Do SSRIs promote more magnanimous attributions about others?
Creators: name: Moutoussis, Michael
name: Barnby, Joseph
name: Durant, Anais
name: Croal, Megan
name: Rutledge, Robb
name: Mason, Liam
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Psychiatry and Psychology
subject: Medicine and Health Sciences

description: Introduction This document lays out pre-registered hypotheses, to be tested using the data from the Attribution Task, a variant of the Repeated Dictator task (Barnby et al., 2020). The data for this study has been collected, but the data required to test the hypotheses outlined below has not been made fully available to the researchers that will carry out the analyses (Dr. Moutoussis &amp; supervisees; henceforth, the “modellers”), who have not tested any of the hypotheses described here. The task has been administered to two groups of healthy young participants, with group allocation being unknown to the analysts at the point of submission of this document. The experimental task was administered on day 1 of the study before taking citalopram or placebo, and also at a follow-up session after 1 week of either Citalopram 20mg/day or placebo treatment. The task is an extended, laboratory version of the one we have used and modelled successfully before (Barnby et al., 2022). In 12 trials, the participant is the ‘receiver’ in a Dictator game, where their computer partner, the ‘dictator’, splits 10 units of play-money either fairly (5 units each player) or unfairly (keeps all 10 units). After the split, the participant is asked for point estimates, from 0 to 100%, of the degree to which the dictator may have been motivated by a (a) wish for the participant to earn as little as possible (henceforth Harm Intent, or HI) or a (b) wish for themselves to win as much as possible (henceforth Self-interest Intent, SI). The task was modified for this study from the previous version in the following ways: 1. Four consecutive blocks were administered at baseline and four blocks at follow-up. A photo of which dictator was involved always appeared on the screen. This is different from our previous work, and the standard framing of neuroeconomic games, to increase ecological validity and allow an opportunity to examine spontaneous coalitional affiliation (operationalised below). 2. Before each split, the participant was asked for the degree of conviction, from 0 to 100%, that the dictator would split fairly. We also aim to model the effect of coalitional threat on attributions, using demographic data (Greenburgh et al., 2019). We will study self-reported gender, ethnicity and subjective socio-economic status with respect to their influence on HI and SI, and their interaction with Citalopram administration. No demographic data are, at the time of this pre-registration, available to the analysts, and neither is drug allocation. These data will be made available to the analysts once this document is publicly available. The task data is currently available to the modellers, but has not been analysed with respect to the hypotheses below. Main hypotheses A. Citalopram group will make more magnanimous attributions, operationalised as the average HI and SI ratings, transformed if necessary to allow for parametric statistics. B. A Bayesian model formulated within the active-inference framework and including the following parameters will provide a better account, over the entire dataset, compared to any nested models: a. Prior point-estimate conviction that Dictators of Harmful Intent, pH0 b. Same for Selfishness Intent, pS0 c. Policy precision, alpha (analogous to policy precision Uπ in our previous work) d. Prior precision over HI and SI (analogous to uSI0 and uHI0 in our previous work, but here expressed in terms of notional evidence for the prior beliefs) e. Threshold, or equivalently bias parameter w0 quantifying the reluctance to predict unfair return given a specific level of HI and SI. C. Fitting model parameters simultaneously to the ‘prediction’ and ‘attribution’ outcomes will improve psychometric reliability, compared to fitting the same parameters to attribution data only. Secondary hypotheses D. More magnanimous attributions induced by Citalopram, as in main hypothesis A., will entail, more specifically, reduced attributions of HI vs placebo. E. Coalitional threat will increase HI between participants. F. Coalitional threat will increase HI within participants. G. Citalopram will not interact with coalitional threat in the generation of larger attributions of HI. H. Models that explain all session data with one set of parameters, plus simple learning from one dictator to the next, will be preferable to models fitting one dictator at a time in terms of the balance of parsimony over accuracy. I. Our published Bayesian model will be equivalent, in terms of model-fit, to the active-inference version used here. Participant data will be better fit by Bayesian models, which take full account of uncertainty, as opposed to associative models with no or heuristic adjustment of learning rates, the better participants are at estimating the likelihood of fair return. Analytic Approach The active-inference models will be novel, and will discretize continuous data into six ordered attribution-report states (Hula et al., 2021), corresponding to bins of [0,⅙),[⅙,2/6)...