working memory in an editing task

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2007 24: 283Written CommunicationJohn R. Hayes and N. Ann Chenoweth

Working Memory in an Editing Task

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Working Memoryin an Editing TaskJohn R. HayesCarnegie Mellon University, Pittsburgh, PennsylvaniaN. Ann ChenowethUniversity of Texas–Pan American, Edinburg

A number of studies have found that writers produce text in bursts of lan-guage. That is, when creating a text, writers produce a few words, pause, producea few more words, pause, and so on. Chenoweth and Hayes (2003) hypothe-sized that language bursts occur when writers translate ideas in to new lan-guage. This study tested this hypothesis against the following two alternativehypotheses: (a) Language bursts are caused by proposing new ideas ratherthan by translating ideas in to written language and (b) language bursts dependon the form of the input to the writing process rather than on the translationprocess. The study employed an editing task in which participants wererequired to translate a written language input. The alternative hypotheses ledto contradictory predictions about writers’ performance in this task. The studyalso explored the impact of working memory restrictions on task performance.

Keywords: language bursts; writing theory; cognitive processes; translation

Anumber of studies have found that writers typically produce texts inlanguage bursts (Chenoweth & Hayes, 2001, 2003; Friedlander, 1989;

Kaufer, Hayes, & Flower, 1986). That is, writing proceeds by fits and starts.While writing, people rapidly produce a word string (of varying length butaveraging say 6 to 10 words), then pause, produce another word string,pause again, and so on. The frequency of the pauses and the length of thelanguage bursts depend on the writer’s capabilities. Kaufer et al. (1986)found that more experienced writers produced longer bursts than less expe-rienced writers. Friedlander (1989) found that ESL writers tended to pro-duce very short bursts. Chenoweth and Hayes (2001) found that writersproduced longer bursts in L1 than in L2 and that students with more expe-rience in L2 produced longer bursts than students with less experience in

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L2. Chenoweth and Hayes (2003) found that burst length could be reducedby limiting the writer’s working memory resources.

Chenoweth and Hayes (2003) proposed the model shown in Figure 1 todescribe the text production process. This model includes four processes:

• a proposer that generates ideas for expression,• a translator that converts the proposed ideas into linguistic strings,• a transcriber that converts the linguistic strings into text,• an evaluator/reviser that evaluates proposed and written language.

Chenoweth and Hayes (2003) hypothesized that language bursts occurbecause of limitations in the resources available to the translator. They sug-gested that the translator can process only a limited amount of input to producea language string. When the translator produces as much language as it can,

284 Written Communication

Figure 1Model of the Text Production Process

Source: Chenoweth and Hayes (2003, p. 113).




translation stops and the resulting language string is transcribed. When tran-scription is complete, the translation process is restarted. Providing resources tothe translator increases the average length of bursts and restricting resourcesdecreases it. By this account, it is limitations of the translation process that areresponsible for language bursts. We will call this the Translator Hypothesis.

The translator hypothesis is consistent with all of the studies, cited earlier,in which language bursts have been examined. All of the studies that reportedfrequent bursts involved translation of source material into new language.Kaufer et al. (1986), Friedlander (1989), and Chenoweth and Hayes (2001)required participants to translate ideas drawn largely from long-term memoryinto text. Chenoweth and Hayes (2003) required participants to translate anonverbal input (a wordless cartoon) into text. In addition, the position thatbursts depend on the translation process is consistent with the results ofHayes and Chenoweth (2006) who showed that language bursts are rarewhen participants simply transcribe text from one computer window toanother, a task that does not require translation.

However, there are two alternative hypotheses that are also consistentwith these results:

Proposer Hypothesis: It might be that the language bursts are caused by propos-ing new ideas rather than by translating those ideas into written language.None of the studies cited earlier allow one to differentiate between effectscaused by the proposer and effects caused by the translator.

Input Hypothesis: It is also possible that the language bursts depend on the formof the input to the writing process rather than on the translation process. Theonly study that did not find frequent interruptions in the language productionprocess, Hayes and Chenoweth (2006), was the only study that did notinvolve translation. But it was also the only study that had written languageinput. The input hypothesis is that language bursts do not occur when theinput is written language but that they do occur with other inputs.

The primary purpose of this study was to test these hypotheses in a taskin which the Translator Hypothesis predicted results different from thosethat would be expected if either the Proposer Hypothesis or the InputHypothesis were correct.

