1. Innovative Approaches to Displaying Words -- The effect of segmentation on word identification Yu-Chi Tai, Shun-nan Yang, John R. Hayes, & James Sheedy College of Optometry Pacific University Forest Grove, Oregon June 3-5, 2006

2. How are complex words represented in the mind? e.g., delivery government speculation truthfulness quasiregular …

3. The Big Debate: How are words with complex structures processed? Direct encoding of the whole word Parse a word into morphemes to minimize redundancy Associate syllables with sounds

4. Syllable: Link visuals to sound Syllables are determined by sound. –A single or a set of vocal sounds uttered with a single uninterrupted articulation –Equal or larger than a phoneme (single sound) –knowledge of syllables is often implicit: one can follow the rules even though one cannot state them

6. Morpheme: link visuals to meaning The smallest meaning-bearing units of a word –Stems: core meaning units e.g., SUNBAKED = sun + baked –Affixes e.g., farmer faithful enable –Rul-ebased syntactic affixes e.g.,Tom’s gives displayed e.g., Untruthfulness = un- + true + -th + -ful + -ness

7. Stronger effect from stem frequency than word frequency on word recognition (Carr and Pollatsek, 1985) Morphological primes affect on lexical decision, but not visual- or unrelated primes (Murrell & Morten, 1974) cars – car card- - car book – car Pseudowords with morphological stems are harder to reject (dejuvenate ) (Taft & Forster, 1975)

8. Models of word recognition

9. The assumption Visually segmenting a word into units based on these hypothesized processes would differentially affect the accuracy and latency of lexical access  If one process is more critically utilized in lexical access than the others, the corresponding segmentation method should result in greater benefit in lexical access.

10. Method Subjects: 54 native English speakers (age 18-40) Stimuli: –Words of 7- to 13-letters with similar frequency –All words in 12-point Consolas –3 segment conditions (by inserting 2 extra pixels of spaces between segments): Syllable-based segmentation Morpheme-based segmentation No segmentation Three tasks: –Threshold word recognition –Within-word letter recognition –Lexical decision

11. EXP 1. Threshold word recognition Read aloud words presented at designated angluar sizes decreased by increasing viewing distance Correct responses were transformed into logMAR to represent the smallest visual angle for recognizing a word

12. EXP 1. Result Threshold Word Recognition is… best with syllable-based segmentation poorest with no segmentation Figure 1. Effect of segmentation on threshold word recognition. Smaller logMARs indicate the word can be resolved at smaller angular size. Outside white box = 95% CI of the estimated mean; Inner gray box = 84% CI of the mean. Lack of overlap means statistically significant difference of α <.05 for the conditions.

13. EXP 2. Within-word letter recognition Task: Identify which of two letters was presented briefly on the tested location before

14. EXP 2. Result Accuracy & RT were similarly facilitated by syllable- based segmentation.

15. EXP 3. Lexical decision Task: Judge whether the presented word can be used as a noun

16. EXP 3. Result Accuracy in lexicon decision was best for syllable- based segmentation No sig. difference on RT

17. Conclusions For skilled native English readers, segmenting a complex word into chunks improves threshold word recognition (Syllable > Morpheme > Original) Syllable-based segmentation enhances word processing at various levels.  Demonstrate strong facilitation effect of syllabic segmentation on phonological processing Application: An innovative approach to display words that potentially facilitates readers’ word processing by placing extra space between syllables.

18. Acknowledgement This study was supported by the Advance Reading Group of Microsoft Corporation.

20. Future Study

21. Study 1: Single word recognition Task: –Decide whether the masked word is a real word or non word. Manipulation: –Original word –Original word with wider spacing (less lateral interference) –Original word with arbitry chunking (less interference & meaningless chunking) –Syllable-segmenetd word –Morpheme-segmented word –Nonword with similar shape (holistic route) –Nonword homophones (syllable-phonological route) –Related words with the same origins (morpheme-meaning, semantic route) –Unpronuncible nonword Measuring: RT & Accuracy

22. Study 2. Segmentation effect on word identification in sentence reading Task: Recognize the disappearing word in the sentence. Factors: syllabic-, morpheme-, no-segmented word before disappearance Measurement: RT (& accuracy)

23. Study 3. Whole-passage reading 3 versions of text: –No segmentation –Syllable-segmented –Morpheme-segmented Expected responses for easier format: –Shorter, fewer fixations –Longer saccades –Fewer regressions –Faster overall reading speed –Better comprehension Challenges (confounding factors): –# segment (usually more for syllable-segment  more fixations?) –Word width (longer for segmented words  more fixations?) –Increase inter-letter spacing to maintain word width the same.

24. Study 4. Segmentation effect in non- word identification during reading Task: Identify a pseudoword in a passage Factor: original vs. syllable- and morpheme-segmented target (pseudowords) Assumptions: –If segmentation helps word identification, it will be slower to identify a segmented pseudoword; –If word identification is holistic, then there should be no difference. Measurements: –RT –Fix# –Fix dur