A social inference model of idealization and devaluation

People often form polarized beliefs, imbuing objects (e.g., themselves or others) with unambiguously positive or negative qualities. In clinical settings, this is referred to as dichotomous thinking or "splitting" and is a feature of several psychiatric disorders. Here, we introduce a Bayesian model of splitting that parameterizes a tendency to rigidly categorize objects as either entirely "Bad" or "Good," rather than to flexibly learn dispositions along a continuous scale. Distinct from the previous descriptive theories, the model makes quantitative predictions about how dichotomous beliefs emerge and are updated in light of new information. Specifically, the model addresses how splitting is context-dependent, yet exhibits stability across time. A key model feature is that phases of devaluation and/or idealization are consolidated by rationally attributing counter-evidence to external factors. For example, when another person is idealized, their less-than-perfect behavior is attributed to unfavorable external circumstances. However, sufficient counter-evidence can trigger switches of polarity, producing bistable dynamics. We show that the model can be fitted to empirical data, to measure individual susceptibility to relational instability. For example, we find that a latent categorical belief that others are "Good" accounts for less changeable, and more certain, character impressions of benevolent as opposed to malevolent others among healthy participants. By comparison, character impressions made by participants with borderline personality disorder reveal significantly higher and more symmetric splitting. The generative framework proposed invites applications for modeling oscillatory relational and affective dynamics in psychotherapeutic contexts. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Dreading the pain of others? Altruistic responses to others' pain underestimate dread

A dislike of waiting for pain, aptly termed ‘dread’, is so great that people will increase pain to avoid delaying it. However, despite many accounts of altruistic responses to pain in others, no previous studies have tested whether people take delay into account when attempting to ameliorate others' pain. We examined the impact of delay in 2 experiments where participants (total N = 130) specified the intensity and delay of pain either for themselves or another person. Participants were willing to increase the experimental pain of another participant to avoid delaying it, indicative of dread, though did so to a lesser extent than was the case for their own pain. We observed a similar attenuation in dread when participants chose the timing of a hypothetical painful medical treatment for a close friend or relative, but no such attenuation when participants chose for a more distant acquaintance. A model in which altruism is biased to privilege pain intensity over the dread of pain parsimoniously accounts for these findings. We refer to this underestimation of others' dread as a ‘Dread Empathy Gap’

Planck 2013 results. XXIX. Planck catalogue of Sunyaev-Zeldovich sources

We describe the all-sky Planck catalogue of clusters and cluster candidates derived from Sunyaev-Zeldovich (SZ) effect detections using the first 15.5 months of Planck satellite observations. The catalogue contains 1227 entries, making it over six times the size of the Planck Early SZ (ESZ) sample and the largest SZ-selected catalogue to date. It contains 861 confirmed clusters, of which 178 have been confirmed as clusters, mostly through follow-up observations, and a further 683 are previously-known clusters. The remaining 366 have the status of cluster candidates, and we divide them into three classes according to the quality of evidence that they are likely to be true clusters. The Planck SZ catalogue is the deepest all-sky cluster catalogue, with redshifts up to about one, and spans the broadest cluster mass range from (0.1 to 1.6) × 1015 M⊙. Confirmation of cluster candidates through comparison with existing surveys or cluster catalogues is extensively described, as is the statistical characterization of the catalogue in terms of completeness and statistical reliability. The outputs of the validation process are provided as additional information. This gives, in particular, an ensemble of 813 cluster redshifts, and for all these Planck clusters we also include a mass estimated from a newly-proposed SZ-mass proxy. A refined measure of the SZ Compton parameter for the clusters with X-ray counter-parts is provided, as is an X-ray flux for all the Planck clusters not previously detected in X-ray surveys.The development of Planck has been supported by: ESA; CNES and CNRS/INSU-IN2P3-INP (France); ASI, CNR, and INAF (Italy); NASA and DoE (USA); STFC and UKSA (UK); CSIC, MICINN and JA (Spain); Tekes, AoF and CSC (Finland); DLR and MPG (Germany); CSA (Canada); DTU Space (Denmark); SER/SSO (Switzerland); RCN (Norway); SFI (Ireland); FCT/MCTES (Portugal); and PRACE (EU).Peer Reviewe

