Drafts by T. Florian Jaeger
Accurate word recognition is facilitated by context. Some relevant context, however, occurs after... more Accurate word recognition is facilitated by context. Some relevant context, however, occurs after the word. Efficient use of such “right context” requires comprehenders to maintain uncertainty about the word, still allowing for consideration of multiple possible alternatives when they encounter relevant right context. However, influential models suggest that uncertainty is not maintained in this way. A classic study (Connine et al., 1991, Experiment 1) examined right context effects using word pairs that differed in voicing by manipulating VOT (e.g., dent/tent). The results were interpreted as evidence for limited uncertainty maintenance. Right context effects were limited to fewer than six syllables downstream and even then were only found for highly ambiguous VOTs near the category boundary. With small modifications in procedure and analysis, we report that uncertainty is maintained for at least six to eight syllables and equally so for the entire VOT continuum (rather than only ambiguous cases). We show that an ideal recognizer, which optimally combines acoustic information with right context, correctly predicts our results. This suggests that, at least under some conditions, listeners combine acoustic information with right context rationally.
As lifelong statistical learners, humans are remarkably sensitive to the unfolding of elements an... more As lifelong statistical learners, humans are remarkably sensitive to the unfolding of elements and events in their surroundings. In the present work, we examine the bi-directional influence of prediction-based processing and learning as adult participants were exposed to a visual artificial grammar containing a non-adjacent dependency. Using a self-paced moving window display, we recorded response times as learners progressed through a series of structured glyph sequences. After accounting for general task adaptation effects, we quantified the growing influence of element predictability on those response times. We find that, as a function of exposure, participants generally processed the grammar increasingly faster; however, the facilitatory benefit was significantly greater for the perfectly predictable items of the grammar. In turn, this progressive processing benefit on predictable elements was uniquely correlated with off-line performance on a post-test. Our results indicate that participants who develop implicit predictions as they learn, and have their expectations met, achieve higher learning outcomes. Links between these findings, obtained with novel stimuli in an experimental context, and the role of prediction in natural language comprehension are considered.
The extent to which language processing involves prediction of upcoming
inputs remains a question... more The extent to which language processing involves prediction of upcoming
inputs remains a question of ongoing debate. One important data point comes from DeLong et al. (2005) who reported that an N400-like event-related potential correlated with a probabilistic index of upcoming input. This result is often cited as evidence for gradient probabilistic prediction of form and/or semantics, prior to the bottom-up input becoming available. However, a recent multi-lab study reports a failure to find these effects (Nieuwland et al., 2017). We review the evidence from both studies, including differences in the design and analysis approach between them. Building on over a decade of research on prediction since DeLong et al. (2005)’s original study, we also begin to spell out the computational nature of predictive processes that one might expect to correlate with ERPs that are evoked by a functional element whose form is dependent on an upcoming predicted word. For paradigms with this type of design, we propose an index of anticipatory processing, Bayesian surprise, and apply it to the updating of semantic predictions. We motivate this index both theoretically and empirically. We show that, for studies of the type discussed here, Bayesian surprise can be closely approximated by another, more easily estimated information theoretic index, the surprisal (or Shannon information) of the input. We re-analyze the data from Nieuwland and colleagues using surprisal rather than raw probabilities as an index of prediction. We find that surprisal is gradiently correlated with the amplitude of the N400, even in the data shared by Nieuwland and colleagues. Taken together, our review suggests that the evidence from both studies is compatible with anticipatory semantic processing. We do, however, emphasize the need for future studies to further clarify the nature and degree of form prediction, as well as its neural signatures, during language comprehension.
It is often assumed that language development occurs during a critical period and that the plasti... more It is often assumed that language development occurs during a critical period and that the plasticity of the brain areas involved decreases afterwards, making language acquisition difficult or impossible. Yet, adults also exhibit implicit language learning, for example, when adapting to novel accents. However, these are typically regarded as separate processes because acquisition and adaptation occur over vastly different time-scales—a single mechanism would not seem to be sufficient. Focusing on one specific phonetic contrast (voicing), we find that the same statistical learning mechanism can explain speech development in infancy and adaptation in adulthood. This is achieved without any changes in plasticity or different learning mechanisms reflecting a critical period. The model we present calls into question the need for critical periods to explain phonological acquisition and adaptation, and it shows a way forward in addressing this question for a broader range of problems in language acquisition.
Unpublished ms., Stanford University, Jan 1, 2004
The art of the state: Mixed effects regression modeling in the visual world
21st Annual CUNY Conference on Human Sentence Processing, Chapel Hill, North Carolina, 2008
The art of the state: Mixed effects regression modeling in the visual world
21st Annual CUNY Conference on Human Sentence Processing, Chapel Hill, North Carolina, 2008
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Drafts by T. Florian Jaeger
inputs remains a question of ongoing debate. One important data point comes from DeLong et al. (2005) who reported that an N400-like event-related potential correlated with a probabilistic index of upcoming input. This result is often cited as evidence for gradient probabilistic prediction of form and/or semantics, prior to the bottom-up input becoming available. However, a recent multi-lab study reports a failure to find these effects (Nieuwland et al., 2017). We review the evidence from both studies, including differences in the design and analysis approach between them. Building on over a decade of research on prediction since DeLong et al. (2005)’s original study, we also begin to spell out the computational nature of predictive processes that one might expect to correlate with ERPs that are evoked by a functional element whose form is dependent on an upcoming predicted word. For paradigms with this type of design, we propose an index of anticipatory processing, Bayesian surprise, and apply it to the updating of semantic predictions. We motivate this index both theoretically and empirically. We show that, for studies of the type discussed here, Bayesian surprise can be closely approximated by another, more easily estimated information theoretic index, the surprisal (or Shannon information) of the input. We re-analyze the data from Nieuwland and colleagues using surprisal rather than raw probabilities as an index of prediction. We find that surprisal is gradiently correlated with the amplitude of the N400, even in the data shared by Nieuwland and colleagues. Taken together, our review suggests that the evidence from both studies is compatible with anticipatory semantic processing. We do, however, emphasize the need for future studies to further clarify the nature and degree of form prediction, as well as its neural signatures, during language comprehension.