This study explored three interrelated questions concerning the lexical processing of complex words in auditory comprehension. First, is there a uniqueness point after the uniqueness point? More specifically, given the evidence for lexical entries for complex words as witnessed by robust and ubiquitous whole-word frequency effects, one would expect to find evidence for a second uniqueness point – after the standard uniqueness point – at which morphologically related
competitors have dropped from the cohort. Our auditory lexical decision experiment with Danish complex words provided evidence for the usefulness of such a second Complex Uniqueness Point (CUP). Words with a late CUP elicited longer response latencies.
The CUP effect shows that the incoming speech input is matched against a lexicon that contains entries for complex words, both inflected and derived. Unsurprisingly, a whole-word frequency effect also supported the hypothesis of a comprehensive lexicon. Does this imply that the internal structure of complex words is irrelevant? The absence of a base form frequency effect suggests an affirmative answer. However, the presence of a family size effect and the presence of an effect of suffix frequency suggest that the internal structure of complex words also plays a role. Of special interest is the interaction of whole-word frequency and affix frequency that emerged from our data.
When one of these two variables has a low value, the effect of the other predictor is facilitatory. But when one of the variables assumes higher values, this facilitation for the other predictor reverses and may even become inhibitory. Apparently, when both retrieval of the full form from memory and constituent-driven comprehension are running simultaneously, processing is delayed instead of enhanced. The frequency effects and their interaction were significantly enhanced for females as compared to males. Thanks to their superior verbal memory (Kimura, 2000; Ullman et al., 2002), females are probably better at retrieving both full forms as well as their constituents from the lexicon. Hence they suffer most when the whole word and the constituents are highly frequent.
Methodologically, this result indicates that it is important to take the sex of the participants into account when attempting to generalise from experimental work.
Our third question addressed the overall advantage or disadvantage of morphological complexity.
High frequency words with high frequency affixes have a processing disadvantage compared to words for which one of these frequencies is not high. How does the average balance of storage and computation fall out for complex words? To answer this question, we compared simple and
complex words. Other things being equal (i.e. statistically controlled in the regression model), complex words emerged with a processing advantage compared to simple words. Although at the extreme high end of the frequency ranges, the whole-word and decompositional routes function suboptimally, on average the joint availability of word and morpheme representations allows for more optimal processing.
Our findings have several consequences for models of morphological processing. Frauenfelder &
Schreuder (1992) hypothesized that in a race model with two independent routes, complex words
When the normally fast direct route happens to be slow, the normally slow parsing route might happen to be fast, and complete access first. Although we do see, on average, a processing advantage for complex words, the interaction of suffix frequency by whole-word frequency
suggests the two routes are not independent and that optimal conditions for each route separately do not guarantee optimal processing for the system as a whole. Since the idea of statistical facilitation is based on the assumption that the two routes race independently, while our data suggest that the two routes are not independent, statistical facilitation may not be the correct interpretation for the processing advantage of complex words.
The CUP measure provides further support for the importance of properly conditioning of
processing measures. The CRUP construct developed by Wurm (1997) provides further refinement of the UP by conditioning the cohort for the base on the preceding prefix. The present CUP
construct conditions the uniqueness point on the presence of the base. The CRUP narrows down the traditional cohort by considering only the bases that can occur in combination with the given prefix, while the CUP draws in more words than those considered by traditional cohort theory. Both in the work of Wurm and colleagues and in this study, the pattern of uniqueness effects shows how sensitive auditory processing is to the context of the different morphemes: for prefixed words, the base is processed given its prefix; for suffixed words, it seems that the same is happening for the suffix given its base. Interestingly, this sensitivity emerges although the phoneme-based UPs are relatively crude measures, given the amount of subphonemic detail that listeners are sensitive to (see e.g. Goldinger, 1998; Kemps, Wurm, Ernestus, Schreuder, & Baayen, 2005; Davis et al., 2002). A further possibility in relation to the CUP measure is that what matters is not only the existence of morphological competitors up until the CUP, but also their frequency relative to the target, similar to the pattern of morphological family effects found by Meunier & Segui (1999),
who observed an inhibitory effect of a larger number of family members with a higher frequency than the target. However, the number of family members of a higher frequency than the target, which was inhibitory in the French experiment of Meunier & Segui, was not predictive for the present Danish experiment. This could be because the larger morphological families in Danish make the frequency rank in the family less distinctive than it is in French. This suggests that the relative frequency of the target in its pre-CUP cohort would not be predictive for languages with large families, like Danish, but could play a role for languages with smaller families. Alternatively, the fact that Danish is stress-timed, while French is syllable-timed, could mean that there are durational cues in Danish which help the listener identify precisely which of the family members is the target, thus reducing the possibility of inhibition from higher-frequency family members. The absence of such durational cues in French could make other cues, such as the relative frequency in the family, more important. Further research is required to clarify this.
