Behavior Research Methods最新文献

Researchers using eye tracking are heavily dependent on software and hardware tools to perform their studies, from recording eye tracking data and visualizing it, to processing and analyzing it. This article provides an overview of available tools for research using eye trackers and discusses considerations to make when choosing which tools to adopt for one's study.

Frequently, we perceive emotional information through multiple channels (e.g., face, voice, posture). These cues interact, facilitating emotional perception when congruent (similar across channels) compared to incongruent (different). Most previous studies on this congruency effect used stimuli from different sets, compromising their quality. In this context, we created and validated a new static stimulus set (ECIFBSS) featuring 1952 facial and body expressions of basic emotions in congruent and incongruent situations. We photographed 40 actors expressing facial emotions and body postures (anger, disgust, happiness, neutral, fear, surprise, and sadness) in both congruent and incongruent situations. The validation was conducted in two parts. In the first part, 76 participants performed a recognition task on facial and bodily expressions separately. In the second part, 40 participants performed the same recognition task, along with an evaluation of four features: intensity, authenticity, arousal, and valence. All emotions (face and body) were well recognized. Consistent with the literature, facial emotions were recognized better than body postures. Happiness was the most recognized facial emotion, while fear was the least. Among body expressions, anger had the highest recognition, while disgust was the least accurately recognized. Finally, facial and bodily expressions were considered moderately authentic, and the evaluation of intensity, valence, and arousal aligned with the dimensional model. The ECIFBSS offers static stimuli for studying facial and body expressions of basic emotions, providing a new tool to explore integrating emotional information from various channels and their reciprocal influence.

Perception of emotion conveyed through language is influenced by embodied experiences obtained from social interactions, which may vary across different cultures. To explore cross-cultural differences in the perception of emotion between Chinese and English speakers, this study collected norms of valence and arousal from 322 native Mandarin speakers for 4923 Chinese words translated from Warriner et al., (Behavior Research Methods, 45, 1191-1207, 2013). Additionally, sensory experience ratings for each word were collected. Analysis demonstrated that the reliability of this dataset is satisfactory, as indicated by comparisons with previous datasets. We examined the distributions of valence and arousal for the entire dataset, as well as for positive and negative emotion categories. Further analysis suggested that valence, arousal, and sensory experience correlated with various psycholinguistic variables, including the number of syllables, number of strokes, imageability, familiarity, concreteness, frequency, and age of acquisition. Cross-language comparison indicated that native speakers of Chinese and English differ in their perception of emotional valence and arousal, largely due to cross-cultural variations associated with ecological, sociopolitical, and religious factors. This dataset will be a valuable resource for research examining the impact of emotional and sensory information on Chinese lexical processing, as well as for bilingual research investigating the interplay between language and emotion across different cultural contexts.

Humans perceive discrete events such as "restaurant visits" and "train rides" in their continuous experience. One important prerequisite for studying human event perception is the ability of researchers to quantify when one event ends and another begins. Typically, this information is derived by aggregating behavioral annotations from several observers. Here, we present an alternative computational approach where event boundaries are derived using a large language model, GPT-3, instead of using human annotations. We demonstrate that GPT-3 can segment continuous narrative text into events. GPT-3-annotated events are significantly correlated with human event annotations. Furthermore, these GPT-derived annotations achieve a good approximation of the "consensus" solution (obtained by averaging across human annotations); the boundaries identified by GPT-3 are closer to the consensus, on average, than boundaries identified by individual human annotators. This finding suggests that GPT-3 provides a feasible solution for automated event annotations, and it demonstrates a further parallel between human cognition and prediction in large language models. In the future, GPT-3 may thereby help to elucidate the principles underlying human event perception.

The complex interplay between low- and high-level mechanisms governing our visual system can only be fully understood within ecologically valid naturalistic contexts. For this reason, in recent years, substantial efforts have been devoted to equipping the scientific community with datasets of realistic images normed on semantic or spatial features. Here, we introduce VISIONS, an extensive database of 1136 naturalistic scenes normed on a wide range of perceptual and conceptual norms by 185 English speakers across three levels of granularity: isolated object, whole scene, and object-in-scene. Each naturalistic scene contains a critical object systematically manipulated and normed regarding its semantic consistency (e.g., a toothbrush vs. a flashlight in a bathroom) and spatial position (i.e., left, right). Normative data are also available for low- (i.e., clarity, visual complexity) and high-level (i.e., name agreement, confidence, familiarity, prototypicality, manipulability) features of the critical object and its embedding scene context. Eye-tracking data during a free-viewing task further confirms the experimental validity of our manipulations while theoretically demonstrating that object semantics is acquired in extra-foveal vision and used to guide early overt attention. To our knowledge, VISIONS is the first database exhaustively covering norms about integrating objects in scenes and providing several perceptual and conceptual norms of the two as independently taken. We expect VISIONS to become an invaluable image dataset to examine and answer timely questions above and beyond vision science, where a diversity of perceptual, attentive, mnemonic, or linguistic processes could be explored as they develop, age, or become neuropathological.

