| Literature DB >> 30840217 |
Luca Cilibrasi1,2,3, Vesna Stojanovik4, Patricia Riddell4, Douglas Saddy4.
Abstract
A number of studies in different languages have shown that speakers may be sensitive to the presence of inflectional morphology in the absence of verb meaning (Caramazza et al. in Cognition 28(3):297-332, 1988; Clahsen in Behav Brain Sci 22(06):991-1013, 1999; Post et al. in Cognition 109(1):1-17, 2008). In this study, sensitivity to inflectional morphemes was tested in a purposely developed task with English-like nonwords. Native speakers of English were presented with pairs of nonwords and were asked to judge whether the two nonwords in each pair were the same or different. Each pair was composed either of the same nonword repeated twice, or of two slightly different nonwords. The nonwords were created taking advantage of a specific morphophonological property of English, which is that regular inflectional morphemes agree in voicing with the ending of the stem. Using stems ending in /l/, thus, we created: (1) nonwords ending in potential inflectional morphemes, vɔld, (2) nonwords without inflectional morphemes, vɔlt, and (3) a phonological control condition, vɔlb. Our new task endorses some strengths presented in previous work. As in Post et al. (2008) the task accounts for the importance of phonological cues to morphological processing. In addition, as in Caramazza et al. (1988) and contrary to Post et al. (2008), the task never presents bare-stems, making it unlikely that the participants would be aware of the manipulation performed. Our results are in line with Caramazza et al. (1988), Clahsen (1999) and Post et al. (2008), and offer further evidence that morphologically inflected nonwords take longer to be discriminated compared to uninflected nonwords.Entities:
Keywords: Inflectional morphology; Minimal pairs; Morphophonology; Nonwords
Mesh:
Year: 2019 PMID: 30840217 PMCID: PMC6513900 DOI: 10.1007/s10936-019-09629-y
Source DB: PubMed Journal: J Psycholinguist Res ISSN: 0090-6905
Materials
| Condition | Morphological condition | Non-morphological condition | Phonological control condition |
|---|---|---|---|
| Examples | /vɔld/ /vɔlz/ | /vɔlt/ /vɔls/ | /vɔlb/ /vɔlm/ |
| Manner of articulation | Plosive/fricative | Plosive/fricative | Plosive/nasal |
| Voicing | Voicing coherent | Voicing incoherent | Voicing coherent |
| Presence of inflectional morpheme | Present | Absent | Absent |
Mean reaction times
| Same pairs | Different pairs | |||
|---|---|---|---|---|
| Mean | SE | Mean | SE | |
| Morphological | 843 | 16 | 926 | 21 |
| Non-morphological | 778 | 16 | 802 | 14 |
| Control | 827 | 14 | 888 | 14 |
SE standard error
Results of the ANOVA performed on the linear mixed model
| Mean square | NumDF | DenDF | F |
| |
|---|---|---|---|---|---|
| cond | 303,30 | 2 | 129.95 | 3.8891 | < .001 |
| type | 101,626 | 1 | 147.62 | 13.03 | < .001 |
| duration | 95,401 | 1 | 138.26 | 12.23 | < .001 |
| cond:type | 23,446 | 2 | 129.85 | 3.006 | 0.05 |
| cond:duration | 17,524 | 2 | 128.85 | 2.24 | 0.1 |
| type:duration | 68,767 | 1 | 138.13 | 11.12 | 0.001 |
