Behavior Research Methods最新文献

Researchers often require validated and well-rounded sets of image stimuli. For those interested in understanding the various visual attentional biases toward threatening stimuli, a dataset containing a variety of such objects is urgently needed. Here, our goal was to create an image database of animate and inanimate objects, including those that people find threatening and those that are visually similar to them but are not considered threatening. To do this, we recruited participants (N = 77) for an online survey in which they were asked to name threatening objects and try to come up with a visually similar counterpart. We then used the survey results to create a list of 32 objects, including eight from each crossing of threatening versus nonthreatening and animate versus inanimate. We obtained 20 exemplar images from each category (640 unique images in total, all copyright-free and openly shared). An independent sample of participants (N = 191) judged the similarity of these images using the spatial arrangement method. Data were then modeled using multidimensional scaling. Our results present modeling outcomes using a "map" of animate and inanimate objects (separately) that spatially conveys the perceived similarity relationships between them. We expect that this image set will be widely used in future visual attention studies and more.

Ceiling or floor effects pose analytic challenges in behavioral and psychological research. In this study, we developed novel Tobit modeling approaches, estimated using maximum likelihood (ML) or Bayesian methods, to address these effects for widely used statistical analyses, including the dependent-sample t-test and moderated regression. Simulation studies were conducted to compare the performance of the proposed modeling approaches to the conventional approach where ceiling or floor data are treated as if true values. The conventional approach was found to yield biased estimates, inflated Type I error rates, and poor confidence interval coverage, even with as little as 10% ceiling data. In contrast, the proposed approaches with either ML or Bayesian estimation provided accurate estimates and inference results across most studied conditions (e.g., with 30% ceiling data). Real data examples further illustrated the impact of modeling choices. To facilitate implementations of the proposed Tobit modeling approaches, we provide simulated datasets along with R and Mplus scripts online. Implications of the findings and future research directions were discussed.

When we encounter words, we activate not only the social information provided by the speaker, but also the rich semantics of the words' meaning, and quantifying this information is a key challenge for the cognitive and behavioural sciences. Although there are many resources available that quantify affective and sensorimotor information, there are relatively few resources available that provide information on social dimensions of meaning. We present the SocioLex-CZ norms, where the primary focus is on socio-semantic dimensions of meaning. Across two experiments, we introduce normative estimates along five dimensions-gender, political alignment, location, valence and age-for a large set of Czech words (Experiment 1) and images (Experiment 2) from 1,709 participants. We provide a series of analyses demonstrating that the norms have good reliability, and present exploratory analyses examining how the variables interact with one another within and between words/images. These norms present a valuable dataset that quantifies socio-semantic representations at scale, which we hope will be used for a range of novel and multidisciplinary applications, thereby opening up new pathways for innovative research. We make the data, code and analysis available at https://osf.io/pv9md/ and also provide an interactive web app at https://tinyurl.com/sociolex-cz-app .

Neural network modeling has played a central role in psycholinguistic studies of lexical processing, but the recent advent of large language models (LLMs) offers a different approach that may yield new insights into the mental lexicon. Four LLMs were prompted across three experiments to test how they generate psycholinguistic ratings of words in comparison with humans. LLM ratings, averaged across varying list contexts, were found to be highly correlated with human ratings, and differences in correlation strengths were partly explained by differences in rating ambiguity. LLM context manipulations strengthened correlations with human ratings through better calibration, and variability in LLM ratings was correlated with human inter-rater variability. Additional results from testing LLM generation of word naming latencies showed functional deviations from factors that underlie human word naming, indicating that lexical function assembly in LLMs is currently limited by patterns of co-occurrence in textual data. Patterns at finer-grained timescales are needed in the training data to model online lexical processes. We conclude that LLMs used context to guide the assembly of generalized lexical functions, rather than recalling ratings and latencies from training data.

Eye tracking has become an increasingly important tool in cognitive and developmental research, providing insights into processes that are difficult to measure otherwise. The majority of eye-tracking studies rely on accurate identification of fixations and saccades in raw data using event classification algorithms (sometimes called fixation filters). Subsequently, it is common to analyze whether fixations or saccades fall into specific areas of interest (AOI). The choice of algorithms can significantly influence study outcomes, especially in special populations such as young children or individuals with neurodevelopmental conditions, where data quality is often compromised by factors such as signal loss, poor calibration, or movement artifacts. It is therefore crucial to examine how available fixation classification algorithms affect the data set at hand as part of the eye-tracking analysis. Here, we introduce the kollaR package, an open-source R library for performing the main steps of an eye-tracking analysis from event classification to AOI-based analyses and visualizations of individual or group-level data for publications. The kollaR package was specifically designed to facilitate the selection and comparison of different event classification algorithms through visualizations. In a validation analysis, we show that results from fixation classification in kollaR are consistent with those from other software implementations of the same algorithms. We demonstrate the use of kollaR with real data from typically developing individuals and individuals with neurodevelopmental conditions, and illustrate how potential threats to validity can be identified in both high- and low-quality data.

