Acquiring and Producing Sentences: Whether Learners Use Verb-Specific or Verb-General Information Depends on Cue Validity

Thothathiri, Malathi; Rattinger, Michelle G.

doi:10.3389/fpsyg.2016.00404

ORIGINAL RESEARCH article

Front. Psychol., 23 March 2016

Sec. Psychology of Language

Volume 7 - 2016 | https://doi.org/10.3389/fpsyg.2016.00404

Acquiring and Producing Sentences: Whether Learners Use Verb-Specific or Verb-General Information Depends on Cue Validity

$\r\nMalathi Thothathiri*$ Malathi Thothathiri^*

Michelle G. Rattinger

Department of Speech and Hearing Science, The George Washington University, Washington, DC, USA

Learning to produce sentences involves learning patterns that enable the generation of new utterances. Language contains both verb-specific and verb-general regularities that are relevant to this capacity. Previous research has focused on whether one source is more important than the other. We tested whether the production system can flexibly learn to use either source, depending on the predictive validity of different cues in the input. Participants learned new sentence structures in a miniature language paradigm. In three experiments, we manipulated whether individual verbs or verb-general mappings better predicted the structures heard during learning. Evaluation of participants’ subsequent production revealed that they could use either the structural preferences of individual verbs or abstract meaning-to-form mappings to construct new sentences. Further, this choice varied according to cue validity. These results demonstrate flexibility within the production architecture and the importance of considering how language was learned when discussing how language is used.

Introduction

As speakers, we do not simply repeat the same set of sentences every day but produce new sentences to describe events in the world as well as our internal thoughts. Thus, an important aspect of learning to talk is knowing how to generate novel utterances that obey the grammatical rules of language. A persistent question for research is whether the sentence production system accomplishes this feat by preferentially encoding and using knowledge about how specific verbs are used in different types of sentences or knowledge about how broad meanings are mapped to different word orders independent of specific verbs. Many theories of language acquisition and language production give priority to one of the two sources of structural information. Here we use a cue validity framework to test the idea that the mental architecture for learning to produce sentences might flexibly adapt to use either type of information depending on statistical regularities in the input.

Sentence production involves converting a thought into a sequence of sounds. There is consensus that this process includes the steps of conceptualization, grammatical encoding, and phonological encoding, before articulation (Bock and Levelt, 1994). The focus of this paper is grammatical encoding (Levelt, 1989). This is the stage at which different words are assigned to different grammatical functions (e.g., subject, direct object) and assembled into an ordered sequence. The challenge for the speaker is that any given language contains multiple possible word orders. For example, when describing the scenario of a dog running after a cat, an English speaker could say that “the dog chased the cat” or that “the cat was chased by the dog.” Similarly, when talking about Lily transferring a container of flowers to John, a speaker could say either that “Lily gave John the vase” or that “Lily gave the vase to John.” In contrast, “The dog chased the cat a ball” and “Lily gave John” are ungrammatical. Speakers seldom, if at all, make such errors, suggesting that they implicitly know that a given word order can be used in some scenarios but not others. What are the constraints that enable speakers to produce novel grammatical utterances while avoiding ungrammatical utterances?

One possible constraint on word order, illustrated in part by the above examples, is the main verb in the sentence. “Chase” describes a transitive action and requires two nouns in the sentence (the entity doing the chasing and the entity being chased) while “give” describes a transfer action and requires three nouns in the sentence (the entity giving, the thing that is given, and the entity receiving). Additionally, some verbs appear to be idiosyncratic with respect to the types of sentences in which they appear. For example, native English listeners judge “Bob donated the art collection to the museum” as being more acceptable than “Bob donated the museum the art collection.” Such observations have led to theories that assign importance to individual verbs in how syntactic knowledge is learned, represented and used, within linguistics (Chomsky, 1981, 1986; Kaplan and Bresnan, 1982; Pollard and Sag, 1994; Ferreira, 2000), language acquisition (Tomasello, 2000, 2003), language production (Bock and Levelt, 1994), and language comprehension (MacDonald et al., 1994; Trueswell and Tanenhaus, 1994). For example, it has been proposed that children initially learn to speak by using verb-specific sentence frames and that generalizations that apply to different verbs only emerge later (Tomasello, 2000, 2003). During production, it is postulated that speakers first retrieve the lemma associated with the verb, which provides the syntactic information necessary to construct a sentence (Bock and Levelt, 1994). Similarly, dominant constraint-based theories of language comprehension assume that listeners use the syntactic information associated with individual lexical items for sentence interpretation (MacDonald et al., 1994; Trueswell and Tanenhaus, 1994). We refer to such theories collectively hereafter as “lexicalist.” Although the different theories vary widely in many respects (e.g., how much syntactic knowledge is assumed to be innate vs. learned), they share the idea that individual lexical items, particularly verbs, play an important role in sentence production and comprehension.

