Journal of Experimental Psychology: Human Perception and Performance 1997, Vol. 23. No. 6, 1792-1797
Copyright 1997 by the American Psychological Association, Inc. 0096-1523197/$3.00
Word Frequency Effects at Brief Exposure Durations: Comment on Paap and Johansen (1994) P h i l i p A. A l l e n , A l b e r t E S m i t h , and Mei-Ching Lien Cleveland State University
T i m o t h y A. W e b e r University of Illinois at Urbana-Champaign
D a v i d J. M a d d e n Duke University Medical Center K. R. Paap and L. S. Johansen (1994) proposed that word frequency effects do not occur on a lexical decision task (LDT) when postmasked target exposure duration is sufficiently brief because such a task prevents verification--their hypothesized locus of the word frequency effect. In making this assertion, they proposed that the activation interpretation ofA. R. Dobbs, A. Friedman, and J. Lloyd (1985) and of P. A. Allen, M. McNeal, and D. Kvak (1992) was flawed. However, evidence that Paap and Johansen's conclusions were wrong and that their experimental design contained flaws is provided here. In Experiment 1 of the present study, word frequency effects were evident on an LDT at the 75% accuracy level proposed by Paap and Johansen as being sufficiently low to prevent verification. In Experiment 2 the mental lexica of participants from the same population as that used for Experiment 1 contained very-low-frequency words. Thus, the present results are consistent with an activation locus.
Paap and Johansen (1994), in an article published in this journal, questioned the validity of research that found effects of word frequency on recognition at brief exposure durations (Allen, McNeal, & Kvak, 1992; Dobbs, Friedman, & Lloyd, 1985). In arguing that word frequency effects are not observed in word recognition tasks with brief exposure durations, Paap and Johansen unfairly characterized our earlier work (Allen et al., 1992) and used flawed methods that invalidate their conclusion that word frequency effects are eliminated under brief exposure durations. We discuss these issues and report a new experiment, the results of which are consistent with our earlier finding that word frequency effects occur under conditions in which, according to Paap and Johansen, they should not. Whether word frequency influences word recognition performance at brief exposure durations is critical to Paap and Johansen's (1994) theory because their activationverification model "eschews the possibility that encoding is frequency sensitive" (p. 1130). According to this model, "because verification relies on a comparison that involves
Philip A. Allen, Albert E Smith, and Mei-Ching Lien, Department of Psychology, Cleveland State University; Timothy A. Weber, Department of Psychology, University of Illinois at UrbanaChampaign; David J. Madden, Center for the Study of Aging and Human Development, Duke University Medical Center. Mei-Ching Lien is now at the Department of Psychology, Purdue University. This research was supported by Grant AG09282 from the National Institutes of Health/National Institute on Aging. Correspondence concerning this article should be addressed to Philip A. Alien, Department of Psychology, Cleveland State University, Euclid Avenue at East 24th Street, Cleveland, Ohio 44115. Electronic mail may be sent via Internet to r0500@ vmcms.csuohio,edu or to
[email protected].
continuing perceptual analysis of the stimulus, the potential contribution of verification should be severely attenuated whenever a backward mask overwrites or erases the sensory buffer" (Paap, Newsome, McDonald, & Schvaneveldt, 1982, p. 574). In other words, verification, assumed to be frequency sensitive, should not occur when brief exposure durations and masking disrupt physical visual representations up to and including iconic memory. Word Frequency Effects W h e n Visual Representations Are Disrupted Dobbs et al. (1985) attempted to prevent verification in several lexical decision task (LDT) experiments by using a brief exposure duration (16.7-ms exposure duration and a target-postmask stimulus onset asynchrony of 67 ms). In both Experiments 3 and 4, Dobbs et al. found word frequency effects in both the control (longer exposure duration) and masking (brief exposure duration) conditions. Thus, word frequency effects were observed under conditions that were likely, according to Paap et al. (1982), to disrupt the visual representation before verification had time to occur. Paap and Johansen (1994) claimed that two aspects of Dobbs et al.'s (1985) study undermined the conclusion that word frequency effects had occurred in the absence of verification: First, Paap and Johansen argued that high error rates on very-low-frequency (VLF) items by the group with the longer exposure duration in Experiment 4 implied that participants may not have had all these words stored in their lexica (although the control error rate for Experiment 3 was quite low, which, it seems, should have allayed this concern). Second, Paap and Johansen asserted that overall error rates in the masked conditions of these experiments were sufficiently low to suggest that verification was indeed 1792