A Social Inference Model of Idealization and Devaluation

People often form polarized beliefs about others. In a clinical setting this is referred to as a dichotomous or ‘split’ representation of others, whereby others are not imbued with possessing mixtures of opposing properties. Here, we formalise these accounts as an oversimplified categorical model of others’ internal, intentional, states. We show how a resulting idealization and devaluation of others can be stabilized by attributing unexpected behaviour to fictive external factors. For example, under idealization, less-than-perfect behaviour is attributed to unfavourable external conditions, thereby maintaining belief in the other’s goodness. This feature of the model accounts for how extreme beliefs are buffered against counter-evidence, while at the same time being prone to precipitous changes of polarity. Equivalent inference applied to the self creates an oscillation between self-aggrandizement and self-deprecation, capturing oscillatory relational and affective dynamics. Notably, such oscillatory dynamics arise out of the Bayesian nature of the model, wherein a subject arrives at the most plausible explanation for their observations, given their current expectations. Thus, the model we present accounts for aspects of splitting that appear ‘defensive’, without the need to postulate a specific defensive intention. By contrast, we associate psychological health with a fine-grained representation of internal states, constrained by an integrated prior, corresponding to notions of ‘character’. Finally, the model predicts that extreme appraisals of self or other are associated with causal attribution errors

Social redistribution of pain and money

People show empathic responses to others' pain, yet how they choose to apportion pain between themselves and others is not well understood. To address this question, we observed choices to reapportion social allocations of painful stimuli and, for comparison, also elicited equivalent choices with money. On average people sought to equalize allocations of both pain and money, in a manner which indicated that inequality carried an increasing marginal cost. Preferences for pain were more altruistic than for money, with several participants assigning more than half the pain to themselves. Our data indicate that, given concern for others, the fundamental principle of diminishing marginal utility motivates spreading costs across individuals. A model incorporating this assumption outperformed existing models of social utility in explaining the data. By implementing selected allocations for real, we also found that while inequality per se did not influence pain perception, altruistic behavior had an intrinsic analgesic effect for the recipient

Anticipation-discounting functions.

<p>Anticipation-discounting functions are constructed from a linear combination of the conventionally discounted value of an outcome, i.e. its instantaneous anticipation, and the prospective sum of anticipation whilst waiting for the outcome, displayed here for an outcome with positive utility. <b>A</b> Where prospective anticipation (savoring) dominates, the overall value of the outcome decreases as it draws nearer, due to decreasing prospective anticipation. <b>B</b> Where discounting dominates, the overall value of the outcome increases as it draws nearer due to increasing instantaneous anticipation.</p

Heuristic model fits.

<p><b>A</b> The observed distribution of consumption by all 30 participants included in the analysis. Warmer colors indicate that a higher proportion of participants chose to consume that amount of relief on a particular trial. Black arrows indicate spending zero relief, which becomes more prominent during the middle of the experiment. <b>B</b> Group-Level distribution of relief consumption predicted by alternative heuristic models. These plots denote the mean probability across all participants of consuming an amount of relief, <i>c_t</i>, on each trial, <i>t</i>, given a vector of the total remaining relief for each participant on each trial, trial, <i>s_t</i>, <i>s_t</i>₊₁, <i>s_t</i>₊₂, … s_<i>T</i>, at the maximum likelihood parameterization, <i>θ</i>, of each model. The Direct Action model combines the three key observed behavioral tendencies as heuristics to either spend close zero relief until the mean relief remaining reaches the maximum allowable spend (save-now-spend-later), to spending close to the mean relief remaining per trial (spread-spending) or close to the maximum allowable relief (spend-now-suffer-later). The Income Maximization model extends this model, such that the saving tendency is implemented as the attempt to dynamically maximize the mean remaining relief per trial, over a limited future horizon. This model captures the relatively greater tendency to save relief during the middle of the experiment (as indicated by the black arrows). <b>C</b> The proportion of variance explained by each model. Mean predicted consumption levels simulated from the maximum likelihood parameterizations of each model over each 10 trials of the experiment for each participant are plotted against the same metric derived from the observed data. Least squares fits indicate an R-squared value of 0.56 for the Direct Action model and 0.80 for the Income Maximization model.</p

Anticipation-discounting and dynamic utility maximization.

<p><b>A</b> Four anticipation-discounting functions. From left to right: predominant discounting, no discounting, predominant savoring, discounted savoring. The parameters of each function are displayed on the plot. <b>B</b> Simulated optimal consumption paths under the same four discount functions, with concave utility, <i>U</i>(<i>c</i>) = <i>c</i>^0.75 Green circles represent simulated consumption paths for a fully naïve decision-maker (See Main Text). Red circles represent consumption for a fully sophisticated decision-maker. <b>C</b> Plans for future consumption made in the first three time periods for a naïve decision-maker. The red circles indicate planned consumption from the perspective of <i>t</i> = 1, the blue circles from the perspective of <i>t</i> = 2 and the green circles from the perspective of <i>t</i> = 3. Where discounting dominates (left panel), the naïve decision-maker consumes more than planned, where savoring dominates (right hand two panels), the naïve decision-maker consumes less than planned.</p