REPAIRING PHONOLOGICAL SPEECH ERRORS IN NOVEL PHRASES AND PHRASAL LEXICAL ITEMS Sieb Nooteboom UiL OTS, Utrecht University
PHRASAL LEXICAL ITEMS (PLIs): proverbs, sayings, idioms, clichés, collocations Levelt & Meyer 2000,PLI in mental lexicon: SUPERLEMMA lemma lemma lemma lemma lemma SET A THIEF TO CATCH A THIEF set a thief to catch a thief
1) Production more automatic for PLIs than for NPs. • 2) Production less closely monitored for PLIs than for NPs • PREDICTIONS: • In PLIs less speech errors than in NPs. • Speech errors less often detected and repaired in PLIs than in NPs.
CORPUS: Utrecht corpus of speech errors: 2455 speech errors in spontaneous Dutch Here we focus on: Syntagmatic phonological speech errors with source and target phoneme in different words, excluding speech errors in foreign words excluding combinations of given and family name excluding combinations of function words This leaves us with 635 speech errors
Each speech error >> minimal intended error-less form, including source and target. English example (Fromkin 1973) Speech error: *it was such a good force because we had informants changed into course because we had informants Reason: errors varied widely in the amount of material preceding and / or following the minimal string.
All 635 word strings presented to 3 judges. Each word string assigned to one of these categories: 1 typical (part of) PLI 2 perhaps (part of) PLI? 3 typical (part of) NP
635 speech errors: • 386 cases: 3 judges agreed • 215 cases: 2 judges agreed • 34 cases: 3 different judgements. • These 34 were assigned code 2, reflecting uncertainty as to lexical status. • This resulted in the following distribution: • 1. PLI: 92 word strings • 2. PLI?: 75 word strings • 3. NP: 468 word strings • ________________________________________________________________________________________ • Sum total: 635 word strings
Search Engine Count Estimates (SECEs) were assessed for all word strings, using Yahoo constrained to Dutch and to precise symbol sequences by using double quotes. The SECEs were transformed by (1) changing all zeros to ones, and (2) by taking the 10log.
Univariate one-way ANOVA: Significant effect of category on 10log frequency (p<0.001) Post hoc analysis (Tukey’s): Significant difference between PLI and NP (p<0.05), and between PLI? and NP (p<0.05). No significant difference between PLI and PLI? (p>0.05). PLI and PLI? Collapsed in further analysis.
Of each of 635 speech errors we now have the following information: estimated lexicality (NP vs PLI) speech error class (anticipation vs perseveration vs exchange) repair status (repaired vs unrepaired) This was the input for further analysis.
Strange data: Exchanges have two chances of being detected and repaired, against only one chance for anticipations and perseverations. Why, then, is fraction repaired lower and not higher for exchanges?
How can we explain these strange data? 1) Repaired anticipations in the corpus often of the form heft..left hemisphere. 2) In inner speech this might have been heft hemisphere, but also heft lemisphere. 3) An anticipation in inner speech has only a single erroneous form, an exchange has two erroneous forms that may trigger detection and repair. 4) Probably 2/3 of repaired anticipations in corpus really derive from half-way repaired exchanges.
No way of knowing whether a particular repaired anticipationstems from an anticipation or an exchange. Therefore anticipations and exchanges collapsed in further analysis as anticipations+ in further analysis.
Logistic regression: Fraction repaired higher for antic+ than for persev (p<0.003). This can be explained by assuming a considerable contribution of repaired exchanges to antic+. Fraction repaired higher for NP than for PLI. This confirms the main hypothesis of this paper.
CONCLUSION Speech errors are less often detected and repaired in Phrasal Lexical Items than in Novel Phrases. This supports a proposal by Levelt & Meyer: A Phrasal Lexical Itemmentally represented as a Superlemma from which the constituting Sublemmas are activated in one go, making mental preparation more automatic for Phrasal Lexical Items than for Novel Phrases.
Table 1. Estimated parameters for the best fitting binomial logistic regression model of fraction repaired using effect coding. The grand mean was used as 'intercept'. For fixed effects, regression coefficients are given, with standard errors, t values and p values. Due to the structure of the data set with no repetitions for speakers or for word sequences, there are no random effects.