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In the present study, we investigated younger and older Persian preschoolers' response tendency and accuracy toward yes/no questions about a coloring activity. Overall, 107 three- to four-year-olds and five- to six-year-old children were asked positive and negative yes/no questions about a picture coloring activity. The questions focused on three question contents namely, actions, environment and person. As for children's response tendency, they showed a compliance tendency. That is, they provided yes and no responses to positively and negatively formed questions respectively. Children especially younger ones were more compliant toward positive questions and their tendency decreased by age. In addition, the results revealed children's highest rate of compliance tendency toward environment inquiries. Concerning response accuracy, the effects of age and question content were significant. Specifically, older children provided more accurate responses than their younger counterparts, especially to yes/no questions asked about the actions performed during the activity. The findings suggest that depending on the format and the content of yes/no questions younger and older children's response accuracy and tendency differ.
Yet another Theta-System
(2002)
There is a wealth of evidence showing that increasing the distance between an argument and its head leads to more processing effort, namely, locality effects: these are usually associated with constraints in working memory (DLT: Gibson, 2000: activation-based model: Lewis and Vasishth, 2005). In SOV languages, however, the opposite effect has been found: antilocality (see discussion in Levy et al., 2013). Antilocality effects can be explained by the expectation based approach as proposed by Levy (2008) or by the activation-based model of sentence processing as proposed by Lewis and Vasishth (2005). We report an eye-tracking and a self-paced reading study with sentences in Spanish together with measures of individual differences to examine the distinction between expectation- and memory based accounts, and within memory-based accounts the further distinction between DLT and the activation-based model. The experiments show that (i) antilocality effects as predicted by the expectation account appear only for high-capacity readers; (ii) increasing dependency length by interposing material that modifies the head of the dependency (the verb) produces stronger facilitation than increasing dependency length with material that does not modify the head; this is in agreement with the activation-based model but not with the expectation account; and (iii) a possible outcome of memory load on low-capacity readers is the increase in regressive saccades (locality effects as predicted by memory-based accounts) or, surprisingly, a speedup in the self-paced reading task; the latter consistent with good-enough parsing (Ferreira et al., 2002). In sum, the study suggests that individual differences in working memory capacity play a role in dependency resolution, and that some of the aspects of dependency resolution can be best explained with the activation-based model together with a prediction component.
There is a wealth of evidence showing that increasing the distance between an argument and its head leads to more processing effort, namely, locality effects; these are usually associated with constraints in working memory (DLT: Gibson, 2000; activation-based model: Lewis and Vasishth, 2005). In SOV languages, however, the opposite effect has been found: antilocality (see discussion in Levy et al., 2013). Antilocality effects can be explained by the expectation-based approach as proposed by Levy (2008) or by the activation-based model of sentence processing as proposed by Lewis and Vasishth (2005). We report an eye-tracking and a self-paced reading study with sentences in Spanish together with measures of individual differences to examine the distinction between expectation- and memory-based accounts, and within memory-based accounts the further distinction between DLT and the activation-based model. The experiments show that (i) antilocality effects as predicted by the expectation account appear only for high-capacity readers; (ii) increasing dependency length by interposing material that modifies the head of the dependency (the verb) produces stronger facilitation than increasing dependency length with material that does not modify the head; this is in agreement with the activation-based model but not with the expectation account; and (iii) a possible outcome of memory load on low-capacity readers is the increase in regressive saccades (locality effects as predicted by memory-based accounts) or, surprisingly, a speedup in the self-paced reading task; the latter consistent with good-enough parsing (Ferreira et al., 2002). In sum, the study suggests that individual differences in working memory capacity play a role in dependency resolution, and that some of the aspects of dependency resolution can be best explained with the activation-based model together with a prediction component.
