Behavior Research Methods最新文献

We introduce a sentence corpus with eye-movement data in traditional Chinese (TC), based on the original Beijing Sentence Corpus (BSC) in simplified Chinese (SC). The most noticeable difference between TC and SC character sets is their visual complexity. There are reaction time corpora in isolated TC character/word lexical decision and naming tasks. However, up to now natural TC sentence reading corpus with recorded eye movements has not been available for general public. We report effects of word frequency, visual complexity, and predictability on eye movements on fixation location and duration based on 60 native TC readers. In addition, because the current BSC-II sentences are nearly identical to the original BSC sentences, we report similarities and differences of the linguistic influences on eye movements for the two varieties of written Chinese. The results shed light on how visual complexity affects eye movements. Together, the two sentence corpora comprise a useful tool to establish cross-script similarities and differences in TC and SC.

While viewing a visual stimulus, we often cannot tell whether it is inherently memorable or forgettable. However, the memorability of a stimulus can be quantified and partially predicted by a collection of conceptual and perceptual factors. Higher-level properties that represent the "meaningfulness" of a visual stimulus to viewers best predict whether it will be remembered or forgotten across a population. Here, we hypothesize that the feelings evoked by an image, operationalized as the valence and arousal dimensions of affect, significantly contribute to the memorability of scene images. We ran two complementary experiments to investigate the influence of affect on scene memorability, in the process creating a new image set (VAMOS) of hundreds of natural scene images for which we obtained valence, arousal, and memorability scores. From our first experiment, we found memorability to be highly reliable for scene images that span a wide range of evoked arousal and valence. From our second experiment, we found that both valence and arousal are significant but weak predictors of image memorability. Scene images were most memorable if they were slightly negatively valenced and highly arousing. Images that were extremely positive or unarousing were most forgettable. Valence and arousal together accounted for less than 8% of the variance in image memorability. These findings suggest that evoked affect contributes to the overall memorability of a scene image but, like other singular predictors, does not fully explain it. Instead, memorability is best explained by an assemblage of visual features that combine, in perhaps unintuitive ways, to predict what is likely to stick in our memory.

Model estimation for SEM analyses in commonly used software typically involves iterative optimization procedures, which can lead to nonconvergence issues. In this paper, we propose using random starting values as an alternative to the current default strategies. By drawing from uniform distributions within data-driven lower and upper bounds (see De Jonckere et al. (2022) Structural Equation Modeling: A Multidisciplinary Journal, 29(3), 412-427), random starting values are generated for each (free) parameter in the model. Through three small simulation studies, we demonstrate that incorporating such bounded random starting values significantly reduces the nonconvergence rate, resulting in increased convergence rates ranging between 87% and 96% in the first two studies. In essence, bounded random starting values seem to offer a promising alternative to the default starting values that are currently used in most software packages.

The Rapid Online Assessment of Reading (ROAR) is a web-based lexical decision task that measures single-word reading abilities in children and adults without a proctor. Here we study whether item response theory (IRT) and computerized adaptive testing (CAT) can be used to create a more efficient online measure of word recognition. To construct an item bank, we first analyzed data taken from four groups of students (N = 1960) who differed in age, socioeconomic status, and language-based learning disabilities. The majority of item parameters were highly consistent across groups (r = .78-.94), and six items that functioned differently across groups were removed. Next, we implemented a JavaScript CAT algorithm and conducted a validation experiment with 485 students in grades 1-8 who were randomly assigned to complete trials of all items in the item bank in either (a) a random order or (b) a CAT order. We found that, to achieve reliability of 0.9, CAT improved test efficiency by 40%: 75 CAT items produced the same standard error of measurement as 125 items in a random order. Subsequent validation in 32 public school classrooms showed that an approximately 3-min ROAR-CAT can achieve high correlations (r = .89 for first grade, r = .73 for second grade) with alternative 5-15-min individually proctored oral reading assessments. Our findings suggest that ROAR-CAT is a promising tool for efficiently and accurately measuring single-word reading ability. Furthermore, our development process serves as a model for creating adaptive online assessments that bridge research and practice.

Aberrant responses (e.g., careless responses, miskeyed items, etc.) often contaminate psychological assessments and surveys. Previous robust estimators for dichotomous IRT models have produced more accurate latent trait estimates with data containing response disturbances. However, for widely used Likert-type items with three or more response categories, a robust estimator for estimating latent traits does not exist. We propose a robust estimator for the graded response model (GRM) that can be applied to Likert-type items. Two weighting mechanisms for downweighting "suspicious" responses are considered: the Huber and the bisquare weight functions. Simulations reveal the estimator reduces bias for various test lengths, numbers of response categories, and types of response disturbances. The reduction in bias and stable standard errors suggests that the robust estimator for the GRM is effective in counteracting the harmful effects of response disturbances and providing more accurate scores on psychological assessments. The robust estimator is then applied to data from the Big Five Inventory-2 (Ober et al., 2021) to demonstrate its use. Potential applications and implications are discussed.