In the present study, we required participants to use ideas presented inwritten language as an input for text production but we also required themto translate that input in order to create a text. Specifically, the task requiresparticipants to write an active sentence with the same content as a passivesentence that is presented on screen. Because the task does not require theproposer to generate new ideas, the Proposer Hypothesis would predict thatthe task would generally be completed in a single burst. Furthermore, since

Hayes, Chenoweth / Working Memory in a Task 285




the input to the task is written language, the Input Hypothesis would alsopredict that the task would generally be completed in a single burst. However,since the task requires translation, the Translator Hypothesis predicts thatparticipants will generally not be able to complete the task in a single burst.

Therefore, if participants are typically able to complete this task withoutan interruption, that is, in a single burst, that would be evidence againstthe Translator Hypothesis and supporting one or both of the alternativehypotheses. However, if participants typically require two or more burststo complete the task, that would be evidence supporting the TranslatorHypothesis and against both of the alternative hypotheses.

The study has a secondary purpose. Chenoweth and Hayes (2003) attrib-uted language bursts to the translation process and claimed that restrictionsof working memory resources act on the translation process to increase thenumber of bursts and decrease burst length. If language bursts are observedin this study with written language input, we wondered if limiting workingmemory would change the numbers of bursts and the burst length in thisstudy as it did in the Chenoweth and Hayes (2003) study where the inputwas pictorial.

To summarize, this study addresses two specific questions:

Question 1: Do participants typically require two or more bursts to completethe task posed in this study?

Question 2: Will limiting working memory increase the number of bursts andreduce burst length?

Method

Participants

The 11 participants in this study received course credit for participation.They were all adult native speakers of English.

The Task

The task was to rewrite doubly passive sentences into active voice. Forexample, sentences such as John was robbed by the man who was hit by theFed-Ex truck were to be rewritten as The Fed-Ex truck hit the man whorobbed John.





Procedure

Participants were run individually. To introduce them to this task, theywere shown a sentence such as that at the top of Figure 2 and asked to rewriteit in active voice. The experimenter then commented on the accuracy of therewrite and showed the participant a good and two less good rewrites suchas those at the bottom of Figure 2.

There were 4 practice trials and 26 experimental trials. The sequence ofsentences was the same for all participants. In all of the trials, participantsperformed a secondary task.

In half of the trials (the experimental trials), the task was to say “tap” intime to a metronome that clicked 120 times per minute. In the other halfof the trials (the control trials), the task was to tap a foot in time to themetronome. The secondary task in the experimental trials was designed toreduce working memory. Baddeley and Hitch (1974) showed that repeatinga syllable, for example, la la la or the the the, reduces working memorycapacity. This effect is called articulatory suppression (AS). The secondarytask in the control condition, tapping a foot, has been shown to have littleeffect on working memory (Chenoweth & Hayes, 2003).

The 1st and 3rd practice trials were control trials and the 2nd and 4thpractice trials were experimental trials. The 26 test trials consisted of 13blocks of 2 trials each. In each block, 1 trial was experimental and the otherwas control. The order of conditions within each block was randomizedseparately for each participant.

Design

We used a within-participants design with two repeated measures: tapcondition (voice-tap or foot-tap) and block (1-13).


Figure 2An Example Illustrating the Rewriting Task

Groceries bought by the hungry travelers were eaten by a bear

Good: A bear ate the groceries that the hungry travelers bought

Less good: A bear ate the groceries bought by the hungry travelers.

Less good: The hungry travelers bought groceries that were eaten by a bear.




Analysis

The software started timing when the stimulus sentence first appeared,recorded the identity and time of each keystroke (including deletes), andstored the final version of the sentence that the participant wrote. Thesedata allowed us to calculate the following measures:

• number of bursts (the number of bursts to type each sentence),• burst length (the number of newly proposed words in each burst),• start time (the time between the appearance of the stimulus sentence and

the participant’s first key stroke),• number of words in the final version of the sentence,• writing time (the time between the participant’s first and last keystroke),• rate (the number of words divided by the writing time).

Following Chenoweth and Hayes (2001), we defined a burst as a group ofwords produced by the writer bounded by breaks in the production process.A break is either a pause of at least 2 seconds or a grammatical discontinuityindicating that previously written language was being revised. Bursts thatare terminated by a pause are called P-bursts. Bursts that are terminated byrevision are called R-bursts.

