Lugel Lacount, 1988; Schwanenflugel Shoben, 1985; Stanovich West, 1983), naming (Forster, 1981; McClelland PM01183MedChemExpress Lurbinectedin O’Regan, 1981; Stanovich West, 1979, 1981, 1983; Traxler Foss, 2000), gating (Grosjean, 1980), and speech monitoring (Cole Perfetti, 1980; Marslen-Wilson, Brown, Tyler, 1988). Moreover, eye-tracking studies show that readers fixate less on predictable than unpredictable words (Balota, Pollatsek, Rayner, 1985; Ehrlich Rayner, 1981; Rayner, Binder, Ashby, Pollatsek, 2001; Rayner Well, 1996; see also Boston, Hale, Kliegl, Patil, Vasishth, 2008; Demberg Keller, 2008; Demberg, Keller, Koller, 2013; Frank Bod, 2011; McDonald Shillcock, 2003; Smith Levy, 2013; see Staub, 2015 for a recent review). And, as early as 1980, Kutas and Hillyard reported evidence for a reduced neural signal — the N400 event-related potential (ERP) — to semantically predictable versus unpredictable words in sentence contexts (see also DeLong, Urbach, Kutas, 2005; Kutas Federmeier, 2011; Kutas Hillyard, 1984). The simple point we wish to make at this stage is that it is logically impossible to explain these effects without assuming that the context influences the state of the language processing system before the bottom-up input is observed. This is the minimal sense in which the language processing system must be predictive. And, indeed, as we will discuss in section 1, almost all models of syntactic parsing and lexico-semantic processing posit that the comprehender has anticipated some structure or some semantic information prior to encountering new bottom-up information. Given this logic, the role of Aprotinin biological activity prediction in language processing should not be so controversial. Yet, debates about its contributions have been central to psycholinguistic theory for decades, with researchers taking strong positions on both sides. Some, for example, have argued that, given the inherently combinatorial nature of human language, predicting upcoming information ahead of time would be an unnecessary waste of processing resources (see Jackendoff, 2002 and Van Petten Luka, 2012 for discussion). Others have argued that, given the noisiness, ambiguity and speed of our linguistic input, prediction is the mostLang Cogn Neurosci. Author manuscript; available in PMC 2017 January 01.Kuperberg and JaegerPageefficient solution for fast, efficient and accurate comprehension (e.g. Kleinschmidt Jaeger, 2015). These debates can be quite nuanced, with researchers focusing on different aspects of prediction. Some have distinguished expectation or anticipation from prediction (e.g. Van Petten Luka, 2012); some have distinguished predictive pre-activation from predictive commitment (e.g. Lau, Holcomb, Kuperberg, 2013). Finally, within the computational psycholinguistics literature, the term prediction has been used in yet other ways, in relation to a growing number of probabilistic models of language processing (e.g., Bejjanki, Clayards, Knill, Aslin, 2011; Demberg et al., 2013; Feldman, Griffiths, Morgan, 2009; Hale, 2011; Jurafsky, 1996; Keller, 2003; Kleinschmidt Jaeger, 2015; Norris McQueen, 2008; Smith Levy, 2013). The end result is that prediction has come to mean quite different things to different people. Indeed, our review of the literature led us to the conclusion that different subfields and different researchers have critically different conceptions of what it means to predict during language comprehension. This has led to much.Lugel Lacount, 1988; Schwanenflugel Shoben, 1985; Stanovich West, 1983), naming (Forster, 1981; McClelland O’Regan, 1981; Stanovich West, 1979, 1981, 1983; Traxler Foss, 2000), gating (Grosjean, 1980), and speech monitoring (Cole Perfetti, 1980; Marslen-Wilson, Brown, Tyler, 1988). Moreover, eye-tracking studies show that readers fixate less on predictable than unpredictable words (Balota, Pollatsek, Rayner, 1985; Ehrlich Rayner, 1981; Rayner, Binder, Ashby, Pollatsek, 2001; Rayner Well, 1996; see also Boston, Hale, Kliegl, Patil, Vasishth, 2008; Demberg Keller, 2008; Demberg, Keller, Koller, 2013; Frank Bod, 2011; McDonald Shillcock, 2003; Smith Levy, 2013; see Staub, 2015 for a recent review). And, as early as 1980, Kutas and Hillyard reported evidence for a reduced neural signal — the N400 event-related potential (ERP) — to semantically predictable versus unpredictable words in sentence contexts (see also DeLong, Urbach, Kutas, 2005; Kutas Federmeier, 2011; Kutas Hillyard, 1984). The simple point we wish to make at this stage is that it is logically impossible to explain these effects without assuming that the context influences the state of the language processing system before the bottom-up input is observed. This is the minimal sense in which the language processing system must be predictive. And, indeed, as we will discuss in section 1, almost all models of syntactic parsing and lexico-semantic processing posit that the comprehender has anticipated some structure or some semantic information prior to encountering new bottom-up information. Given this logic, the role of prediction in language processing should not be so controversial. Yet, debates about its contributions have been central to psycholinguistic theory for decades, with researchers taking strong positions on both sides. Some, for example, have argued that, given the inherently combinatorial nature of human language, predicting upcoming information ahead of time would be an unnecessary waste of processing resources (see Jackendoff, 2002 and Van Petten Luka, 2012 for discussion). Others have argued that, given the noisiness, ambiguity and speed of our linguistic input, prediction is the mostLang Cogn Neurosci. Author manuscript; available in PMC 2017 January 01.Kuperberg and JaegerPageefficient solution for fast, efficient and accurate comprehension (e.g. Kleinschmidt Jaeger, 2015). These debates can be quite nuanced, with researchers focusing on different aspects of prediction. Some have distinguished expectation or anticipation from prediction (e.g. Van Petten Luka, 2012); some have distinguished predictive pre-activation from predictive commitment (e.g. Lau, Holcomb, Kuperberg, 2013). Finally, within the computational psycholinguistics literature, the term prediction has been used in yet other ways, in relation to a growing number of probabilistic models of language processing (e.g., Bejjanki, Clayards, Knill, Aslin, 2011; Demberg et al., 2013; Feldman, Griffiths, Morgan, 2009; Hale, 2011; Jurafsky, 1996; Keller, 2003; Kleinschmidt Jaeger, 2015; Norris McQueen, 2008; Smith Levy, 2013). The end result is that prediction has come to mean quite different things to different people. Indeed, our review of the literature led us to the conclusion that different subfields and different researchers have critically different conceptions of what it means to predict during language comprehension. This has led to much.