Over the past few decades, Swahili-English and Lithuanian-English word pair databases have been extensively utilized in research on learning and memory. However, these normative databases are specifically designed for generating study stimuli in learning and memory research involving native (or fluent) English speakers. Consequently, they are not suitable for investigations that encompass populations whose first language is not English, such as Chinese individuals. Notably, native Chinese speakers constitute a substantial proportion, approximately 18%, of the global population. The current study aims to establish a new database of translation equivalences, specifically tailored to facilitate research on learning, memory, and metacognition among the Chinese population. We present a comprehensive set of normative measures for 200 Swahili-Chinese paired associates, including recall accuracy, recall latency, error patterns, confidence ratings, perceived learning difficulty, judgments of learning, and perceived learning interestingness for the entire word pairs. Additionally, we include word-likeness ratings and word length for the Swahili words, and concreteness ratings, familiarity ratings, word frequency, and number of strokes for the Chinese words. This diverse array of measures, gathered across a substantial number of Swahili-Chinese word pairs, is poised to effectively support future research seeking to investigate the intricate processes of learning, memory and metacognition within the Chinese population.

The self-matching task (SMT) is widely used to investigate the cognitive mechanisms underlying the self-prioritization effect (SPE), wherein performance is enhanced for self-associated stimuli compared to other-associated ones. Although the SMT robustly elicits the SPE, there is a lack of data quantifying the reliability of this paradigm. This is problematic, given the prevalence of the reliability paradox in cognitive tasks: many well-established cognitive tasks demonstrate relatively low reliability when used to evaluate individual differences, despite exhibiting replicable effects at the group level. To fill this gap, this preregistered study investigated the reliability of SPE derived from the SMT using a multiverse approach, combining all possible indicators and baselines reported in the literature. We first examined the robustness of 24 SPE measures across 42 datasets (N = 2250) using a meta-analytical approach. We then calculated the split-half reliability (r) and intraclass correlation coefficient (ICC2) for each SPE measure. Our findings revealed a robust group-level SPE across datasets. However, when evaluating individual differences, SPE indices derived from reaction time (RT) and efficiency exhibited relatively higher, compared to other SPE indices, but still unsatisfied split-half reliability (approximately 0.5). The reliability across multiple time points, as assessed by ICC2, RT, and efficiency, demonstrated moderate levels of test-retest reliability (close to 0.5). These findings revealed the presence of a reliability paradox in the context of SMT-based SPE assessment. We discussed the implications of how to enhance individual-level reliability using this paradigm for future study design.

Estimating how the human body moves in space and time-body kinematics-has important applications for industry, healthcare, and several research fields. Gold-standard methodologies capturing body kinematics are expensive and impractical for naturalistic recordings as they rely on infrared-reflective wearables and bulky instrumentation. To overcome these limitations, several algorithms have been developed to extract body kinematics from plain video recordings. This comes with a drop in accuracy, which however has not been clearly quantified. To fill this knowledge gap, we analysed a dataset comprising 46 human participants exhibiting spontaneous movements of varying amplitude. Body kinematics were estimated using OpenPose (video-based) and Vicon (infrared-based) motion capture systems simultaneously. OpenPose accuracy was assessed using Vicon estimates as ground truth. We report that OpenPose accuracy is overall moderate and varies substantially across participants and body parts. This is explained by variability in movement amplitude. OpenPose estimates are weak for low-amplitude movements. Conversely, large-amplitude movements (i.e., > ~ 10 cm) yield highly accurate estimates. The relationship between accuracy and movement amplitude is not linear (but mostly exponential or power) and relatively robust to camera-body distance. Together, these results dissect the limits of video-based motion capture and provide useful guidelines for future studies.

Measurement of object recognition (OR) ability could predict learning and success in real-world settings, and there is hope that it may reduce bias often observed in cognitive tests. Although the measurement of visual OR is not expected to be influenced by the language of participants or the language of instructions, these assumptions remain largely untested. Here, we address the challenges of measuring OR abilities across linguistically diverse populations. In Study 1, we find that English-Spanish bilinguals, when randomly assigned to the English or Spanish version of the novel object memory test (NOMT), exhibit a highly similar overall performance. Study 2 extends this by assessing psychometric equivalence using an approach grounded in item response theory (IRT). We examined whether groups fluent in English or Spanish differed in (a) latent OR ability as assessed by a three-parameter logistic IRT model, and (2) the mapping of observed item responses on the latent OR construct, as assessed by differential item functioning (DIF) analyses. Spanish speakers performed better than English speakers, a difference we suggest is due to motivational differences between groups of vastly different size on the Prolific platform. That we found no substantial DIF between the groups tested in English or Spanish on the NOMT indicates measurement invariance. The feasibility of increasing diversity by combining groups tested in different languages remains unexplored. Adopting this approach could enable visual scientists to enhance diversity, equity, and inclusion in their research, and potentially in the broader application of their work in society.

This study investigates the performance of mediation analyses, including manipulation check variables, in experimental studies where manipulated psychological attributes are independent variables. We simulated the level of manipulation intensities and measurement errors of the manipulation check variable to test the validity of the analytic practice. Our results showed that when manipulation is successful and measurement error is low, mediation analyses with the manipulation check variable revealed an unstable path coefficient and standard error. Moreover, many of the detected indirect effects were inconsistent mediation situations. However, when individual differences in psychological attributes remained within the condition (low manipulation intensity) and the manipulation check variable contained low measurement error, the indirect effect indicated the validity of the manipulation. We discuss the implications of our findings for the use of manipulation checks in experimental research.