Implicit measures are used extensively in psychological science. One fundamental goal of these measures is to provide information diagnostic of an individual's attitudes or beliefs. After 25 years of research, this goal has not been achieved. We argue that this is because psychologists have not yet even quantified the individual-level precision of implicit measures, much less calibrated them to it. In this paper, we examine the individual-level precision of six different implicit measures across three different attitude domains (race, politics, and self-esteem) using a very large open dataset. Despite some variation, we find that there is substantial room for improvement for the precision of implicit measures as measures of individual attitudes. We recommend that researchers who wish to make theoretical inferences about individuals directly quantify individual-level precision to calibrate their tasks appropriately, both in the context of implicit measures and with tasks in psychological science more broadly.

Both parametric and non-parametric extensions of the multinomial processing tree (MPT) models have been proposed for jointly modeling discrete and continuous variables. Since the two approaches have not yet been compared systematically, we assess their power and robustness in three simulation studies focusing on the weapon identification task. In this context, two statistically equivalent MPT models have been proposed, namely, the preemptive-conflict-resolution model (PCRM) and the default-interventionist model (DIM), which differ only in their assumptions regarding the order of latent processes (i.e., response times, RTs). The first simulation evaluates the calibration and statistical power of the nonstandard goodness-of-fit test for the parametric approach (i.e., the Dzhaparidze-Nikulin statistic), as well as the ability of different distributional assumptions to fit simulated RT data. The second simulation compares nested models to study the power for testing hypotheses about RTs within each model. The third simulation focuses on model-recovery performance for the two non-nested models. In all three simulations, we manipulated the size and nature of discrepancies (location/scale or shape) between latent RT distributions, sample size, and parametric assumptions. Results show that the parametric approach has higher statistical power but is also sensitive to misspecifications of distributional assumptions. In contrast, the non-parametric approach is more robust but less powerful, especially with small samples. We provide recommendations on when to use each approach and highlight the importance of properly specifying and selecting extended MPT models.

In gaze-contingent rendering, the visual stimulus rendered on a display changes based on where the observer is looking. This technique allows researchers to achieve dynamic control over stimulus placement on the retina in the presence of eye movements and is often used to investigate how sensory processing and perception vary across the visual field. Precise stimulus placement using gaze-contingent rendering depends on minimizing the temporal latency between a change in the observer's gaze position, measured using an eye tracker, and the corresponding change to the stimulus. This latency, however, can be challenging to measure reliably. Here, we present a simple method for measuring system latency that requires no additional hardware beyond the eye tracker and display, which are already part of the gaze-contingent system. Two small circles are rendered on the display to simulate the appearance of two pupils. The eye tracker is pointed towards the display to record both pupils simultaneously. One pupil is drawn based on a pre-determined trajectory, for example, moving up and down at a constant speed. The second pupil is "gaze-contingent": it is drawn based on the measured position of the first pupil. The time-lag at which the position of the second pupil matches the first pupil gives the closed-loop latency of the entire system. To validate this method, we added artificial rendering delays to our system and produced measured latencies that precisely corresponded to predictions, given the refresh rate of the display. This method provides a simple, low-cost way of precisely quantifying gaze-contingent rendering latencies, with no additional hardware required.

Changes in pupil size can lead to apparent gaze shifts in data recorded with video-based eye trackers in the absence of physical eye rotation. This is known as the pupil-size artifact (PSA). While the PSA is widely reported in desktop eye trackers, it is unknown whether and to what extent it occurs in head-mounted eye trackers. In this paper, we examined the effects of pupil size variations on eye-tracking data quality in four head-mounted eye trackers: the Pupil Core, the Pupil Neon, the SMI ETG 2w, and the Tobii Pro Glasses 2, in addition to a widely used desktop eye tracker, the SR Research EyeLink 1000 Plus. Participants viewed a central target on a monitor while we systematically varied the screen brightness to induce controlled pupil size changes. All head-mounted eye trackers exhibited PSA, with apparent gaze shifts ranging from 0.94 $_{}^{\circ }$ for the Pupil Neon to 3.46 $_{}^{\circ }$ for the Pupil Core. Except for the Pupil Neon, all eye trackers exhibited a significant change in accuracy due to pupil size variations. Precision measures showed device-specific effects of pupil size changes, with some eye trackers performing better in the bright condition and others in the dark condition. These findings demonstrated that, just like desktop eye trackers, head-mounted video-based eye trackers exhibited PSA.

In the present study, we developed a novel self-report measurement method for the rubber hand illusion (RHI) strength based on inverse multidimensional scaling (MDS). In the preregistered study consisting of two experiments, participants experienced the RHI in synchronous and asynchronous conditions (Experiment 1) as well as the RHI and the arm immobilization imaginative suggestion (Experiment 2). In each condition, participants repeatedly arranged items related to distinct bodily-related experiences (including RHI or suggestion) in accordance with the perceived similarity between them. Proximity data obtained from the arrangements were represented as distances in the multidimensional bodily space. To measure RHI strength, we focused on distances between items representing experimental conditions and two baseline items representing cases of no ownership over an external object and normal bodily feelings. We found that the distance between the rubber hand and an external object was significantly larger in the synchronous than the asynchronous condition, and larger than the distance between the immobilized arm and the normal body, demonstrating stronger shifts in ownership for synchronous RHI. In general, the RHI was associated with moderate ownership and low perceived stimulation, and it clustered with experiences related to a high degree of ownership. MDS-based solutions for the bodily space were consistent within participants and across different experimental conditions. We believe that this method can complement traditional questionnaire-based measurement, offering additional opportunities for a comprehensive self-assessment of RHI strength.