What if they're just not that into you (or your experiment)? On motivation and psycholinguistics

A targeted review of prosodic production in agrammatic aphasia

It is unclear whether individuals with agrammatic aphasia have particularly disrupted prosody, or in fact have relatively preserved prosody they can use in a compensatory way. A targeted literature review was undertaken to examine the evidence regarding the capacity of speakers with agrammatic aphasia to produce prosody. The aim was to answer the question, how much prosody can a speaker "do" with limited syntax? The literature was systematically searched for articles examining the production of grammatical prosody in people with agrammatism, and yielded 16 studies that were ultimately included in this review. Participant inclusion criteria, spoken language tasks, and analysis procedures vary widely across studies. The evidence indicates that timing aspects of prosody are disrupted in people with agrammatic aphasia, while the use of pitch and amplitude cues is more likely to be preserved in this population. Some, but not all, of these timing differences may be attributable to motor speech programming deficits (AOS) rather than aphasia, as these conditions frequently co-occur. Many of the included studies do not address AOS and its possible role in any observed effects. Finally, the available evidence indicates that even speakers with severe aphasia show a degree of preserved prosody in functional communication.

A Deep Learning Approach to Analyzing Continuous-Time Cognitive Processes

The dynamics of the mind are complex. Mental processes unfold continuously in time and may be sensitive to a myriad of interacting variables, especially in naturalistic settings. But statistical models used to analyze data from cognitive experiments often assume simplistic dynamics. Recent advances in deep learning have yielded startling improvements to simulations of dynamical cognitive processes, including speech comprehension, visual perception, and goal-directed behavior. But due to poor interpretability, deep learning is generally not used for scientific analysis. Here, we bridge this gap by showing that deep learning can be used, not just to imitate, but to analyze complex processes, providing flexible function approximation while preserving interpretability. To do so, we define and implement a nonlinear regression model in which the probability distribution over the response variable is parameterized by convolving the history of predictors over time using an artificial neural network, thereby allowing the shape and continuous temporal extent of effects to be inferred directly from time series data. Our approach relaxes standard simplifying assumptions (e.g., linearity, stationarity, and homoscedasticity) that are implausible for many cognitive processes and may critically affect the interpretation of data. We demonstrate substantial improvements on behavioral and neuroimaging data from the language processing domain, and we show that our model enables discovery of novel patterns in exploratory analyses, controls for diverse confounds in confirmatory analyses, and opens up research questions in cognitive (neuro)science that are otherwise hard to study.

Syntactic adaptation leads to updated knowledge for local structural frequencies

Syntactic adaptation has been shown to occur for various temporarily ambiguous structures, wherein an initially unexpected resolution becomes easier to process after repeated exposure. More controversial and less replicated is the claim that this adaptation towards a locally frequent structure occurs due to a strategic shifting of expectations to match short-term statistical regularities such that readers adapt away from the a priori more frequent structure. Experiment 1 replicates the initial adaptation towards a coordination garden path structure using self-paced reading; however, this paradigm has been criticised for its low reliability for detecting such small effects. To this end, Experiments 2 and 3 use a combination of self-paced reading and sentence completion tasks to replicate initial adaptation towards both coordination and reduced relative garden path structures and show evidence for a preference for these structures over their a priori more frequent alternatives. Together, these data reveal that participants may be tracking local structural statistics in real time; however, they may not be able to rapidly use that information to update processing behaviours.

Nuances of knowing: Brain potentials reveal implicit effects of domain knowledge on word processing in the absence of sentence-level knowledge

In previous work investigating the relationship between domain knowledge (of the fictional world of Harry Potter) and sentence comprehension, domain knowledge had a greater impact on electrical brain potentials to words which completed sentences about fictional "facts" participants reported they did not know compared to facts they did. This suggests that individuals use domain knowledge continuously to activate relevant/related concepts as they process sentences, even with only partial knowledge. As that study relied on subjective reports, it may have resulted in response bias related to an individual's overall domain knowledge. In the present study, we therefore asked participants with varying degrees of domain knowledge to complete sentences describing fictional "facts" as an objective measure of sentence-level knowledge. We then recorded EEG as the same individuals (re-)read the same sentences, including their appropriate final words, and sorted these according to their objective knowledge scores. Replicating and extending Troyer et al., domain knowledge immediately facilitated access to meaning for unknown words; greater domain knowledge was associated with reduced N400 amplitudes for unknown words. These findings constitute novel evidence for graded preactivation of conceptual knowledge (e.g., at the level of semantic features and/or relations) in the absence of lexical prediction. Knowledge also influenced post-N400 memory/integration processes for these same unknown words; greater domain knowledge was associated with enhanced late positive components (LPCs), suggesting that deeper encoding during language processing may be engendered when knowledgeable individuals encounter an apparent gap in their knowledge.

The Myth of Normal Reading

We argue that the educational and psychological sciences must embrace the diversity of reading rather than chase the phantom of normal reading behavior. We critically discuss the research practice of asking participants in experiments to read "normally." We then draw attention to the large cross-cultural and linguistic diversity around the world and consider the enormous diversity of reading situations and goals. Finally, we observe that people bring a huge diversity of brains and experiences to the reading task. This leads to four implications: First, there are important lessons for how to conduct psycholinguistic experiments; second, we need to move beyond Anglocentric reading research and produce models of reading that reflect the large cross-cultural diversity of languages and types of writing systems; third, we must acknowledge that there are multiple ways of reading and reasons for reading, and none of them is normal or better or a "gold standard"; and fourth, we must stop stigmatizing individuals who read differently and for different reasons, and there should be increased focus on teaching the ability to extract information relevant to the person's goals. What is important is not how well people decode written language and how fast people read but what people comprehend given their own stated goals.