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In this paper, we introduce an R package that performs automated photo editing effects. Specifically, it is an R implementation of an image-processing algorithm proposed by Boyadzhiev et al. (2015). The software allows the user to manipulate the appearance of objects in photographs, such as emphasizing facial blemishes and wrinkles, smoothing the skin, or enhancing the gloss of fruit. It provides a reproducible method to quantitatively control specific surface properties of objects (e.g., gloss and roughness), which is useful for researchers interested in topics related to material perception, from basic mechanisms of perception to the aesthetic evaluation of faces and objects. We describe the functionality, usage, and algorithm of the method, report on the findings of a behavioral evaluation experiment, and discuss its usefulness and limitations for psychological research. The package can be installed via CRAN, and documentation and source code are available at https://github.com/tsuda16k/materialmodifier. [ABSTRACT FROM AUTHOR]

Online data collection has begun to revolutionize the behavioral sciences. However, conducting carefully controlled behavioral experiments online introduces a number of new of technical and scientific challenges. The project described in this paper, psiTurk, is an open-source platform which helps researchers develop experiment designs which can be conducted over the Internet. The tool primarily interfaces with Amazon's Mechanical Turk, a popular crowd-sourcing labor market. This paper describes the basic architecture of the system and introduces new users to the overall goals. psiTurk aims to reduce the technical hurdles for researchers developing online experiments while improving the transparency and collaborative nature of the behavioral sciences. [ABSTRACT FROM AUTHOR]

Approach–avoidance conflicts are a hallmark of anxiety-related behaviors. A gold standard for assessing anxiety-related behaviors in rodents is the elevated plus-maze (EPM), which was recently translated to humans using immersive virtual reality. Repeated behavioral testing is particularly interesting for clinical and pharmacological research in humans but could be limited by habituation effects. Here, we tested whether comparable strategies that are used in rodents (different environments and inter-trial interval of 28 days) are sufficient to avoid habituation or sensitization effects on the EPM, making it possible to perform repeated measurement of anxiety-related behavior in humans. Moreover, we developed two novel virtual environments for repeated testing to explore whether a scenario resembling the real world is superior to a video game-like EPM in terms of lifelike physiological, emotional, and behavioral responses. On a behavioral level, no significant differences but a high correlation between first and repeated exposure to the human EPM independent of EPM version were found. On a psychophysiological level, salivary alpha-amylase, skin-conductance, and respiratory frequency increased at first and second exposure independent of EPM version. However, at repeated exposure, skin-conductance and heart rate showed indicators for anticipatory anxiety and a small sensitization effect, while no effect of real-world resemblance on these physiological measures was found. This was also reflected in slightly higher subjective anxiety levels at second exposure, although subjective anxiety still correlated strongly between first and second exposure. In conclusion, the human EPM can be used for longitudinal assessments of human anxiety-related behavior when strategies to avoid habituation and sensitization are considered. [ABSTRACT FROM AUTHOR]

Mobile technologies can be used for behavioral assessments to associate changes in behavior with environmental context and its influence on mental health and disease. Research on real-time motor control with a joystick, analyzed using a computational proportion-derivative (PD) modeling approach, has shown that model parameters can be estimated with high reliability and are related both to self-reported fear and to brain structures important for affective regulation, such as the anterior cingulate cortex. Here we introduce a mobile version of this paradigm, the rapid assessment of motor processing (RAMP) paradigm, and show that it provides robust, reliable, and accessible behavioral measurements relevant to mental health. A smartphone version of a previous joystick sensorimotor task was developed in which participants control a virtual car to a stop sign and stop. A sample of 89 adults performed the task, with 66 completing a second retest session. A PD modeling approach was applied to compute Kp (drive) and Kd (damping) parameters. Both Kp and Kd exhibited high test-retest reliabilities (ICC.81 and.78, respectively). Replicating a previous finding from a different sample with the joystick version of the task, both Kp and Kd were negatively associated with self-reported fear. The RAMP paradigm, a mobile sensorimotor assessment, can be used to assess drive and damping during motor control, which is robustly associated with subjective affect. This paradigm could be useful for examining dynamic contextual modulation of affect-related processing, which could improve assessment of the effects of interventions for psychiatric disorders in a real-world context. [ABSTRACT FROM AUTHOR]

