An assessment of the fourth law of Kuryłowicz: does prototypicality of meaning affect language change?

Isabeau De Smet

doi:10.1515/cog-2022-0089

Published by De Gruyter Mouton June 22, 2023

An assessment of the fourth law of Kuryłowicz: does prototypicality of meaning affect language change?

Isabeau De Smet

From the journal Cognitive Linguistics

https://doi.org/10.1515/cog-2022-0089

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Abstract

According to the (in)famous fourth law of Kuryłowicz (K4), when a morphological doublet arises in a language, the newer form becomes associated with the prototypical, basic meaning, while the old form takes a secondary meaning. This paper takes a first attempt at a more thorough inquiry of K4 to assess whether prototypicality of meaning has an effect on morphological change. Three studies on historical Dutch are taken on: -en versus -s plurals, the apocope of schwa and the apocope of -de. The effects of prototypicality are analysed both on a token level (differences in meaning within lemmas) as well as on a type level (differences between lemmas). As proxies for prototypicality of meaning (psycho)linguistic predictors are used, such as concreteness, age of acquisition, chronology of meaning, meaning frequency and metaphor. Results show no clear effect of prototypicality on a token level, but they do suggest an effect on a type level: more concrete meanings tend to show up more often with the newer variant. Yet these results may also be ascribed to iconicity as the newer variants in these cases are the shorter ones and concrete meanings tend to be represented by shorter words than abstract ones.

Keywords: concreteness; Dutch; historical linguistics; prototypicality

Corresponding author: Isabeau De Smet, KU Leuven, Leuven, Belgium; and FWO (Research Foundation Flanders), Brussel, Belgium, E-mail: isabeau.desmet@kuleuven.be

Funding source: Fonds Wetenschappelijk Onderzoek

Award Identifier / Grant number: 12W5522N

Acknowledgments

I want to sincerely thank Stefano De Pascale for his time and effort annotating a subset of my data. I would also like to express my gratitude to Freek Van de Velde, for his feedback on this paper, as well as the three anonymous reviewers for their helpful comments.

Appendix

Table 1:

Fixed effects model 1 plurals – metaphor (token level).

Variable	Estimate	p-Value
Intercept	1.743	0.002
metaphor (figurative)	−0.150	0.666
register (spoken)	1.219	0.002
rhyme (yes)	0.322	0.558
century	1.955	<0.001
earlier variation	−0.172	0.292
lemma frequency	−1.093	0.129

Significant p-values (p < 0.05) are in bold. C-value^a: 0.921, marginal R ²: 0.242, conditional R ²: 0.761,^b bobyqa added. ^aWe used the MiMIn package (Barton 2019). ^bWe used the ModelMetrics package (Hunt 2018).

Table 2:

Random effects model 1 plurals – metaphor (token level).

Groups	Name	Variance	Standard deviation	Correlation
author	Intercept	1.607	1.268
lemma	Intercept	1.568	1.252
	metaphor	0.729	0.854
	rhyme	2.062	1.436
	century	3.039	1.743	0.84

Table 3:

Fixed effects model 2 plurals – chronology of meaning (token level).

Variable	Estimate	p-Value
Intercept	1.586	0.001
chronology of meaning (secondary meaning)	0.239	0.521
register (spoken)	1.233	0.001
rhyme (yes)	0.607	0.094
century	1.841	<0.001
earlier variation	−0.074	0.646
lemma frequency	−0.476	0.443

Significant p-values (p < 0.05) are in bold. C-value: 0.916, marginal R ²: 0.251, conditional R ²: 0.724, bobyqa added.

Table 4:

Random effects model 2 plurals – chronology of meaning (token level).

Groups	Name	Variance	Standard deviation	Correlation
author	Intercept	1.506	1.227
lemma	Intercept	1.096	1.047
	chronology of meaning	1.183	1.088
	century	2.623	1.620	0.65

Table 5:

Fixed effects model 3 plurals – meaning frequency (token level).

Variable	Estimate	p-Value
Intercept	1.718	0.002
meaning frequency (less frequent)	−0.031	0.920
register (spoken)	1.224	0.002
rhyme (yes)	0.336	0.524
century	1.937	<0.001
earlier variation	−0.134	0.412
lemma frequency	−0.862	0.274

Significant p-values (p < 0.05) are in bold. C-value: 0.921, marginal R ²: 0.247, conditional R ²: 0.755, bobyqa added.

