Sunday, August 23, 2020

It is likely that a large proportion of people who purport to believe fake news really do, but this proportion might be significantly smaller than thought; assertion of belief is inflated, we suggest, by insincere report

Levy, Neil L., and Robert M. Ross. 2020. “The Cognitive Science of Fake News.” PsyArXiv. August 23. doi:10.31234/ Forthcoming book chapter to appear in:
Hannon, M. & de Ridder, J. (2021). Routledge Handbook of Political Epistemology.

Abstract: In this chapter, we provide a necessarily brief and partial survey of recent work in the cognitive sciences directly on or closely related to the psychology of fake news, in particular fake news in the political domain. We focus on whether and why people believe fake news. While we argue that it is likely that a large proportion of people who purport to believe fake news really do, we provide evidence that this proportion might be significantly smaller than is usually thought (and smaller than is suggested by surveys). Assertion of belief is inflated, we suggest, by insincere report, whether to express support for one side of political debate or simply for fun. It is also inflated by the use of motivated inference of one sort or another, which lead respondents to report believing things about which they had no opinion prior to being probed. We then turn to rival accounts that aim to explain why people believe in fake news when they do. While partisan explanations, turning on motivated reasoning, are probably best known, we show they face serious challenges from accounts that explain belief by reference to analytic thinking.

Check also Echo Chambers Exist! (But They're Full of Opposing Views). Jonathan Bright, Nahema Marchal, Bharath Ganesh, Stevan Rudinac. arXiv Jan 30 2020. arXiv:2001.11461.

And: The rise in the political polarization in recent decades is not accounted for by the dramatic rise in internet use; claims that partisans inhabit wildly segregated echo chambers/filter bubbles are largely overstated:
Deri, Sebastian. 2019. “Internet Use and Political Polarization: A Review.” PsyArXiv. November 6.

And Testing popular news discourse on the “echo chamber” effect: Does political polarisation occur among those relying on social media as their primary politics news source? Nguyen, A. and Vu, H.T. First Monday, 24 (5), 6. Jun 4 2019.

Check also
Why Smart People Are Vulnerable to Putting Tribe Before Truth. Dan M Kahan. Scientific American, Dec 03 2018.

Baum, J., Rabovsky, M., Rose, S. B., & Abdel Rahman, R. (2018). Clear judgments based on unclear evidence: Person evaluation is strongly influenced by untrustworthy gossip. Emotion,

The key mechanism that generates scientific polarization involves treating evidence generated by other agents as uncertain when their beliefs are relatively different from one’s own:

Scientific polarization. Cailin O’Connor, James Owen Weatherall. European Journal for Philosophy of Science. October 2018, Volume 8, Issue 3, pp 855–875.

Polarized Mass or Polarized Few? Assessing the Parallel Rise of Survey Nonresponse and Measures of Polarization. Amnon Cavari and Guy Freedman. The Journal of Politics,

Tappin, Ben M., and Ryan McKay. 2018. “Moral Polarization and Out-party Hate in the US Political Context.” PsyArXiv. November 2.

Forecasting tournaments, epistemic humility and attitude depolarization. Barbara Mellers, PhilipTetlock, Hal R. Arkes. Cognition,

Does residential sorting explain geographic polarization? Gregory J. Martin & Steven W. Webster. Political Science Research and Methods,

Liberals and conservatives have mainly moved further apart on a wide variety of policy issues; the divergence is substantial quantitatively and in its plausible political impact: intra party moderation has become increasingly unlikely:

Peltzman, Sam, Polarizing Currents within Purple America (August 20, 2018). SSRN:

Does Having a Political Discussion Help or Hurt Intergroup Perceptions? Drawing Guidance From Social Identity Theory and the Contact Hypothesis. Robert M. Bond, Hillary C. Shulman, Michael Gilbert. Bond Vol 12 (2018),

All the interactions took the form of subjects rating stories offering ‘ammunition’ for their own side of the controversial issue as possessing greater intrinsic news importance:

Perceptions of newsworthiness are contaminated by a political usefulness bias. Harold Pashler, Gail Heriot. Royal Society Open Science,

When do we care about political neutrality? The hypocritical nature of reaction to political bias. Omer Yair, Raanan Sulitzeanu-Kenan. PLOS,

Democrats & Republicans were both more likely to believe news about the value-upholding behavior of their in-group or the value-undermining behavior of their out-group; Republicans were more likely to believe & want to share apolitical fake news:

Pereira, Andrea, and Jay Van Bavel. 2018. “Identity Concerns Drive Belief in Fake News.” PsyArXiv. September 11.