By examining the sentences the participants produced, we were also ableto calculate the number of mechanical errors remaining after the completionof each sentence.

Results and Discussion

Question 1: Do Participants Typically Require Two or MoreBursts to Complete the Task Posed in This Study?

Table 1 shows the mean number of bursts (P- and R-bursts combined)for each participant in the control (foot-tap) condition. On average, partic-ipants required more than two bursts to complete the task. One participant,Number 11, completed the task in one burst on seven, or more than half, ofthe trials. Another participant, Number 7, never completed the task in lessthan two bursts. Each of the remaining 9 participants required two or morebursts on more than half of their trials. Of the 143 trials in this study (11participants by 13 sentences), 29% were completed in a single burst; 36%required two bursts; and the remaining 36% required three or more bursts.It is fair to conclude that participants typically required two or more burststo complete the task. These results strongly support the Chenoweth and





Hayes (2003) position that language bursts occur when inputs are translatedin to new language. They are not consistent with the alternative hypotheses.

The data on P-burst length also support this conclusion. On average, par-ticipants wrote a sentence of 12.11 words to complete the task. The averageburst length of 7.17 words was not long enough to allow participants rou-tinely to finish the task in a single burst.

To put these results in context, we have compared the present study toChenoweth and Hayes (2003) where participants translated a pictorial input into text and Hayes and Chenoweth (2006) where participants simply transcribed


Table 1Mean and Range for Numbers of Bursts and P-BurstLength for Each Participant in the Control Condition

Participant

Grand1 2 3 4 5 6 7 8 9 10 11 Mean

Number of Mean 2.77 2.23 2.39 3.23 2.15 2.23 3.15 2.31 2.23 1.69 1.77 2.38bursts

Range 1-6 1-6 1-5 1-8 1-5 1-4 2-6 1-4 1-4 1-3 1-5

P-burst Mean 7.15 7.83 6.63 5.99 7.74 6.90 4.52 6.74 6.50 9.58 9.30 7.17length

Range 1-14 2-18 1-14 1-13 1-16 1-12 1-9 1-13 1-13 3-17 1-20

Note: P-burst = bursts that are terminated by a pause.

Table 2A Comparison of Burst Length in Three Studies

Average Average Number AverageBursts of Words in Burst

Translation? Per Task Final Text Length

This study Yes 2.38 12.11 7.17Chenoweth and Yes 2.11 21.65 12.41

Hayes (2003)a

Hayes and No 1.77 139.08 81.95Chenoweth (2006)b

a. Burst length was computed for the foot-tap control condition, which is comparable to thecontrol condition in the present study.b. Hayes and Chenoweth (2006) did not calculate burst lengths in their study because of thelarge number of cases in which the transcription task was completed in a single burst. We haveused their data to calculate burst lengths in the usual way for purposes of comparison.




written language. The first two studies involved translation and the third didnot. These comparisons are shown in Table 2. The average burst length inthe transcription study is more than 10 times that in the present study. Thedifference is significant at the .001 level by independent samples t test. Theaverage burst length in the Chenoweth and Hayes (2003) study is also sub-stantially larger than in the present study. This difference is also significantat the .001 level by independent samples t test. Differences in the numbersof bursts per task were not statistically significant.

One might worry about the validity of these comparisons of tasks of verydifferent length. The present study and Chenoweth and Hayes (2003)required the participant to write a single sentence. In contrast, in Hayes andChenoweth (2006), the participants transcribed many paragraphs of text.The completion of the task necessarily limited the length of the final burst.Perhaps the short burst length in the present study and in Chenoweth andHayes (2003) was due to the short task length. We believe that burst lengthprobably is underestimated because of the inclusion of the last burst in cal-culating average burst length. However, since participants respond to thetasks in each study with about the same number of bursts, it is not clear thatburst length would be more severely underestimated in one study than inany of the others. In any case, we do not believe such underestimation canaccount for the 10 to 1 difference in burst length that we have observed. Wedo believe that in studies where it is important to estimate the absoluterather than the relative value of burst length, it would be best to omit thefinal burst when calculating burst length.1

Question 2: Will Limiting Working Memory Increasethe Number of Bursts and Reduce Burst Length?

Frequency of bursts. P-bursts were more frequent than R-bursts. Weobserved a total of 807 bursts, of which 140 or 17.3% were R-bursts.Chenoweth and Hayes (2003) found that R-bursts accounted for 12.9% ofthe total number bursts they observed.

