No scientific conclusion follows automatically from a statistically non-significant result, yet people routinely use non-significant results to guide conclusions about the status of theories (or the effectiveness of practices). To know whether a non-significant result counts against a theory, or if it just indicates data insensitivity, researchers must use one of: power, intervals (such as confidence or credibility intervals), or else an indicator of the relative evidence for one theory over another, such as a Bayes factor. I argue Bayes factors (...) allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches. Specifically, Bayes factors use the data themselves to determine their sensitivity in distinguishing theories (unlike power), and they make use of those aspects of a theory’s predictions that are often easiest to specify (unlike power and intervals, which require specifying the minimal interesting value in order to address theory). Bayes factors provide a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive. They allow accepting and rejecting the null hypothesis to be put on an equal footing. Concrete examples are provided to indicate the range of application of a simple online Bayes calculator, which reveal both the strengths and weaknesses of Bayes factors. (shrink)

Research using the intermodal preferential looking paradigm (IPLP) has consistently shown that English‐learning children aged 2 can associate transitive argument structure with causal events. However, studies using the same methodology investigating 2‐year‐old children’s knowledge of the conjoined agent intransitive and semantic role assignment have reported inconsistent findings. The aim of the present study was to establish at what age English‐learning children have verb‐general knowledge of both transitive and intransitive argument structure using a new method: the forced‐choice pointing paradigm. The results (...) suggest that young 2‐year‐olds can associate transitive structures with causal (or externally caused) events and can use transitive structure to assign agent and patient roles correctly. However, the children were unable to associate the conjoined agent intransitive with noncausal events until aged 3;4. The results confirm the pattern from previous IPLP studies and indicate that children may develop the ability to comprehend different aspects of argument structure at different ages. The implications for theories of language acquisition and the nature of the language acquisition mechanism are discussed. (shrink)

Within the cognitive sciences, most researchers assume that it is the job of linguists to investigate how language is represented, and that they do so largely by building theories based on explicit judgments about patterns of acceptability – whereas it is the task of psychologists to determine how language is processed, and that in doing so, they do not typically question the linguists' representational assumptions. We challenge this division of labor by arguing that structural priming provides an implicit method of (...) investigating linguistic representations that should end the current reliance on acceptability judgments. Moreover, structural priming has now reached sufficient methodological maturity to provide substantial evidence about such representations. We argue that evidence from speakers' tendency to repeat their own and others' structural choices supports a linguistic architecture involving a single shallow level of syntax connected to a semantic level containing information about quantification, thematic relations, and information structure, as well as to a phonological level. Many of the linguistic distinctions often used to support complex (or multilevel) syntactic structure are instead captured by semantics; however, the syntactic level includes some specification of “missing” elements that are not realized at the phonological level. We also show that structural priming provides evidence about the consistency of representations across languages and about language development. In sum, we propose that structural priming provides a new basis for understanding the nature of language. (shrink)

Structural priming offers a powerful method for experimentally investigating the mental representation of linguistic structure. We clarify the nature of our proposal, justify the versatility of priming, consider alternative approaches, and discuss how our specific account can be extended to new questions as part of an interdisciplinary programme integrating linguistics and psychology as part of the cognitive sciences of language.