Behavior Research Methods最新文献

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.

To facilitate research into the language development of preschool children, this article presents the Chinese Preschool Children's Spoken Lexical Database (CPCSLD), which is a lexical database built from a corpus of spontaneous speech production by 648 Chinese children aged between 3 and 6 years. The corpus comprises 1,199,851 word tokens, which include 21,372 unique words, 1,147 unique tonal syllables, and 400 unique atonal syllables. CPCSLD provides multiple distributional characteristics of both word-level and syllable-level information, including word frequency, token frequency, word length, word syntactic categories, tonal syllable frequency, and atonal syllable frequency, for words in the entire corpus as well as in the three grade-level sub-corpora (K1, K2, and K3). Using CPCSLD, we describe the developmental changes in word syntactic categories and frequency across K1, K2, and K3. Validation analyses showed an advantage for CPCSLD over existing databases in predicting children's picture-naming performance, but not in the semantic decision task. Correlation analyses further revealed distinct developmental patterns in lexical properties: CPCSLD's syllable frequencies were highly correlated with those from other child-based databases, whereas its word frequencies showed low correlations. These findings highlight CPCSLD's sensitivity to early lexical development and its value for research on language production in 3-6-year-old children.

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.

A growing number of psycholinguistic studies use methods from corpus linguistics to examine the language that children encounter in their environment, to understand how they might acquire different aspects of linguistic knowledge. Many of these studies focus on child-directed speech or children's literature, while there is a paucity of work focusing on children's television and video media. We describe the creation and contents of the Corpus of Children's Video Media (CCVM), a specialised corpus designed to represent the spoken language in television and online videos popular among 3-5-year-old children in the UK (available as a scrambled database of tokens). The CCVM was designed to be comparable to an existing corpus of child-directed speech (CDS). We used a dual sampling approach: inclusion decisions were guided by (a) a survey of parents with children in our target age group, and (b) a survey of programmes available on popular streaming platforms. The corpus consists of 233,471 tokens across 161 transcripts (43.12 h of video) and is available on the Open Science Framework (OSF) as a scrambled database of tokens (including gloss, stem, and lemma forms, and part-of-speech tags), organised within transcripts, together with relevant metadata for each transcript. We discuss the challenges of creating a corpus that is comparable to existing datasets and highlight the importance of transparency in this process. We take an open science approach, sharing a detailed data collection and processing protocol, code, and data so that the corpus can be evaluated, extended, and used appropriately by other research teams.