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Inferences about hypotheses are ubiquitous in the cognitive sciences. Bayes factors provide one general way to compare different hypotheses by their compatibility with the observed data. Those quantifications can then also be used to choose between hypotheses. While Bayes factors provide an immediate approach to hypothesis testing, they are highly sensitive to details of the data/model assumptions and it's unclear whether the details of the computational implementation (such as bridge sampling) are unbiased for complex analyses. Hem, we study how Bayes factors misbehave under different conditions. This includes a study of errors in the estimation of Bayes factors; the first-ever use of simulation-based calibration to test the accuracy and bias of Bayes factor estimates using bridge sampling; a study of the stability of Bayes factors against different MCMC draws and sampling variation in the data; and a look at the variability of decisions based on Bayes factors using a utility function. We outline a Bayes factor workflow that researchers can use to study whether Bayes factors are robust for their individual analysis. Reproducible code is available from haps://osf.io/y354c/. <br /> Translational Abstract <br /> In psychology and related areas, scientific hypotheses are commonly tested by asking questions like "is [some] effect present or absent." Such hypothesis testing is most often carried out using frequentist null hypothesis significance testing (NIIST). The NHST procedure is very simple: It usually returns a p-value, which is then used to make binary decisions like "the effect is present/abscnt." For example, it is common to see studies in the media that draw simplistic conclusions like "coffee causes cancer," or "coffee reduces the chances of geuing cancer." However, a powerful and more nuanced alternative approach exists: Bayes factors. Bayes factors have many advantages over NHST. However, for the complex statistical models that arc commonly used for data analysis today, computing Bayes factors is not at all a simple matter. In this article, we discuss the main complexities associated with computing Bayes factors. This is the first article to provide a detailed workflow for understanding and computing Bayes factors in complex statistical models. The article provides a statistically more nuanced way to think about hypothesis testing than the overly simplistic tendency to declare effects as being "present" or "absent".
Background:
Aphasia therapy software applications (apps) can help achieve recommendations regarding aphasia treatment intensity and duration.
However, we currently know very little about speech and language therapists' (SLTs) preferences with regards to these apps.
This may be problematic, as clinician acceptance of novel treatments and technology are a key factor for successful translation from research evidence to practice.
Aim:
This research aimed to increase our understanding of clinicians' experiences with aphasia therapy apps and their perceived barriers and facilitators to the use of aphasia apps. Furthermore, we wanted to explore the influence of some demographic factors (age, country, and SLT availability in the client's hometown) on SLTs' attitudes towards these apps.
Method & Procedures:
35 Dutch and 29 Australian SLTs completed an online survey. The survey contained 9 closed-ended questions and 3 open-ended questions. Responses to the closed-ended questions were summarised through the use of descriptive statistics. The responses to the open questions were analysed and coded into recurring themes that were derived from the data. Logistic regression analyses were performed to explore the relationship between the demographic variables and the responses to the closed-ended questions.
Outcomes & results:
Participants were overwhelmingly positive about aphasia therapy apps and saw the potential for their clients to use apps independently. As facilitators of app use, participants reported accessibility and inclusion of different language modalities, while high costs, absence of a compatible device, and clients' potential computer illiteracy were listed as barriers. None of the analysed demographic factors consistently influenced differences in participants' attitudes towards aphasia therapy apps.
Conclusions:
The positive, extensive and insightful feedback from speech and language therapists is both useful and encouraging for app developers and aphasia researchers, and should facilitate the development of appropriate, high-quality therapy apps.
The study of perceptual flexibility in speech depends on a variety of tasks that feature a large degree of variability between participants. Of critical interest is whether measures are consistent within an individual or across stimulus contexts. This is particularly key for individual difference designs that are deployed to examine the neural basis or clinical consequences of perceptual flexibility. In the present set of experiments, we assess the split-half reliability and construct validity of five measures of perceptual flexibility: three of learning in a native language context (e.g., understanding someone with a foreign accent) and two of learning in a non-native context (e.g., learning to categorize non-native speech sounds). We find that most of these tasks show an appreciable level of split-half reliability, although construct validity was sometimes weak. This provides good evidence for reliability for these tasks, while highlighting possible upper limits on expected effect sizes involving each measure.