In self-judgment, the "best option illusion" leads to Dunning-Kruger (failure to recognize our own incompetence). In social judgment, it leads to the Cassandra quandary (failure to identify when another person’s competence exceeds our own): The best option illusion in self and social assessment. David Dunning. Self and Identity,

People are more inaccurate when forecasting their own future prospects than when forecasting others, in part the result of biased visual experience. People orient visual attention and resolve visual ambiguity in ways that support self-interests: "Visual experience in self and social judgment: How a biased majority claim a superior minority." Emily Balcetis & Stephanie A. Cardenas. Self and Identity,

Can we change our biased minds? Michael Gross. Current Biology, Volume 27, Issue 20, 23 October 2017, Pages R1089–R1091.
Summary: A simple test taken by millions of people reveals that virtually everybody has implicit biases that they are unaware of and that may clash with their explicit beliefs. From policing to scientific publishing, all activities that deal with people are at risk of making wrong decisions due to bias. Raising awareness is the first step towards improving the outcomes.

People believe that future others' preferences and beliefs will change to align with their own:
The Belief in a Favorable Future. Todd Rogers, Don Moore and Michael Norton. Psychological Science, Volume 28, issue 9, page(s): 1290-1301,

Kahan, Dan M. and Landrum, Asheley and Carpenter, Katie and Helft, Laura and Jamieson, Kathleen Hall, Science Curiosity and Political Information Processing (August 1, 2016). Advances in Political Psychology, Forthcoming; Yale Law & Economics Research Paper No. 561. Available at SSRN:
Abstract: This paper describes evidence suggesting that science curiosity counteracts politically biased information processing. This finding is in tension with two bodies of research. The first casts doubt on the existence of “curiosity” as a measurable disposition. The other suggests that individual differences in cognition related to science comprehension - of which science curiosity, if it exists, would presumably be one - do not mitigate politically biased information processing but instead aggravate it. The paper describes the scale-development strategy employed to overcome the problems associated with measuring science curiosity. It also reports data, observational and experimental, showing that science curiosity promotes open-minded engagement with information that is contrary to individuals’ political predispositions. We conclude by identifying a series of concrete research questions posed by these results.

Facebook news and (de)polarization: reinforcing spirals in the 2016 US election. Michael A. Beam, Myiah J. Hutchens & Jay D. Hmielowski. Information, Communication & Society,

The Partisan Brain: An Identity-Based Model of Political Belief. Jay J. Van Bavel, Andrea Pereira. Trends in Cognitive Sciences,

The Parties in our Heads: Misperceptions About Party Composition and Their Consequences. Douglas J. Ahler, Gaurav Sood. Aug 2017,

The echo chamber is overstated: the moderating effect of political interest and diverse media. Elizabeth Dubois & Grant Blank. Information, Communication & Society,

Processing political misinformation: comprehending the Trump phenomenon. Briony Swire, Adam J. Berinsky, Stephan Lewandowsky, Ullrich K. H. Ecker. Royal Society Open Science, published on-line March 01 2017. DOI: 10.1098/rsos.160802,

Competing cues: Older adults rely on knowledge in the face of fluency. By Brashier, Nadia M.; Umanath, Sharda; Cabeza, Roberto; Marsh, Elizabeth J. Psychology and Aging, Vol 32(4), Jun 2017, 331-337.

Stanley, M. L., Dougherty, A. M., Yang, B. W., Henne, P., & De Brigard, F. (2017). Reasons Probably Won’t Change Your Mind: The Role of Reasons in Revising Moral Decisions. Journal of Experimental Psychology: General.

