Chasing consistency: On the measurement error in self-reported affect in experiments.

How feelings change over time is a central topic in emotion research. To study these affective fluctuations, researchers often ask participants to repeatedly indicate how they feel on a self-report rating scale. Despite widespread recognition that this kind of data is subject to measurement error, the extent of this error remains an open question. Complementing many daily-life studies, this study aimed to investigate this question in an experimental setting. In such a setting, multiple trials follow each other at a fast pace, forcing experimenters to use a limited number of questions to measure affect during each trial. A total of 1398 participants completed a probabilistic reward task in which they were unknowingly presented with the same string of outcomes multiple times throughout the study. This allowed us to assess the test-retest consistency of their affective responses to the rating scales under investigation. We then compared these consistencies across different types of rating scales in hopes of finding out whether a given type of scale led to a greater consistency of affective measurements. Overall, we found moderate to good consistency of the affective measurements. Surprisingly, however, we found no differences in consistency across rating scales, which suggests that the specific rating scale that is used does not influence the measurement consistency.

Efficient estimation of bounded gradient-drift diffusion models for affect on CPU and GPU.

Computational modeling plays an important role in a gamut of research fields. In affect research, continuous-time stochastic models are becoming increasingly popular. Recently, a non-linear, continuous-time, stochastic model has been introduced for affect dynamics, called the Affective Ising Model (AIM). The drawback of non-linear models like the AIM is that they generally come with serious computational challenges for parameter estimation and related statistical analyses. The likelihood function of the AIM does not have a closed form expression. Consequently, simulation based or numerical methods have to be considered in order to evaluate the likelihood function. Additionally, the likelihood function can have multiple local minima. Consequently, a global optimization heuristic is required and such heuristics generally require a large number of likelihood function evaluations. In this paper, a Julia software package is introduced that is dedicated to fitting the AIM. The package includes an implementation of a numeric algorithm for fast computations of the likelihood function, which can be run both on graphics processing units (GPU) and central processing units (CPU). The numerical method introduced in this paper is compared to the more traditional Euler-Maruyama method for solving stochastic differential equations. Furthermore, the estimation software is tested by means of a recovery study and estimation times are reported for benchmarks that were run on several computing devices (two different GPUs and three different CPUs). According to these results, a single parameter estimation can be obtained in less than thirty seconds using a mainstream NVIDIA GPU.

Testing a computational model of subjective well-being: a preregistered replication of Rutledge et al. (2014).

Subjective well-being changes over time. While the causes of these changes have been investigated extensively, few attempts have been made to capture these changes through computational modelling. One notable exception is the study by Rutledge et al. [Rutledge, R. B., Skandali, N., Dayan, P., & Dolan, R. J. (2014). A computational and neural model of momentary subjective well-being. Proceedings of the National Academy of Sciences, 111(33), 12252-12257. https://doi.org/10.1073/pnas.1407535111], in which a model that captures momentary changes in subjective well-being was proposed. The model incorporates how an individual processes rewards and punishments in a decision context. Using this model, the authors were able to successfully explain fluctuations in subjective well-being observed in a gambling paradigm. Although Rutledge et al. reported an in-paper replication, a successful independent replication would further increase the credibility of their results. In this paper, we report a preregistered close replication of the behavioural experiment and analyses by Rutledge et al. The results of Rutledge et al. were mostly confirmed, providing further evidence for the role of rewards and punishments in subjective well-being fluctuations. Additionally, the association between personality traits and the way people process rewards and punishments was examined. No evidence for such associations was found, leaving this an open question for future research.

Nonlinearity in affect dynamics persists after accounting for the valence of daily-life events.

In recent years, increased attention has gone to studying nonlinear characteristics of affective time series. An example of such nonlinear features is multimodality-the presence of more than one mode in an affective time series-which might mark the presence of discrete-like transitions between one and another affective state. In an attempt to capture these nonlinear features, Loossens et al. (2020) proposed the Affective Ising Model (AIM) as a model of affect dynamics. This model was validated on daily-life data, but these data did not contain any information on potential environmental factors that might have influenced a participant's affective state. Unfortunately, this omission may have led to erroneously concluding that nonlinearity is a defining characteristic of the affective system, even when it is solely driven by extrinsic influences. To accommodate this limitation, we applied the AIM on daily-life data in which the valence of such external events was measured. Overall, we found that nonlinearity persisted after accounting for the valence of daily-life events, suggesting that nonlinearity is a defining characteristic of affect and should thus be accounted for. Interestingly, this effect was more pronounced for composite compared to single-item measures of affect. While in line with previous research, these results should be replicated in a larger, more representative sample. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Stimulus-Driven Affective Change: Evaluating Computational Models of Affect Dynamics in Conjunction with Input.

The way in which emotional experiences change over time can be studied through the use of computational models. An important question with regard to such models is which characteristics of the data a model should account for in order to adequately describe these data. Recently, attention has been drawn on the potential importance of nonlinearity as a characteristic of affect dynamics. However, this conclusion was reached through the use of experience sampling data in which no information was available about the context in which affect was measured. However, affective stimuli may induce some or all of the observed nonlinearity. This raises the question of whether computational models of affect dynamics should account for nonlinearity, or whether they just need to account for the affective stimuli a person encounters. To investigate this question, we used a probabilistic reward task in which participants either won or lost money at each trial. A number of plausible ways in which the experimental stimuli played a role were considered and applied to the nonlinear Affective Ising Model (AIM) and the linear Bounded Ornstein-Uhlenbeck (BOU) model. In order to reach a conclusion, the relative and absolute performance of these models were assessed. Results suggest that some of the observed nonlinearity could indeed be attributed to the experimental stimuli. However, not all nonlinearity was accounted for by these stimuli, suggesting that nonlinearity may present an inherent feature of affect dynamics. As such, nonlinearity should ideally be accounted for in the computational models of affect dynamics. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-022-00118-5.

No persistent attenuation of fear memories in humans: A registered replication of the reactivation-extinction effect.

It has been proposed that memory retrieval can destabilize consolidated memories, after which they need to be reconsolidated in order to be retained. The presentation of relevant information during memory reconsolidation could then result in the modification of a destabilized memory trace, by allowing the memory trace to be updated before being reconsolidated. In line with this idea, Schiller et al. (2010) have demonstrated that memory retrieval shortly before extinction training can prevent the later recovery of conditioned fear responding that is observed after regular extinction training. Those findings have been the subject of considerable controversy, due in part to theoretical reasons but also due to a number of failures to obtain similar results in conceptual replication attempts. Here, we report the results of a highly powered, direct, independent replication of the critical conditions of Schiller et al. (2010, Experiment 1). Due to misrepresentation of the exclusion criteria in the original Schiller et al. (2010) report, data collection was considerably delayed. When we eventually managed to attain our pre-registered sample size, we found that we could not observe any benefit of reactivation-extinction over regular extinction training in preventing recovery of conditioned fear. The results of the present study, along with the mixed findings in the literature and the misreporting in Schiller et al. (2010), give cause to question whether there is robust evidence that reactivation-extinction prevents the return of fear in humans.