2020 Annual Conference Abstracts
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2020 Annual Conference • Symposium Society for Affective Science Symposium 1: Overview Symposium 1B STATISTICAL AND COMPUTATIONAL MODELS OF AFFECTIVE DERIVING AFFECTIVE DYNAMICS VIA EXPERIENCE SAMPLING: A DYNAMICS STUDY OF MOMENTARY AFFECT IN BIPOLAR PSYCHOPATHOLOGY Bennett, Daniel—Princeton University; Koval, Peter—The University of Sperry, Sarah—Medical University of South Carolina; Kwapil, Thomas R— Melbourne University of Illinois at Urbana-Champaign Descriptors: computational science, emotion regulation, cognitive, methods, Descriptors: clinical, emotion regulation, mental health, positive emotion mental health Research on affective dysregulation in bipolar spectrum psychopathology (BSP) Human affect is fundamentally dynamic. Affective states ebb and flow, evolving has largely concentrated on studying mood elevations (depression, mania) over time in response to complex interactions between a person and their over long time scales. However, little is understood about moment-to-moment environment. Individual differences in affective dynamics have been related or day-to-day perturbations in affect. Thus, the present study aimed to examine to psychological well-being and are thought to be implicated in a number of affective dynamics to better understand affective dysregulation in BSP across psychiatric disorders. However, a number of fundamental questions concerning multiple timescales. Young-adults (n=233) oversampled for BSP completed affective dynamics remain to be addressed, and further progress calls for self-report questionnaires and 14 days of experience sampling assessing empirical methods and quantitative tools that go beyond traditional laboratory high and low arousal negative affect (NA) and positive affect (PA). The time- studies. This symposium presents four projects that encompass two distinct series of each participant was used to derive variability, instability, and acute research approaches in this field. The first approach (abstracts 1 and 2) involves instability at the momentary level (within day) and day level (between days). statistical modelling of repeatedly assessed affective states obtained using All analyses controlled for mean NA and PA. Within days, BSP was associated naturalistic methods such as experience sampling. Here, researchers use affect with variability of high arousal NA (beta=.02, p=.03), instability of high and low ratings collected intensively (e.g., hourly) over several days or weeks, and derive arousal NA (gamma=.18, p=.002) and high arousal PA (gamma=.15, p=.02), statistical measures from these time-series to quantify various parameters of and a high probability of acute increases in NA, but not PA (gamma=.01, affective dynamics, (e.g., mean, variance, and autocorrelation of affect). The p=.008). BSP showed similar associations at the between-day level; however, second approach (abstracts 3 and 4), works from the bottom up, building BSP was additionally associated with acute increases (gamma=.01, p=.04) computational models of the psychological processes that produce affective and variability (beta=.05, p=.008) of high arousal PA. Examining affective dynamics. These models can then be used to simulate synthetic affective time- dynamics across multiple timescales enhances our understanding of affective series whose dynamics can be compared to real data. These two approaches dysregulation in BSP and can be applied transdiagnostically to understand have largely arisen in parallel literatures, without strong mutual influence. This mood psychopathology. symposium will redress this by presenting them side-by-side as complementary tools for understanding affective dynamics. Symposium 1C THE AFFECTIVE ISING MODEL: A COMPUTATIONAL ACCOUNT OF Symposium 1A HUMAN AFFECT DYNAMICS TEMPORAL DYNAMICS OF AFFECT IN DAILY LIFE ARE PARTIALLY Loossens, Tim—KU Leuven; Mestdagh, Merijn—KU Leuven; Dejonckheere, MEDIATED BY MOMENTARY USE OF EMOTION REGULATION Egon—KU Leuven; Kuppens, Peter—KU Leuven; Tuerlinckx, Francis—KU STRATEGIES Leuven; Verdonck, Stijn—KU Leuven Koval, Peter—The University of Melbourne & KU Leuven; Hinton, Jordan— Australian Catholic University; Gleeson, John—Australian Catholic University; Descriptors: computational science, positive emotion Hollenstein, Tom—Queen’s University; Kuppens, Peter—KU Leuven The typical (continuous-time) vector autoregressive (VAR) models that are Descriptors: emotion regulation, methods used in the field of affect research cannot capture empirical phenomena that are often encountered in affect data such as skewness, bimodality and non- Feelings change not only in response to perceived threats and opportunities, linear relations of the positive and negative affect dimensions (PA and NA). The but also