Agreement attraction is a cross-linguistic phenomenon where a verb occasionally agrees not with its subject, as required by grammar, but instead with an unrelated noun ("The key to the cabinets were horizontal ellipsis ").
Despite the clear violation of grammatical rules, comprehenders often rate these sentences as acceptable. Contenders for explaining agreement attraction fall into two broad classes: Morphosyntactic accounts specifically designed to explain agreement attraction, and more general sentence processing models, such as the Lewis and Vasishth model, which explain attraction as a consequence of how linguistic structure is stored and accessed in content-addressable memory.
In the present research, we disambiguate between these two classes by testing a surprising prediction made by the Lewis and Vasishth model but not by the morphosyntactic accounts, namely, that attraction should not be limited to morphosyntax, but that semantic features of unrelated nouns equally induce attraction.
A recent study by Cunnings and Sturt provided initial evidence that this may be the case. Here, we report three single-trial experiments in English that compared semantic and agreement attraction and tested whether and how the two interact.
All three experiments showed strong semantically induced attraction effects closely mirroring agreement attraction effects. We complement these results with computational simulations which confirmed that the Lewis and Vasishth model can faithfully reproduce the observed results.
In sum, our findings suggest that attraction is a more general phenomenon than is commonly believed, and therefore favor more general sentence processing models, such as the Lewis and Vasishth model.
A comprehensive theory of child language acquisition requires an evidential base that is representative of the typological diversity present in the world's 7000 or so languages. However, languages are dying at an alarming rate, and the next 50 years represents the last chance we have to document acquisition in many of them. Here, we take stock of the last 45 years of research published in the four main child language acquisition journals: Journal of Child Language, First Language, Language Acquisition and Language Learning and Development. We coded each article for several variables, including (1) participant group (mono vs multilingual), (2) language(s), (3) topic(s) and (4) country of author affiliation, from each journal's inception until the end of 2020. We found that we have at least one article published on around 103 languages, representing approximately 1.5% of the world's languages. The distribution of articles was highly skewed towards English and other well-studied Indo-European languages, with the majority of non-Indo-European languages having just one paper. A majority of the papers focused on studies of monolingual children, although papers did not always explicitly report participant group status. The distribution of topics across language categories was more even. The number of articles published on non-Indo-European languages from countries outside of North America and Europe is increasing; however, this increase is driven by research conducted in relatively wealthy countries. Overall, the vast majority of the research was produced in the Global North. We conclude that, despite a proud history of crosslinguistic research, the goals of the discipline need to be recalibrated before we can lay claim to truly a representative account of child language acquisition.
Languages differ in whether or not they allow discontinuous noun phrases. If they do, they further vary in the ways the nominal projections interact with the available syntactic operations. Yucatec Maya has two left-peripheral configurations that differ syntactically: a preverbal position for foci or wh-elements that is filled in by movement, and the possibility to adjoin topics at the highest clausal layer. These two structural options are reflected in different ways of the formation of discontinuous patterns. Subextraction from nominal projections to the focus position yielding discontinuous NPs is possible, but subject to several restrictions. It observes conditions on extraction domains, and does not apply to the left branch of nominal structures. The topic position also appears to license discontinuity, typically involving a non-referential nominal expression as the topic and quantifiers/adjectives that form an elliptical nominal projection within the clause proper. Such constructions can involve several morphological and syntactic mismatches between their parts that are excluded for continuous noun phrases, and they are not sensitive to syntactic island restrictions. Thus, in a strict sense, discontinuities involving the topic position are only apparent, because the construction involves two independent nominal projections that are semantically linked.