Table 6:

Random effects model 3 plurals – meaning frequency (token level).

Groups	Name	Variance	Standard deviation	Correlation
author	Intercept	1.622	1.274
lemma	Intercept	1.623	1.274
	meaning frequency	0.768	0.877
	rhyme	1.724	1.313
	century	2.976	1.725	0.63

Table 7:

Fixed effects model 4 plurals – concreteness (token level).

Variable	Estimate	p-Value
Intercept	1.873	<0.001
concreteness (abstract)	−1.406	<0.001
register (spoken)	1.145	0.003
rhyme (yes)	0.449	0.406
century	1.904	<0.001
earlier variation	−0.150	0.380
lemma frequency	−1.065	0.177

Significant p-values (p < 0.05) are in bold. C-value: 0.927, marginal R ²: 0.261, conditional R ²: 0.749, bobyqa added.

Table 8:

Random effects model 4 plurals – concreteness (token level).

Groups	Name	Variance	Standard deviation	Correlation
author	Intercept	2.430	1.559
	concreteness	1.381	1.175	−0.79
lemma	Intercept	1.604	1.267
	rhyme	1.863	1.365
	century	2.328	1.526	0.56

Table 9:

Fixed effects model 5 schwa apocope – metaphor (token level).

Variable	Estimate	p-Value
Intercept	−0.811	0.248
metaphor (figurative)	0.361	0.281
register (spoken)	1.303	0.003
rhyme (yes)	1.890	0.003
century	2.287	<0.001
earlier variation	−0.328	0.109
lemma frequency	−0.435	0.485

Significant p-values (p < 0.05) are in bold. C-value: 0.916, marginal R ²: 0.313, conditional R ²: 0.752, bobyqa added.

Table 10:

Random effects model 5 schwa apocope – metaphor (token level).

Groups	Name	Variance	Standard deviation
author	Intercept	2.830	1.682
lemma	Intercept	0.716	0.846
	century	2.261	1.504

Table 11:

Fixed effects model 6 schwa apocope – chronology of meaning ^a (token level).

Variable	Estimate	p-Value
Intercept	0.907	0.168
chronology of meaning (secondary)	−0.574	0.235
register (spoken)	1.284	0.014
rhyme (yes)	1.905	0.005
century	2.522	<0.001
earlier variation	−0.482	0.020
lemma frequency	−0.395	0.488

Significant p-values (p < 0.05) are in bold. C-value: 0.935, marginal R ²: 0.292, conditional R ²: 0.777, bobyqa added. ^aIn this model, we had to delete both the correlation parameters from the random effects structure, even though they made the model significantly better. Even with the optimizer bobyqa the model did not converge otherwise. All estimates and p-values remain largely the same.

Table 12:

Random effects model 6 schwa apocope – chronology of meaning (token level).

Groups	Name	Variance	Standard deviation
author	Intercept	3.171	1.781
	chronology of meaning	1.245	1.116
lemma	Intercept	0.403	0.635
	chronology of meaning	0.920	1.648
	century	2.714	1.648

Table 13:

Fixed effects model 7 schwa apocope –meaning frequency ^a (token level).

Variable	Estimate	p-Value
Intercept	0.809	0.262
meaning frequency (less frequent)	−0.168	0.795
register (spoken)	1.505	0.002
rhyme (yes)	1.820	0.005
century	2.445	<0.001
earlier variation	−0.279	0.200
lemma frequency	−0.328	0.512

Significant p-values (p < 0.05) are in bold. C-value: 0.931, marginal R ²: 0.310, conditional R ²: 0.747, bobyqa added. ^aIn this model, we had to delete both the correlation parameters from the random effects structure, even though they made the model significantly better. Even with the optimizer bobyqa the model did not converge otherwise. All estimates and p-values remain largely the same.

Table 14:

Random effects model 7 schwa apocope – meaning frequency (token level).

Groups	Name	Variance	Standard deviation	Correlation
author	Intercept	2.909	1.706
lemma	Intercept	1.612	1.270
	meaning frequency	3.489	1.868	−0.91
	century	1.465	1.210

Table 15:

Fixed effects model 8 schwa apocope – concreteness (token level).