| cond:type:duration | 25,488 | 2 | 128.76 | 3.26 | 0.04 |
cond condition, NumDF numerator degrees of freedom, DenDF denominator degrees of freedom
Summary of the linear mixed model effects for reaction times
| Estimate | SE |
| t value |
| |
|---|---|---|---|---|---|
| Intercept | 1168.65 | 196.90 | 243.55 | 5.93 | < 0.001 |
| cond2 | − 447.75 | 218.79 | 193.21 | − 2.04 | 0.04 |
| cond3 | − 465.45 | 214.86 | 229.29 | − 2.16 | 0.03 |
| typesame | − 577.47 | 217.19 | 202.15 | − 2.65 | 0.008 |
| duration | − 56.11 | 41.50 | 253.89 | − 1.35 | 0.1 |
| cond2:typesame | 183.59 | 259.70 | 151.08 | 0.707 | 0.48 |
| cond3:typesame | 518.98 | 246.27 | 178.96 | 2.107 | 0.03 |
| cond2:duration | 74.96 | 47.27 | 191.96 | 1.58 | 0.11 |
| cond3:duration | 98.69 | 46.14 | 237.33 | 2.13 | 0.03 |
| typesame:durat | 119.42 | 46.86 | 202.86 | 2.54 | 0.01 |
| cond2:typesame:duration | − 24.73 | 59.07 | 142.59 | − 0.41 | 0.6 |
| cond3:typesame:duration | − 116.01 | 54.51 | 175.83 | − 2.128 | 0.03 |
SE standard error, cond1 morphological condition, cond2 non-morphological condition, cond3 control condition, df degrees of freedom
Fig. 1Reaction times. These graphs plot the predictions from coefficients in same and different minimal pairs
Results of the ANOVA performed on the logistic regression
|
| F |
| |
|---|---|---|---|
| Condition | 2 | 54.12 | < 0.001 |
| Type | 1 | 46.96 | < 0.001 |
| Duration | 1 | 41.87 | < 0.001 |
| Condition:type | 2 | 6.69 | < 0.05 |
df degrees of freedom
Summary of the linear mixed model effects for accuracy
| Coefficient | SE | z |
| |
|---|---|---|---|---|
| Intercept | 6.23 | 0.984 | 6.336 | < 0.001 |
| cond2 | 2.06 | 0.292 | 7.056 | < 0.001 |
| cond3 | 0.22 | 0.234 | 0.965 | 0.33 |
| typesame | 0.55 | 0.243 | 2.276 | 0.02 |
| duration | − 0.007 | 0.001 | − 6.737 | < 0.001 |
| cond2:typesame | − 1.11 | 0.393 | − 2.837 | 0.004 |
| cond3:typesame | 0.35 | 0.328 | 1.080 | 0.28 |
SE standard error, cond1 morphological condition, cond2 non-morphological condition, cond3 control condition
Fig. 2Accuracy. This graph plots the raw data (proportion of correct answers out of given answers) across different types (same/different) and across different conditions (morphological, non-morphological and control) as well as their interaction
Fig. 3Scatterplots of item raw reaction-times/duration across condition in different and same minimal pairs
List of nonwords used in the task
| Stem number | Morpho | Morpho | Non-morpho | Non-morpho | Control | Control | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | vɪld | 2 | vɪlz | 41 | vɪlt | 42 | vɪls | 81 | vɪlb | 82 | vɪlm |
| 2 | 3 | vɛld | 4 | vɛlz | 43 | vɛlt | 44 | vɛls | 83 | vɛlb | 84 | vɛlm |
| 3 | 5 | væld | 6 | vælz | 45 | vælt | 46 | væls | 85 | vælb | 86 | vælm |
| 4 | 7 | vɔld | 8 | vɔlz | 47 | vɔlt | 48 | vɔls | 87 | vɔlb | 88 | vɔlm |
| 5 | 9 | vʌld | 10 | vʌlz | 49 | vʌlt | 50 | vʌls | 89 | vʌln | 90 | vʌlm |
| 6 | 11 | nɪld | 12 | nɪlz | 51 | nɪlt | 52 | nɪls | 91 | nɪlb | 92 | nɪlm |
| 7 | 13 | naɪld | 14 | naɪlz | 53 | naɪlt | 54 | naɪls | 93 | naɪlb | 94 | naɪlm |
| 8 | 15 | næld | 16 | nælz | 55 | nælt | 56 | næls | 95 | nælb | 96 | nælm |
