Behavior Research Methods

Remote photoplethysmography (rPPG) is a low-cost technique to measure physiological parameters such as heart rate by analyzing videos of a person. There has been growing attention to this technique due to the increased possibilities and demand for running psychological experiments on online platforms. Technological advancements in commercially available cameras and video processing algorithms have led to significant progress in this field. However, despite these advancements, past research indicates that suboptimal video recording conditions can severely compromise the accuracy of rPPG...

Can an inclusive test of face cognition meet or exceed the psychometric properties of a prominent less inclusive test? Here, we norm and validate an updated version of the influential Reading the Mind in the Eyes Test (RMET), a clinically significant neuropsychiatric paradigm that has long been used to assess theory of mind and social cognition. Unlike the RMET, our Multiracial Reading the Mind in the Eyes Test (MRMET) incorporates racially inclusive stimuli, nongendered answer choices, ground-truth referenced answers, and more accessible vocabulary...

Responding to rating scale items is a multidimensional process, since not only the substantive trait being measured but also additional personal characteristics can affect the respondents' category choices. A flexible model class for analyzing such multidimensional responses are IRTree models, in which rating responses are decomposed into a sequence of sub-decisions. Different response processes can be involved in item responding both sequentially across those sub-decisions and as co-occurring processes within sub-decisions...

Meta-analysis is often recognized as the highest level of evidence due to its notable advantages. Therefore, ensuring the precision of its findings is of utmost importance. Insufficient reporting in primary studies poses challenges for meta-analysts, hindering study identification, effect size estimation, and meta-regression analyses. This manuscript provides concise guidelines for the comprehensive reporting of qualitative and quantitative aspects in primary studies. Adhering to these guidelines may help researchers enhance the quality of their studies and increase their eligibility for inclusion in future research syntheses, thereby enhancing research synthesis quality...

Multinomial processing tree (MPT) models are a broad class of statistical models used to test sophisticated psychological theories. The research questions derived from these theories often go beyond simple condition effects on parameters and involve ordinal expectations (e.g., the same-direction effect on the memory parameter is stronger in one experimental condition than another) or disordinal expectations (e.g., the effect reverses in one experimental condition). Here, we argue that by refining common modeling practices, Bayesian hierarchical models are well suited to estimate and test these expectations...

Computer code plays a vital role in modern science, from the conception and design of experiments through to final data analyses. Open sharing of code has been widely discussed as being advantageous to the scientific process, allowing experiments to be more easily replicated, helping with error detection, and reducing wasted effort and resources. In the case of psychology, the code used to present stimuli is a fundamental component of many experiments. It is not known, however, the degree to which researchers are sharing this type of code...

This paper presents a novel approach known as the cross estimation network (CEN) for fitting the datasets obtained from psychological or educational tests and estimating the parameters of item response theory (IRT) models. The CEN is comprised of two subnetworks: the person network (PN) and the item network (IN). The PN processes the response pattern of individual respondent and generates an estimate of the underlying ability, while the IN takes in the response pattern of individual item and outputs the estimates of the item parameters...

Measuring the variability in persons' behaviors and experiences using ecological momentary assessment is time-consuming and costly. We investigate whether interval responses provided through a dual-range slider (DRS) response format can be used as a simple and efficient alternative: Respondents indicate variability in their behavior in a retrospective rating by choosing a lower and an upper bound on a continuous, bounded scale. We investigate the psychometric properties of this response format as a prerequisite for further validation...

Unconscious processing has been widely examined using diverse and well-controlled methodologies. However, the extent to which these findings are relevant to real-life instances of information processing without awareness is limited. Here, we present a novel inattentional blindness (IB) paradigm in virtual reality (VR). In three experiments, we managed to repeatedly induce IB while participants foveally viewed salient stimuli for prolonged durations. The effectiveness of this paradigm demonstrates the close relationship between top-down attention and subjective experience...

This work introduces the English Sublexical Toolkit, a suite of tools that utilizes an experience-dependent learning framework of sublexical knowledge to extract regularities from the English lexicon. The Toolkit quantifies the empirical regularity of sublexical units in both the reading and spelling directions (i.e., grapheme-to-phoneme and phoneme-to-grapheme) and at multiple grain sizes (i.e., phoneme/grapheme and onset/rime unit size). It can extract multiple experience-dependent regularity indices for words or pseudowords, including both frequency indices (e...