The CUP effect and the whole-word frequency effect bear witness to the importance of knowledge of the lexical entries for complex words. These two effects challenge models that reject pervasive storage of whole complex forms and that rely instead on rule-based comprehension (e.g., Pinker, 1997; Pinker & Ullman, 2002). The presence of whole-word frequency and CUP effects for regular inflection side by side with semi-regular derivation adds to this challenge. At the same time, our data also argue against models in which the role of morphological structure is marginalized. Except for extreme values of whole-word and suffix frequency, we observe synergy between the two routes, a synergy without which we suspect it would not be profitable for languages to have morphology at all.
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Table 1
Comparison of largest morphological family sizes attested in three Germanic languages and Finnish. Note that for English spaced compounds are not counted and that for Finnish, the largest count cited is the largest family of an item used in the experiment of Moscoso del Prado Martín, Bertram, Häikiö, Schreuder & Baayen (2004).
Language Max Source
English 187 CELEX (Baayen, Piepenbrock, & Gullikers 1995), 18 million words
Dutch 550 CELEX (Baayen et al. 1995), 42 million words (Schreuder & Baayen 1997)
Danish 3476 Danish corpus described below, 43.5 million words Finnish 6029 Turun Sanomat newspaper corpus, 22.7 million
(Moscoso del Prado Martín et al. 2004)
Table 2
Properties of the suffixes used on the complex items in the experiment. Word class covers adjectives (A), nouns (N) and verbs (V). Homonymy refers to homonymy with other affixes.
Suffix Word
* Homographic but not homonymic with adjectival comparative suffix.
Table 3
Lexical predictors for the complex items. The variables marked with an asterisk are frequency counts per million.
Median (s.d.) Range
Whole-word frequency* 2.9 (45.9) 0 to 388.2
Base form frequency* 15.8 (83.8) 0.1 to 691
Suffix token frequency* 5397.9 (8753.1) 199 to 29327
Morphological family size 184 (451.1) 1 to 2165
Continuation forms 2 (40.8) 0 to 458
Semantic transparency rating 5.26 (1.59) 1.25 to 6.97
Length in msec 590 (91) 364 to 820
UP in msec 288 (77) 154 to 523
Complex UP in msec 458 (95) 203 to 682
Table 4
Correlations between the significant predictors for the complex items, with previous RTs included as principal components (PCs), semantic transparency residualised from morphological family size, family size from word and suffix frequency, and suffix frequency from word frequency as described in the text. All pairwise (absolute) correlations are smaller than 0.51, and hence stay well below the traditional most simple warning of collinearity.
Previous
Table 5
Summary of the mixed-effects analysis of covariance for variables predicting auditory lexical decision time to complex items only. Df = 3214.
Estimate MCMC
Intercept 13.1803 13.0321 7.4414 18.4922 0.0002 0.0000
Previous RT PC1 -0.0002 -0.0002 -0.0002 -0.0001 0.0001 0.0000 Affix repetition -0.0037 -0.0037 -0.0053 -0.0019 0.0001 0.0000 Prev. PC1*Affix repetition 0.0000 0.0000 0.0000 0.0000 0.0008 0.0003
Previous RT PC2 0.0000 0.0000 0.0000 0.0001 0.0008 0.0008
Previous response: error 0.0343 0.0329 0.0113 0.0568 0.0060 0.0038
Log UP 0.0970 0.0985 0.0603 0.1388 0.0001 0.0000
Log CUP (linear) -2.5289 -2.4809 -4.3458 -0.7103 0.0082 0.0101 Log CUP (quadratic) 0.2269 0.2229 0.0746 0.3737 0.0036 0.0049 Log msec from CUP to offset 0.0154 0.0152 0.0086 0.0215 0.0001 0.0000
Residualised semantic transparency -0.0086 -0.0084 -0.0163 -0.0007 0.0352 0.0474 Residualised family size -0.0085 -0.0084 -0.0146 -0.0024 0.0072 0.0093
Log word frequency -0.0127 -0.0128 -0.0396 0.0145 0.3320 0.0001
SexM 0.0057 0.0056 -0.0403 0.0516 0.8102 0.8530 SexM*Log word frequency 0.0015 0.0015 -0.0389 0.0455 0.9494 0.6366 Residualised suffix frequency -0.0429 -0.0430 -0.0671 -0.0199 0.0004 0.0008 SexM*Resid. suffix frequency 0.0255 0.0251 0.0036 0.0468 0.0236 0.0190 Resid. suffix freq*Log word freq. 0.0095 0.0096 0.0047 0.0140 0.0002 0.0001 SexM*suffix freq.*word freq. -0.0063 -0.0063 -0.0107 -0.0016 0.0066 0.0056
Table 6
Summary of binomial regression model for correctness of the responses to the complex items, with correct responses scored as 0 and incorrect as 1. Df = 3454.