In this study, we investigated the cognitive-emotional interplay by measuring the effects of executive competition (Pessoa, 2013), i.e., how inhibitory control is influenced when emotional information is encountered. Sixty-three children (8 to 9 years of age) participated in an inhibition task (central task) accompanied by happy, sad, or neutral emoticons (displayed in the periphery). Typical interference effects were found in the main task for speed and accuracy, but in general, these effects were not additionally modulated by the peripheral emoticons indicating that processing of the main task exhausted the limited capacity such that interference from the task-irrelevant, peripheral information did not show (Pessoa, 2013). Further analyses revealed that the magnitude of interference effects depended on the order of congruency conditions: when incongruent conditions preceded congruent ones, there was greater interference. This effect was smaller in sad conditions, and particularly so at the beginning of the experiment. These findings suggest that the bottom-up perception of task-irrelevant emotional information influenced the top-down process of inhibitory control among children in the sad condition when processing demands were particularly high. We discuss if the salience and valence of the emotional stimuli as well as task demands are the decisive characteristics that modulate the strength of this relation.
Sonority is a fundamental notion in phonetics and phonology, central to many descriptions of the syllable and various useful predictions in phonotactics. Although widely accepted, sonority lacks a clear basis in speech articulation or perception, given that traditional formal principles in linguistic theory are often exclusively based on discrete units in symbolic representation and are typically not designed to be compatible with auditory perception, sensorimotor control, or general cognitive capacities. In addition, traditional sonority principles also exhibit systematic gaps in empirical coverage. Against this backdrop, we propose the incorporation of symbol-based and signal-based models to adequately account for sonority in a complementary manner. We claim that sonority is primarily a perceptual phenomenon related to pitch, driving the optimization of syllables as pitch-bearing units in all language systems. We suggest a measurable acoustic correlate for sonority in terms of periodic energy, and we provide a novel principle that can account for syllabic well-formedness, the nucleus attraction principle (NAP). We present perception experiments that test our two NAP-based models against four traditional sonority models, and we use a Bayesian data analysis approach to test and compare them. Our symbolic NAP model outperforms all the other models we test, while our continuous bottom-up NAP model is at second place, along with the best performing traditional models. We interpret the results as providing strong support for our proposals: (i) the designation of periodic energy as the acoustic correlate of sonority; (ii) the incorporation of continuous entities in phonological models of perception; and (iii) the dual-model strategy that separately analyzes symbol-based top-down processes and signal-based bottom-up processes in speech perception.
We investigated the processing of morphologically complex words adopting an approach that goes beyond estimating average effects and allows testing predictions about variability in performance. We tested masked morphological priming effects with English derived ('printer') and inflected ('printed') forms priming their stems ('print') in non-native speakers, a population that is characterized by large variability. We modeled reaction times with a shifted-lognormal distribution using Bayesian distributional models, which allow assessing effects of experimental manipulations on both the mean of the response distribution ('mu') and its standard deviation ('sigma'). Our results show similar effects on mean response times for inflected and derived primes, but a difference between the two on the sigma of the distribution, with inflectional priming increasing response time variability to a significantly larger extent than derivational priming. This is in line with previous research on non-native processing, which shows more variable results across studies for the processing of inflected forms than for derived forms. More generally, our study shows that treating variability in performance as a direct object of investigation can crucially inform models of language processing, by disentangling effects which would otherwise be indistinguishable. We therefore emphasize the importance of looking beyond average performance and testing predictions on other parameters of the distribution rather than just its central tendency.
Language processing requires memory retrieval to integrate current input with previous context and making predictions about upcoming input. We propose that prediction and retrieval are two sides of the same coin, i.e. functionally the same, as they both activate memory representations. Under this assumption, memory retrieval and prediction should interact: Retrieval interference can only occur at a word that triggers retrieval and a fully predicted word would not do that. The present study investigated the proposed interaction with event-related potentials (ERPs) during the processing of sentence pairs in German. Predictability was measured via cloze probability. Memory retrieval was manipulated via the position of a distractor inducing proactive or retroactive similarity-based interference. Linear mixed model analyses provided evidence for the hypothesised interaction in a broadly distributed negativity, which we discuss in relation to the interference ERP literature. Our finding supports the proposal that memory retrieval and prediction are functionally the same.