Categorical cutpoints used to assess the adequacy of various statistics—like small, medium, and large for correlation coefficients of.10,.30, and.50 (Cohen, Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.)—are as useful as they are arbitrary, but not all statistics are suitable candidates for categorical cutpoints. One such is kappa, a statistic that gauges inter-observer agreement corrected for chance (Cohen Educational and Psychological Measurement, 20(1), 37–46, Cohen, Educational and Psychological Measurement 20:37–46, 1960). Depending on circumstances, a specific value of kappa may be judged adequate in one case but not in another. Thus, no one value of kappa can be regarded as universally acceptable and the question for investigators should be, are observers accurate enough, not is kappa big enough. A principled way to assess whether a specific value of kappa is adequate is to estimate observer accuracy—how accurate would simulated observers need to be to have generated a specific value of kappa obtained by actual observers, given specific circumstances. Estimating observer accuracy based on a kappa table the user provides is what KappaAcc, the program described here, does. [ABSTRACT FROM AUTHOR]

Some human behaviors have serious societal consequences, but these consequences tend to be neglected in online research on societally relevant behaviors. For example, human activities contribute to climate change and biodiversity loss, but pro-environmental behavior is often studied using inconsequential self-reports and hypothetical scenarios. Such measures can easily be administered online, but suffer from severe validity problems. To address these problems, we developed a multi-trial web-based procedure for the study of consequential pro-environmental behavior. On the Work for Environmental Protection Task (WEPT), participants can choose to exert voluntary extra efforts screening numerical stimuli in exchange for donations to an environmental organization. They thus have the opportunity to produce actual environmental benefits at actual behavioral costs (i.e., to show actual pro-environmental behavior). In a preregistered validation study (N = 209), we found WEPT performance to systematically vary with these consequences, that is, the implemented costs and benefits were large enough for participants to effectively take them into account. In addition, aggregated WEPT performance was found to be highly reliable and to be correlated to self-reports and objective observations of other pro-environmental behaviors and conceptually related measures. These findings support the validity of the WEPT as an online procedure for the study of actual pro-environmental behavior. We discuss how the WEPT can advance the experimental analysis of pro-environmental behavior, help to address problems of common-method variance in individual difference research, and be adapted for the consequential study of other societally relevant behaviors. [ABSTRACT FROM AUTHOR]

Analysis of rodents' behavior/activity is of fundamental importance in many research fields. However, many behavioral experiments still rely on manual scoring, with obvious problems in reproducibility. Despite important advances in video-analysis systems and computational ethology, automated behavior quantification is still a challenge. The need for large training datasets, background stability requirements, and reduction to two-dimensional analysis (impairing full posture characterization), limit their use. Here we present a novel integrated solution for behavioral analysis of individual rats, combining video segmentation, tracking of body parts, and automated classification of behaviors, using machine learning and computer vision methods. Low-cost depth cameras (RGB-D) are used to enable three-dimensional tracking and classification in dark conditions and absence of color contrast. Our solution automatically tracks five anatomical landmarks in dynamic environments and recognizes seven distinct behaviors, within the accuracy range of human annotations. The developed free software was validated in experiments where behavioral differences between Wistar Kyoto and Wistar rats were automatically quantified. The results reveal the capability for effective automated phenotyping. An extended annotated RGB-D dataset is also made publicly available. The proposed solution is an easy-to-use tool, with low-cost setup and powerful 3D segmentation methods (in static/dynamic environments). The ability to work in dark conditions means that natural animal behavior is not affected by recording lights. Furthermore, automated classification is possible with only ~30 minutes of annotated videos. By creating conditions for high-throughput analysis and reproducible quantitative measurements of animal behavior experiments, we believe this contribution can greatly improve behavioral analysis research. [ABSTRACT FROM AUTHOR]

We present a novel set of 200 Western tonal musical stimuli (MUST) to be used in research on perception and appreciation of music. It consists of four subsets of 50 stimuli varying in balance, contour, symmetry, or complexity. All are 4 s long and designed to be musically appealing and experimentally controlled. We assessed them behaviorally and computationally. The behavioral assessment (Study 1) aimed to determine whether musically untrained participants could identify variations in each attribute. Forty-three participants rated the stimuli in each subset on the corresponding attribute. We found that inter-rater reliability was high and that the ratings mirrored the design features well. Participants' ratings also served to create an abridged set of 24 stimuli per subset. The computational assessment (Study 2) required the development of a specific battery of computational measures describing the structural properties of each stimulus. We distilled nonredundant composite measures for each attribute and examined whether they predicted participants' ratings. Our results show that the composite measures indeed predicted participants' ratings. Moreover, the composite complexity measure predicted complexity ratings as well as existing models of musical complexity. We conclude that the four subsets are suitable for use in studies that require presenting participants with short musical motifs varying in balance, contour, symmetry, or complexity, and that the stimuli and the computational measures are valuable resources for research in music psychology, empirical aesthetics, music information retrieval, and musicology. The MUST set and MATLAB toolbox codifying the computational measures are freely available at osf.io/bfxz7. [ABSTRACT FROM AUTHOR]

