Is structural priming between different languages a learning effect? Modelling priming as error-driven implicit learning

Figures & data

Figure 1. Bilingual Dual-path, the model used in our Spanish-English priming experiment. The model is an SRN-based model (the lower path, via the “compress” layers) that is augmented with a semantic stream (upper path) that contains information about concepts, thematic roles, event semantics, and the target language. The number of units per layer for the model used in Experiment 1 is shown in parentheses. The numbers of units for the hidden and compress layers vary across simulations.

Table 1. Sentence types in the artificial language input for the Spanish-English model.

Sentence type	Spanish	English
Animate intransitive	la gata vieja está reir -ger.	the old cat is laugh -prg.
	(La gata vieja está riendo.)	(The old cat is laughing.)
Animate with-intransitive	él ha caminar -prf con la abuela.	he has walk -par with the grandmother.
	(El ha caminado con la abuela.)	(He has walked with the grandmother.)
Inanimate intransitive	el cacto estába caer -ger.	the cactus was fall -prg.
	(El cacto estaba cayendo.)	(The cactus was falling.)
Locative	un camarero pequeño saltar en la bañera.	a small waiter jump in the bath.
	(Un camarero pequeño salta en la bañera.)	(A small waiter jumps in the bath.)
Transitive (active)	el padre romper -pas la botella.	the father break -pst the bottle.
	(El padre rompió la botella.)	(The father broke the bottle.)
Transitive (passive)	la botella es romper -prf por el padre.	the bottle is break -par by the father.
	(La botella es rota por el padre.)	(The bottle is broken by the father.)
Cause-motion	ella está deslizar -ger el café por el pueblo	she is slide -prg the coffee by the village
	(Ella está deslizarando el café por el pueblo.)	(She is sliding the coffee by the village.)
Benefactive transitive	la tía grande reparar un cacto para el perro	the big aunt repair a cactus for the dog
	(La tía grande repara un cacto para el perro.)	(The big aunt repairs a cactus for the dog.)
State-change	ella llenar -pas la botella con naranja	she fill -pst the bottle with orange
	(Ella llenó la botella con naranja.)	(She filled the bottle with orange.)
Locative alternation (LT)	el niño cepillar el lavabo con leche	the boy brush the sink with milk
	(El niño cepilla el lavabo con leche.)	(The boy brushes the sink with milk.)
Locative alternation (TL)	el niño cepillar leche sobre un lavabo	the boy brush milk on the sink
	(El niño cepilla leche sobre el lavabo.)	(The boy brushes milk on the sink.)

Example sentences are given for each sentence type in each of the two artificial languages, and the natural language sentences they represent are given underneath in parentheses.

Figure 2. Percentage of responses in Experiment 1 (Spanish-English) that had a passive structure after either an active prime or a passive prime, for cross-language trials (on the left) or within-language trials (on the right). The thick black lines visualise the priming effect across all analysed trials by connecting the percentage of passive responses after active primes to the percentage of passive responses after passive primes. The thin grey lines show the same for each individual simulated participant.

Figure 3. Posterior distributions of the estimate for the Prime Structure predictor (top) and of the estimate for the interaction between Language Combination and Prime Structure (bottom) in Experiment 1 (Spanish-English).

Table 2. Summary of the fixed effects in the Bayesian logistic mixed-effects model ($N=45,310$) for Experiment 1.

Predictor	Est.	95% CrI	P(Est. > 0)
Intercept	1.62	[1.23, 1.99]	1.00
Prime Structure	3.08	[2.65, 3.50]	1.00
Language Combination	0.29	[0.12, 0.45]	1.00
Prime Language	0.71	[0.59, 0.83]	1.00
Target-Message Bias	11.55	[11.21, 11.90]	1.00
Language Combination × Prime Structure	0.42	[0.09, 0.76]	1.00

For each predictor are shown its estimate (Est.) with 95% Bayesian credible interval (CrI) and the posterior probability that the estimate is positive (P(Est. > 0)). Estimated are log-odds (for the Intercept), log-odds ratios (for the Prime Structure, Language Combination, Prime Language, and Target-Message Bias predictors), and a ratio of log-odds ratios (for the interaction between Language Combination and Prime Structure).

Table 3. Sentence types in the artificial language input for the Dutch-English models.