OBSERVATIONS occurring. This would have been a moot point had Dobbs et al. manipulated stimulus quality within subjects and found a larger error rate for masked VLF items than for control VLF items. However, Dobbs et al. varied stimulus quality between subjects. Allen et al. (1992) manipulated exposure duration within subjects, using exposure durations of 100 ms, 200 ms, and 400 ms. In two LDT experiments, words and nonwords were followed immediately by a 100-ms postmask. In the first experiment, nonwords were formed by changing the final letter of a real word; in the second, nonwords were formed by reversing the order of letters of real words. In analyses of response times (RTs) from both experiments, Allen et al. (1992) found both a main effect of exposure duration and an effect of word frequency in the 100-ms exposure-duration condition. Thus, using a within-subjects design, they replicated the between-subjects findings of Dobbs et al. (1985). However, Paap and Johansen (1994) correctly discovered that Allen et ai. (1992), when collecting RTs, had started the clock at mask onset rather than at stimulus onset. This requires that the reported data be corrected by adding to all cell means 100 ms in the 100-ms exposure-duration condition, 200 ms in the 200-ms exposure-duration condition, and 400 ms in the 400-ms exposure-duration condition. Although this correction eliminates the main effect for exposure duration for RT, it does not affect the finding regarding word frequency in the 100-ms exposure-duration condition: Word frequency effects for both RT and errors were nevertheless present in the 100-ms exposure condition in both experiments. Paap and Johansen (1994) appear to have been much too eager to cast aside the data of Allen et al. (1992). In both experiments, Allen et ai. (1992) found that perceptual sensitivity (Ag; Bamber, 1975) decreased as exposure duration decreased and that sensitivity depended significantly on word frequency. Perhaps even more damaging for the activation-verification model, though, were the error results from the first experiment of Allen et al. (1992). Participants committed more errors for briefly presented words than for words presented for longer durations. Furthermore, this experiment showed a word frequency effect for the 100-ms exposure-duration condition. Given that exposure duration was a within-subjects factor, Paap and Johansen cannot simply claim that some of the VLF words were not in these participants' lexica, because the briefer exposure duration clearly caused a performance decrement beyond any cost of any missing lexical entries. The missing-entry hypothesis cannot be used to explain why a significant increase in errors occurred for the briefest exposure duration compared with the longer exposure durations. Empirical Support for the Verification Model? The primary evidence for a verification locus of word frequency effects is the result of two experiments reported by Paap and Johansen (1994). Experirnent 1 was an LDT in which items were presented until a response was made. Paap and Johansen found that word frequency affected accuracy and suggested that presentation until response allowed
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sufficient processing time for verification to occur. They further suggested that an operational definition that we believe offers a fair test of our encoding assumptions would be to individually titrate performance for each subject to about 75% correct (a level midway between chance and perfect performance) on a binary forced-choice task, such as Schvaneveldt and McDonald's (1981) masked lexical decision task (LDT) or Reicher's (1969) masked letter identification task. (Paap & Johansen, 1994, p. 1130) In Experiment 2, Paap and Johansen combined a Reicher task with a masked LDT and found no effect of word frequency. From these results, Paap and Johansen concluded that the verification stage is the locus of word frequency effects. Paap and Johansen's (1994) design contained at least two flaws that make it difficult to interpret their results. First, the procedure of their Experiment 2 differs in two confounded ways from that of their Experiment 1, and one cannot determine which of these two factors was responsible for the difference in the outcomes of the experiments (i.e., the presence vs. the absence of a word frequency effect). Paap and Johansen reasoned that if participants showed a word frequency effect for the words presented until response in Experiment 1 but showed no such word frequency effect in Experiment 2, when the same stimuli were presented for briefer exposure durations, then the nullification of the word frequency effect in Experiment 2 resulted from the exposureduration manipulation. However, exposure duration was not the only difference between Experiment 1 and Experiment 2: In Experiment 2 the LDT followed a letter detection task on every trial, whereas in Experiment 1, only a lexical decision was required. The absence of a word frequency effect in the LDT of Experiment 2 may have resulted from participants' having completed lexical processing long before the lexical decision response was made. Second, Paap and Johansen (1994) omitted VLF words from their analyses of Experiment 2. They felt that the error rates of participants in Experiment 1 for these items were too high and suggested that these items may not have been contained in the participants' mental lexica. However, the word frequency effect is much greater when these stimuli are included in an LDT (e.g., Allen, Wallace, & Weber, 1995). Probably the strongest argument for including VLF words (using Allen et ai.'s, 1995, criterion of one to five instances in the corpus of Kucera & Francis, 1967) is that they constitute the vast majority of English words. Indeed, in Kucera and Francis (1967), pages 1-36 list words that occur at least six times in the corpus. Pages 37-137, however, list words that occur one to five times in the corpus. Thus, eliminating VLF words is tantamount to eliminating approximately three fourths of the words in the entire corpus of Kucera and Francis! Where Do We Stand? Paap and Johansen (1994) did not present conclusive evidence that word frequency effects for an LDT are eliminated when brief exposure durations were used. Their design did not allow them to determine whether their failure