People often interact with environments that can provide only a finite number of items as resources. Eventually a book contains no more chapters, there are no more albums available from a band, and every Pokémon has been caught. When interacting with these sorts of environments, people either actively choose to quit collecting new items, or they are forced to quit when the items are exhausted. Modeling the distribution of how many items people collect before they quit involves untangling these two possibilities, We propose that censored geometric models are a useful basic technique for modeling the quitting distribution, and, show how, by implementing these models in a hierarchical and latent-mixture framework through Bayesian methods, they can be extended to capture the additional features of specific situations. We demonstrate this approach by developing and testing a series of models in two case studies involving real-world data. One case study deals with people choosing jokes from a recommender system, and the other deals with people completing items in a personality survey. [ABSTRACT FROM AUTHOR]

There is growing interest among organizational researchers in tapping into alternative sources of data beyond self-reports to provide a new avenue for measuring behavioral constructs. Use of alternative data sources such as wearable sensors is necessary for developing theory and enhancing organizational practice. Although wearable sensors are now commercially available, the veracity of the data they capture is largely unknown and mostly based on manufacturers’ claims. The goal of this research is to test the validity and reliability of data captured by one such wearable badge (by Humanyze) in the context of structured meetings where all individuals wear a badge for the duration of the encounter. We developed a series of studies, each targeting a specific sensor of this badge that is relevant for structured meetings, and we make specific recommendations for badge data usage based on our validation results. We have incorporated the insights from our studies on a website that researchers can use to conduct validation tests for their badges, upload their data, and assess the validity of the data. We discuss this website in the corresponding studies. [ABSTRACT FROM AUTHOR]

If the automatic item generation is used for generating test items, the question of how the equivalence among different instances may be tested is fundamental to assure an accurate assessment. In the present research, the question was dealt by using the knowledge space theory framework. Two different ways of considering the equivalence among instances are proposed: The former is at a deterministic level and it requires that all the instances of an item template must belong to exactly the same knowledge states; the latter adds a probabilistic level to the deterministic one. The former type of equivalence can be modeled by using the BLIM with a knowledge structure assuming equally informative instances; the latter can be modeled by a constrained BLIM. This model assumes equality constraints among the error parameters of the equivalent instances. An approach is proposed for testing the equivalence among instances, which is based on a series of model comparisons. A simulation study and an empirical application show the viability of the approach. [ABSTRACT FROM AUTHOR]

Sustained attention (SA) is among the most studied faculties of human cognition, and thought to be crucial for many aspects of behavior. Measuring SA often relies on performance on a continuous, low-demanding task. Such continuous performance tasks (CPTs) have many variations, and sustained attention is typically estimated based on variability in reaction times. While relying on reaction times may be useful in some cases, it can pose a challenge when working with clinical populations. To increase interpersonal variability in task parameters that do not rely on speed, researchers have increased demands for memory and response inhibition. These approaches, however, may be confounded when used to assess populations that suffer from multiple cognitive deficits. In the current study, we propose a new approach for increasing task variability by increasing the attentional demands. In order to do so, we created a new variation of a CPT - a masked version, where inattention is more likely to cause misidentifying a target. After establishing that masking indeed decreases target detection, we further investigated which task parameter may influence response biases. To do so, we contrasted two versions of the CPT with different target/distractor ratio. We then established how perceptual parameters can be controlled independently in a CPT. Following the experimental manipulations, we tested the MCCPT with aging controls and chronic stroke patients to assure the task can be used with target populations. The results confirm the MCCPT as a task providing high sensitivity without relying on reaction speed, and feasible for patients. [ABSTRACT FROM AUTHOR]