Educational researchers have a long-lasting interest in the strategies examinees employ when responding to items in an assessment. Mixture item response theory (IRT) modeling is a popular class of approaches to studying examinees' item-response strategies. In the present study, we introduce a response time (RT)-based mixture IRT model for flexible modeling of examinee-and-item-specific item-response strategies. We posit that examinees may alternate between ability-based and non-ability-based strategies across different test items. Our proposed model identifies such within-examinee strategy switches without the need to predefine the non-ability-based strategies. Instead, our proposed approach allows for inferring the nature of these strategies from model parameter estimates. We illustrated the proposed approach using empirical data from PISA 2018 Science test and evaluated it through simulation studies. We concluded the article with discussions of limitations and future research directions.

This article addresses the problem of measurement invariance in psychometrics. In particular, its focus is on the invariance assumption of item parameters in a class of models known as Rasch models. It suggests a mixed-effects or random intercept model for binary data together with a conditional likelihood approach of both estimating and testing the effects of multiple covariates simultaneously. The procedure can also be viewed as a multivariate multiple regression analysis which can be applied in longitudinal designs to investigate effects of covariates over time or different experimental conditions. This work also derives four statistical tests based on asymptotic theory and a parameter-free test suitable in small sample size scenarios. Finally, it outlines generalizations for categorical data in more than two categories. All procedures are illustrated on real-data examples from behavioral research and on a hypothetical data example related to clinical research in a longitudinal design.

Sighting dominance is an important behavioral property which has been difficult to measure quantitatively with high precision. We developed a measurement method that is grounded in a two-camera model that satisfies these aims. Using a simple alignment task, this method quantifies sighting ocular dominance during binocular viewing, identifying each eye's relative contribution to binocular vision. The method involves placing a physical target between the viewer and a display. The viewer indicates the perceived target's projection on the display with both eyes open and with only one eye open. The relative location of the binocular projection in relation to the two monocular projections is the index of dominance. The method produces a continuous variable with robust test-retest reliability (ICC = 0.96). The unit of measurement for the computed quantity is physiologically grounded: it is proportional to the distance between the monocular projections, which we show is predictable from interpupillary distance and phoria. Comparisons with the classic 'hole in card' sighting dominance test show good agreement, but also hint at potential bias for determining right-eye dominance in the latter. Interestingly, we find that some individuals systematically demonstrate nearly balanced vision, a phenomenon previously construed as mixed dominance or noisy responses. We also present ways to quantify and mitigate sources of random noise in this measurement. Overall, this new method allows for precise estimation of sighting dominance during binocular viewing. We expect it will allow a more effective understanding of the neural basis of dominance and improved effectiveness when using sighting dominance as a covariate in more complex analyses.

The degree of semantic equivalence of translation pairs is typically measured by asking bilinguals to rate the semantic similarity of them or comparing the number and meaning of dictionary entries. Such measures are subjective, labor-intensive, and unable to capture the fine-grained variation in the degree of semantic equivalence. Thompson et al. (in Nature Human Behaviour, 4(10), 1029-1038, 2020) propose a computational method to quantify the extent to which translation equivalents are semantically aligned by measuring the contextual use across languages. Here, we refine this method to quantify semantic alignment of English-Chinese translation equivalents using word2vec based on the proposal that the degree of similarity between the contexts associated with a word and those of its multiple translations vary continuously. We validate our measure using semantic alignment from GloVe and fastText, and data from two behavioral datasets. The consistency of semantic alignment induced across different models confirms the robustness of our method. We demonstrate that semantic alignment not only reflects human semantic similarity judgment of translation equivalents but also captures bilinguals' usage frequency of translations. We also show that our method is more cognitively plausible than Thompson et al.'s method. Furthermore, the correlations between semantic alignment and key psycholinguistic factors mirror those between human-rated semantic similarity and these variables, indicating that computed semantic alignment reflects the degree of semantic overlap of translation equivalents in the bilingual mental lexicon. We further provide the largest English-Chinese translation equivalent dataset to date, encompassing 50,088 translation pairs for 15,734 English words, their dominant Chinese translation equivalents, and their semantic alignment Rc values.