drive structural priming. For example, one idea is that the mechanism that underlies structural priming in children is the same as the one that underlies
47
Chapter 2 analogical reasoning (Goldwater, Tomlinson, Echols, & Love, 2011). Another
view is that priming is driven by a domain-general mechanism so that the abstraction of structural representations is not limited to linguistic representations. On this view, it is the overall shape of the representation that is primed, and thus the mechanism that enables abstraction of a linguistic structure is the same one that enables abstraction of a mathematical one (Scheepers, Sturt, Martin, Myachykov, Teevan, & Viskupova, 2011).
A theory that has received attention recently, however, is that structural priming is the consequence of (implicit) error-based learning. This idea has been conceptualised in Chang, Dell, and Bock’s (2006) frequency-
based connectionist model of syntactic development - the Dual-path model.
The model has a dual-pathway architecture made up of a simple recurrent network (SRN) and a (hidden) meaning network. The meaning network contains the intended message of the sentence and is important in this model because one sentence may differ structurally from another but may still convey a similar message. For example, the act of object transfer can be
expressed by either a double-object dative (DOD; The boy handed his mum
the note) or a prepositional object dative (PD; The boy handed the note to his
mum). Syntax learning occurs because the syntax system in the model
generates a prediction about the next word in a sentence based on sequential restraints (i.e., the previous word) and information from the meaning network about the type of message that is being conveyed (i.e., the context). It then calculates the difference (or error) between the predicted word and the actual word and uses this prediction error to make gradual
48
Chapter 2 changes in the weights that support syntactic knowledge in the system.
Increasing experience and continual feedback strengthen the model’s predictive abilities so that, over time, it is able to make more accurate predictions about the next word in a sentence. This type of distributional learning enables the model to gradually develop abstract syntactic categories. Then, using meaning, it is able to sequence these abstract categories to generate sentences. Thus, the small weight changes in the model eventually converge on the representations that support adult-like sentence production. This not only allows the model to learn syntactic structure, but also enables it to develop lexical-structural representations such as verb argument structure preferences (verb bias).
Figure 2.1SimplifiedDual-path model: the acquisition of verb bias (taken
from Chang et al., 2012)
Figure 2.1 is an example of how verb bias acquisition is conceptualised in the Dual-path model. The SRN in the model that tracks which verbs and structures tend to co-occur is the same mechanism that enables the model to
learn verb-structure regularities. In Figure 2.1 below, the three verbs push,
throw, and give are linked to the node that signifies a PD structure (node 1:
49
Chapter 2 signifies a DOD structure (node 2: NP-NP). Because the model’s experience
with give is that it tends to occur more often in a DOD structure, the link
between the give node and node 2 is stronger than the link between the give
node and node 1 (denoted by a thicker blue line in Figure 2.1). This creates a bias for the DOD structure for this verb. Similarly, because the model is
presented with push more frequently in a PD structure, the link between the
push node and node 1 is stronger than the link between the push node and
node 2 (again, denoted by a thicker blue line). This creates a bias for the PD structure for this verb.
Other work has also shown that verb biases are learned in this way: Twomey, Chang, and Ambridge (2014) presented a version of the Dual-path model that gradually learned locative verb biases over development, and Twomey, Chang, and Ambridge (2015) showed that both children and adults used lexical distribution to learn verb classes for novel locative verbs after as little as two exposures. Thus, the Dual-path model provides an account in which syntax acquisition and verb bias acquisition is the result of a common verb-structure mechanism: error-based learning.
Abstract structural priming effects are also caused by this very same error-based learning mechanism. To understand how structural priming effects can be simulated in the Dual-path model, let us consider the following example taken from Chang, Janciauskas, and Fitz (2012) in which the model
is presented with a prime sentence that uses a DOD structure: John threw
the man a ball. The model is tested for priming by presenting the prime sentence with error-based learning left ON.
50
Chapter 2
Figure 2.2SimplifiedDual-path model: structural priming (taken from Chang
et al., 2012)
The prediction error for the prime is used to make changes to the weights in the network - some of which are made to abstract structural representations. Then, the model’s meaning network is presented with a new target message. For instance, the model might be presented with a message that describes an event in which a book is transferred between a child and Sally (see Figure 2.2). The model recognises that this type of message can be described by
either a DOD so that the recipient (Sally) immediately follows the verb give
(e.g., The child gave Sally a book), or by a PD so that the theme (a book)
immediately follows the verb give (e.g., The child gave a book to Sally).
However, the slight changes in the connection weights (as a result of the prediction error caused when processing the prime sentence) are enough to bias the model’s target description so that it is more likely to use the structure of the prime sentence - which in this case was a DOD. Thus, the Dual-path
51
Chapter 2
model is able to show that DOD prime sentences like: John threw the man a
ball will result in the production of a target sentence like: The child gave Sally
a book.
2.4. What has structural priming told us about the nature of adult