Recognising and reacting to angry and happy facial expressions: a diffusion model analysis.

Researchers have reported two biases in how people recognise and respond to angry and happy facial expressions: (1) a gender-expression bias (Becker et al. in J Pers Soc Psychol, 92(2):179-190, https://doi.org/10.1037/0022-3514.92.2.179 , 2007)-faster identification of male faces as angry and female faces as happy and (2) an approach-avoidance bias-faster avoidance of people who appear angry and faster approach responses people who appear happy (Heuer et al. in Behav Res The, 45(12):2990-3001, https://doi.org/10.1016/j.brat.2007.08.010 2007; Marsh et al. in Emotion, 5(1), 119-124, https://doi.org/10.1037/1528-3542.5.1.119 , 2005; Rotteveel and Phaf in Emotion 4(2):156-172, https://doi.org/10.1037/1528-3542.4.2.156 , 2004). The aim of the current research is to gain insight into the nature of such biases by applying the drift diffusion model to the results of an approach-avoidance task. Sixty-five participants (33 female) identified faces as either happy or angry by pushing and pulling a joystick. In agreement with the original study of this effect (Solarz 1960) there were clear participant gender differences-both the approach avoidance and gender-expression biases were larger in magnitude for female compared to male participants. The diffusion model results extend recent research (Krypotos et al. in Cogn Emot 29(8):1424-1444, https://doi.org/10.1080/02699931.2014.985635 , 2015) by indicating that the gender-expression and approach-avoidance biases are mediated by separate cognitive processes

A many-analysts approach to the relation between religiosity and well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N=10,535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β=0.120). For the second research question, this was the case for 65% of the teams (median reported β=0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

A Many-analysts Approach to the Relation Between Religiosity and Well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N = 10, 535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β = 0.120). For the second research question, this was the case for 65% of the teams (median reported β = 0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models

Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non-decision time. Inferences about these psychological factors, hinge upon the validity of the models’ parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants’ behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler’s degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models

Related to Anxiety: Arbitrarily Applicable Relational Responding and Experimental Psychopathology Research on Fear and Avoidance

Humans have an unparalleled ability to engage in arbitrarily applicable relational responding (AARR). One of the consequences of this ability to spontaneously combine and relate events from the past, present, and future may, in fact, be a propensity to suffer. For instance, maladaptive fear and avoidance of remote or derived threats may actually perpetuate anxiety. In this narrative review, we consider contemporary AARR research on fear and avoidance as it relates to anxiety. We first describe laboratory-based research on the emergent spread of fear- and avoidance-eliciting functions in humans. Next, we consider the validity of AARR research on fear and avoidance and address the therapeutic implications of the work. Finally, we outline challenges and opportunities for a greater synthesis between behavior analysis research on AARR and experimental psychopathology