Variable	Estimate	p-Value
Intercept	0.739	0.241
concreteness (abstract)	0.117	0.871
register (spoken)	1.289	0.012
rhyme (yes)	1.821	0.005
century	2.620	<0.001
earlier variation	−0.451	0.029
lemma frequency	−0.390	0.594

Significant p-values (p < 0.05) are in bold. C-value: 0.933, marginal R ²: 0.315, conditional R ²: 0.798, bobyqa added.

Table 16:

Random effects model 8 schwa apocope – concreteness (token level).

Groups	Name	Variance	Standard deviation
author	Intercept	2.472	1.572
	concreteness	2.209	1.486
lemma	Intercept	0.255	0.505
	concreteness	2.386	1.545
	century	2.666	1.633

Table 17:

Fixed effects model 9 de apocope – metaphor (token level).^a

Variable	Estimate	p-Value
Intercept	−0.749	0.134
metaphor (figurative)	0.469	−0.033
register (spoken)	0.939	0.010
rhyme (yes)	1.754	<0.001
century	1.188	<0.001
earlier variation	0.070	0.554
lemma frequency	0.331	0.230

Significant p-values (p < 0.05) are in bold. C-value: 0.903, marginal R ²: 0.159, conditional R ²: 0.626, bobyqa added. ^aBecause the final model obtained through the steps described above did not converge with the optimizer bobyqa, we did exclude a random slope (i.e., century by lemma) that made the model significantly better. Estimates and p-values only differ slightly from the model that included these items in the random structure.

Table 18:

Random effects model 9 de apocope – metaphor (token level).

Groups	Name	Variance	Standard deviation
author	Intercept	4.089	2.022
lemma	Intercept	0.021	0.146

Table 19:

Fixed effects model 10 de apocope – chronology of meaning ^a (token level).

Variable	Estimate	p-Value
Intercept	−1.039	0.029
chronology of meaning (secondary)	−0.172	0.329
register (spoken)	0.905	0.014
rhyme (yes)	1.732	<0.001
century	1.161	<0.001
earlier variation	0.135	0.232
lemma frequency	0.449	0.046

Significant p-values (p < 0.05) are in bold. C-value: 0.901, marginal R ²: 0.153, conditional R ²: 0.624, bobyqa added. ^aBecause the final model obtained (with a random slope for chronology of meaning by lemma and a correlated random slope for chronology of meaning by author) through the steps described above did not converge with the optimizer bobyqa, we had to exclude random factors until convergence. We started out with the correlated term, then both random slopes and finally the random effect for lemma. Estimates and p-values only differ slightly from the model that included these items in the random structure.

Table 20:

Random effects model 10 de apocope – chronology of meaning (token level).

Groups	Name	Variance	Standard deviation
author	Intercept	4.116	2.029

Table 21:

Fixed effects model 11 de apocope – meaning frequency ^a (token level).

Variable	Estimate	p-Value
Intercept	−1.074	0.024
meaning frequency (less frequent)	−0.068	0.718
register (spoken)	0.925	0.012
rhyme (yes)	1.754	<0.001
century	1.166	<0.001
earlier variation	0.127	0.261
lemma frequency	0.471	0.050

Significant p-values (p < 0.05) are in bold. C-value: 0.901, marginal R ²: 0.153, conditional R ²: 0.624, bobyqa added. ^aBecause the final model obtained through the steps described above did not converge with the optimizer bobyqa, we had to exclude all random slopes (starting with the one that made the least difference until convergence). Estimates and p-values only differ slightly from the model with the random slopes.

Table 22:

Random effects model 11 de apocope – meaning frequency (token level).

Groups	Name	Variance	Standard deviation
author	Intercept	4.117	2.029
lemma	Intercept	0.002	0.042

Table 23:

Fixed effects model 12 de apocope – concreteness (token level).^a

Variable	Estimate	p-Value
Intercept	−1.095	0.039
concreteness (abstract)	0.187	0.700
register (spoken)	0.936	0.014
rhyme (yes)	1.659	<0.001
century	1.022	<0.001
earlier variation	0.062	0.648
lemma frequency	0.384	0.400

Significant p-values (p < 0.05) are in bold. C-value: 0.920, marginal R ²: 0.110, conditional R ²: 0.675, bobyqa added. ^aBecause the final model obtained through the steps described above did not converge with the optimizer bobyqa, we had to exclude the correlation parameter, even though it made the model significantly better. Estimates and p-values only differ slightly from the model with the random slopes.