Sentence type	Dutch	English
Animate intransitive	de oude kat is lachen -dur.	the old cat is laugh -prg.
	(De oude kat is aan het lachen.)	(The old cat is laughing.)
Animate with-intransitive	hij heeft wandelen -vdw met de oma.	he has walk -par with the grandmother.
	(Hij heeft gewandeld met de oma.)	(He has walked with the grandmother.)
Inanimate intransitive	de cactus was vallen -dur.	the cactus was fall -prg.
	(De cactus was aan het vallen.)	(The cactus was falling.)
Locative	een kleine kelner springen in het bad.	a small waiter jump in the bath.
	(Een kleine kelner springt in het bad.)	(A small waiter jumps in the bath.)
Transitive (active)	de vader breken -ver de fles.	the father break -pst the bottle.
	(De vader brak de fles.)	(The father broke the bottle.)
Transitive (verb-final Dutch passive)	de fles wordt door de vader breken -vdw.
	(De fles wordt gebroken door de vader.)	the bottle is break -par by the father.
Transitive (verb-medial Dutch passive)	de fles wordt breken -vdw door de vader.	(The bottle is broken by the father.)
	(De fles wordt gebroken door de vader.)
Theme-experiencer (active)	de nieuwe tante verbazen een gastvrouw	the new aunt surprise a hostess
	(De nieuwe tante verbaast een gastvrouw.)	(The new aunt surprises a hostess.)
Theme-experiencer (verb-medial Dutch passive)	een gastvrouw wordt verbazen -vdw door de nieuwe tante
	(Een gastvrouw wordt verbaasd door de nieuwe tante.)	a hostess is surprise -par by the new aunt
Theme-experiencer (verb-final Dutch passive)	een gastvrouw wordt door de nieuwe tante verbazen -vdw	(A hostess is surprised by the new aunt.)
	(Een gastvrouw wordt door de nieuwe tante verbaasd.)
Cause-motion	zij is de koffie rondom het dorp schuiven -dur.	she is slide -prg the coffee around the village.
	(Zij is de koffie rondom het dorp aan het schuiven.)	(She is sliding the coffee around the village.)
Benefactive transitive	de grote tante repareren een cactus voor de hond.	the big aunt repair a cactus for the dog.
	(De grote tante repareert een cactus voor de hond.)	(The big aunt repairs a cactus for the dog.)
State-change	zij vullen -ver de fles met sinaasappel.	she fill -pst the bottle with orange.
	(Zij vulde de fles met sinaasappel.)	(She filled the bottle with orange.)
Locative alternation (LT)	de jongen borstelen een wastafel met melk.	the boy brush the sink with milk.
	(De jongen borstelt een wastafel met melk.)	(The boy brushes the sink with milk.)
Locative alternation (TL)	de jongen borstelen melk op een wastafel.	the boy brush milk on the sink.
	(De jongen borstelt melk op een wastafel.)	(The boy brushes milk on the sink.)

Example sentences are given for each sentence type in each of the two artificial languages, and the natural language sentences they represent are given underneath in parentheses.

Figure 4. Percentage of responses in Experiment 2a (verb-final Dutch-English, left panel) and Experiment 2b (verb-medial Dutch-English, right panel) that had a passive structure after either an active or a passive prime, split by within- or cross-language trials. The thick black lines visualises the priming effect across all analysed trials by connecting the percentage of passives responses after active primes to the percentage of passive responses after passive primes. The thin grey lines show the same for each individual simulated participant.

Table 4. Summary of the fixed effects in the Bayesian logistic mixed-effects model ($N=42,462$) for Experiment 2a (verb-final Dutch-English).

Predictor	Est.	95% CrI	P(Est. > 0)
Intercept	0.84	[0.49, 1.20]	1.00
Prime Structure	2.37	[2.00, 2.74]	1.00
Language Combination	0.32	[0.20, 0.43]	1.00
Prime Language	0.36	[0.26, 0.46]	1.00
Target-Message Bias	9.02	[8.80, 9.24]	1.00
Language Combination × Prime Structure	0.53	[0.30,0.77]	1.00

For each predictor are shown its estimate (Est.) with 95% Bayesian credible interval (CrI) and the posterior probability that the estimate is positive (P(Est. > 0)). Estimated are log-odds (for the Intercept), log-odds ratios (for the Prime Structure, Language Combination, Prime Language, and Target-Message Bias predictors), and a ratio of log-odds ratios (for the interaction between Language Combination and Prime Structure).

Table 5. Summary of the fixed effects in the Bayesian logistic mixed-effects model ($N=52,176$) for Experiment 2b (verb-medial Dutch-English).

Predictor	Est.	95% CrI	P(Est. > 0)
Intercept	1.45	[1.10, 1.78]	1.00
Prime Structure	3.08	[2.81, 3.34]	1.00
Language Combination	0.12	[0.02, 0.22]	0.99
Prime Language	0.30	[0.21, 0.38]	1.00
Target-Message Bias	9.28	[9.08, 9.49]	1.00
Language Combination × Prime Structure	0.33	[0.12, 0.54]	1.00

For each predictor are shown its estimate (Est.) with 95% Bayesian credible interval (CrI) and the posterior probability that the estimate is positive (P(Est. > 0)). Estimated are: log-odds (for the Intercept), log-odds ratios (for the Prime Structure, Language Combination, Prime Language, and Target-Message Bias predictors), and a ratio of log-odds ratios (for the interaction between Language Combination and Prime Structure).

Table 6. Summary of the fixed effects in the Bayesian logistic mixed-effects model ($N=52,176$) for the combined analysis of the cross-language trials from Experiment 2a and 2b (verb-final and verb-medial Dutch-English).

Predictor	Est.	95% CrI	P(Est. > 0)
Intercept	1.14	[0.82, 1.45]	1.00
Prime Structure	2.79	[2.55, 3.04]	1.00
Dutch Passive Structure	0.61	[0.31, 0.90]	1.00
Prime Language	0.43	[0.34, 0.53]	1.00
Target-message Bias	9.28	[9.07, 9.50]	1.00
Dutch Passive Structure ×Prime Structure	0.51	[0.06, 0.95]	0.99

For each predictor are shown its estimate (Est.) with 95% Bayesian credible interval (CrI) and the posterior probability that the estimate is positive (P(Est. > 0)). Estimated are: log-odds (for the Intercept), log-odds ratios (for the Prime Structure, Dutch Passive Structure, Prime Language, and Target-Message Bias predictors), and a ratio of log-odds ratios (for the interaction between Dutch Passive Structure and Prime Structure).

Figure 5. Posterior distributions of the estimates for the Prime Structure predictors (top) and of the interaction between Language Combination and Prime Structure (bottom) for Experiment 2a (verb-final Dutch-English, solid line) and Experiment 2b (verb-medial Dutch-English, dashed line).

Figure 6. Posterior distribution of the estimate for the interaction between Dutch Passive Structure and Prime Structure.