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OBSERVATIONS
to find an effect of word frequency resulted from presenting a letter discrimination task before an LDT or from brief exposure duration. However, Paap and Johansen proposed specific conditions under which verification should and should not occur, and we evaluated performance under these conditions in Experiment 1. We tested whether Paap and Johansen's criterion of 75% accuracy would prevent verification on a binary, masked LDT in which word frequency varied. To address Paap and Johansen's concern about the status of VLF items in participants' mental lexica, we administered a test of such items to a sample of individuals similar to those who participated in Experiment 1. Experiment 1 The goal of Experiment 1 was to evaluate the effect of word frequency on recognition performance in a study in which the overall accuracy level of each participant was 75%. We used a 100-ms target-mask onset asynchrony for all trials and then varied luminance (25 cd/m 2, 21 cd/m 2, and 19.6 cd/m 2) over three separate trial blocks to titrate performance. Responses from the block in which accuracy was closest to 75% were used, given that accuracy in a block was between 65% and 81%. Of 44 individuals tested, the performances of 25 individuals fit into this accuracy interval; their average accuracy level was 74.55%.
Me~od Participants. Forty-four undergraduate psychology students from Cleveland State University participated for research credit. All participants were native English speakers and reported normal or corrected-normal vision. Apparatus. Stimulus presentation, timing, and data collection were controlled using an IBM PS/2 Model 30 (80286) microcomputer driven by Micro Experimental Laboratory software (Schneider, 1988). Stimuli were presented in white on a black background by means of an IBM color monitor controlled by an EGA graphics card. Predisplay and postdisplay fixation fields (i.e., masks), as well as target stimuli, were presented in the center of the screen so that masks and targets always spatially overlapped. All stimuli were presented in lowercase letters. Each letter in a display subtended a visual angle of 0.28 ° horizontally and 0.56 ° vertically, with approximately 0.03 ° of visual angle separating individual letters. The predisplay and postdisplay fixation fields and the six-letter words and nonwords subtended 1.85° of total horizontal visual angle. Participants viewed stimuli from a distance of approximately 50 cm, and luminance was approximately 25 cd/m2, 21 cd/m2, or 19.6 cd/m2 in the three luminance conditions, respectively. Postdisplay masks were a suing of six uppercase Xs. Half of the participants responded to words using the left-arrow key and to nonwords using the right-arrow key; the other half of the participants responded to words using the right-arrow key and to nonwords with the left-arrow key. Materials. The base stimulus set consisted of the words in Allen et al.'s (1995) Appendix (also see Allen & Emerson, 1991). These included 36 from each of the 12 classes defined by crossing four levels of word frequency with three levels of word length. The four levels of word frequency were very high frequency (VHF; 240-1,016 occurrences in the Kucera & Francis, 1967, norms), medium high frequency (MHK 151-235 occurrences), low frequency (LF, 40-54 occurrences), and VLF (1-5 occurrences). The
three levels of word length were four, five, and six letters. For this experiment, the nonwords were formed by changing the final letter of half of the words in each class so that another word was not formed (e.g., gold to golp). Thus, for this experiment, there were 216 words and 216 nonwords. It is important to note that the stimulus set was divided into three parts for Experiment I--one for each luminance condition. That is, word frequency, string length, and response type were comparable across all three luminance conditions. To further ensure the comparability of the stimuli across the three luminance groups, we used two different orderings of these stimuli across the three luminance conditions (and stimulus ordering was a betweensubjects variable). Thus, 15 of the analyzed participants received the List 1 ordering of the stimuli across luminance, and the remaining 10 participants received the List 2 ordering of stimuli across luminance conditions. Procedure. An LDT was used: On each trial, participants decided whether letter strings formed an English word. We instructed participants to respond as rapidly as possible while maintaining accuracy. There were 432 experimental trials (216 words and 216 nonwords) preceded by 20 practice trials (10 words and 10 nonwords). Word frequency, response type, and string length were balanced across trial blocks. Each trial began with the presentation of the target string for 100 ms. The postmask immediately replaced the target string and remained on the screen for 100 ms. Participants used their index and middle fingers on their right hands to respond. Half of the participants responded "word" with their index finger, and half of the participants responded "word" with their middle finger. Response latencies were measured from target onset to when a participant responded. Only the latencies from correct trials with RTs greater than 100 ms and less than 3,000 ms were included in the RT analysis.