Estimate Std.
Error
z p (z)
Intercept 6.7261 4.2124 1.597 0.1103
Previous RT PC1 0.0008 0.0004 2.029 0.0425
Log Complex UP -1.6260 0.6962 -2.336 0.0195
Log whole-word frequency -0.1880 0.0751 -2.503 0.0123
Table 7
Summary of the mixed-effects analysis of covariance for variables predicting auditory lexical decision time for both complex and simple items. Df = 4710.
Estimate MCMC Affix repetition -0.0042 -0.0042 -0.0059 -0.0026 0.0001 0.0000 Prev. PC1*Affix repetition 0.0000 0.0000 0.0000 0.0000 0.0188 0.0220
Previous RT PC2 0.0000 0.0000 0.0000 0.0001 0.0001 0.0001
Previous response: error 0.0288 0.0287 0.0087 0.0476 0.0020 0.0030
Log UP (linear) -1.3669 -1.3709 -2.4213 -0.3594 0.0104 0.0090 Log UP (quadratic) 0.1413 0.1416 0.0515 0.2331 0.0028 0.0021
Log UP to CUP 0.0729 0.0730 0.0531 0.0928 0.0001 0.0000
Log msec CUP to offset (linear) -0.0160 -0.0161 -0.0372 0.0036 0.1204 0.1259 Log msec CUP to offset (quadratic) 0.0051 0.0051 0.0012 0.0092 0.0116 0.0122
Log whole-word frequency -0.0149 -0.0149 -0.0193 -0.0101 0.0001 0.0000
Affix type: Derivation -0.2609 -0.2610 -0.3600 -0.1579 0.0001 0.0000 Affix type: Inflection -0.2781 -0.2782 -0.3831 -0.1768 0.0001 0.0000 Word class: Noun 0.0200 0.0198 0.0016 0.0399 0.0594 0.0556 Word class: Verb 0.0287 0.0285 0.0061 0.0509 0.0116 0.0128
Figure 1
Partial effects of the context-related control predictors on auditory lexical decision times, adjusted to the median values of the other significant continuous predictors and the reference levels
CORRECT for the factor PREVIOUS RESPONSE and FEMALE for the factor SEX. Previous PC1 and Previous PC2 are the two strongest of four principal components based on the four previous reaction times.
Figure 2
Partial effects of the form-related predictors on auditory lexical decision times, adjusted to the median values of the other significant continuous predictors and the reference levels CORRECT for the factor PREVIOUS RESPONSE and FEMALE for the factor SEX.
Figure 3
Partial effects of the semantic predictors on auditory lexical decision times, adjusted to the median values of the other significant continuous predictors and the reference levels CORRECT for the factor PREVIOUS RESPONSE and FEMALE for the factor SEX.
Figure 4
Effects of word and suffix frequency split between the two sexes. The left panels show the effects for female participants, the right panels, the effects for male participants. The top panels show the partial effect of whole-word frequency on RT, split between words with minimum (dotted), median (solid) and maximum (dashed) suffix frequency, and adjusted to the median values of the other significant continuous predictors and the reference level CORRECT for the factor PREVIOUS
RESPONSE. The bottom panels show the partial effect of suffix frequency on lexical decision times, split between words with minimum (dotted), median (solid) and maximum (dashed) word
frequency, similarly adjusted for the other variables in the model.
Figure 5
The effect of whole-word frequency for different intervals of suffix frequency, with suffix frequency increasing from bottom left to top right. The intervals are determined by dividing the items into six groups of the same size according to residualised suffix frequency and plotting the effect of whole-word frequency for each of these intervals.
Log whole-word frequency
Figure 6
Partial effects of morphological complexity on auditory lexical decision times, adjusted to the median values of the other significant continuous predictors and the reference levels CORRECT for the factor PREVIOUS RESPONSE and ADJECTIVE for the factor WORD CLASS.