There is a wealth of evidence showing that increasing the distance between an argument and its head leads to more processing effort, namely, locality effects; these are usually associated with constraints in working memory (DLT: Gibson, 2000; activation-based model: Lewis and Vasishth, 2005). In SOV languages, however, the opposite effect has been found: antilocality (see discussion in Levy et al., 2013). Antilocality effects can be explained by the expectation-based approach as proposed by Levy (2008) or by the activation-based model of sentence processing as proposed by Lewis and Vasishth (2005). We report an eye-tracking and a self-paced reading study with sentences in Spanish together with measures of individual differences to examine the distinction between expectation- and memory-based accounts, and within memory-based accounts the further distinction between DLT and the activation-based model. The experiments show that (i) antilocality effects as predicted by the expectation account appear only for high-capacity readers; (ii) increasing dependency length by interposing material that modifies the head of the dependency (the verb) produces stronger facilitation than increasing dependency length with material that does not modify the head; this is in agreement with the activation-based model but not with the expectation account; and (iii) a possible outcome of memory load on low-capacity readers is the increase in regressive saccades (locality effects as predicted by memory-based accounts) or, surprisingly, a speedup in the self-paced reading task; the latter consistent with good-enough parsing (Ferreira et al., 2002). In sum, the study suggests that individual differences in working memory capacity play a role in dependency resolution, and that some of the aspects of dependency resolution can be best explained with the activation-based model together with a prediction component.
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".
Words as social tools
(2019)
This study provides a synthesis of corpus-based and experimental investigations of word-order preferences in German infinitival complementation. We carried out a systematic analysis of present-day German corpora to establish frequency distributions of different word-order options: extraposition, intraposition, and 'third construction'. We then examined, firstly, whether and to what extent corpus frequencies and processing economy constraints can predict the acceptability of these three word-order variants, and whether subject raising and subject control verbs form clearly distinguishable subclasses of infinitive-embedding verbs in terms of their word-order behaviour. Secondly, our study looks into the issue of coherence by comparing acceptability ratings for monoclausal coherent and biclausal incoherent construals of intraposed infinitives, and by examining whether a biclausal incoherent analysis gives rise to local and/or global processing difficulty. Taken together, our results revealed that (i) whilst the extraposition pattern consistently wins out over all other word-order variants for control verbs, neither frequency nor processing-based approaches to word-order variation can account for the acceptability of low-frequency variants, (ii) there is considerable verb-specific variation regarding word-order preferences both between and within the two sets of raising and control verbs under investigation, and (iii) although monoclausal coherent intraposition is rated above biclausal incoherent intraposition, the latter is not any more difficult to process than the former. Our findings indicate that frequency of occurrence and processing-related constraints interact with idiosyncratic lexical properties of individual verbs in determining German speakers' structural preferences.
There is evidence that infants start extracting words from fluent speech around 7.5 months of age (e.g., Jusczyk & Aslin, 1995) and that they use at least two mechanisms to segment words forms from fluent speech: prosodic information (e.g., Jusczyk, Cutler & Redanz, 1993) and statistical information (e.g., Saffran, Aslin & Newport, 1996). However, how these two mechanisms interact and whether they change during development is still not fully understood.
The main aim of the present work is to understand in what way different cues to word segmentation are exploited by infants when learning the language in their environment, as well as to explore whether this ability is related to later language skills. In Chapter 3 we pursued to determine the reliability of the method used in most of the experiments in the present thesis (the Headturn Preference Procedure), as well as to examine correlations and individual differences between infants’ performance and later language outcomes. In Chapter 4 we investigated how German-speaking adults weigh statistical and prosodic information for word segmentation. We familiarized adults with an auditory string in which statistical and prosodic information indicated different word boundaries and obtained both behavioral and pupillometry responses. Then, we conducted further experiments to understand in what way different cues to word segmentation are exploited by 9-month-old German-learning infants (Chapter 5) and by 6-month-old German-learning infants (Chapter 6). In addition, we conducted follow-up questionnaires with the infants and obtained language outcomes at later stages of development.
Our findings from this thesis revealed that (1) German-speaking adults show a strong weight of prosodic cues, at least for the materials used in this study and that (2) German-learning infants weight these two kind of cues differently depending on age and/or language experience. We observed that, unlike English-learning infants, 6-month-old infants relied more strongly on prosodic cues. Nine-month-olds do not show any preference for either of the cues in the word segmentation task. From the present results it remains unclear whether the ability to use prosodic cues to word segmentation relates to later language vocabulary. We speculate that prosody provides infants with their first window into the specific acoustic regularities in the signal, which enables them to master the specific stress pattern of German rapidly. Our findings are a step forwards in the understanding of an early impact of the native prosody compared to statistical learning in early word segmentation.