Variability indices are a key measure of interest across diverse fields, in and outside psychology. A crucial problem for any research relying on variability measures however is that variability is severely confounded with the mean, especially when measurements are bounded, which is often the case in psychology (e.g., participants are asked "rate how happy you feel now between 0 and 100?"). While a number of solutions to this problem have been proposed, none of these are sufficient or generic. As a result, conclusions on the basis of research relying on variability measures may be unjustified. Here, we introduce a generic solution to this problem by proposing a relative variability index that is not confounded with the mean by taking into account the maximum possible variance given an observed mean. The proposed index is studied theoretically and we offer an analytical solution for the proposed index. Associated software tools (in R and MATLAB) have been developed to compute the relative index for measures of standard deviation, relative range, relative interquartile distance and relative root mean squared successive difference. In five data examples, we show how the relative variability index solves the problem of confound with the mean, and document how the use of the relative variability measure can lead to different conclusions, compared with when conventional variability measures are used. Among others, we show that the variability of negative emotions, a core feature of patients with borderline disorder, may be an effect solely driven by the mean of these negative emotions. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Choice reaction time (RT) experiments are an invaluable tool in psychology and neuroscience. A common assumption is that the total choice response time is the sum of a decision and a nondecision part (time spent on perceptual and motor processes). While the decision part is typically modeled very carefully (commonly with diffusion models), a simple and ad hoc distribution (mostly uniform) is assumed for the nondecision component. Nevertheless, it has been shown that the misspecification of the nondecision time can severely distort the decision model parameter estimates. In this article, we propose an alternative approach to the estimation of choice RT models that elegantly bypasses the specification of the nondecision time distribution by means of an unconventional convolution of data and decision model distributions (hence called the D*M approach). Once the decision model parameters have been estimated, it is possible to compute a nonparametric estimate of the nondecision time distribution. The technique is tested on simulated data, and is shown to systematically remove traditional estimation bias related to misspecified nondecision time, even for a relatively small number of observations. The shape of the actual underlying nondecision time distribution can also be recovered. Next, the D*M approach is applied to a selection of existing diffusion model application articles. For all of these studies, substantial quantitative differences with the original analyses are found. For one study, these differences radically alter its final conclusions, underlining the importance of our approach. Additionally, we find that strongly right skewed nondecision time distributions are not at all uncommon.

The Ising Decision Maker (IDM) is a new formal model for speeded two-choice decision making derived from the stochastic Hopfield network or dynamic Ising model. On a microscopic level, it consists of 2 pools of binary stochastic neurons with pairwise interactions. Inside each pool, neurons excite each other, whereas between pools, neurons inhibit each other. The perceptual input is represented by an external excitatory field. Using methods from statistical mechanics, the high-dimensional network of neurons (microscopic level) is reduced to a two-dimensional stochastic process, describing the evolution of the mean neural activity per pool (macroscopic level). The IDM can be seen as an abstract, analytically tractable multiple attractor network model of information accumulation. In this article, the properties of the IDM are studied, the relations to existing models are discussed, and it is shown that the most important basic aspects of two-choice response time data can be reproduced. In addition, the IDM is shown to predict a variety of observed psychophysical relations such as Piéron's law, the van der Molen-Keuss effect, and Weber's law. Using Bayesian methods, the model is fitted to both simulated and real data, and its performance is compared to the Ratcliff diffusion model. The speeded two-choice response time (RT) task is a wellestablished paradigm in experimental psychology for investigating the principles underlying simple decision making. In the psychological literature, several successful models have been proposed based on the idea of the accumulation of noisy evidence over time (Link & Heath, 1975;Ratcliff, 1978;Stone, 1960;Usher & McClelland, 2001;Vickers, 1970). An important class of accumulator models, of which the drift diffusion model is the prime example, relies on a single or a few linear stochastic differential equations (SDEs). Decades of careful research resulted in excellent fits between the best accumulator models and behavioral data from speeded twochoice RT tasks. Initially, these models were conceived as abstract representations of the decision process. In the last decade however, there has been an increasing trend of investigating their neurophysiological underpinnings (

Can we experience positive (PA) and negative affect (NA) separately (i.e., affective independence), or do these emotional states represent the mutually exclusive ends of a single bipolar continuum (i.e., affective bipolarity)? Building on previous emotion theories, we propose that the relation between PA and NA is not invariable, but rather fluctuates in response to changing situational demands. Specifically, we argue that our affective system shifts from relative independence to stronger bipolarity when we encounter events or situations that activate personally relevant concerns. We test this idea in an experience sampling study, in which we tracked the positive and negative emotional trajectories of 101 first-year university students who received their exam results, an event that potentially triggers a personally significant concern. Using multilevel piecewise regression, we show that running PA-NA correlations become increasingly more negative in the anticipation of results release, indicating stronger affective bipolarity, and ease back towards greater independence as time after this event passes. Furthermore, we show that this dynamic trajectory is particularly apparent for event-related PA and NA, and not affect in general, and that such shifts are partly a function of the importance people attribute to that event. We suggest that such flexible changes in the affect relation may function as an emotional compass by signalling personally relevant information, and create a motivational push to respond to these meaningful events in an appropriate manner.

Open people show greater interest in situations that are complex, novel, and difficult to understandsituations that may also be experienced as confusing. Here we investigate the possibility that openness/ intellect is centrally characterized by more positive relations between interest and confusion. Interest and confusion are key states experienced during engagement with information and learning. However, little is known about the within-person relation between them, let alone individual differences in this relation. We tested our hypotheses by making use of different paradigms, stimuli, and participants. Across five studies (N ϭ 640) we tested the relation between openness/intellect and within-person interest-confusion relations in response to art (Study 1); science, philosophy, and art (Study 2); psychology lectures (Study 3); a poem (Study 4); and a complex problem solving task (Study 5). Average interest-confusion relations varied between different studies, but for all studies the distributions of the relations went from highly negative to highly positive-individual differences in direction rather than just degree. In all but 1 study we found consistent support for our hypotheses-openness/intellect is associated with more positive relations between interest and confusion. No other personality domain or intelligence was consistently related to interest-confusion relations. Together, these findings suggest a new phenomenological aspect of being open-curiosity toward confusing situations. Our findings support the link between openness/ intellect and sensitivity to the value of complex information, and are discussed with regards to their relevance for engagement with information and learning.