Table 24:

Random effects model 12 de apocope – concreteness (token level).

Groups	Name	Variance	Standard deviation
author	Intercept	4.918	2.218
	concreteness	1.825	1.351
lemma	Intercept	<0.001	0.022
	century	0.171	0.414

Table 25:

Fixed effects model 13 plurals – age of acquisition (type level).

Variable	Estimate	p-Value
Intercept	2.566	<0.001
age of acquisition	−0.062	0.773
register (spoken)	0.853	0.035
rhyme (yes)	0.685	0.046
century	2.526	<0.001
earlier variation	0.357	0.045
frequency	0.396	0.348

Significant p-values (p < 0.05) are in bold. C-value: 0.967, marginal R ²: 0.292, conditional R ²: 0.863, bobyqa added.

Table 26:

Random effects model 13 plurals – age of acquisition (type level).

Groups	Name	Variance	Standard deviation	Correlation
author	Intercept	1.654	1.286
lemma	Intercept	7.498	2.738
	register	1.430	1.196
	century	3.407	1.846	0.78

Table 27:

Fixed effects model 14 plurals – concreteness (type level).

Variable	Estimate	p-Value
Intercept	2.286	<0.001
concreteness	0.765	0.010
register (spoken)	0.901	0.024
rhyme (yes)	0.668	0.055
century	2.286	<0.001
earlier variation	0.605	0.001
frequency	0.485	0.201

Significant p-values (p < 0.05) are in bold. C-value: 0.970, marginal R ²: 0.356, conditional R ²: 0.854, bobyqa added.

Table 28:

Random effects model 14 plurals – concreteness (type level).

Groups	Name	Variance	Standard deviation	Correlation
author	Intercept	1.689	1.300
	concreteness	0.336	0.580
lemma	Intercept	5.357	2.314
	register	1.116	1.056
	century	2.932	1.712	0.78

Table 29:

Fixed effects model 15 plurals – polysemy (type level).

Variable	Estimate	p-Value
Intercept	2.657	<0.001
polysemy	−0.003	0.992
register (spoken)	1.015	0.013
rhyme (yes)	0.648	0.062
century	2.256	<0.001
earlier variation	0.376	0.037
frequency	0.479	0.326

Significant p-values (p < 0.05) are in bold. C-value: 0.969, marginal R ²: 0.305, conditional R ²: 0.864, bobyqa added.

Table 30:

Random effects model 15 plurals – polysemy (type level).

Groups	Name	Variance	Standard deviation	Correlation
author	Intercept	1.576	1.256
	polysemy	0.274	0.524
lemma	Intercept	7.571	2.752
	register	1.137	1.066
	century	3.192	1.787	0.78

Table 31:

Fixed effects model 16 schwa apocope – age of acquisition (type level).

Variable	Estimate	p-Value
Intercept	1.394	0.018
age of acquisition	0.084	0.795
register (spoken)	1.104	0.002
rhyme (yes)	0.439	0.143
century	1.040	<0.001
earlier variation	−0.325	0.022
frequency	0.479	0.326

Significant p-values (p < 0.05) are in bold. C-value: 0.930, marginal R ²: 0.058, conditional R ²: 0.754, bobyqa added.

Table 32:

Random effects model 16 schwa apocope – age of acquisition (type level).

Groups	Name	Variance	Standard deviation	Correlation
author	Intercept	1.188	1.090
	age of acquisition	0.452	0.672
lemma	Intercept	5.188	2.278
	register	1.323	1.150
	rhyme	0.419	0.647
	century	1.494	1.222	0.69

Table 33:

Fixed effects model 17 schwa apocope – concreteness (type level).

Variable	Estimate	p-Value
Intercept	1.438	0.026
concreteness	0.625	0.097
register (spoken)	1.130	<0.001
rhyme (yes)	0.554	0.081
century	0.934	0.003
earlier variation	−0.225	0.077
frequency	0.270	0.606

Significant p-values (p < 0.05) are in bold. C-value: 0.931, marginal R ²: 0.077, conditional R ²: 0.749, bobyqa added.