A contrasting viewpoint is provided by theories that emphasize the role of abstract or verb-independent structural knowledge. Such abstract knowledge is postulated in part based on observations that people can produce and understand novel verbs (e.g., the first use of “google” as a verb as in “She googled the directions to his place”), use known verbs in unusual sentence structures (e.g., “She smiled herself an upgrade” Goldberg, 2003), and show structural or syntactic priming between sentences containing different verbs (e.g., see Thothathiri and Snedeker, 2008a,b). These capacities necessarily require that the language user represent and use mappings between meaning and sentence form that are not tied to individual lexical items. As with lexicalist theories above, “abstractionist” theories have been proposed within language acquisition (Fisher, 2002a,b), language production (Bock, 1990; Bock and Loebell, 1990; Konopka and Bock, 2009), and language comprehension (Bencini and Goldberg, 2000; Kaschak and Glenberg, 2000). These theories share the idea that abstract structural knowledge, which is not tied to specific lexical items, is used routinely to understand and produce sentences.

The balance between lexical conservatism and broad generalization has been investigated extensively within language acquisition. Different theories have attempted to account for the balance in different ways. For example, Pinker (1989) proposed that children initially rely on innate “broad-range” linking rules that tie abstract, non-verb-specific meanings to sentence structures. This enables correct generalization of structures from one verb to another but also leads to overgeneralization errors. Under the model proposed by Pinker (1989), children can prune such errors later during development by acquiring “narrow range” linking rules that tie specific classes of verbs to specific structures based on meaning. For example, children could learn that while many dative verbs alternate between the double-object and prepositional-object constructions (e.g., “Lily gave John the vase,” “Lily gave the vase to John”), verbs that encode the meaning of “continuous imparting of force” (e.g., carry) are not (entirely) grammatical in the double-object construction. This approach is called “semantic bootstrapping” because knowledge about the meanings of verbs is used to restrict structural choices. It has come under criticism because crosslinguistically, the structures that verbs appear in are not entirely predictable from verb semantics (see e.g., Bowerman and Brown, 2008). An alternative distributional or statistical learning approach proposes that speakers learn to produce utterances by tracking the statistical distribution of different lexical items in different constructions in the input (see e.g., Twomey et al., 2014). This approach has gained momentum in the last two to three decades due to demonstrations of impressive statistical learning abilities in infants, children, and adults. In particular, studies under this approach have investigated how learners deal with structural variation associated with different syntactic categories (Nouns: Hudson Kam and Newport, 2005, 2009; Smith and Wonnacott, 2010; Wonnacott, 2011. Verbs: Wonnacott et al., 2008, 2012; Perek and Goldberg, 2015). Here we focus on the learning of verb-related distributional information.

