An integrated theory of language production and comprehension

Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume that actors construct forward models of their actions before they execute those actions, and that perceivers of others' actions covertly imitate those actions, then construct forward models of those actions. We use these accounts of action, action perception, and joint action to develop accounts of production, comprehension, and interactive language. Importantly, they incorporate well-defined levels of linguistic representation (such as semantics, syntax, and phonology). We show (a) how speakers and comprehenders use covert imitation and forward modeling to make predictions at these levels of representation, (b) how they interweave production and comprehension processes, and (c) how they use these predictions to monitor the upcoming utterances. We show how these accounts explain a range of behavioral and neuroscientific data on language processing and discuss some of the implications of our proposal

Self-, other-, and joint monitoring using forward models

In the psychology of language, most accounts of self-monitoring assume that it is based on comprehension. Here we outline and develop the alternative account proposed by Pickering and Garrod (2013), in which speakers construct forward models of their upcoming utterances and compare them with the utterance as they produce them. We propose that speakers compute inverse models derived from the discrepancy (error) between the utterance and the predicted utterance and use that to modify their production command or (occasionally) begin anew. We then propose that comprehenders monitor other people’s speech by simulating their utterances using covert imitation and forward models, and then comparing those forward models with what they hear. They use the discrepancy to compute inverse models and modify their representation of the speaker’s production command, or realize that their representation is incorrect and may develop a new production command. We then discuss monitoring in dialogue, paying attention to sequential contributions, concurrent feedback, and the relationship between monitoring and alignment

Towards a complete multiple-mechanism account of predictive language processing [Commentary on Pickering & Garrod]

Although we agree with Pickering & Garrod (P&G) that prediction-by-simulation and prediction-by-association are important mechanisms of anticipatory language processing, this commentary suggests that they: (1) overlook other potential mechanisms that might underlie prediction in language processing, (2) overestimate the importance of prediction-by-association in early childhood, and (3) underestimate the complexity and significance of several factors that might mediate prediction during language processing

Simulating background settings during spoken and written sentence comprehension

Previous findings from the sentence-picture verification task demonstrated that comprehenders simulate visual information about intrinsic attributes of described objects. Of interest is whether comprehenders may also simulate the setting in which an event takes place, such as, for example, the light information. To address this question, four experiments were conducted in which participants (total N = 412) either listened to (Experiment 1) or read (Experiment 3) sentences like “The sun is shining onto a bench” followed by a picture with the matching object (bench) and either the matching lighting condition of the scene (sunlit bench against the sunlit background) or the mismatching one (moonlit bench against the moonlit background). In both experiments, response times (RTs) were shorter when the lighting condition of the pictured scene matched the one implied in the sentence. However, no difference in RTs was observed when the processing of spoken sentences was interfered with visual noise (Experiment 2). Specifically, the results showed that visual interference disrupted incongruent visual content activated by listening to the sentences, as evidenced by faster responses on mismatching trials. Similarly, no difference in RTs was observed when the lighting condition of the pictured scene matched sentence context, but the target object presented for verification mismatched sentence context (Experiment 4). Thus, the locus of simulation effect is on the lighting representation of the target object rather than the lighting representation of the background. These findings support embodied and situated accounts of cognition, suggesting that comprehenders do not simulate objects independently of background settings.info:eu-repo/semantics/publishedVersio

Comprehension in-situ: how multimodal information shapes language processing

The human brain supports communication in dynamic face-to-face environments where spoken words are embedded in linguistic discourse and accompanied by multimodal cues, such as prosody, gestures and mouth movements. However, we only have limited knowledge of how these multimodal cues jointly modulate language comprehension. In a series of behavioural and EEG studies, we investigated the joint impact of these cues when processing naturalistic-style materials. First, we built a mouth informativeness corpus of English words, to quantify mouth informativeness of a large number of words used in the following experiments. Then, across two EEG studies, we found and replicated that native English speakers use multimodal cues and that their interactions dynamically modulate N400 amplitude elicited by words that are less predictable in the discourse context (indexed by surprisal values per word). We then extended the findings to second language comprehenders, finding that multimodal cues modulate L2 comprehension, just like in L1, but to a lesser extent; although L2 comprehenders benefit more from meaningful gestures and mouth movements. Finally, in two behavioural experiments investigating whether multimodal cues jointly modulate the learning of new concepts, we found some evidence that presence of iconic gestures improves memory, and that the effect may be larger if information is presented also with prosodic accentuation. Overall, these findings suggest that real-world comprehension uses all cues present and weights cues differently in a dynamic manner. Therefore, multimodal cues should not be neglected for language studies. Investigating communication in naturalistic contexts containing more than one cue can provide new insight into our understanding of language comprehension in the real world