Results Responses to words. We analyzed the data of participants whose accuracy performance in a block of trials fell within the range defined by our absolute cutoffs of 65% and 81%. Data from only one trial block p e r participant were used--that is, the data from the trial block with accuracy closest to 75%. For Experiment 1, the data of 12 participants from the 25 cd/m 2 luminance condition (mean percent correct = 74.2%), 6 participants from the 21 cd/m 2 luminance condition (mean percent correct = 72.3%), and 7 participants from the 19.6 cd/m 2 luminance condition (mean percent correct = 77.8%) were used. The RT, error, and sensitivity (A') data for these 25 participants are analyzed in the following sections. Mean response times for the correct responses to words are presented in Table 1. The 4 (word frequency) X 3 (word length: four-, five-, and six-letters) analysis of variance (ANOVA) for RT data revealed a main effect for frequency, F(3, 72) = 3.03, p < .05 (VHF = 625 ms, MHF = 650 ms, LF = 646 ms, VLF = 679 ms), but no other statistically significant effects. For each participant, we regressed RT on word frequency. The mean slope was significantly less than zero, T(24) = - 2 . 5 8 , p < .01. Thus, we observed a significant word frequency effect even when task accuracy was limited to approximately 75% (74.55%, to be more precise). The accuracy data for Experiment 1 (see Table 1) also revealed a main effect for word frequency, F(3, 72) = 23.28, p < .0001 (mean percent error: VHF = 9.1%,
OBSERVATIONS
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Table 1
Reaction Time (RT,. in ms) and Mean Errors for Words From Experiment 1 as a Function of Word Frequency, Word Length, and Recognition Sensitivity (A ' ) Based on Word and Nonword Data Word frequency Very high
A' Five letters RT % error A' Six letters RT % error A'
Low
SD
M
SD
M
SD
M
SD
613.0 8.0 .863
105
626.0 12.7 .859
108
650.0 18.0 .827
100
666.0 25.3 .780
131
652.0 10.7 .832
79
647.0 14.0 .853
139
647.0 20.0 .840
121
674.0 25.3 .748
108
610.0 8.7 .850
71
678.0 10.0 .886
153
642.0 16.0 .822
126
697.0 30.7 .784
157
MHF = 12.2%, LF = 18.0%, VLF = 27.1%), with no other significant effects (ps > .50). Sensitivity (A') analyses. For the recognition sensitivity analyses (A'; see Pollock & Norman, 1964, and Table 1 for means), there was a main effect for word frequency, F(3, 72) = 9.87, p < .001 (mean A' values: VHF = .85, MHF = .87, LF = .83, VLF = .77), but no other effects were significant (ps > .50). Paired comparisons showed that sensitivity was higher for VHF, MHF, and LF words than for VLF words (all ps < .05) but that sensitivity values between pairs of the three higher frequency categories did not significantly differ from each other (p > . 18).
Combined analyses of words and nonwords.
The 2
(response type) × 3 (word length) repeated measures ANOVA for the latency data (see Table 2 for means) revealed that participants took longer to respond to nonwords (762 ms) than to words (650 ms), F(1, 24) = 32.84, p < .001. Also, there was a main effect for letter-string length (four letters = 690 ms, five letters = 715 ms, six letters = 713 ms), F(2, 48) = 4.19, p < .05. For the analysis
of overall errors, there was also a main effect for response type, F(1, 24) = 51.01, p < .001 (mean percent error: words = 16.6%, nonwords = 34.3%), but no other effects were statistically significant (ps > .39).