Broadly speaking, empirical evidence from statistical learning experiments suggests that learners can acquire and use both verb-specific and verb-independent information extracted from the input. In one study, adult participants learned a miniature language containing novel vocabulary and two novel syntactic structures (Wonnacott et al., 2008). During training, the researchers manipulated each individual verb’s relative preference for one structure vs. the other. Subsequent production and comprehension tasks showed that participants tended to use verbs in the structures with which they were statistically associated during training, consistent with the acquisition of verb-specific syntactic knowledge (Wonnacott et al., 2008). These findings agree with results from a number of paradigms, which show that individual verbs’ statistical associations with different sentence structures (hereafter “verb bias”) influence language comprehension and production in both adults and children (e.g., Garnsey et al., 1997; Stallings et al., 1998; Snedeker and Trueswell, 2004; Wilson and Garnsey, 2009). Empirical support also exists for the alternate perspective. Wonnacott et al. (2012) taught 5-year-old children a novel sentence form that corresponded to a novel meaning. Subsequent to training, children were tested on comprehension and production of the novel construction. Critically, a subset of the test trials involved new verbs not heard during training. Children successfully generalized from verbs heard during training to verbs heard during test, demonstrating the learning and use of verb-independent form-meaning mappings (Wonnacott et al., 2012, See also Casenhiser and Goldberg, 2005). Results from other paradigms similarly indicate that adults and children use abstract structural knowledge during language comprehension and production (e.g., Gertner et al., 2006; Thothathiri and Snedeker, 2008a,b; Konopka and Bock, 2009; Rowland et al., 2012).

Although debates within language acquisition and language processing are often framed in exclusionary terms, the two types of structural knowledge are not mutually exclusive (Thothathiri and Snedeker, 2008a). This is explicitly acknowledged by linguistic frameworks that have suggested a continuum of abstraction rather than a dichotomy (e.g., Jackendoff, 2002; Goldberg, 2003). A recent computational demonstration comes from a connectionist model of learning to talk that incorporated multiple cues, including lexically specific knowledge about word order and abstract mappings between event roles and sentential positions, which operated together during language acquisition (Chang et al., 2006). The model successfully mimicked aspects of linguistic behavior that have been observed in adults and children. This is proof of principle that the two sources of structural information can co-exist in learners’ minds. The important remaining question is how the language system weights the two types of information in different situations (Thothathiri and Snedeker, 2008a; Konopka and Bock, 2009; Rowland et al., 2012). While previous studies have documented the use of one or the other type of information, principled investigations of what guides the speaker to use one source vs. the other are rare [but see Perfors et al. (2010) for a hierarchical Bayesian account]. One exception is a recent study by Perek and Goldberg (2015). In this study, participants learned a language containing two novel word orders (Subject-Object-Verb and Pronoun-Subject-Verb). As in the Wonnacott et al. (2008) study, the researchers manipulated the statistical preference of different verbs for one word order or the other during training and showed that participants were able to learn and use such verb-specific information. However, participants’ production during test also depended on the discourse context. In the full-noun-phrase-biased context, participants were asked to describe scenes in response to the prompt “what happened here?” In the pronoun-biased context, the prompt was “what happened to the <animal>?” The key finding was that participants tended to use the construction that was discourse appropriate (e.g., Pronoun-Subject-Verb in the pronoun-biased context) even if that involved overriding verb-specific statistical preferences. Thus, Perek and Goldberg (2015) showed that speakers’ reliance on verb-specific or verb-general information could be shifted by the context under which language production occurs. In the present study, we tackle the problem exclusively from the perspective of how language input affects language production. Without manipulating discourse context or nudging the speakers in any way during the test phase, we ask whether different statistical properties encountered during the training phase alone can predictably lead speakers to rely on one source of information or the other.

A critical insight from connectionist frameworks is that the architecture of language processing is influenced by how language is learned (Plaut et al., 1996; Joanisse and Seidenberg, 1999; Gordon and Dell, 2003; Chang et al., 2006). The neural networks in these models do not just learn specific instantiations within a fixed architecture but reorganize and create interesting divisions of labor between different processes that capture behavioral dissociations observed in humans (Plaut et al., 1996; Chang, 2002; Gordon and Dell, 2003). These divisions of labor arise from competition between cues during learning such that reliable cues come to influence processing more than less reliable cues. We adopted a similar perspective and asked whether the human sentence production system can flexibly prioritize different sources of structural information under different learning conditions. We used a cue validity framework (Bates and MacWhinney, 1987; Goldberg et al., 2005; MacWhinney, 2013) to set up and test hypotheses about learning to produce sentences. In three experiments, we trained participants in a miniature language containing higher cue validity for verb-specific over abstract structural information, or a miniature language containing the opposite pattern. After training, we examined participants’ sentence production in the new language to evaluate whether they relied more on verb-specific or verb-independent knowledge. If the language learning architecture is flexible, we would expect different degrees of reliance on the two sources in different types of languages. Alternatively, if the sentence production system has a fixed architecture that prioritizes one source of information over the other, we would expect no effect of the type of language. The results have implications for theories of language acquisition and processing.