Predicting (in)correctly: listeners rapidly use unexpected information to revise their predictions

Comprehenders can incorporate rich contextual information to predict upcoming input on the fly, and cues that conflict with their predictions are quickly detected. The present study examined whether and how comprehenders may revise their existing predictions upon encountering a prediction-inconsistent cue. We took advantage of the rich classifier system in Mandarin Chinese and tracked participants’ eye-movements as they listened to sentences in which the final noun is preceded by a classifier which was either compatible with the most expected noun, incompatible with the most expected noun but indicative of another contextually suitable noun, or uninformative. We found that, upon hearing a prediction-inconsistent classifier, listeners quickly directed their eye gaze away from the originally expected object and immediately onto the (initially) unexpected but contextually suitable object. This provides initial evidence that listeners can quickly use prediction-mismatching cues to revise their existing predictions on the fly

Principle B constrains the processing of cataphora: Evidence for syntactic and discourse predictions

We tested whether comprehenders can use Binding Principle B (Chomsky, 1981) to guide antecedent search during the processing of cataphoric pronouns. We ran two self-paced reading experiments using the gender mismatch paradigm (van Gompel & Liversedge, 2003) as an index of active prediction of coreference between a cataphor and a main clause subject. In both experiments, we find gender mismatch effects at the main clause subject when coreference with the cataphor is grammatically acceptable. We do not find comparable gender mismatch effects in conditions where coreference is ruled out by Principle B. Our results are broadly consistent with models in which grammatical constraints serve as early filters on anaphora resolution processes in comprehension. We illustrate how the parser can integrate syntactic and discourse-level information to achieve grammatical sensitivity during incremental referential processing

Speaker-specific processing of anomalous utterances

Existing work shows that readers often interpret grammatical errors (e.g., The key to the cabinets *were shiny) and sentence-level blends (“without-blend”: Claudia left without her headphones *off) in a non-literal fashion, inferring that a more frequent or more canonical utterance was intended instead. This work examines how interlocutor identity affects the processing and interpretation of anomalous sentences. We presented anomalies in the context of “emails” attributed to various writers in a self-paced reading paradigm and used comprehension questions to probe how sentence interpretation changed based upon properties of the item and properties of the “speaker.” Experiment 1 compared standardised American English speakers to L2 English speakers; Experiment 2 compared the same standardised English speakers to speakers of a non-Standardised American English dialect. Agreement errors and without-blends both led to more non-literal responses than comparable canonical items. For agreement errors, more non-literal interpretations also occurred when sentences were attributed to speakers of Standardised American English than either non-Standardised group. These data suggest that understanding sentences relies on expectations and heuristics about which utterances are likely. These are based upon experience with language, with speaker-specific differences, and upon more general cognitive biases

Flexible predictions during listening comprehension: Speaker reliability affects anticipatory processes

Available online 9 October 2019During listening comprehension, the identification of individual words can be strongly influenced by properties of the preceding context. While sentence context can facilitate both behavioral and neural responses, it is unclear whether these effects can be attributed to the pre-activation of lexico-semantic features or the facilitated integration of contextually congruent words. Moreover, little is known about how statistics of the broader language environment, or information about the current speaker, might shape these facilitation effects. In the present study, we measured neural responses to predictable and unpredictable words as participants listened to sentences for comprehension. Critically, we manipulated the reliability of each speaker’s utterances, such that individual speakers either tended to complete sentences with words that were highly predictable (reliable speaker) or with words that were unpredictable but still plausible (unreliable speaker). As expected, the amplitude of the N400 was reduced for locally predictable words, but, critically, these context effects were also modulated by speaker identity. Sentences from a reliable speaker showed larger facilitation effects with an earlier onset, suggesting that listeners engaged in enhanced anticipatory processing when a speaker’s behavior was more predictable. This finding suggests that listeners can implicitly track the reliability of predictive cues in their environment and use these statistics to adaptively regulate predictive processing.This research was partially funded by NSF (1024003) and NIH (R21 11601946)

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings