Behavior Research Methods最新文献

Research on the interaction between object and scene processing has a long history in the fields of perception and visual memory. Most databases have established norms for pictures where the object is embedded in the scene. In this study, we provide a diverse and controlled stimulus set comprising real-world pictures of 375 objects (e.g., suitcase), 245 scenes (e.g., airport), and 898 object-scene pairs (e.g., suitcase-airport), with object and scene presented separately. Our goal was twofold. First, to create a database of object and scene pictures, normed for the same variables to have comparable measures for both types of pictures. Second, to acquire normative data for the semantic relationships between objects and scenes presented separately, which offers more flexibility in the use of the pictures and allows disentangling the processing of the object and its context (the scene). Along three experiments, participants evaluated each object or scene picture on name agreement, familiarity, and visual complexity, and rated object-scene pairs on semantic congruency. A total of 125 septuplets of one scene and six objects (three congruent, three incongruent), and 120 triplets of one object and two scenes (in congruent and incongruent pairings) were built. In future studies, these objects and scenes can be used separately or combined, while controlling for their key features. Additionally, as object-scene pairs received semantic congruency ratings along the entire scale, researchers may select among a wide range of congruency values. ObScene is a comprehensive and ecologically valid database, useful for psychology and neuroscience studies of visual object and scene processing.

To evaluate model fit in confirmatory factor analysis, researchers compare goodness-of-fit indices (GOFs) against fixed cutoff values (e.g., CFI > .950) derived from simulation studies. Methodologists have cautioned that cutoffs for GOFs are only valid for settings similar to the simulation scenarios from which cutoffs originated. Despite these warnings, fixed cutoffs for popular GOFs (i.e., χ², χ²/df, CFI, RMSEA, SRMR) continue to be widely used in applied research. We (1) argue that the practice of using fixed cutoffs needs to be abandoned and (2) review time-honored and emerging alternatives to fixed cutoffs. We first present the most in-depth simulation study to date on the sensitivity of GOFs to model misspecification (i.e., misspecified factor dimensionality and unmodeled cross-loadings) and their susceptibility to further data and analysis characteristics (i.e., estimator, number of indicators, number and distribution of response options, loading magnitude, sample size, and factor correlation). We included all characteristics identified as influential in previous studies. Our simulation enabled us to replicate well-known influences on GOFs and establish hitherto unknown or underappreciated ones. In particular, the magnitude of the factor correlation turned out to moderate the effects of several characteristics on GOFs. Second, to address these problems, we discuss several strategies for assessing model fit that take the dependency of GOFs on the modeling context into account. We highlight tailored (or "dynamic") cutoffs as a way forward. We provide convenient tables with scenario-specific cutoffs as well as regression formulae to predict cutoffs tailored to the empirical setting of interest.

Sentence processing is affected by the sentence context and word expectancy. To investigate sentence comprehension experimentally, it is useful to have sentence completion norms with both context constraint and word expectancy measures. In this study, two experiments were conducted to collect norms for completion of 807 European Portuguese sentences. Context constraint was measured through type-token ratio and proportion idiosyncratic responses, while word expectancy was assessed by cloze probability. Besides establishing norms for a large sample of sentences, the study investigated the impact of the production procedure and method of analysis. In Experiment 1, a single-production procedure was used, i.e., participants completed each sentence fragment with only a single response, whereas in Experiment 2, a multiple-production procedure was used, i.e., participants have to provide up to three completion words for each sentence fragment. In Experiment 2, the analyses were obtained using two distinct methods: first-response analysis and combined-response analysis. The results showed that cloze and context measures are comparable between production paradigms and that the results from both analysis methods were correlated. The advantages of each production procedure and analysis method are discussed.

Methods of causal discovery and direction of dependence to evaluate causal properties of variable relations have experienced rapid development. The majority of causal discovery methods, however, relies on the assumption of causal effect homogeneity, that is, the identified causal structure is expected to hold for the entire population. Because causal mechanisms can vary across subpopulations, we propose combining methods of model-based recursive partitioning and non-Gaussian causal discovery to identify such subpopulations. The resulting algorithm can discover subpopulations with potentially varying magnitude and causal direction of effects under mild parameter inequality assumptions. Feasibility conditions are described and results from synthetic data experiments are presented suggesting that large effects and large sample sizes are beneficial for detecting causally competing subgroups with acceptable statistical performance. In a real-world data example, the extraction of meaningful subgroups that differ in the causal mechanism underlying the development of numerical cognition is illustrated. Potential extensions and recommendations for best practice applications are discussed.

Studying how sensory signals from different sources (sensory cues) are integrated within or across multiple senses allows us to better understand the perceptual computations that lie at the foundation of adaptive behaviour. As such, determining the presence of precision gains - the classic hallmark of cue combination - is important for characterising perceptual systems, their development and functioning in clinical conditions. However, empirically measuring precision gains to distinguish cue combination from alternative perceptual strategies requires careful methodological considerations. Here, we note that the majority of existing studies that tested for cue combination either omitted this important contrast, or used an analysis approach that, unknowingly, strongly inflated false positives. Using simulations, we demonstrate that this approach enhances the chances of finding significant cue combination effects in up to 100% of cases, even when cues are not combined. We establish how this error arises when the wrong cue comparator is chosen and recommend an alternative analysis that is easy to implement but has only been adopted by relatively few studies. By comparing combined-cue perceptual precision with the best single-cue precision, determined for each observer individually rather than at the group level, researchers can enhance the credibility of their reported effects. We also note that testing for deviations from optimal predictions alone is not sufficient to ascertain whether cues are combined. Taken together, to correctly test for perceptual precision gains, we advocate for a careful comparator selection and task design to ensure that cue combination is tested with maximum power, while reducing the inflation of false positives.

Mental fatigue is a commonplace human experience which is the focus of a growing body of research. Whilst researchers in numerous disciplines have attempted to uncover the origins, nature, and effects of mental fatigue, the literature is marked by many contradictory findings. We identified two major methodological problems for mental fatigue research. First, researchers rarely use objective measures of mental fatigue. Instead, they rely heavily on subjective reports as evidence that mental fatigue has been induced in participants. We aimed to develop a task which led to not only a subjective increase in mental fatigue, but a corresponding performance decrement in the mentally fatiguing task as an objective measure. Secondly, current mental fatigue paradigms have low ecological validity - in most prior studies participants have been fatigued with a single repetitive task such as the n-back or Stroop. To move towards a more ecologically valid paradigm, our participants undertook a battery of diverse cognitive tasks designed to challenge different aspects of executive function. The AX-CPT, n-back, mental rotation, and visual search tasks were chosen to challenge response inhibition, working memory, spatial reasoning, and attention. We report results from 45 participants aged 19 to 63 years who completed a two-hour battery comprising four different cognitive tasks. Subjective fatigue ratings and task performance were measured at the beginning and end of the battery. Our novel method resulted in an increase in subjective ratings of fatigue (p < 0.001) and a reduction in task performance (p = 0.008). Future research into mental fatigue may benefit from using this task battery.

Online research methods have the potential to facilitate equitable accessibility to otherwise-expensive research resources, as well as to more diverse populations and language combinations than currently populate our studies. In psycholinguistics specifically, webcam-based eye tracking is emerging as a powerful online tool capable of capturing sentence processing effects in real time. The present paper asks whether webcam-based eye tracking provides the necessary granularity to replicate effects-crucially both large and small-that tracker-based eye tracking has shown. Using the Gorilla Experiment Builder platform, this study set out to replicate two psycholinguistic effects: a robust one, the verb semantic constraint effect, first reported in Altmann and Kamide,  Cognition 73(3), 247-264 (1999), and a smaller one, the lexical interference effect, first examined by Kukona et al. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(2), 326 (2014). Webcam-based eye tracking was able to replicate both effects, thus showing that its functionality is not limited to large effects. Moreover, the paper also reports two approaches to computing statistical power and discusses the differences in their outputs. Beyond discussing several important methodological, theoretical, and practical implications, we offer some further technical details and advice on how to implement webcam-based eye-tracking studies. We believe that the advent of webcam-based eye tracking, at least in respect of the visual world paradigm, will kickstart a new wave of more diverse studies with more diverse populations.

Growth mixture modeling (GMM) is an analytical tool for identifying multiple unobserved sub-populations in longitudinal processes. In particular, it describes change patterns within each latent sub-population and investigates between-individual differences in within-individual change for each sub-group. A key research interest in using GMMs is examining how covariates influence the heterogeneity in change patterns. Liu & Perera (2022b) extended mixture-of-experts (MoE) models, which primarily focus on time-invariant covariates, to allow covariates to account for both within-group and between-group differences and investigate the heterogeneity in nonlinear trajectories. The present study further extends Liu & Perera, 2022b by examining the effects of time-varying covariates (TVCs) on trajectory heterogeneity. Specifically, we propose methods to decompose a TVC into an initial trait (the baseline value of the TVC) and a set of temporal states (interval-specific slopes or changes of the TVC). The initial trait is allowed to account for within-group differences in growth factors of trajectories (i.e., baseline effect), while the temporal states are allowed to impact observed values of a longitudinal process (i.e., temporal effects). We evaluate the proposed models using a simulation study and real-world data analysis. The simulation study demonstrates that the proposed models are capable of separating trajectories into several clusters and generally producing unbiased and accurate estimates with target coverage probabilities. The proposed models reveal the heterogeneity in initial trait and temporal states of reading ability across latent classes of students' mathematics performance. Additionally, the baseline and temporal effects on mathematics development of reading ability are also heterogeneous across the clusters of students.

In Bayesian statistics, the most widely used criteria of Bayesian model assessment and comparison are Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC). We use a multilevel mediation model as an illustrative example to compare different types of DIC and WAIC. More specifically, we aim to compare the performance of conditional and marginal DICs and WAICs, and investigate their performance with missing data. We focus on two versions of DIC ( $DI{C}_1$ and $DI{C}_2$ ) and one version of WAIC. In addition, we explore whether it is necessary to include the nuisance models of incomplete exogenous variables in likelihood. Based on the simulation results, whether $DI{C}_2$ is better than $DI{C}_1$ and WAIC and whether we should include the nuisance models of exogenous variables in likelihood functions depend on whether we use marginal or conditional likelihoods. Overall, we find that the marginal likelihood based- $DI{C}_2$ that excludes the likelihood of covariate models generally had the highest true model selection rates.

Pupillometry has been widely implemented to investigate cognitive functioning since infancy. Like most psychophysiological and behavioral measures, it implies hierarchical levels of arbitrariness in preprocessing before statistical data analysis. By means of an illustrative example, we checked the robustness of the results of a familiarization procedure that compared the impact of audiovisual and visual stimuli in 12-month-olds. We adopted a multiverse approach to pupillometry data analysis to explore the role of (1) the preprocessing phase, that is, handling of extreme values, selection of the areas of interest, management of blinks, baseline correction, participant inclusion/exclusion and (2) the modeling structure, that is, the incorporation of smoothers, fixed and random effects structure, in guiding the parameter estimation. The multiverse of analyses shows how the preprocessing steps influenced the regression results, and when visual stimuli plausibly predicted an increase of resource allocation compared with audiovisual stimuli. Importantly, smoothing time in statistical models increased the plausibility of the results compared to those nested models that do not weigh the impact of time. Finally, we share theoretical and methodological tools to move the first steps into (rather than being afraid of) the inherent uncertainty of infant pupillometry.