Behavior Research Methods最新文献

In typing proficiency tests, like those used in job recruitment or research studies, individuals are evaluated based on their speed and accuracy. However, the difficulty of the typed text, its 'typability', can impact typing performance, introducing variability that is unrelated to skill. To ensure valid comparisons across individuals, time, and conditions, it is crucial to control for this variation in text difficulty. To address this issue, we develop the Typability Index, a model that predicts the relative typing speed of text. Building on earlier attempts to quantify typing difficulty from the 1940s, we create a more advanced typability model using the 136 Million (136 M) Keystrokes Dataset (Dhakal et al., Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1-12, 2018), where over 168,000 participants each typed 15 sentences from a pool of 1,525 items. Through random forest regression, we identify eight key predictors from 30 candidate variables, including the proportion of lowercase letters, word frequency, and syllables per word. Trained on 80% of the dataset and validated on the remaining 20% and a novel dataset, the Typability Index explained 68-88% of the variance in typability, compared to the 34% explained by an earlier leading model (Bell, Unpublished Doctor's Dissertation, University of Oklahoma, 1949). To promote higher control in typing research and assessments, we introduce a web-based tool to facilitate accurate measurement and fair comparisons of text typability.

Psycholinguistic norms quantify dimensions of word form, use, and/or meaning and are important for studies involving word stimuli. Among the most commonly used norms are those for concreteness, quantifying the degree to which a word's referent can be understood through the senses. The dimension of concreteness has been found to be related to performance in language processing and memory tasks, yet recent research suggests that (1) ratings of concreteness may be conflated with how specific a word's meaning is, and (2) concreteness and specificity may have opposite effects on lexical-semantic processing. In the present study, we collected specificity scores for a large sample of English words, assessed their reliability and validity, and tested their relationships to lexical processing and memory accuracy. We found that specificity and concreteness are indeed correlated and have different behavioural effects. This large dataset of specificity scores will be an important resource for future lexical-semantic studies.

Continuous flash suppression (CFS) is widely used in research on unconscious visual processing due to its long-lasting masking. While CFS effectively masks static stimuli, its application to motion stimuli remains challenging. To resolve this issue, our previous work developed the Chameleon-1 paradigm (Zhao & Bao, 2022), an enhanced CFS technique that enables robust masking of translational motion stimuli for up to 10 s through precise spatiotemporal matching of color dynamics between target and masking stimuli. The current study systematically evaluated and optimized this paradigm through three studies. We first assessed the masking efficacy of the Chameleon-1 paradigm across different motion parameters and patterns (Study 1). Because Chameleon-1 failed to effectively mask biological motion (BM) stimuli, we then upgraded the paradigm to accommodate BM stimulus characteristics (Study 2). The results demonstrated that this Chameleon-2 paradigm achieved superior masking efficacy for BM stimuli, with average breakthrough time extended by over two-fold compared to Chameleon-1 and breakthrough ratios approximately 75% for upright and 45% for inverted BM stimuli during 10-s of BM presentation. We further employed this paradigm to investigate the neural correlates of conscious and unconscious BM processing using functional near-infrared spectroscopy in Study 3. Our work establishes a robust paradigm for sustained masking of BM stimuli and validates its utility in unconscious processing research. We also provide new insights into the neural mechanisms underlying unconscious BM perception.

This study examined how well large language models (LLMs) approximate human psychological ratings for early-acquired English words. We used four state-of-the-art LLMs, including GPT-4o and Meta-Llama-3.1, to evaluate 21 static psychological features for 695 words and compared these estimates with human norms. The results showed that LLMs aligned well with human ratings for some features (e.g., Concreteness, Bodily Interactiveness) in terms of rank correlations (rs > .82) and distributional similarities but diverged notably for others (e.g., Iconicity, Arousal; rs < .48). Compared with content words, function words showed more pronounced discrepancies between human and LLM ratings. We also assessed how similarly human- and LLM-derived psychological features predicted words' age of acquisition (AoA), revealing both strong correspondences and systematic biases, depending on the model (differences in correlations ranged from -.27 to .28). Based on these analyses, we identified which features may be reliably estimated using LLMs, which require further refinement, and what methodological considerations are necessary for applying LLM-based measures in cognitive science. We discuss the implications of using LLMs as methodological tools in psychology and cognitive science, highlighting both their practical advantages (e.g., data coverage and data collection efficiency) and theoretical relevance. The present study provides a novel framework for evaluating the cognitive plausibility of LLMs by using lexical psychological features, complementing existing benchmarks.

