Title: Predictive coding accelerates word recognition and learning in the early stages of language development Running head: Predictions in children’s word recognition and learning

The ability to predict future events in the environment and learn from them is a fundamental component of adaptive behavior across species. Here we propose that inferring predictions facilitates speech processing and word learning in the early stages of language development. Twelveand 24-month olds’ electrophysiological brain responses to heard syllables are faster and more robust when the preceding word context predicts the ending of a familiar word. For unfamiliar, novel word forms, however, word-expectancy violation generates a prediction error response, the strength of which significantly correlates with children’s vocabulary scores at 12 months. These results suggest that predictive coding may accelerate word recognition and support early learning of novel words, including not only the learning of heard word forms but also their mapping to meanings. Prediction error may mediate learning via attention, since infants' attention allocation to entire learning situation in natural environments could account for the link between prediction error and the understanding of word meanings. On the whole, the present results on predictive coding support the view that principles of brain function reported across domains in humans and non-human animals apply to language and its development in the infant brain. Introduction In both humans and non-human animals, the capacity to predict future events on the basis of probabilistic inference is crucial for adaptation to the environment and, in some cases, even for survival. Predictive coding theory (Rao & Ballard, 1999; Friston, 2005, 2009, 2010) sheds light on the neural underpinnings of such predictions. According to the theory, predictive coding is an implicit process that creates an internal model about sensory input with the aim of minimizing surprise. It is based on bidirectional information flow in a hierarchical neural network. The inferred causes of the incoming sensory input are encoded by representational units located at a higher level of the network. They send predictions to prediction error units located at a lower level, where bottom-up input is compared with these top-down predictions. A match between input and prediction results in a suppressed neural response, whereas a mismatch elicits a prediction error response, which is projected back to the higher level to adjust the internal model. As a result, only input that does not match the internal model requires further processing resources. As recently reviewed by Pouget, Beck, Ma, and Latham (2013), the brain uses probabilistic inference to solve many tasks, ranging from sensory processing that is shared by species to higher cognitive functions that are specific to humans. One of the neurocognitive processes observed across species and linked with probabilistic inference is the elicitation of the mismatch negativity (MMN) component of auditory event-related potential (ERP) (Näätänen, Gaillard, & Mäntysalo, 1978; for reviews on MMN, see Näätänen, Paavilainen, Rinne, & Alho, 2007; Winkler, 2007; Näätänen, Astikainen, Ruusuvirta, & Huotilainen, 2010). MMN is typically elicited in an oddball paradigm, which refers to a stimulus sequence with a high probability of repetitive standard stimuli and occasional deviant stimuli. MMN reflects automatic auditory processing in the brain, since it is elicited even when participant's attention is directed away from the sounds. Winkler (2007) has proposed that the brain uses the regularities of the auditory environment to create a model that predicts the sounds that will follow. Violations of these predictions elicit the MMN, which is in line with the predictive coding theory. Furthermore, Friston (2005) has explicitly proposed that the MMN belongs to the family of prediction error responses (see also Wacongne et al., 2011). Interestingly, recent studies have suggested that the domain-general principles of predictive coding seem to also serve the processing of language. Gagnepain, Henson, and Davis (2012) have proposed that human adults use predictive coding to recognize spoken words. Specifically, their pattern of findings is compatible with the interpretation that compared with brain responses to unpredicted speech sounds, speech sounds predicted on the basis of long-term memory word representations generate reduced responses in superior temporal cortex. In contrast, speech sounds that do not match word-based predictions generate enhanced brain responses, interpreted as prediction error signals. Thus, predictive coding may, together with several other factors, contribute to speech recognition. However, the principle of predictive coding has not been associated with recognition only, but also with learning, because predictions promote reward-based associative learning in non-human animals (Schultz, Dayan, & Montague, 1997; Steinberg et al., 2013). Bridging the gap between these