| 9 | 17 | nɔld | 18 | nɔlz | 57 | nɔlt | 58 | nɔls | 97 | nɔlb | 98 | nɔlm |
| 10 | 19 | nʌld | 20 | nʌlz | 59 | nʌlt | 60 | nʌls | 99 | nʌlb | 100 | nʌlm |
| 11 | 21 | θɪld | 22 | θɪlz | 61 | θɪlt | 62 | θɪls | 101 | θɪlb | 102 | θɪlm |
| 12 | 23 | θaɪld | 24 | θaɪlz | 63 | θaɪlt | 64 | θaɪls | 103 | θaɪlb | 104 | θaɪlm |
| 13 | 25 | θæld | 26 | θælz | 65 | θælt | 66 | θæls | 105 | θælb | 106 | θælm |
| 14 | 27 | θɔld | 28 | θɔlz | 67 | θɔlt | 68 | θɔls | 107 | θɔlb | 108 | θɔlm |
| 15 | 29 | θʌld | 30 | θʌlz | 69 | θʌlt | 70 | θʌls | 109 | θʌlb | 110 | θʌlm |
| 16 | 31 | dʒald | 32 | dʒalz | 71 | dʒalt | 72 | dʒals | 111 | dʒalb | 112 | dʒalm |
| 17 | 33 | dʒaɪld | 34 | dʒaɪlz | 73 | dʒaɪlt | 74 | dʒaɪls | 113 | dʒaɪlb | 114 | dʒaɪlm |
| 18 | 35 | dʒæld | 36 | dʒælz | 75 | dʒælt | 76 | dʒæls | 115 | dʒælb | 116 | dʒælm |
| 19 | 37 | dʒɔld | 38 | dʒɔlz | 77 | dʒɔlt | 78 | dʒɔls | 117 | dʒɔlb | 118 | dʒɔlm |
| 20 | 39 | dʒʌld | 40 | dʒʌlz | 79 | dʒʌlt | 80 | dʒʌls | 119 | dʒʌlb | 120 | dʒʌlm |
List of models tested (only the first 4 converged and were compared)
| Models | |
|---|---|
| M1 | lmer(rt_corr ~ cond*type*length + (1|part) + (1|item), REML = TRUE, na.action = na.omit) |
| M2 | lmer(rt_corr ~ cond*type*length + (cond|part) + (1|item), REML = TRUE, na.action = na.omit) |
| M3 | lmer(rt_corr ~ cond*type*length + (type|part) + (1|item), REML = TRUE, na.action = na.omit) |
| M4 | lmer(rt_corr ~ cond*type*length + (cond + type|part) + (1|item), REML = TRUE, na.action = na.omit) |
| M5 | lmer(rt_corr ~ cond*type*length + (cond*type|part) + (1|item), REML = TRUE, na.action = na.omit) |
| M6 | lmer(rt_corr ~ cond*type*length + (1|part) + (cond|item), REML = TRUE, na.action = na.omit) |
| M7 | lmer(rt_corr ~ cond*type*length + (1|part) + (type|item), REML = TRUE, na.action = na.omit) |
| M8 | lmer(rt_corr ~ cond*type*length + (1|part) + (cond + type|item), REML = TRUE, na.action = na.omit) |
| M9 | lmer(rt_corr ~ cond*type*length + (1|part) + (cond*type|item), REML = TRUE, na.action = na.omit) |
| M10 | lmer(rt_corr ~ cond*type*length + (cond|part) + (cond|item), REML = TRUE, na.action = na.omit) |
| M11 | lmer(rt_corr ~ cond*type*length + (cond|part) + (type|item), REML = TRUE, na.action = na.omit) |
| M12 | lmer(rt_corr ~ cond*type*length + (cond|part) + (cond + type|item), REML = TRUE, na.action = na.omit) |
| M13 | lmer(rt_corr ~ cond*type*length + (cond|part) + (cond*type|item), REML = TRUE, na.action = na.omit) |
| M14 | lmer(rt_corr ~ cond*type*length + (type|part) + (cond|item), REML = TRUE, na.action = na.omit) |
| M15 | lmer(rt_corr ~ cond*type*length + (type|part) + (type|item), REML = TRUE, na.action = na.omit) |
| M16 | lmer(rt_corr ~ cond*type*length + (type|part) + (cond + type|item), REML = TRUE, na.action = na.omit) |
| M17 | lmer(rt_corr ~ cond*type*length + (type|part) + (cond*type|item), REML = TRUE, na.action = na.omit) |
| M18 | lmer(rt_corr ~ cond*type*length + (cond + type|part) + (cond + type|item), REML = TRUE, na.action = na.omit) |
| M19 | lmer(rt_corr ~ cond*type*length + (cond*type|part) + (cond*type|item), REML = TRUE, na.action = na.omit) |