Using simple mathematical models of choice behavior, we present a Bayesian adaptive algorithm to assess measures of impulsive and risky decision making. Practically, these measures are characterized by discounting rates and are used to classify individuals or population groups, to distinguish unhealthy behavior, and to predict developmental courses. However, a constant demand for improved tools to assess these constructs remains unanswered. The algorithm is based on trial-by-trial observations. At each step, a choice is made between immediate (certain) and delayed (risky) options. Then the current parameter estimates are updated by the likelihood of observing the choice, and the next offers are provided from the indifference point, so that they will acquire the most informative data based on the current parameter estimates. The procedure continues for a certain number of trials in order to reach a stable estimation. The algorithm is discussed in detail for the delay discounting case, and results from decision making under risk for gains, losses, and mixed prospects are also provided. Simulated experiments using prescribed parameter values were performed to justify the algorithm in terms of the reproducibility of its parameters for individual assessments, and to test the reliability of the estimation procedure in a group-level analysis. The algorithm was implemented as an experimental battery to measure temporal and probability discounting rates together with loss aversion, and was tested on a healthy participant sample. [ABSTRACT FROM AUTHOR]

As visual media spread to all domains of public and scientific life, nonverbal behavior is taking its place as an important form of communication alongside the written and spoken word. An objective and reliable method of analysis for hand movement behavior and gesture is therefore currently required in various scientific disciplines, including psychology, medicine, linguistics, anthropology, sociology, and computer science. However, no adequate common methodological standards have been developed thus far. Many behavioral gesture-coding systems lack objectivity and reliability, and automated methods that register specific movement parameters often fail to show validity with regard to psychological and social functions. To address these deficits, we have combined two methods, an elaborated behavioral coding system and an annotation tool for video and audio data. The NEUROGES-ELAN system is an effective and user-friendly research tool for the analysis of hand movement behavior, including gesture, self-touch, shifts, and actions. Since its first publication in 2009 in Behavior Research Methods, the tool has been used in interdisciplinary research projects to analyze a total of 467 individuals from different cultures, including subjects with mental disease and brain damage. Partly on the basis of new insights from these studies, the system has been revised methodologically and conceptually. The article presents the revised version of the system, including a detailed study of reliability. The improved reproducibility of the revised version makes NEUROGES-ELAN a suitable system for basic empirical research into the relation between hand movement behavior and gesture and cognitive, emotional, and interactive processes and for the development of automated movement behavior recognition methods. [ABSTRACT FROM AUTHOR]

Previous research (e.g., cultural consensus theory (Romney, Weller, & Batchelder, American Anthropologist, 88, 313-338, 1986); cultural mixture modeling (Mueller & Veinott, 2008)) has used overt response patterns (i.e., responses to questionnaires and surveys) to identify whether a group shares a single coherent attitude or belief set. Yet many domains in social science have focused on implicit attitudes that are not apparent in overt responses but still may be detected via response time patterns. We propose a method for modeling response times as a mixture of Gaussians, adapting the strong-consensus model of cultural mixture modeling to model this implicit measure of knowledge strength. We report the results of two behavioral experiments and one simulation experiment that establish the usefulness of the approach, as well as some of the boundary conditions under which distinct groups of shared agreement might be recovered, even when the group identity is not known. The results reveal that the ability to recover and identify shared-belief groups depends on (1) the level of noise in the measurement, (2) the differential signals for strong versus weak attitudes, and (3) the similarity between group attitudes. Consequently, the method shows promise for identifying latent groups among a population whose overt attitudes do not differ, but whose implicit or covert attitudes or knowledge may differ. [ABSTRACT FROM AUTHOR]

The directionality of finger counting (i.e., from left to right or right to left) is supposed to compete with the orientation of the mental number line in determining number mental representations. Indeed, Western individuals who count on their fingers from right to left present a weaker SNARC effect than do individuals for whom the directionality of counting is the same as the mental number line. Observations of natural behavior should be the preferred methodology for classifying individuals according to their counting habits. Yet, to perform such classification, researchers usually rely on questionnaires or reports of imagined behaviors. However, we show in a series of three experiments that, on average, 26 % of a sample of adults reported the opposite behavior from the one they actually implemented spontaneously when tested with an original ecological task. In a fourth experiment, this new task proved reliable, using a test-retest method. These results suggest that future studies about counting habits could benefit from the use of more ecological and functional tasks, rather than depending on noncontextualized questionnaires. [ABSTRACT FROM AUTHOR]