[⅚,1]. We will use a generative model structured like our pre-printed reversal Bayesian model (Barnby et al., 2022). Responses will be explicitly modelled, rather than correlated with internal-belief states. The motivation to report a particular belief will be modelled as an internal reward (C-matrix goal, in active inference terms) with probabilistically more attractive actions that are congruent rather than incongruent with the underlying belief. We will use the standard Active Inference software suite (Smith et al., 2022) in matlab or octave (2019 or later). As well as the parameters above, we will extract the following descriptive measures: Average HI (HIAv); Average SI (SIAv); Average prediction of fair return, predAv; and first-trial observations of the same. Coalitional threat will be quantified as 1) self-perception of socio-economic status according to the McArthur scale, with lower SES interpreted as higher coalitional threat, to be used across participants 2) Congruence of BAME status of the participant vs. the dictator depicted on the screen (with status simply dichotomised as white majority vs. BAME, randomised over experimental days and fair/unfair dictator types), to be used within participants. We will test hypothesis B. above by using the log-likelihood ratio test for nested models, and the Bayesian Information Criterion as an approximation to model evidence. If the hypothesised model is not preferred, and non-nested models have to be compared, then use a protected exceedance probability comparison. The BIC is known to over-penalise more complex models; while this is in our case an acceptably conservative policy, whenever possible we will use more precise, free-energy approximations to model evidence. For secondary hypothesis I, i.e. comparison with our established models rather than within the class of active inference models, we will again use the BIC as an approximation to model evidence. Hypothesis C will be tested by examining a) split-half reliability across all sessions, halves being split by dictator and b) test-retest stability from baseline to follow-up. Once parameters and descriptive measures are extracted, all hypotheses will be tested by fitting linear mixed effects models, using the effect to be tested as fixed effect and participant as random effect using the ‘lme’ function in R (R Core Team, 2020) and averaging over candidate linear regression models (Barnby et al., 2022). The specified, directional hypotheses above will be tested at conventional significance threshold of 0.05. Any exploratory analyses, over and above the above hypotheses, will be family-wise corrected for multiple comparisons. References Barnby, J. M., Bell, V., Mehta, M. A., &amp; Moutoussis, M. (2020). Reduction in social learning and increased policy uncertainty about harmful intent is associated with pre-existing paranoid beliefs: Evidence from modelling a modified serial dictator game. PLOS Computational Biology, 16(10), e1008372. https://doi.org/10.1371/journal.pcbi.1008372 Barnby, J. M., Mehta, M., &amp; Moutoussis, M. (2022). The computational relationship between reinforcement learning and social inference in paranoia. PsyArXiv. https://doi.org/10.31234/osf.io/x4d3f Greenburgh, A., Bell, V., &amp; Raihani, N. (2019). Paranoia and conspiracy: Group cohesion increases harmful intent attribution in the Trust Game. PeerJ, 7, e7403. https://doi.org/10.7717/peerj.7403 Hula, A., Moutoussis, M., Will, G.-J., Kokorikou, D., Reiter, A. M., Ziegler, G., Consortium, N., Bullmore, E., Jones, P. B., Goodyer, I., Fonagy, P., Montague, P. R., &amp; Dolan, R. J. (2021). Multi-Round Trust Game Quantifies Inter-Individual Differences in Social Exchange from Adolescence to Adulthood. Computational Psychiatry, 5(1), 102–118. https://doi.org/10.5334/cpsy.65 R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/. Smith, R., Friston, K. J., &amp; Whyte, C. J. (2022). A step-by-step tutorial on active inference and its application to empirical data. Journal of Mathematical Psychology, 107, 102632. https://doi.org/10.1016/j.jmp.2021.102632