Researchers agree that comprehenders regularly predict upcoming language, but they do not always agree on what prediction is (and how to differentiate it from integration) or what constitutes ...evidence for it. After defining prediction, we show that it occurs at all linguistic levels from semantics to form, and then propose a theory of which mechanisms comprehenders use to predict. We argue that they most effectively predict using their production system (i.e., prediction-by-production): They covertly imitate the linguistic form of the speaker's utterance and construct a representation of the underlying communicative intention. Comprehenders can then run this intention through their own production system to prepare the predicted utterance. But doing so takes time and resources, and comprehenders vary in the extent of preparation, with many groups of comprehenders (non-native speakers, illiterates, children, and older adults) using it less than typical native young adults. We thus argue that prediction-by-production is an optional mechanism, which is augmented by mechanisms based on association. Support for our proposal comes from many areas of research (electrophysiological, eye-tracking, and behavioral studies of reading, spoken language processing in the context of visual environments, speech processing, and dialogue).
Public Significance Statement
This theoretical review shows that people regularly predict upcoming language. Importantly, it also shows that in most cases people rely on their own ability to produce language to make predictions that are compatible with both the speaker's language and their intended message. This form of prediction aids, but it is not necessary for, language understanding.
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CEKLJ, FFLJ, NUK, ODKLJ, PEFLJ, UPUK
Conversation is the natural setting for language learning and use, and a key property of conversation is the smooth taking of turns. In adult conversations, delays between turns are minimal ...(typically 200 ms or less) because listeners display a striking ability to predict what their partner will say, and they formulate a response before their partner’s turn ends. Here, we tested how this ability to coordinate comprehension and production develops in preschool children. In an interactive paradigm, 106 children (ages 3–5 years) and 48 adults responded to questions that varied in predictability but were controlled for linguistic complexity. Using a novel distributional approach to data analysis, we found that when children can predict a question’s ending, they leave shorter gaps before responding, suggesting that they can optimize the timing of their conversational turns like adults do. In line with a recent ethological theory of turn taking, this early competency helps explain how conversational contexts support language development.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
Is children’s acquisition of structural knowledge driven by prediction errors? Error-driven models of language acquisition propose that children generate expectations about upcoming words ...(prediction), compare them to the input, and, when they detect a mismatch (i.e., prediction error signal), update their long-term linguistic knowledge. But we only have limited empirical evidence for this learning mechanism. Using a novel touch-screen app and a pre-post training between-subjects design, we tested the effect of prediction errors on 120 English-learning 4-year-olds’ understanding of challenging direct object datives. We hypothesized that children who are exposed to input that encourages the generation of prediction error signals should show greater improvements in their post-test comprehension scores. Consistent with error-driven models of language learning, we found that children exposed to sentences that encouraged the generation of incorrect linguistic predictions improved numerically more than those who were exposed to sentences that did not support predictions. However, we caution that these preliminary findings need to be confirmed by additional testing on much larger samples (we only tested 20–30 children per training condition). If confirmed, these findings would provide some of the strongest empirical support to date for the role of prediction error in the acquisition of linguistic structure.
In dialogue, people represent each other’s utterances to take turns and communicate successfully. In previous work, speakers who were naming single pictures or picture pairs represented whether ...another speaker was engaged in the same task (vs a different or no task) concurrently but did not represent in detail the content of the other speaker’s utterance. Here, we investigate the co-representation of whole sentences. In three experiments, pairs of speakers imagined each other producing active or passive descriptions of transitive events. Speakers took longer to begin speaking when they believed their partner was also preparing to speak, compared to when they did not. Interference occurred when speakers believed their partners were preparing to speak at the same time as them (synchronous production and co-representation; Experiment 1), and also when speakers believed that their partner would speak only after them (asynchronous production and co-representation; Experiments 2a and 2b). However, interference was generally no greater when speakers believed their partner was preparing a different compared to a similar utterance, providing no consistent evidence that speakers represented what their partners were preparing to say. Taken together, these findings indicate that speakers can represent another’s intention to speak even as they are themselves preparing to speak, but that such representation tends to lack detail.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
Influential theories and computational models suggest error-based learning plays an important role in language acquisition: Children learn new words by generating predictions about upcoming ...utterances and revising those predictions when they are erroneous. Critically, revising stronger (rather than weaker) predictions should further enhance learning. Although previously demonstrated in adults, such prediction error boost has not been conclusively shown in children. To close this gap, we tested 107 participants between the ages of 5 and 10. We found little evidence that word learning in this age group benefits from a prediction error boost. Moreover, we also failed to replicate previous evidence for such an effect in adults. Based on a detailed task analysis, we suggest the variation in adult findings may be partly explained by differences in encoding strategies and that, relatedly, the protracted development of the episodic memory system might explain why children do not experience robust benefits from having stronger (rather than weaker) predictions disconfirmed.
