Evaluating the Effect of Neighborhood Size on Chinese Word Naming and Lexical Decision

1. Evaluating the Effect of Neighborhood Size on Chinese Word Naming and Lexical Decision Meng-Feng Li1, Jei-Tun WU1*, Wei-Chun Lin1 and Fu-Ling Yang1 1National Taiwan University, Taiwan *e-mail: jtwu@ntu.edu.tw ICPEAL 2012 Session Number: 26P-31 • Introduction • Character frequency is defined as the summation of frequencies of all words sharing that particular character. It is then easily inferred that character frequency would covariate with the number of words embedding that character. Without considering this covariance, any experiment manipulating neighbor words size without frequency of the commonly shared character being controlled should not derive a logical and clear conclusion. Therefore, the manipulation variables of the study must go beyond the level of characters, in order to further explore the role of neighborhood size effects in Chinese word recognition. • The neighborhood size in Chinese compound words is defined as the number of neighbors that share the same leading character with the target word. For instance, a two-character word (e.g., 色彩, se4 tsai3,color) has the neighbors sharing to the leading character (such as 色澤, se4 tza2; color shades) or the second character (such as 彩虹, tsai3 hung2, rainbow). • Huang et al. (2006) and Tsai et al. (2006) manipulated word frequency and neighborhood size to explore their effects on word lexical decision in the same way. Both studies obtained the same pattern of word frequency effect but conflicting patterns of the neighborhood size effect. Therefore, in the present study, it is crucial to resolve the conflict between these two studies and further clarify the impact of neighborhood size effects on Chinese compound words recognition. • Method • Participants • For each experiment, 40 valid participants were assigned randomly to two experiments as indicated below. • Experiments • Experiment 1: Lexical decision task (LDT). • Experiment 2: Naming task. • Design and Stimuli • Participants received the same 2×2 within-subjects factorial of two factors of word frequency (higher vs. lower), and orthographic neighborhood size (larger vs. smaller). This formed a 2×2 two-way factorial design. • One hundred two-characters words were selected as a stimulus set from four character databases commonly used in Taiwan. The leading character frequency of the target word and the number of strokes of constituent characters were also equated among different conditions. The assignment of each target word into the different conditions was counter-balanced between participants. A total of 20 additional words were also selected for practice trials. • For the lexical decision task, equal numbers of pseudo-word foil targets were created. Half of them have many word neighbors sharing the leading character and the other half with few word neighbors sharing the leading character. For the naming task, on the other hand, did not contain pseudo-word foil targets. • Procedures • The software used for controlling experimental procedures and data handing was adopted from Wu (1995) and E-Prime v2.0 Professional. Participants received 20 practice trails each with feedback. Erroneous responses in the practice trials were repeated until a correct response was made. No feedback was given on subsequent experimental trials. • On each trial, the following sequence of events occurred: (a) an asterisk (+), used as a fixation point, was presented at the center of the monitor and lasted for 500 ms accompanied with a 100 Hz warning tone for 200 ms, and then disappeared to leave the screen blank for 500 ms; (b) the target word occupying a 30×50 dot matrix area which subtended a visual arc of approximately 2 degrees, from the viewing distance of 70 cm, was then remained there until the computer detected the participant’s key stroke (or pronunciation), the RT timing started from the presentation of the target word until the response was made; and (c) the whole screen was immediately erased and there was a blank for 1000 ms before an asterisk accompanied with a warning tone for the next trial was presented. • Data analysis • Data with at least correct-response rate 85 % would be analyzed.Trails with correct RT data over 2.5 standard deviations from means for each condition in each participant would be deleted. Two two-way ANOVAs were applied and both by subjects and by items analyses would be performed. Results Table 1 Mean correct latencies in milliseconds as a function of task for Experiment 1 and 2 Note: Percentages of errors are given in parentheses. NS = neighborhood size. • Experiment 1 • The results showed that the main effect of word frequency was highly significant, F1 (1, 39) = 198.11, MSe = 1668.68, p < .001, ω2 = .55; F2 (1, 96) = 96.65, MSe = 2449.02, p < .001, ω2 = .49, the decision latencies were much shorter for high- than for low-frequency words. The main effect of NS was also significant, F1 (1, 39) = 6.31, MSe = 1076.68, p < .05, ω2 = .03; F2 (1, 96) = 4.79, MSe = 2449.02, p < .05, ω2 = .04, the decision responses were faster for large- than for small-NS. The interaction effect between word frequency and orthographic neighborhood size was not significant, F1 (1, 39) < 1; F2 (1, 96) < 1. • Experiment 2 • The results showed thatthe main effect of word frequency was highly significant, F1 (1, 39) = 85.01, MSe = 238.51, p < .001, ω2 = .34; F2 (1, 96) = 23.87, MSe = 586.57, p < .001, ω2 = .19, the naming latencies were much shorter for high- than for low-frequency words. The main effect of NS was also highly significant, F1 (1, 39) = 57.80, MSe = 177.05, p < .001, ω2 = .26; F2 (1, 96) = 11.84, MSe = 586.57, p < .01, ω2 = .10, the naming responses were faster for large- than for small-NS. The interaction effect between word frequency and orthographic neighborhood size was not significant, F1 (1, 39) < 1; F2 (1, 96) < 1. • Experiment 1 vs. Experiment 2 • Discussion • Word frequency effect • In the Experiment 1 and 2, the results showed that the word frequency is the most powerful variable in the recognition process. • Neighborhood size effect • In the two experiments, we demonstrated that orthographic neighbors exert stable facilitative effects on target lexical decision and naming. • The Chinese written system contains more than 100,000 different words constitutedfrom about 5,000 common characters, and the particular character shared between the neighbors has certain semantics, so there is a semantic relationship between orthographic neighbors.When the bottom level shares the same character with the higher level, the higher semantic priming will provide a strong top-down support to activate the bottom level, accelerate the target word identification to reach the threshold in the lexical access processing. • The effect sizes of word frequency between these two tasks • The results showed that lexical decision responses manifested a larger word frequency effect than naming responses, and the response latencies for lexical decisions were distinctly slower than those for naming. The experimental results exhibited a very similar pattern with Frost et al.’s(1987) study, indicating that pre-lexical phonology was involved in multi-character word processing.