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The authors examine the role of similarity in artificial grammar learning (AGL; A. S. Reber, 1989). A standard finite-state language was used to create stimuli that were arrangements of embedded geometric shapes (Experiment 1), connected lines (Experiment 2), and sequences of shapes (Experiment 3). Main effects for well-known predictors from the literature (grammaticality, associative global and anchor chunk strength, novel global and anchor chunk strength, length of items, and edit distance) were observed, thus replicating previous work. However, the authors extend previous research by using a widely known similarity-based exemplar model of categorization (the generalized context model; R. M. Nosofsky, 1989) to fit grammaticality judgments, by nested regression analyses. The results suggest that any explanation of AGL that is based on the existing theories is incomplete without a similarity process as well. Also, the results provide a foundation for further interpreting AGL in the wider context of categorization research.

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Scalar implicatures often incur a processing cost in sentence comprehension tasks. We used a novel mouse-tracking technique in a sentence verification paradigm to test different accounts of this effect. We compared a two-step account, in which people access a basic meaning and then enrich the basic meaning to form the scalar implicature, against a one-step account, in which the scalar implicature is directly incorporated into the sentence representation. Participants read sentences and used a computer mouse to indicate whether each sentence was true or false. Three experiments found that when verifying sentences like "some elephants are mammals", average mouse paths initially moved towards the true target and then changed direction mid-flight to select the false target. This supports the two-step account of implicatures.We discuss the results in relation to previous findings on scalar implicatures and theoretical accounts of pragmatic inference. Scalar implicatures 3To communicate efficiently, speakers often imply information instead of explicitly stating it. Consider this exchange: 1A) Nowadays, teenagers are tethered to their smart phones. 1B) Some are.Here, B is a teenager who distances himself from people of his age who seemingly never put down their mobile phones. By saying, "some are," he confirms that there are indeed teenagers who match A's description. More importantly for the purposes of this paper, he also implies that there is a significant group of teenagers who do not use their phones excessively.In order to understand inferences like those above, the listener must know which of an infinite number of potential inferences the speaker intended her to draw. Moreover, for the sake of efficiency and communicative fluency, the inferences must be derived in a very short space of time. Grice's (1975; maxims of communication describe abstract principles that could guide the listener in drawing inferences. However, something like Grice's maxims might be realized by any number of processing mechanisms. In this paper, we test between two processing models of scalar implicatures (see also, Bott & Noveck, 2004;Breheny, Katsos & Williams, 2006;and Huang & Snedeker, 2009). The first model assumes the listener derives the implicature in a single processing step -a one-step model -and the second assumes the listener initially derives a literal, or basic, meaning, and then enriches this to form the implicature -a two step model. The structure of the paper is as follows. We first introduce scalar implicatures in more detail and present a summary of the relevant linguistic literature. We then present the two processing models in more detail and discuss how they account for previous findings on processing scalar implicatures. Finally, we introduce the paradigm that we use to test between the models and describe three experiments that test the model predictions.