Experiment 1

In Experiment 1, participants were trained in a miniature language that contained two novel structures for describing transitive events (Table 1). The two structures both began with the verb. They differed in whether the agent of the transitive action was placed before the patient (hereafter agent-before-patient or AP order) or the patient before the agent (hereafter patient-before-agent or PA order). During training, we manipulated verb bias such that some verbs appeared solely in AP order (hereafter AP-only verbs), some solely in PA order (hereafter PA-only verbs), and others equally in the two orders (hereafter Alternating or Alt verbs). After training, participants were asked to describe new events. We analyzed the proportions of AP and PA orders produced in different conditions.

TABLE 1

TABLE 1. Miniature language input provided to participants in the three experiments.

If learners exclusively encode and use abstract structural representations, we would expect no differences between the different bias conditions. This is because all verbs in this experiment referred to transitive actions and this meaning could be mapped to either the AP or the PA order. In contrast, if learners encode and use verb-specific structural associations, we would expect systematic differences between bias conditions, with the lowest PA order proportion for AP-only verbs (0% PA order during training) and the highest proportion for PA-only verbs (100% PA order during training), and an in-between proportion for Alt verbs (50% PA order during training). We sought to replicate the results from a closely related previous study, which showed an effect of verb bias on participants’ language production (Wonnacott et al., 2008). Further, we attempted to address a potential confound in that study and explore the nature of the statistical associations acquired by the participants. In the previous study, the verb bias effects could have arisen from participants learning the associations between sentence structures and individual verb lemmas (e.g., the novel verb “fenk”) or the associations between sentences structures and the depicted action meanings (e.g., hitting). To tease apart the two possibilities, we included synonymous verbs in the current study. The synonymous verbs described the same physical action but were associated with different lemmas and word orders (cf. I like that and That pleases me in English). This meant that the action was linked 50–50 to AP and PA order but the individual verbs were associated 100% with a single sentence structure. Therefore, if participants learned action-structure associations only, we would expect the synonymous verbs to behave like Alt verbs. Alternatively, if participants learned lemma-structure associations, we would expect the synonymous verbs to pattern according to their statistical biases.

To summarize, the primary goals of Experiment 1 were to replicate previous findings of verb bias effects and to explore the nature of the underlying representations that lead to such effects. In the context of learning sentences, cue validity is defined as the conditional probability of a sentence structure given the cue. The present study manipulated two types of cues, namely individual verbs and abstract mappings between meaning and structure. In Experiment 1, transitive events were described equally often using the AP or the PA structure, leading to 50% cue validity for abstract meaning-to-structure mappings. In comparison, the average cue validity for individual verbs was higher (100% for AP-only and PA-only verbs, and 50% for Alt verbs). Therefore, a cue validity framework would predict that learners would encode and use individual verb biases for production under these conditions (consistent with the previous results and our predictions for this experiment).

Materials and Methods

Participants

Thirty right-handed native English speakers from the Washington, D.C. area completed all portions of the study (18–24 years, mean = 19.86, 24 female). Eleven were excluded because they did not produce any verb-first structures, leaving a total of nineteen in the final analyses. All participants gave consent under a protocol approved by the Institutional Review Board at The George Washington University.

Stimuli

The miniature language was adapted from Wonnacott et al. (2008). The language contained English nouns, but novel verbs and sentence structures. The 12 novel verbs (fenk, flern, frag, glim, gofe, gund, parn, pelk, pruf, semz, sig, stoom) were used to describe transitive actions. Ten of the verbs described 10 distinct actions (hit, hug, jump on, kiss, lift, poke, pull, push, shake, tap). Two other synonymous verbs described the same physical action (stroke). The verb forms corresponding to the different meanings were counterbalanced across five lists. Each participant was pseudorandomly assigned to a list.