We report on the development of a novel shared touch-panel apparatus for examining a diverse range of topics in great ape social cognition and interaction. Our apparatus-named the Arena System-is composed of a single multitouch monitor that spans across two separate testing booths, so that individuals situated in each booth have tactile access to half of the monitor and visual access to the whole monitor. Additional components of the system include a smart-film barrier able to restrict visual access between the booths, as well as two automated feeding devices that dispense food rewards to the subjects. The touch-panel, smart-film, and feeders are controlled by a PC that is also responsible for running the experimental tasks. We present data from a pilot behavioral game theory study with two chimpanzees in order to illustrate the efficacy of our method, and we suggest applications for a range of topics including animal social learning, coordination, and behavioral economics. The system enables fully automated experimental procedures, which means that no human participation is needed to run the tasks. The novel use of a touch-panel in a social setting allows for a finer degree of data resolution than do the traditional experimental apparatuses used in prior studies on great ape social interaction. [ABSTRACT FROM AUTHOR]

A new method, with an application program in Matlab code, is proposed for testing item performance models on empirical databases. This method uses data intraclass correlation statistics as expected correlations to which one compares simple functions of correlations between model predictions and observed item performance. The method rests on a data population model whose validity for the considered data is suitably tested and has been verified for three behavioural measure databases. Contrarily to usual model selection criteria, this method provides an effective way of testing under-fitting and over-fitting, answering the usually neglected question "does this model suitably account for these data?" [ABSTRACT FROM AUTHOR]

In previous attempts to identify dynamical systems properties in patterns of play in team sports, only 2-D analysis methods have been used, implying that the plane of motion must be preselected and that movements out of the chosen plane are ignored, in the present study, we examined the usefulness of 3-D methods of analysis for establishing the presence of dynamical systems properties, such as phase transitions and symmetry-breaking processes in the team sport of rugby. Artificial neural networks (ANNs) were employed to reconstruct the 3-D performance space in a typical one-versus-one subphase of rugby. Results confirm that ANNs are reliable tools for reconstructing a 3-D performance space and may be instrumental in identifying pattern formation in team sports generally. [ABSTRACT FROM AUTHOR]

We have worked on automatically measuring the behavior of dairy cows during automatic milking. A milking robot offers a unique possibility for a dynamic measurement of physical data. Four strain gauge scales were installed into a milking robot in order to measure the weight of each leg separately, and a laser distance sensor was placed next to the robot in order to measure the radial movement of the cow's body surface. The data were collected into a PC. Three video cameras were installed to observe the system, and the data were recorded digitally. From the data, the dynamic weight or load of each leg and the respiration rate of a cow could be measured. Different stages of milking were observed, and the changes in behavior during milking were analyzed. The acquired information could be used to judge a cow's restlessness and welfare--fur example, leg health and stress. [ABSTRACT FROM AUTHOR]

The Iowa gambling task in humans is, in principle, suited for the study of the long-term efficiency of behavior in a biologically relevant context. Key features of this task are uncertainty of outcomes and a conflict between the immediate and the long-term payoff options. Animal models allow us to study the underlying neurobiology of decision-making processes and the long-term efficiency of behavior in more detail and at a greater depth than is possible in humans. Therefore, we set out to develop a model of this task in rodents, using the task's key features. In this article, we describe the results of the first series of experiments with rats and mice. The data thus far suggest that mice and rats behave in a way similar to humans; that is, they tend to choose the option with the best long-term payoff more often as the test progresses. [ABSTRACT FROM AUTHOR]

The zebrafish has been popular in developmental biology and genetics, but its brain function has rarely been studied. High-throughput screening of mutation or drug-induced changes in brain function requires simple and automatable behavioral tests. This article compares three behavioral quantification methods in four simple behavioral paradigms that test a range of characteristics of adult zebrafish, including novelty-induced responses, social behavior, aggression, and predator-model-induced responses. Two quantification methods, manual recording and computerized videotracking of location and activity, yielded very similar results, suggesting that automated videotracking reliably measures activity parameters and will allow high-throughput screening. However, observation-based event recording of posture patterns was found generally not to correlate with videotracking measures, suggesting that further refinement of automated behavior quantification may be considered. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented which features topics discussed at the 40th Annual Meeting of the Society for Computers in Psychology (SCiP), including behavior analysis, text processing and affective computing.