Table 34:

Random effects model 17 schwa apocope – concreteness (type level).

Groups	Name	Variance	Standard deviation
author	Intercept	1.089	1.044
	concreteness	0.546	0.749
lemma	Intercept	4.662	2.159
	register	0.985	0.993
	rhyme	0.515	0.717
	century	1.725	1.314

Table 35:

Fixed effects model 18 schwa apocope – polysemy (type level).

Variable	Estimate	p-Value
Intercept	1.270	0.016
polysemy	−0.249	0.428
register (spoken)	0.891	0.008
rhyme (yes)	0.522	0.073
century	0.941	<0.001
earlier variation	−0.136	0.351
frequency	−0.271	0.472

Significant p-values (p < 0.05) are in bold. C-value: 0.929, marginal R ²: 0.048, conditional R ²: 0.717, bobyqa added.

Table 36:

Random effects model 18 schwa apocope – polysemy (type level).

Groups	Name	Variance	Standard deviation	Correlation
author	Intercept	1.203	1.100
	polysemy	0.234	0.484
lemma	Intercept	4.193	2.048
	register	1.150	1.072
	rhyme	0.357	0.597
	century	1.287	1.135	0.78

Table 37:

Fixed effects model 19 de apocope – age of acquisition (type level).

Variable	Estimate	p-Value
Intercept	−2.358	0.001
age of acquisition	1.108	0.028
register (spoken)	0.932	0.008
rhyme (yes)	2.144	<0.001
century	0.757	0.003
earlier variation	−0.025	0.815
frequency	−0.304	0.633

Significant p-values (p < 0.05) are in bold. C-value: 0.939, marginal R ²: 0.172, conditional R ²: 0.756, bobyqa added.

Table 38:

Random effects model 19 de apocope – age of acquisition (type level).

Groups	Name	Variance	Standard deviation
author	Intercept	3.132	1.770
	age of acquisition	0.239	0.489
lemma	Intercept	3.769	1.941
	rhyme	0.781	0.884
	century	0.504	0.710

Table 39:

Fixed effects model 20 de apocope – concreteness (type level).^a

Variable	Estimate	p-Value
Intercept	−3.391	<0.001
concreteness	1.502	<0.001
register (spoken)	0.862	0.013
rhyme (yes)	2.152	<0.001
century	0.716	0.004
earlier variation	−0.047	0.658
frequency	0.431	0.340

Significant p-values (p < 0.05) are in bold. C-value: 0.939, marginal R ²: 0.172, conditional R ²: 0.756, bobyqa added. ^aBecause the final model did not converge, we took out the random slope that made the least difference to the model, i.e., concreteness by author. Estimates and p-values only differ slightly.

Table 40:

Random effects model 20 de apocope – concreteness (type level).

Groups	Name	Variance	Standard deviation
author	Intercept	3.143	1.773
lemma	Intercept	1.577	1.256
	rhyme	1.161	1.077
	century	0.471	0.686

Table 41:

Fixed effects model 21 de apocope – polysemy (type level).

Variable	Estimate	p-Value
Intercept	−2.276	0.004
polysemy	−0.198	0.767
register (spoken)	0.726	0.062
rhyme (yes)	2.216	<0.001
century	0.773	0.003
earlier variation	−0.031	0.077
frequency	0.190	0.773

Significant p-values (p < 0.05) are in bold. C-value: 0.940, marginal R ²: 0.086, conditional R ²: 0.7552, bobyqa added.

Table 42:

Random effects model 21 de apocope – polysemy (type level).

Groups	Name	Variance	Standard deviation
author	Intercept	3.149	1.775
	polysemy	0.227	0.476
lemma	Intercept	4.548	2.133
	register	0.284	0.533
	rhyme	0.584	0.764
	century	0.542	0.736

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Received: 2022-11-10

Accepted: 2023-06-01

Published Online: 2023-06-22

Published in Print: 2023-05-25

An assessment of the fourth law of Kuryłowicz: does prototypicality of meaning affect language change?

Abstract

Acknowledgments

References

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