findings, we postulate that predictive coding could support both spoken-word recognition and word learning in infants and young children, as they show the ability of predictive coding from birth (Trainor, 2012; Haden, Nemeth, Torok, & Winkler, 2015). To test this hypothesis with recordings of auditory event-related potential (ERP), we presented 12and 24-month-old children with native language syllables in an oddball paradigm, where syllable patterns were expected to generate predictions at different levels. These predictions can be probed by violating them, which elicits the mismatch response (child equivalent of the MMN) or prediction error. The hypothesis on different levels of prediction is based on previous studies that have, on the one hand, suggested that MMN reflects hierarchical predictions derived from probabilities within sound sequences (Wacongne et al., 2011; Basirat, Dehaene, & Dehaene-Lambertz, 2014) and, on the other hand, shown that predictions and consequent prediction error responses may also be driven by word representations in the long-term memory (Gagnepain et al., 2012). Thus, when an oddball paradigm includes isolated syllables, they most likely induce predictions about the following syllables on the basis of stimulus probabilities in the stimulus sequence. Violations of these sequence-level predictions about the following items are expected to elicit mismatch responses. In contrast, when the same syllables are presented within words as second syllables, the word beginnings may induce predictions about these syllables at the level of word representations. In this case, the violations of predictions are expected to generate prediction error responses at word level. In addition to probing predictions with their violations, the oddball paradigm can be used to study the activation of long-term memory representations for words (for a review, see Pulvermüller & Shtyrov, 2006). When presented among pseudowords, words typically elicit larger MMN responses than pseudowords, because deviant syllables completing words activate the corresponding word representations resulting in enhanced responses (Pulvermüller et al. 2001; Garagnani, Wennekers, & Pulvermüller, 2008; Garagnani & Pulvermüller, 2011). According to the neurocomputational model by Garagnani and Pulvermüller (2011), local inhibition of cortical activity or the combination of inhibition and neuronal adaptation may account for MMN responses in non-speech tones, but not in 1 Since in infants and young children the brain responses elicited in an oddball paradigm and resembling MMN with respect to their function can be negative or positive in polarity, we will further use the terms mismatch response to refer to this response in children and MMN to refer to it in adults. words. The most likely explanation for their pattern of results in words is the “ignition” and reverberation of activity in the neural network covering distant brain areas, including areas involved in speech perception and production (Garagnani & Pulvermüller, 2011, see also Pulvermüller, 1999). To reveal the effect of word-level predictions and lexical activations in a setup controlling for the effect of sequence-level predictions and neuronal adaptation in repetitive stimulation, we compared ERP responses in the two conditions that used the same syllables of interest. Firstly, in No context condition (Fig.1A), we presented the standard syllable, occurring with a high probability, in isolation. Secondly, in Context condition (Fig. 1B), we presented the same standard syllable after a familiar word beginning, enabling to interpret the syllable of interest as the word ending. Thus, the critical difference between the two conditions was the absence or presence of the context syllable. In both conditions, the standard syllable was expected to create sequence-level predictions of hearing this particular syllable (Fig. 1C and D, longer arrows). In Context condition, however, the context syllable representing a familiar word beginning was expected to create predictions at another level, namely, to create word-level predictions of hearing the ending of some familiar word (Fig. 1D, shorter arrows; for discussion on knowledge-based predictions, see Poeppel & Monahan, 2011; for phonological predictions in pseudowords, see Ylinen et al., submitted). To probe the predictions at sequence and word levels, we presented two deviant syllables (Fig. 1A and B). In No context condition, both deviants were equally unexpected with respect to sequence-level predictions and hypothesized to elicit typical mismatch responses (see Table 1). In Context condition, however, the same deviant syllables either completed a familiar word, the meaning of which the children knew, or a novel word, the meaning of which the children could not know (Fig. 1B). The involvement of words in Context condition was hypothesized to enable the interaction of sequence-level and word-level predictions and to result in different brain responses to the deviant syllables (see Table 1): The deviant syllable completing the