Discussion These results show that word frequency influences RT, error, and sensitivity (A'), even when participants' accuracy averaged 74.55%. Keeping mean accuracy below 75% was designed to achieve the verification-prevention criterion prescribed by Paap and Johansen (1994). Note that even though the word frequency effect for the A' analyses was confined to the VLF items, the word frequency effect for RT occurred for VHF words as compared with the two middlefrequency categories, and then again for the two middlefrequency categories as compared with the VHF words. We viewed these results, using Paap and Johansen's proposed operationalization for elimination of verification, as persuasive evidence that word frequency effects occur in the absence of verification.
Table 2
Reaction Time (RT," in ms) and Mean Errors for Words and Nonwords in Experiment I as a Function of Response Type and Word Length Words Word length Four letters RT % error Five letters RT % error Six letters RT % error
Very low
M
Word length Four letters RT % error
Medium high
Nonwords
M
SD
M
SD
639.0 16.0
111
740.0 33.2
190
655.0 17.5
112
775.0 36.2
235
657.0 16.3
127
772.0 33.5
189
Experiment 2 One of Paap and Johansen's (1994) major concerns was that VLF words were absent from the mental lexica of their participants, and so they excluded these words from their analyses. Paap and Johansen also criticized the conclusions of Dobbs et al. (1985) by making a similar assumption about their participants. We decided to evaluate directly the vocabulary knowledge of our participants through the use of an untimed paper-and-pencil test.
Method We tested 40 undergraduate participants who were drawn from the same participant pool as was used in Experiment 1. For each of 30 letter strings from the VLF category of Experiment 1, partici-
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OBSERVATIONS
pants indicated first whether the string was a word or nonword and, if a string was a word, circled the correct definition from a list of four options (i.e., if the person asserted that a stimulus was a word, the probability of guessing the correct definition by chance was 25%). These 30 items were selected randomly from 108 VLF stimuli used in Experiment 1 (54 words and 54 nonwords). Definitions for words were obtained from Webster's (1983) New Universal UnabridgedDictionary. We constructed distractor definitions so that they would have parallel structure to the correct definition (e.g., for chew the correct definition was "to bite and grind with the teeth," and one of the distractors was "to shop for a gift for a friend"). Twenty of the letter strings were words and 10 items were nonwords.
Results Mean accuracy on the LDT component of the task was 95.8%, and 37 out of 40 (92.5%) of the participants showed accuracy levels of at least 90% (the lowest score was 80%). Note that if cues were present in the definitions for word trials that made them easy to select correctly, then accuracy should have been substantially higher for word trials than for nonword trials (i.e., there were no correct options for these trials). However, as Table 3 demonstrates, participants made more errors on word trials than on nonword trials, even though they were biased to respond "word" (i.e., although participants were not informed of this, there were 20 words but only 10 nonwords on the test). With regard to definitions, the probability that the participant responded correctly given a correct response on the LDT was .9645. In fact, 23 out of 40 participants correctly defined every word to which they responded.
Discussion These results show that undergraduate students from the same population as the participants in Experiment 1 can correctly classify words and nonwords and that they know the definitions of VLF words well enough to define them at better than 90% accuracy. It appears that even the time stress in presentation-until-response RT conditions results in higher error rates than a task that does not involve timed responses. However, this elevated error rate does not indicate lack of knowledge. Our data show quite clearly that the majority of the VLF words used in Experiment 1 are known to our undergraduate students.