Analysis of P-bursts showed that restricting working memory increasedthe number of P-bursts. Again, there was a significant effect for conditions(F = 21.492, df = 1, 10; p = .001) and for blocks (F = 2.353, df = 12, 120; p< .009) but not for the interaction of blocks and conditions. The averagenumber of P-bursts per sentence was 1.89 in the control condition and 2.88in the AS condition.

In contrast, analysis of R-bursts alone showed no effect of workingmemory restriction. There were no significant effects for condition, block,or the interaction of condition and block. The average number of R-bursts





per sentence was 0.50 in the control condition and 0.48 in the AS condition.Although working memory restriction increased the frequency of P-bursts,it did not appear to increase the frequency of R-bursts.

Burst length. Restricting working memory resources decreased P-burstlength. There was a significant effect for condition (F = 18.26, df = 1, 10;p < .002) and for block (F = 2.308, df = 12, 120; p < .011). The interactionwas not significant. In the control condition, average P-burst length was7.17 words and in the AS condition, it was 5.36 words.

Because there were relatively few R-bursts, there were too many emptycells in our design to allow for an ANOVA of the effects of the experimen-tal variables on R-burst length. However, averaging the lengths of the avail-able R-bursts over both conditions yielded a rough estimate of R-burstlength of 3.95 words. This is substantially smaller than the comparableaverage for P-bursts of 6.27 words. In a similar analysis, Chenoweth andHayes (2003) reported that R-bursts were shorter than P-bursts. Both ofthese results are consistent with the idea that R-bursts are P-bursts that havebeen cut short by the revision process.

We can conclude that the answer to Question 2 is yes for P-bursts butprobably no for R-bursts.

Other Results

In addition to measuring the effects of working memory restriction onnumber and length of bursts, we measured its effect on several variablesthat were also studied in Chenoweth and Hayes (2003). These were starttime, number of words in the final text, writing time, writing rate, andmechanical errors. Here are the results for this study.

Start time was not influenced by condition or the interaction of blockand condition. However, there was a significant effect for block (F = 2.449,df = 12, 120; p < .007). The average start time in the control condition was9.42 seconds and in the AS condition, it was 8.95 seconds

Number of words written was not influenced by condition or the interac-tion of block and condition. However, there was a significant effect for block(F = 29.824, df = 12, 120; p < .001). The average number of words per sen-tence in the control condition was 12.11 and in the AS condition, it was 12.05

Writing time was significantly influenced by condition (F = 7.215, df = 1, 10;p = .023) and by block (F = 6.854, df = 12, 120; p < .001). The interactioncondition and block was not significant. Average writing time in the controlcondition was 30.97 seconds and in the AS condition, it was 37.16 seconds.





Writing rate was significantly influenced by condition (F = 7.518, df = 1,10; p = .021) and by block (F = 7.048, df = 12, 120; p < .001). The interactionwas not significant. The average writing rate in the control condition was30.71 words per minute and in the AS condition, it was 25.29.

There were no significant differences between conditions in the numberof mechanical errors made, the number of errors corrected, or in the numberof errors remaining in the final text.

Generally, the results of this study parallel those of Hayes andChenoweth (2003). Both studies find that working memory restrictionsincrease the number of P-bursts, decrease P-burst length, and have no effecton start time and on the number of words in the final text. However, theresults of the studies differ in two ways. Although Chenoweth and Hayes(2003) found a moderately significant increase in mechanical errors leftuncorrected in the final texts, no such difference was found in this study.Second, although block effects were absent in Chenoweth and Hayes, theywere consistently present in this study.

The Block Effects

Because there was a significant effect for block for most of the depen-dent measures, we carried out a correlational analysis to determine if theseblock effects were due to practice or to other differences among the blocks.(Remember, a block consisted of two sentences which were randomlyassigned to the experimental or control condition for each participant.) Thevariables were the order of the block (from 1 to 13) and the average valuesof start time, number of words, writing time, writing rate, number of P-bursts, and P-burst length for each of the blocks. A parallel analysis wascarried out for the 26 sentences

The results are shown in Table 3. As can be seen in the table, neitherblock order nor sentence order is significantly correlated with any of thedependent variables. This suggests that the block effects described previ-ously were not due to changes in performance with practice. Rather, webelieve that the block effects result from variability in the difficulty of thetask from sentence to sentence. Some stimulus sentences appear to be moredifficult to transform into active voice than others.