following deliberate regulation. Yet, few studies have investigated how Affective Ising Model (AIM) is a nonlinear stochastic model for the dynamics emotion regulation (ER) relates to affect dynamics. Previous research has shown of positive and negative affect that was put forward to better explain these that positive (PA) and negative affect (NA) dynamically interact over time, and phenomena and unify them in one modeling framework. It incorporates show bi-directional associations with ER strategies. However, the possibility principles of statistical mechanics and is inspired by neurophysiological and that ER strategies may mediate temporal associations among PA and NA has behavioral evidence about auto-excitation and mutual inhibition of the PA not previously been tested. To address this question, we analysed data from an and NA dimensions. Applying the AIM to two large experience sampling existing experience sampling study using dynamic structural equation modeling studies on the occurrence of PA and NA in daily life in both normality and to estimate how PA and NA mutually predict each other and themselves over mood disorder, including 318 participants and 685 distinct timeseries, it has time, both directly and indirectly via the use of ER strategies. Data were from been shown by means of the parametric bootstrap method that the AIM is community participants (n=176) who reported their momentary experiences indeed able to reproduce the aforementioned non-Gaussian features observed of PA and NA and their use of several ER strategies 9-10 times daily for 21 in data, which is not true for typical (continuous-time) VAR models. Using the consecutive days, using a smartphone app. Two ER strategies emerged as the same methodology, it has been shown that the AIM’s recovery of Gaussian most consistent mediators of affect dynamics: rumination was the strongest measures, like the correlation between PA and NA, is comparable to that of the mediator of NA inertia (indirect effect=0.021, 95% CI [0.013, 0.029]) and the VAR models. The predictive performance of the AIM is also better for 63% of the only mediator of NA’s inhibitory effect on PA (indirect effect=-0.012, 95% CI timeseries in a leave-one-out context and is skewed in favor of the AIM; it can [-0.002, -0.004]). In contrast, situation selection was the strongest mediator of significantly outperform the VAR models but the converse is not true. Currently, PA inertia (indirect effect=0.017, 95% CI [0.010, 0.024]) and the only mediator a manuscript of the model is being reviewed. of PA’s inhibitory effect on NA (indirect effect=-0.009, 95% CI [-0.015, 0.004]). Results suggest that spontaneous ER partly accounts for affective persistence and change over time in daily life. Data collection for this study was funded by an Australian Research Council (ARC) Discovery Project (DP160102252) and Peter Koval is currently supported by an ARC Discovery Early Career Researcher Award (DE190100203). 1 2020 Annual Conference • Symposium Society for Affective Science Symposium 1D Symposium 2A A MODEL OF MOOD AS INTEGRATED ADVANTAGE VISUALIZING CORTICAL CIRCUITS ENCODING PAIN Bennett, Daniel—Princeton University; Niv, Yael—Princeton University UNPLEASANTNESS Corder, Gregory—University of Pennsylvania; McCall, Norma—University of Descriptors: computational science, cognitive Pennsylvania; Wojick, Jessica—University of Pennsylvania Our moment-to-moment experiences have complex, non-linear effects on our Descriptors: neuroscience, animal, comparative, physiology, biomarkers, methods mood. One way of studying these dynamics is to formally specify a theory of how experience influences mood within a computational model. The model can Damage to the basolateral amygdala (BLA) can induce a rare phenomenon then be simulated to investigate the mood dynamics that are produced under where noxious stimuli are devoid of perceived unpleasantness, while anterior different parameter settings or in different environments. Here, we present a cingulate cortex (ACC) disruption renders unpleasant pain experiences “less novel computational model of mood, and demonstrate its utility for modelling bothersome”. This indifference to pain affect is similar to the phenomenology affective dynamics. The Integrated Advantage model instantiates the hypothesis of opioid analgesia, suggesting a mu opioid receptor (MOR)-expressing pain that the valence of mood reflects a moving average of the “advantage” of one’s circuitry in the ACC. How brain circuits transform emotionally inert nociceptive actions (i.e., how much better/worse were the results of those actions compared information into an affective pain percept remains unclear. Here, using in to alternative actions). This model parsimoniously accounts for numerous vivo calcium imaging and neural activity manipulation