The language contained two novel structures [verb agent patient, verb patient agent particle (“ka”) Wonnacott et al. (2008)]. The two structures both began with the verb. Both encoded a transitive meaning. They differed in whether they contained AP or PA order. During training, we manipulated verb bias. Four of the 12 verbs appeared only in AP order (AP-only verbs), four only in PA order (PA-only verbs), and two equally in both orders (Alt verbs). The remaining two verbs were the synonymous verbs – one appeared only in AP order, the other only in PA order.

Actions were depicted using videos involving puppets. Each video was roughly 4 s long and showed one puppet acting on another. During training, each video was accompanied by a sentence in the miniature language (Figure 1). A female native English speaker recorded each word at different sentential positions. These recordings were combined to form different sentences using the SoX command line utility (sox.sourceforge.net).

FIGURE 1

FIGURE 1. Example stills from training videos in Experiment 1. (A) Action = zebra jumps on giraffe, accompanied by a spoken AP order sentence (e.g., pelk zebra giraffe). (B) Action = lion kisses giraffe, accompanied by a spoken PA order sentence (e.g., fenk giraffe lion ka).

Procedure

Stimuli were presented using E-prime. Each participant underwent three training sessions and one testing session. The three training sessions were identical. They were conducted on three separate days, with no more than 3 days between sessions. In each training session, participants watched 144 videos where animal puppets acted on one another (e.g., zebra jumping on giraffe). Each video was accompanied by a pre-recorded sentence (e.g., pelk zebra giraffe). Participants were instructed to listen to the sentence, repeat it out loud, and then press any key to move on to the next trial. The 144 trials consisted of 48 trials with AP-only verbs, 48 trials with PA-only verbs, 24 trials with Alt verbs, and 24 trials with the synonymous verbs. Each Alt and synonymous verb appeared 12 times per session. Half of the AP-only and PA-only verbs appeared at a lower frequency (6 times per session) and half at a higher frequency (18 times per session). This variation in verb frequency [similar to the manipulation in Wonnacott et al. (2008)] was included to investigate the potential effects of frequency on learning and using verb bias. Previous results had suggested that participants might be more likely to adhere to verb bias with high frequency verbs [see Wonnacott et al. (2008) for discussion]. However, in the current study, we found no main effects of or interactions with frequency. Therefore, all results are reported collapsed across this variable.

After training, participants returned within 3 days for a final testing session. During the test, they watched new videos and described them using the miniature language. Critically, the test videos involved new animal puppets (and correspondingly new nouns) compared to training (Appendix Table A1). Thus, participants could not rely on memorized word combinations from training. They had to generate new sentences, which allowed us to evaluate their syntactic knowledge.

The test began with a familiarization phase where participants were asked to name the new animal puppets one by one. The experimenter provided feedback if necessary. Subsequently on each test trial, they saw a miniature language verb to use in describing the upcoming video, watched the video clip (4000 ms), and then provided a verbal response (maximum 5000 ms). Forty-eight test trials were split across two blocks (16 AP-only, 16 PA-only, 8 Alt, 8 synonymous). The test session lasted approximately 20–30 min.

Coding

We transcribed participants’ responses to the test videos and coded the structures as containing AP or PA order. Only trials containing the correct verb and the correct two nouns were classified into the AP/PA categories. All other responses, including incorrect verb or noun(s), repeats, and incomplete responses, were classified as “Other.” For four randomly selected participants, a different coder transcribed and coded the responses. There was high inter-rater reliability (Cohen’s Kappa = 0.96).