Co-actors represent and integrate each other's actions, even when they need not monitor one another. However, monitoring is important for successful interactions, particularly those involving ...language, and monitoring others' utterances probably relies on similar mechanisms as monitoring one's own. We investigated the effect of monitoring on the integration of self- and other-generated utterances in the shared-Stroop task. In a solo version of the Stroop task (with a single participant responding to all stimuli; Experiment 1), participants named the ink colour of mismatching colour words (incongruent stimuli) more slowly than matching colour words (congruent). In the shared-Stroop task, one participant named the ink colour of words in one colour (e.g. red), while ignoring stimuli in the other colour (e.g. green); the other participant either named the other ink colour or did not respond. Crucially, participants either provided feedback about the correctness of their partner's response (Experiment 3) or did not (Experiment 2). Interference was greater when both participants responded than when they did not, but only when their partners provided feedback. We argue that feedback increased interference because monitoring one's partner enhanced representations of the partner's target utterance, which in turn interfered with self-monitoring of the participant's own utterance.
We discuss two limitations of Hickok's account. First, we propose that ideas from motor control and planning should be brought wholesale into psycholinguistics so that processing at every level of ...the linguistic hierarchy (from concepts to sounds) should be recast in terms of forward model predictions and implementation. Second, we argue that motor involvement can sometimes enhance perception. We conclude that our account is consistent with a dual route model of comprehension in which different routes to prediction can interact.
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BFBNIB, NUK, PILJ, SAZU, UL, UM, UPUK
How do we update our linguistic knowledge? In seven experiments, we asked whether error-driven learning can explain under what circumstances adults and children are more likely to store and retain a ...new word meaning. Participants were exposed to novel object labels in the context of more or less constraining sentences or visual contexts. Both two-to-four-year-olds (Mage = 38 months) and adults were strongly affected by expectations based on sentence constraint when choosing the referent of a new label. In addition, adults formed stronger memory traces for novel words that violated a stronger prior expectation. However, preschoolers' memory was unaffected by the strength of their prior expectations. We conclude that the encoding of new word-object associations in memory is affected by prediction error in adults, but not in preschoolers.
•Adults form stronger memory traces for novel words violating stronger expectations.•2-to-4-year-olds' memory is unaffected by the strength of prior expectations.•Prediction error drives word encoding in adults, but not in preschoolers.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Word segmentation is a crucial step in children's vocabulary learning. While computational models of word segmentation can capture infants’ performance in small‐scale artificial tasks, the ...examination of early word segmentation in naturalistic settings has been limited by the lack of measures that can relate models’ performance to developmental data. Here, we extended CLASSIC (Chunking Lexical and Sublexical Sequences in Children; Jones et al., 2021), a corpus‐trained chunking model that can simulate several memory and phonological and vocabulary learning phenomena to allow it to perform word segmentation using utterance boundary information, and we have named this extended version CLASSIC utterance boundary (CLASSIC‐UB). Further, we compared our model to the performance of children on a wide range of new measures, capitalizing on the link between word segmentation and vocabulary learning abilities. We showed that the combination of chunking and utterance‐boundary information used by CLASSIC utterance boundary allowed a better prediction of English‐learning children's output vocabulary than did other models.
A one‐page Accessible Summary of this article in non‐technical language is freely available in the Supporting Information online and at https://oasis‐database.org
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
•During conversation, interlocutors rarely overlap or leave long gaps between turns.•We investigated how listeners use prediction to achieve such coordination.•Listeners used content predictions to ...prepare their verbal responses early.•However, they did not use these predictions to determine the speaker’s turn-end.•We suggest that content predictions aid early response preparation.
During conversation, there is often little gap between interlocutors’ utterances. In two pairs of experiments, we manipulated the content predictability of yes/no questions to investigate whether listeners achieve such coordination by (i) preparing a response as early as possible or (ii) predicting the end of the speaker’s turn. To assess these two mechanisms, we varied the participants’ task: They either pressed a button when they thought the question was about to end (Experiments 1a and 2a), or verbally answered the questions with either yes or no (Experiments 1b and 2b). Predictability effects were present when participants had to prepare a verbal response, but not when they had to predict the turn-end. These findings suggest content prediction facilitates turn-taking because it allows listeners to prepare their own response early, rather than because it helps them predict when the speaker will reach the end of their turn.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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