Flect a process of adaptation (or learning), in which the comprehender updates her entire internal generative model to better reflect the AMN107 web broader statistical structure of the current environment (see Kuperberg, under review, for further discussion; see also Kuperberg, 2013). On this account, after encountering “plane” (instead of “kite”) following context (3a), the comprehender might update her beliefs about the statistical contingencies between her semantic, syntactic and phonological knowledge (for computational extensions of this type of generative framework to adaptation during language processing, see Fine et al., 2010; Kleinschmidt et al., 2012; Kleinschmidt Jaeger, 2015). A second possibility, which is slightly different although related to the first, is that the late positivities reflect a type of `model switching’. For example, the comprehender might have previously learned (and stored) different generative models that correspond different statistical environments (Kleinschmidt Jaeger, 2015, pp180-181; for related models beyond language processing, see also Qian, Jaeger, Aslin, 2012, and Gershman Ziv, 2012). For example, comprehenders might have learned generative models for particular genres (Fine, Jaeger, Farmer, Qian, 2013; Kuperberg, 2013), dialects (Fraundorf Jaeger, submitted; Niedzielski, 1999), or accents (Hanulikova, van Alphen, van Goch, Weber, 2012). They might even have learned a generative models for situations in which normal statistics completely break down, e.g., when participating in experiment (cf. Jaeger, 2010, p. 53) or when talking to someone one believes to have a language deficit (Arnold, Kam, Tanenhaus, 2007). The late positivities might then reflect a re-allocation of resources associated with inferring (or switching to) these new generative models (for further discussion, see Kuperberg, under review). Distinguishing between these possibilities will be an important step in fleshing out the generative architecture described here.Author Manuscript Author Manuscript Author Manuscript Author ManuscriptSection 5: Towards a hierarchical multi-representational generative framework of language comprehensionIn this review, we considered several ways in which prediction has been discussed in relation to language comprehension. In section 1, we argued that, in its minimal sense, prediction implies that, at any given time, we use high level information within our representation of context to probabilistically infer (hypothesize) upcoming information at this same higher level representation. In section 2, we surveyed a large body of work suggesting that we can use multiple types of information within our representation of context to facilitate the GSK343MedChemExpress GSK343 processing of new bottom-up inputs at multiple other levels of representation, ranging from syntactic, semantic, to phonological, orthographic, and perceptual. In section 3, we discussed evidence that, at least under some circumstances, facilitation at lower level representations results from the use of higher level inferences to predictively pre-activate information at these lower level(s), ahead of new bottom-up information reaching these levels. We also discussed several factors known to influence the degree and representational level(s) to which we predictively pre-activate lower level information, suggesting that these factors might act by influencing the utility of predictive pre-activation by balancing its benefits and costs. Finally, in section 4, w.Flect a process of adaptation (or learning), in which the comprehender updates her entire internal generative model to better reflect the broader statistical structure of the current environment (see Kuperberg, under review, for further discussion; see also Kuperberg, 2013). On this account, after encountering “plane” (instead of “kite”) following context (3a), the comprehender might update her beliefs about the statistical contingencies between her semantic, syntactic and phonological knowledge (for computational extensions of this type of generative framework to adaptation during language processing, see Fine et al., 2010; Kleinschmidt et al., 2012; Kleinschmidt Jaeger, 2015). A second possibility, which is slightly different although related to the first, is that the late positivities reflect a type of `model switching’. For example, the comprehender might have previously learned (and stored) different generative models that correspond different statistical environments (Kleinschmidt Jaeger, 2015, pp180-181; for related models beyond language processing, see also Qian, Jaeger, Aslin, 2012, and Gershman Ziv, 2012). For example, comprehenders might have learned generative models for particular genres (Fine, Jaeger, Farmer, Qian, 2013; Kuperberg, 2013), dialects (Fraundorf Jaeger, submitted; Niedzielski, 1999), or accents (Hanulikova, van Alphen, van Goch, Weber, 2012). They might even have learned a generative models for situations in which normal statistics completely break down, e.g., when participating in experiment (cf. Jaeger, 2010, p. 53) or when talking to someone one believes to have a language deficit (Arnold, Kam, Tanenhaus, 2007). The late positivities might then reflect a re-allocation of resources associated with inferring (or switching to) these new generative models (for further discussion, see Kuperberg, under review). Distinguishing between these possibilities will be an important step in fleshing out the generative architecture described here.Author Manuscript Author Manuscript Author Manuscript Author ManuscriptSection 5: Towards a hierarchical multi-representational generative framework of language comprehensionIn this review, we considered several ways in which prediction has been discussed in relation to language comprehension. In section 1, we argued that, in its minimal sense, prediction implies that, at any given time, we use high level information within our representation of context to probabilistically infer (hypothesize) upcoming information at this same higher level representation. In section 2, we surveyed a large body of work suggesting that we can use multiple types of information within our representation of context to facilitate the processing of new bottom-up inputs at multiple other levels of representation, ranging from syntactic, semantic, to phonological, orthographic, and perceptual. In section 3, we discussed evidence that, at least under some circumstances, facilitation at lower level representations results from the use of higher level inferences to predictively pre-activate information at these lower level(s), ahead of new bottom-up information reaching these levels. We also discussed several factors known to influence the degree and representational level(s) to which we predictively pre-activate lower level information, suggesting that these factors might act by influencing the utility of predictive pre-activation by balancing its benefits and costs. Finally, in section 4, w.