Analyses

Because the dependent variable (structure produced = AP or PA order) was binomial, we used mixed effects logistic regression (glmer function with the bobyqa optimizer. lme4 version 1.1.10 in R. Bates et al., 2015). Two separate models addressed the two questions posed in this experiment. The first model examined whether verb bias affected the proportions of AP/PA orders produced with AP-only, Alt and PA-only verbs. If the proportions vary according to statistical bias, we would expect a linear trend in PA order production (AP-only < Alt < PA-only). The model contained the fixed Bias factor and the maximal random effects structure warranted by the design, including random intercept and slope by participant and random intercept by item (Barr et al., 2013). The bias factor was coded using orthogonal polynomial coding. The second model examined whether Syn-AP verbs (synonymous verbs that only appeared in AP order during training) patterned similarly to AP-only verbs or to Alt verbs. The bias factor was dummy-coded with the Syn-AP condition as the reference level.

Results

Participants showed an overall preference for AP order (65.24% of the responses contained AP order; 28.18% contained PA order, 6.58% were other, mu = 50, t(18) = 3.62, p = 0.002). The proportion of PA order [=PA responses/(PA responses + AP responses)^∗100] for each verb bias condition is shown in Figure 2 (PA order proportion for AP-only = 18.66, Syn-AP = 27.26, Alt = 32.89, PA-only = 42.22). Note that these proportions are reported solely for ease of interpretation; all statistical analyses were performed in log-odds space. Table 2 shows the results from the mixed model analyses. In the model containing AP-only, Alt and PA-only verbs, there was a significant linear trend that was consistent with verb bias (AP-only < Alt < PA-only. Wald’s Z = 2.51, p = 0.01). In the model comparing Syn-AP to AP-only and Alt verbs, neither contrast was significant (Syn-AP vs. AP-only: Wald’s Z = 0.05, p = 0.96; Syn-AP vs. Alt: Wald’s Z = 1.04, p = 0.30). Note: because a substantial number of participants were excluded in this experiment, we conducted the same analyses as above with all subjects included. As in the original analysis, there was a significant AP-only < Alt < PA-only linear trend, consistent with verb bias (Wald’s Z = 2.08, p = 0.04). Syn-AP verbs did not differ from AP-only verbs (Wald’s Z = 0.68, p = 0.49) but they were marginally different from Alt verbs (Wald’s Z = 1.96, p = 0.05). Thus, we confirmed the AP-only < Alt < PA-only pattern, and the non-significant difference between Syn-AP and AP-only verbs, in the entire sample.

FIGURE 2

FIGURE 2. Proportion of PA order responses produced with different verb types in Experiment 1.

TABLE 2

TABLE 2. Mixed models of the likelihood of a PA order response in Experiment 1.

Discussion

Experiment 1 replicated essential aspects of previous findings on verb bias effects during sentence production (Wonnacott et al., 2008). Participants were trained in a miniature language containing two alternative word orders for describing transitive events. We manipulated the statistical bias of different verbs for different structures. Subsequently, participants’ descriptions of new events, which could not rely on memorization, showed that the likelihood of PA order varied according to verb bias. Specifically, the likelihood of PA order showed a linear AP-only < Alt < PA-only trend. We augmented the design of the previous study by testing sentence production with a synonymous verb, whose lemma was linked exclusively to AP order but whose associated action was linked equally to AP and PA orders. Likelihood of PA production for this verb was indistinguishable from AP-only verbs both in the analysis of subjects who passed the inclusion criteria and in the analysis of all subjects. The comparison between the synonymous verb and Alt verbs was not significant in the former analysis but was marginally significant in the latter. This tentatively suggests that production with the Syn-AP verb was more similar to AP-only than Alt verbs and that learners encoded the structural preferences of lemmas and not just action-structure linkages.

The findings corroborate a plethora of previous evidence for lexically specific constraints on language processing (e.g., Garnsey et al., 1997; Snedeker and Trueswell, 2004). They fit quite naturally within constraint-based lexicalist frameworks, which assume rich lexical representations that contain information about how any given word can combine with other words in a sentence (MacDonald, 1994; MacDonald et al., 1994; Trueswell and Tanenhaus, 1994; Trueswell, 1996). Under these frameworks, language users represent and use linkages between individual verbs and structural configurations (Trueswell and Kim, 1998; See also Pickering and Branigan, 1998) for a similar proposal within a structural priming context). The strengths of the links depend on frequency of co-occurrence. Thus, verbs experienced often in one structural configuration (e.g., PA-only verbs in PA order) would likely be used in that configuration later on. Similarly, Chang et al. (2006) connectionist model learns to talk in part by learning how to sequence individual verbs in sentences. This makes the model sensitive to lexically specific structural preferences, particularly early on during language acquisition (Chang et al., 2006). In sum, verb bias effects such as those found here are consistent with dominant models of language learning and processing, which assume that the human language system¹ is capable of encoding and using rich information about how specific words are used.