experiments did not show an effect of word frequency at brief exposure because of a timing error. However, we noted that perceptual sensitivity (Ag) decreased as exposure duration decreased in Allen et al.'s (1992) study. Also, participants committed more errors for more briefly presented words than for words presented for longer durations in Allen et al.'s (1992) Experiment 1. Hence, independent of RT, Paap and Johansen ignored relevant data from Allen et al. (1992) that were inconsistent with their conclusion. More fundamentally, though, we noted that the timing error was irrelevant to the conclusion by Allen et al. (1992) that word frequency affected performance in their 100-ms exposureduration condition. Second, Paap and Johansen's (1994) Experiment 2 contained methodological flaws. For example, Paap and Johansen assumed that having participants make lexical decisions to a remembered target after a letter-identification task constituted a direct test of on-line lexical retrieval. This procedure simply does not allow the conclusion that no on-line word frequency effects were present. Finally, in the present Experiment 1, we used the very criterion that Paap and Johansen (1994) claimed would prevent verification (a 75% accuracy level on a masked LDT), yet we continued to find robust word frequency effects for RT errors and A'. In the A' results, frequency effects were confined to a decrement on VLF words. This would be problematic for our position if this outcome occurred because participants did not know large numbers of those words and hence responded to them as if they were nonwords. However, Experiment 2 showed that participants from the same population as those in Experiment I correctly classified and defined VLF words at high levels of accuracy. Thus, it was quite likely that the participants in Experiment 1 had the VLF words in their mental lexica. Furthermore, the RT results from Experiment 1 produced frequency effects that were not confined to VLF words. The highest frequency words were correctly classified more rapidly than the two middle-frequency levels at the same time that the lowest frequency words were classified more slowly. Consequently, using Paap and Johansen's (1994) prescription for how to prevent verification, we observed word frequency effects in this study, suggesting that word frequency effects can occur at the encoding stage as proposed by Dobbs et al. (1985) and Allen et al. (1992).
General Discussion Paap and Johansen (1994) made several bold statements in their article, First, they claimed that Allen et al.'s (1992) Table 3 Mean Percent Hits, Misses, Correct Rejections, and False Alarms for Words and Nonwords for the Lexical Decision Task Portion of Experiment 3 Participants' responses Stimulus
Words (%)
Nonwords (%)
Word Nonword
94.4 3.5
5.6 96.5
References Allen, E A., & Emerson, E L. (1991). Holism revisited: Evidence for parallel independent word-level and letter-level processors during word recognition. Journal of Experimental Psychology: Human Perception and Performance, 17, 489-511. Allen, E A., McNeal, M., & Kvak, D. (1992). Perhaps the lexicon is coded as a function of word frequency. Journal of Memory and Language, 31, 826-844. Allen, E A., Wallace, B., & Weber, T. A. (1995). Influence of case type, word frequency, and exposure duration on visual word recognition. Journal of Experimental Psychology: Human Perception and Performance, 21, 914-934.
OBSERVATIONS Bamber, D. (1975). The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. Journal of Mathematical Psychology, 12, 387-415. Dobbs, A. R., Friedman, A., & Lloyd, J. (1985). Frequency effects in lexical decisions: A test of the verification model. Journal of Experimental Psychology: Human Perception and Performance, 11, 81-92. Kucera, H., & Francis, W. N. (1967). Computational analysis of present-day American English. Providence, RI: Brown University Press. Paap, K. R., & Johansen, L. S. (1994). The case of the vanishing frequency effect: A retest of the verification model. Journal of Experimental Psychology: Human Perception and Performance, 20, 1129-1157. Paap, K. R., Newsome, S. L., McDonald, J. E., & Schvaneveldt, R. W. (1982). An activation-verification model for letter and word recognition. Psychological Review, 89, 573-594. Pollock, I., & Norman, D. (1964). A non-parametric analysis of recognition experiments. Psychonomic Science, 1, 125-126.
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Reicher, G. M. (1969). Perceptual recognition as a function of meaningfulness of stimulus material. Journal of Experimental Psychology, 81, 275-280. Schneider, W. (1988). Micro Experimental Laboratory: An integrated system for IBM PC compatibles. Behavior Research Methods, Instruments, and Computers, 20, 206-217. Schvaneveldt, R. W., & McDonald, J. E. (1981). Semantic context and the encoding of words: Evidence for two modes of stimulus analysis. Journal of Experimental Psychology: Human Perception and Performance, 7, 673-687. Webster, N. (1983). Webster's new universal unabridged dictionary. Cleveland, OH: Dorset & Baber.
Received June 7, 1995 Revision received October 1, 1996 Accepted October 21, 1996 •
Dannemiller Appointed Editor of DevelopmentalPsychology, 1999-2004 The Publications and Communications Board of the American Psychological Association announces the appointment of James L. Dannemiller, PhD, University of Wisconsin, as editor of Developmental Psychology for a 6-year term beginning in 1999. Effective January 1, 1998, manuscripts should be directed to James L. Dannemiller, PhD Developmental Psychology Journal Office Room 555 Waisman Center University of Wisconsin--Madison 1500 Highland Avenue Madison, WI 53705-2280 email: jldannem@ facstaff.wisc.edu
