Lewellen et al.’s (1993) study, which looked into IDs of lexical familiarity and vocabulary knowledge among undergraduates. It will be further introduced in the review of the IDs of vocabulary knowledge later.

Among the studies pertinent to IDs in word recognition, those examining IDs of vocabulary knowledge are most related to this thesis. Studies on the IDs of vocabulary knowledge (Lewellen et al.,1993; Katz et al., in press; Yap et al., 2012) obtained consistent results— participants’ knowledge of words was positively associated with their performance of word recognition. Each of the studies is reviewed in the subsequent paragraphs.

One study was conducted by Lewellen et al. (1993). They divided participants into two

9

groups depending on three criteria: high/low familiarity with words, vocabulary knowledge, and reading experiences. Their vocabulary test was taken from the Nelson-Denny reading test (Nelson & Denny, 1960), in which examinees should respond in 10 minutes to 100 multiple choices about which word among five options expressed one statement the best.

Results showed that the high group processed words more efficiently than the low group no matter in tasks of naming, lexical decision, or semantic categorization³. However, effects of word length and frequency did not significantly differ between the two groups of subjects.

Lewellen et al. thus claimed that the group differences of word processing proficiency lied on their disparities of working memory capacity, rather than disparities in the automatic processing of word components.

Another study done by Katz et al. (in press) also probed into the relationship between participants’ vocabulary size and response latencies in word recognition, in that they were interested in how much recognition experiments accounted for multiple aspects of reading.⁴ Vocabulary size in the study was measured by the Woodcock-Johnson III Diagnostic Reading Battery (WJ) reading vocabulary test and oral vocabulary test (Wechsler, 1999), the Wechsler Abbreviated Scale of Intelligence (WASI) vocabulary test (Woodcock, Mather, &

Schrank, 2004), and the Peabody Picture Vocabulary Test (PPVT) (Dunn & Dunn, 2007);

3 In a semantic categorization task, participants are required to classify a presented stimuli into one of the semantic categories provided by experimenters, like ANIMALS, TOOLS, or PLANTS.

4 Katz et al. (in press) regarded that people’s vocabulary size represented their reading experience because the size possibly increased with their exposure to print.

10

they were either a test of word production or a test of word choice. Canonical correlations between each of the four vocabulary test and response latencies in naming and lexical decision tasks were carried out. Statistical significance was found, even though the correlation was mild. Besides, the reaction time in lexical decision was more related to vocabulary size than the naming task. Katz et al. (in press) concluded that higher knowledge of oral or written vocabularies led to faster retrieval processing; that is, rich vocabulary facilitated, not inhibited, word recognition.

Concerning the other study, Yap et al. (2012) conducted reliable tests between sessions and trials of each participant’s response latencies, and reported that subtle IDs were reliably found among 1,280 participants’ trial-level data in both lexical decision and naming tasks.

This suggested that each participant possessed a relatively stable processing profile, which was argued to be distinct from his average processing speed. Vocabulary knowledge of participants in Yap et al.’s study resorted to Shipley Institute of Living Scale (Shipley, 1940), which comprised 40 multiple choices of word synonym. Results showed that higher vocabulary scores were related to faster and more accurate responses. Similar to Lewellen et al.’s (1993), in a lexical decision task, the performance of high-vocabulary-knowledge participants did not show smaller effects of structure properties (e.g. the number of letters, and phonological Levenshtein distance) and word frequency/semantics. Nonetheless, in a naming task, the two types of effects and a neighborhood size effect were found to differ

11

between high- and low-vocabulary-knowledge participants. More specifically, the high group’s responses varied less with the three types of word variables than the low group’s responses. The participants’ vocabulary scores in the study were interpreted as the integrity of word representations in their minds. Thereupon, on ground of the results, Yap et al.

argued that participants with more integral word representations recognized word by relying more on automatic processing than control processing.

Lewellen et al. (1993), Katz et al. (in press), and Yap et al. (2012) all measured vocabulary knowledge by adopting tests of word form, meaning, or association (see Table 2.1). It is doubted that how much the score of a vocabulary test represented or reflected a person’s vocabulary knowledge. In the tests, vocabulary knowledge was measured by a restricted set of words and within the range of the scale or score of a given test. Hence, this thesis attempts to investigate the knowledge from a distinct angle— lexical behaviors in participants’ language usage. One merit of doing so is that people’s lexical knowledge will be evaluated not by a small set of vocabularies in a given test, but by the words used by themselves. In this case, a variable’s value assigned to a given participant is personalized and not confined to the scale or the total score of a test. The other merit resides in that the data of language usage can provide a deeper insight into a person’s lexical knowledge, compared with a vocabulary test. If a person is able to use or produce a given word naturally (and frequently), it suggests that the word’s representation has been firmly

12

established in his/her mental lexicon. The test of whether one knows a word, on the contrary, is a relatively superficial measure.

Table 2.1: Measures of vocabulary knowledge in Lewellen et al., Katz et al., and Yap et al.

Paper Task of visual word recognition

WJ reading vocabulary

Production of

The present study examines the IDs of lexical behaviors by focusing on the frequency

index of personal word usage as well as personal word frequency. The latter one refers to

13

usage. It was hypothesized that a participant would respond faster to words that he/she used more frequently, and the personal preference of word usage was considered to be his/her own relative familiarity with the words here. Lewellen et al. (1993) also addressed the impact of the IDs concerning lexical familiarity on word recognition. Yet, it needs to be clarified that the experiment conducted in this thesis differed from theirs. In Lewellen et al.

(1993)’s study, participants were split into high and low groups based on their answers to a lexical familiarity questionnaire; subsequently, whether the two groups behave distinctly in word recognition tasks were examined. In our experiment, however, a person’s familiarity with words would vary with his/her frequency of using those words. Concerning the variable of the frequency index of personal word usage, its hypothesis was drawn from results of a study testifying the correlation between word frequency and word difficulty (Breland, 1996). A review of the study is provided in the following section.

2.2 The Correlation between Word Frequency and