O act on spoken instructions like “Pour the egg in the bowl over the flour”, and showed that anticipatory eye movements, which reflected participants syntactic parse of the sentence, were influenced by whether or not there were pourable liquid eggs in a bowl (versus solid eggs in a bowl that were not pourable). In addition, when we are listening to a lecture or reading text, our overall goal can also influence mechanism of processing, as well as our future recall of its contents — contrast carefully reading an academic paper with reading a novel for pleasure (see van den Broek, Lorch, Linderholm Gustafson, 2001 for discussion). Finally, whether or not we see pre-activation at any particular representational level will likely depend on the speed at which the bottom-up input unfolds: contextual facilitation is greater when linguistic input is presented at slower than faster rates (e.g. Camblin, Ledoux, Boudewyn, Gordon, Swaab, 2007, Wlotko Federmeier, 2015). Moreover, the degree to which predictive pre-activation (versus bottom-up input) drives button presses during selfpaced reading or eye-movements during reading is known to be sensitive to the relative importance of Pan-RAS-IN-1MedChemExpress Pan-RAS-IN-1 comprehension speed versus accuracy (see Norris, 2006 for discussion), which can, in turn, be affected by external reward structures (cf. Bicknell, 2011; Bicknell Levy, 2010; Lewis et al., 2013, see also Lewis, Howes, Singh, 2014). Taken together, all these factors suggest that the question we should be asking is not whether we can use higher level information in our representation of context to predictively preactivate upcoming information at lower levels of representation, but rather when we do so. We now consider the buy SC144 computational issues that may shed light on the question of when, and to what degree, we use higher level information within our internal representation of context to pre-activate upcoming information at lower representational level(s). Computational insights In computational terms, predictive pre-activation can be understood as the use of beliefs at a higher level of representation (level k) to change the prior distribution at a lower level of representation (k-1), ahead of new bottom-up input reaching this lower level representation. So long as such predictive pre-activation is based on the comprehender’s stored probabilisticAuthor Manuscript Author Manuscript Author Manuscript Author ManuscriptLang Cogn Neurosci. Author manuscript; available in PMC 2017 January 01.Kuperberg and JaegerPageknowledge, then, on average, it will serve to reduce the degree of shift that the comprehender expects when she encounters new input at this lower level of representation: it will reduce her expected surprise at k-1. In other words, by shifting her prior beliefs at k-1 prior to encountering new information at k-1, when such new information does reach k-1, any further shift in belief (Bayesian surprise) will, on average, be less than if she had not pre-activated (shifted the prior at k-1) at all. Information that has been pre-activated at k-1 should therefore, on average, be supported by the new bottom-up input to k-1, and its processing should therefore be relatively facilitated. Note that an architecture in which inferences at higher levels of representation lead to the generation of predictions at lower level(s) by changing the prior probability belief distributions at these lower levels, is not only generative in the theoretical sense described in sections 1.O act on spoken instructions like “Pour the egg in the bowl over the flour”, and showed that anticipatory eye movements, which reflected participants syntactic parse of the sentence, were influenced by whether or not there were pourable liquid eggs in a bowl (versus solid eggs in a bowl that were not pourable). In addition, when we are listening to a lecture or reading text, our overall goal can also influence mechanism of processing, as well as our future recall of its contents — contrast carefully reading an academic paper with reading a novel for pleasure (see van den Broek, Lorch, Linderholm Gustafson, 2001 for discussion). Finally, whether or not we see pre-activation at any particular representational level will likely depend on the speed at which the bottom-up input unfolds: contextual facilitation is greater when linguistic input is presented at slower than faster rates (e.g. Camblin, Ledoux, Boudewyn, Gordon, Swaab, 2007, Wlotko Federmeier, 2015). Moreover, the degree to which predictive pre-activation (versus bottom-up input) drives button presses during selfpaced reading or eye-movements during reading is known to be sensitive to the relative importance of comprehension speed versus accuracy (see Norris, 2006 for discussion), which can, in turn, be affected by external reward structures (cf. Bicknell, 2011; Bicknell Levy, 2010; Lewis et al., 2013, see also Lewis, Howes, Singh, 2014). Taken together, all these factors suggest that the question we should be asking is not whether we can use higher level information in our representation of context to predictively preactivate upcoming information at lower levels of representation, but rather when we do so. We now consider the computational issues that may shed light on the question of when, and to what degree, we use higher level information within our internal representation of context to pre-activate upcoming information at lower representational level(s). Computational insights In computational terms, predictive pre-activation can be understood as the use of beliefs at a higher level of representation (level k) to change the prior distribution at a lower level of representation (k-1), ahead of new bottom-up input reaching this lower level representation. So long as such predictive pre-activation is based on the comprehender’s stored probabilisticAuthor Manuscript Author Manuscript Author Manuscript Author ManuscriptLang Cogn Neurosci. Author manuscript; available in PMC 2017 January 01.Kuperberg and JaegerPageknowledge, then, on average, it will serve to reduce the degree of shift that the comprehender expects when she encounters new input at this lower level of representation: it will reduce her expected surprise at k-1. In other words, by shifting her prior beliefs at k-1 prior to encountering new information at k-1, when such new information does reach k-1, any further shift in belief (Bayesian surprise) will, on average, be less than if she had not pre-activated (shifted the prior at k-1) at all. Information that has been pre-activated at k-1 should therefore, on average, be supported by the new bottom-up input to k-1, and its processing should therefore be relatively facilitated. Note that an architecture in which inferences at higher levels of representation lead to the generation of predictions at lower level(s) by changing the prior probability belief distributions at these lower levels, is not only generative in the theoretical sense described in sections 1.