Table 3 reveals some expected correlations and some more surprising ones.As would be expected, writing time was positively correlated with number ofwords written and negatively correlated with writing rate. Burst length is sig-nificantly correlated with number of words produced and with writing rate butnot with writing time. These findings are consistent with the Chenoweth and





Hayes (2001) claim that burst length is positively related to writing fluency.More surprising, the number of P-bursts is not strongly correlated with P-burstlength. Also, start time is strongly correlated with writing time, suggesting thatsentences that are hard to plan are also hard to write.2

Conclusion

The results of this study are consistent with the Translation Hypothesis,Chenoweth and Hayes’s (2003) position that the process of translating ideasinto new language causes language bursts. The source of the ideas, whethermemories, pictures, or written text, does not appear to matter. Thus, theInput Hypothesis appears to be inconsistent with our results. Furthermore,the ideas writers expressed in this study were not new. The participants sim-ply took ideas expressed in passive voice and translated them into activevoice.3 Therefore, the Proposer Hypothesis, that the proposing process is thecause of bursting, could not account for the bursts observed in this study.

These results do not imply that the proposing process cannot cause lan-guage bursts. They simply indicate that the bursts observed in this studywere not caused by the proposing process. It is possible that in tasks thatinvolve both proposing and translating both processes could cause bursting.


Table 3Correlations by Block and by Sentence

Among the Dependent Variables

Start Number Writing Writing Number P-BurstTime of Words Time Rate of P-Bursts Length

Order Block .231 .113 –.098 .170 –.009 .104Sent –.048 .073 –.110 .140 –.028 .187

Start Block .221 .652* –.451 .503 –.190 time Sent .069 .609** –.436* .316 –.136

Number Block .359 .110 .621* .456 of words Sent .410* .150 .587** .605**

Writing Block –.806** .879** –.221 time Sent –.712** .816** –.117

Writing Block –.680* .693** rate Sent –.586** .642**

Number of Block –.187 P-bursts Sent –.960

Note: P-burst = bursts that are terminated by a pause.*p < .05. **p < .01.




Restricting working memory had very much the same impact on writingperformance in this study as it did in Chenoweth and Hayes (2003). Thisreinforces the belief that the writing processes involved in the two studieswere similar even though the source of ideas for writing was different.

Notes

1. In this study, we compared the length of the last burst to the average of earlier bursts forall cases in which there were two or more bursts. We found that the last burst was slightly butnot significantly longer than earlier bursts. Of course, this does not mean that the last burst wasnot underestimated. It may be that last bursts are intrinsically longer than earlier bursts. Forexample, it might be that decisions are made and problems solved early in sentence constructionthat allow writing to proceed more fluently later.

2. It is tempting to try to extract more information from this table by further analysis.However, since this study was not designed with this analysis in mind, it seems more appro-priate to wait for a study specifically designed to explore these relations.

3. It would be interesting to know if other meaning-preserving editing tasks such aschanging wordy into concise text or translating nominals into verbs are also accompanied bylanguage bursts.

References

Baddeley, A. D., & Hitch, G. J. (1974). Working memory. In G. Bower (Ed.), The psychologyof learning and motivation (Vol. 8, pp. 47-90). San Diego, CA: Academic Press.

Chenoweth, N. A., & Hayes, J. R. (2001). Fluency in writing: Generating text in L1 and L2.Written Communication, 18, 80-98.

Chenoweth, N. A., & Hayes, J. R. (2003). The inner voice in writing. Written Communication,20, 99-118.

Friedlander, A. C. (1989). The writer stumbles: Some constraints on composing in English asa second language (Doctoral dissertation, Carnegie Mellon University, 1989). DissertationAbstracts International, 49, 11A.

Hayes, J. R., & Chenoweth, N. A. (2006). Is working memory involved in the transcribing andediting of texts? Written Communication, 23, 135-149.

Kaufer, D. S., Hayes, J. R., & Flower, L. (1986). Composing written sentences. Research inthe Teaching of English, 20, 121-140.

John R. Hayes is a professor of psychology at Carnegie Mellon University, where he is thedirector of the graduate program. His primary interests are in writing theory and in empiricalstudies of writing.

N. Ann Chenoweth was an assistant professor of English at the University of Texas–PanAmerican. She is now retired.





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