In the language used in Experiment 1, verbs were, on average, highly predictive of the sentence structures heard during training. Ten out of the twelve verbs (all except Alt) predicted the correct sentence structure 100% of the time. In contrast, event structure correctly predicted which structure would be heard only 50% of the time. In this context of higher predictive validity of verb cues than abstract meaning cues, we expected and found effects of verb bias. These effects are consistent with but do not exclusively support the cue validity framework. This is because the results are also consistent with a production architecture that prioritizes verb-specific information. Subsequent experiments therefore tested whether learners come to rely on an alternate cue namely, meaning-to-structure mappings, if statistical regularities in the input favor that cue. These experiments used a language in which two different structures each referred to two different types of events. Unlike Experiment 1, event meaning was 100% predictive of sentence structure. The verb bias manipulations stayed the same. In this altered learning context, would participants show greater sensitivity to meaning-to-form mappings than to verb-specific structural preferences?

Experiment 2

The miniature language in this experiment contained two word orders that were associated with two different types of events (instrument and modifier). Instrument events involved the agent acting on the patient using an instrument (e.g., monkey pinching giraffe using a clip). During training, these were associated exclusively with AP order (e.g., gund monkey giraffe clip). Modifier events involved the agent acting on the patient who was holding the object (e.g., monkey brushing donkey who is holding paper). During training, these were associated exclusively with PA order (e.g., flern donkey monkey paper ka). Thus, type of event was 100% predictive of the sentence structures heard during training. The verb bias manipulations were the same as before, with some verbs appearing exclusively in AP order, some exclusively in PA order, and others equally in both (Table 1).

As in Experiment 1, participants were asked to describe new videos during the test phase. Verbs were only tested with the event type(s) with which they were associated during training (i.e., event type and verb bias were not fully crossed). This meant that AP-only, Syn-AP, and PA-only verbs were only tested with one type of event, and Alt verbs were tested with both event types. Therefore, Alt verbs provide the strongest test of whether participants relied on meaning-to-form mappings instead of verb-specific structural preferences. If learners successfully encode and use meaning-to-form mappings, we would expect proportion of PA order to be significantly different when Alt verbs are used to describe modifier events and when the same verbs are used to described instrument events. Alternatively, if learners fail to employ meaning-to-form mappings and rely instead on verb-specific structural preferences, we would expect no difference between the two event types.

Examination of a linear verb-bias trend akin to Experiment 1 was not possible in Experiment 2 because AP-only and PA-only verbs were tested with distinct event types, which resulted in conflation between event type and verb bias. However, we were able to examine whether production differed according to verb bias within each event type. For modifier events, we compared PA-only and Alt verbs (these were the only two verb types that appeared with these events). For instrument events, we compared AP-only, Alt and Syn-AP verbs (again, these were all the verb types that appeared with these events). If verb bias has an effect on sentence production despite the fact that this language contained a more valid cue to structure, we would expect systematic variation between the different conditions within each event type. Alternatively, if participants preferentially rely on meaning-to-form mappings instead of verb-specific information, we would expect no such differences.

Note that as in Experiment 1, the two word orders in Experiment 2 differed in the relative positions of the agent and the patient. We kept the position of the object the same in both structures. Although this created a somewhat unusual word order for the structure used to describe modifier events wherein the modifier was separated from the modified noun (cf. The man answered the door naked), it allowed us to ensure that the word order differences present in the input were comparable to the differences in Experiment 1. Additionally, it could be argued that the presence of a modifier, in whichever position, is infelicitous in contexts containing a single possible referent. However, to preview our results, we show that participants successfully learned to produce this modifier. This supports our claim that the language production system can flexibly learn patterns that are present in the input.