Moving through a dynamic world, humans need to intermittently stabilize gaze targets on their retina to process visual information. Overt attention being thus split into discrete intervals, the automatic detection of such fixation events is paramount to downstream analysis in many eye-tracking studies. Standard algorithms tackle this challenge in the limiting case of little to no head motion. In this static scenario, which is approximately realized for most remote eye-tracking systems, it amounts to detecting periods of relative eye stillness...

Web surveys are often used to collect data for psychological research. However, the inclusion of many inattentive respondents can be a problem. Various methods for detecting inattentive respondents have been proposed, most of which require the inclusion of additional items in the survey for detection or the calculation of variables for detection after data collection. This study proposes a method for detecting inattentive respondents in web surveys using machine learning. The method requires only the collection of response time and the inclusion of a Likert scale, eliminating the need to include special detection items in the survey...

In behavioral research, it is very common to have manage multiple datasets containing information about the same set of individuals, in such a way that one dataset attempts to explain the others. To address this need, in this paper the Tucker3-PCovR model is proposed. This model is a particular case of PCovR models which focuses on the analysis of a three-way data array and a two-way data matrix where the latter plays the explanatory role. The Tucker3-PCovR model reduces the predictors to a few components and predicts the criterion by using these components and, at the same time, the three-way data is fitted by the Tucker3 model...

Short texts generated by individuals in online environments can provide social and behavioral scientists with rich insights into these individuals' internal states. Trained manual coders can reliably interpret expressions of such internal states in text. However, manual coding imposes restrictions on the number of texts that can be analyzed, limiting our ability to extract insights from large-scale textual data. We evaluate the performance of several automatic text analysis methods in approximating trained human coders' evaluations across four coding tasks encompassing expressions of motives, norms, emotions, and stances...

Working memory capacity (WMC) has been measured with a plethora of cognitive tasks. Several preeminent automated batteries of working memory (WM) tasks have been developed recently. However, despite all their advantages, most batteries were programmed in paid platforms and/or only included a single WM paradigm. To address these issues, we developed the OpenWMB, an automated battery comprising seven tasks from three distinct paradigms (complex spans, updating tasks, and binding tasks) that tap into several functional aspects of WM (simultaneous storage and processing, updating, and binding)...

In recent years, much research and many data sources have become digital. Some advantages of digital or Internet-based research, compared to traditional lab research (e.g., comprehensive data collection and storage, availability of data) are ideal for an improved meta-analyses approach.In the meantime, in meta-analyses research, different types of meta-analyses have been developed to provide research syntheses with accurate quantitative estimations. Due to its rich and unique palette of corrections, we recommend to using the Schmidt and Hunter approach for meta-analyses in a digitalized world...

Structural equation models are used to model the relationships between latent constructs and observable behaviors such as survey responses. Researchers are often interested in testing nested models to determine whether additional constraints that create a more parsimonious model are also supported by the data. A popular statistical tool for nested model comparison is the chi-square difference test. However, there is some evidence that this test performs suboptimally when the unrestricted model is misspecified...

The standard approach for detecting and preventing bots from doing harm online involves CAPTCHAs. However, recent AI research, including our own in this manuscript, suggests that bots can complete many common CAPTCHAs with ease. The most effective methodology for identifying potential bots involves completing image-processing, causal-reasoning based, free-response questions that are hand coded by human analysts. However, this approach is labor intensive, slow, and inefficient. Moreover, with the advent of Generative AI such as GPT and Bard, it may soon be obsolete...

Sounds are important sensory cues for food perception and acceptance. We developed and validated a large-scale database of kitchen and food sounds (180 stimuli) capturing different stages of preparing, cooking, serving, and/or consuming foods and beverages and sounds of packaging, kitchen utensils, and appliances. Each sound was evaluated across nine subjective evaluative dimensions (random order), including stimuli-related properties (e.g., valence, arousal) and food-related items (e.g., healthfulness, appetizingness) by a subsample of 51 to 64 participants (Mdn = 54; N = 332; 69...

Synthesizing results across multiple studies is a popular way to increase the robustness of scientific findings. The most well-known method for doing this is meta-analysis. However, because meta-analysis requires conceptually comparable effect sizes with the same statistical form, meta-analysis may not be possible when studies are highly diverse in terms of their research design, participant characteristics, or operationalization of key variables. In these situations, Bayesian evidence synthesis may constitute a flexible and feasible alternative, as this method combines studies at the hypothesis level rather than at the level of the effect size...