The development of large datasets of natural images has galvanized progress in psychology, neuroscience, and computer science. Notably, the THINGS database constitutes a collective effort towards understanding of human visual knowledge by accumulating rich data on a shared set of visual object concepts across several studies. In this paper, we introduce Drawing of THINGS ( DoT ), a novel dataset of 28,627 human drawings of 1854 diverse object concepts, sampled systematically from concrete picturable and nameable nouns in the American English language, mirroring the structure of the THINGS image database. In addition to data on drawings' stroke history, we further collected fine-grained recognition data for each drawing, along with metadata on participant demographics, drawing ability, and mental imagery. We characterize people's ability to communicate and recognize semantic information encoded in drawings and compare this ability to their ability to recognize real-world images of the same visual objects. We also explore the relationship between drawing understanding and the memorability and typicality of the objects contained in THINGS. In sum, we envision DoT as a powerful tool that builds on the THINGS database to advance understanding of how humans express knowledge about visual concepts.

Scale abbreviation is a crucial task for researchers aiming to reduce response burden and optimize data collection when using self-report instruments such as online surveys and questionnaires. Among various data-driven strategies available for scale abbreviation, supervised machine learning (SML) algorithms have emerged as a prominent approach due to their accuracy in predicting total scores from the original instrument. However, previous studies offer limited insights into how SML-abbreviated scales can be evaluated using both SML and psychometric metrics across different feature selection techniques. To address this gap, the current study aims to evaluate the effectiveness of seven feature selection methods: item-total-correlation-based filters (ITC), Minimum-Redundancy-Maximum-Relevance (MRMR), Lasso, Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Genetic Algorithms (GA), and Non-dominated Sorting Genetic Algorithms-II (NSGA-II), all used in conjunction with SML. Additionally, the psychometric properties of these SML methods are compared with two non-SML approaches. Using simulated datasets varying in sample size, model error, and factorial correlations, the study examines predictive accuracy, reliability, and the ability to recover both inter-subscale correlations and external criterion correlations. The findings indicate that no single method consistently excels across all conditions, with specific feature selection techniques performing better under certain circumstances. Key insights are provided to guide researchers in selecting appropriate feature selection methods based on their specific dataset characteristics and goals.

The present study examined individual differences in 24 measures of cognitive ability in a sample of young adults (N = 255). Each measure was completed twice, separated by a period of 2 weeks, to assess test-retest reliability and retesting (i.e., practice) effects. Latent variable modeling was used to assess the convergent and discriminant validity of the measures, as they were selected to measure seven different cognitive constructs (attention control, processing speed, working memory, primary memory, secondary memory, fluid intelligence, and spatial ability). The measures showed adequate to high intrasession and intersession reliability. Construct-level estimates were highly reliable, and the measurement structure was invariant across the two testing occasions. In several instances, correlations among latent variables warranted further testing to ensure adequate discriminability. Finally, latent state-trait modeling indicated that the majority of systematic variance in cognitive measures is due to latent traits, rather than state-specific or task-specific factors. We discuss the practical and theoretical implications of these findings.

In the absence of inter-word spaces, Chinese text sometimes presents word boundary ambiguity. One common case is the overlapping ambiguous string (OAS), a three-character string (ABC) where the middle character can form distinct words with both the character to its left (AB) and the character to its right (BC), creating segmentation ambiguity between AB-C and A-BC. This structure makes OASs a valuable tool for investigating the cognitive mechanisms of Chinese word segmentation. We introduce a comprehensive OAS database consisting of 952,497 OASs, each with 43 types of linguistic information at the character, word, and OAS levels. To illustrate how to use the database, we conducted an eye-tracking reading experiment manipulating whether the first character of the OAS (i.e., character A) could stand alone in sentences. Results showed that when character A could not stand alone, readers were more likely to group it with the next character B, leading to an AB-C segmentation. These findings validate the utility of the OAS database in understanding word segmentation during Chinese reading. The potential applications of the database in artificial intelligence, education, and writing system reform are discussed.

The dynamic nature of our environment allows us to anticipate the onset of relevant events, enhancing our responses to them. Temporal preparation can be assessed in the laboratory using various tasks, including foreperiod tasks, temporal orienting tasks, and rhythmic tasks. However, the existing literature lacks a unified task to measure the most common temporal preparation effects (i.e., foreperiod, sequential, temporal orienting, and rhythmic effects) in a single session. The main goal of the present study was to fill this gap by devising the temporal preparation task (TEP-Task) to measure temporal preparation effects in a single 35-min testing session. Besides its utility in single-session assessments, the TEP-Task may also serve for future research across diverse populations and experimental demands.