Science Denial Across the Political Divide — Liberals and Conservatives Are Similarly Motivated to Deny Attitude-Inconsistent Science. Anthony N. Washburn, Linda J. Skitka. Social Psychological and Personality Science, 10.1177/1948550617731500.

Biased Policy Professionals. Sheheryar Banuri, Stefan Dercon, and Varun Gauri. World Bank Policy Research Working Paper 8113.

Dispelling the Myth: Training in Education or Neuroscience Decreases but Does Not Eliminate Beliefs in Neuromyths. Kelly Macdonald et al. Frontiers in Psychology, Aug 10 2017.

Individuals with greater science literacy and education have more polarized beliefs on controversial science topics. Caitlin Drummond and Baruch Fischhoff. Proceedings of the National Academy of Sciences, vol. 114 no. 36, pp 9587–9592, doi: 10.1073/pnas.1704882114,

Expert ability can actually impair the accuracy of expert perception when judging others' performance: Adaptation and fallibility in experts' judgments of novice performers. By Larson, J. S., & Billeter, D. M. (2017). Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(2), 271–288.

Public Perceptions of Partisan Selective Exposure. Perryman, Mallory R. The University of Wisconsin - Madison, ProQuest Dissertations Publishing, 2017. 10607943.

The Myth of Partisan Selective Exposure: A Portrait of the Online Political News Audience. Jacob L. Nelson, and James G. Webster. Social Media + Society,

Echo Chamber? What Echo Chamber? Reviewing the Evidence. Axel Bruns. Future of Journalism 2017 Conference.

Fake news and post-truth pronouncements in general and in early human development. Victor Grech. Early Human Development,

Consumption of fake news is a consequence, not a cause of their readers’ voting preferences. Kahan, Dan M., Misinformation and Identity-Protective Cognition (October 2, 2017). Social Science Research Network,

Twitter: While partisan opinion leaders are certainly polarized, centrist/non-political voices are much more likely to produce the most visible information; & there is little evidence of echo-chambers in consumption
Mukerjee, Subhayan, Kokil Jaidka, and Yphtach Lelkes. 2020. “The Ideological Landscape of Twitter: Comparing the Production Versus Consumption of Information on the Platform.” OSF Preprints. June 23.

Contrary to this prediction, we found that moderate and uncertain participants showed a nonreciprocal attraction towards extreme and confident individuals:
Zimmerman, Federico, Gerry Garbulsky, Dan Ariely, Mariano Sigman, and Joaquin Navajas. 2020. “The Nonreciprocal and Polarizing Nature of Interpersonal Attraction in Political Discussions.” PsyArXiv. August 21.

Updated after July 5 2021 with old papers not printed above, or papers newer than the original post here:

Politically partisan left-right online news echo chambers are real, but only a minority of approximately 5% of internet news users inhabit them; the continued popularity of mainstream outlets often preclude the formation of large partisan echo chambers:

How Many People Live in Politically Partisan Online News Echo Chambers in Different Countries? Richard Fletcher, Craig T. Robertson, Rasmus Kleis Nielsen. Journal of Quantitative Description: Digital Media, Vol. 1 (2021). Aug 4 2021.

Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don't Talk to Conservatives. Siqi Wu, Paul Resnick. arXiv Apr 12 2021.

Auditing Partisan Audience Bias within Google Search. Ronald E. Robertson et al. Proceedings of the ACM on Human-Computer Interaction - CSCW archive. Volume 2 Issue CSCW, November 2018, Article No. 148, doi: 10.1145/3274417.

Few people are actually trapped in filter bubbles. Why do they like to say that they are? Plus: Are your Google results really that different from your neighbor’s? Laura Hazard Owen. NiemanLab, Dec 07 2018.

Overall, research indicates that the risk of getting stuck in a filter bubble on intermediaries such as Google News, Apple News, Facebook, or Twitter is low and often exaggerated:

News recommender systems: a programmatic research review. Eliza Mitova et al. Annals of the International Communication Association, Nov 11 2022.

Echo chambers and filter bubble are largely just a figment of the minds of political pundits:

Echo chambers, filter bubbles, and polarisation: a literature review. Amy Ross Arguedas et al. Reuters Institute for the Study of Journalism, Jan 19 2022.


In the self-experienced (vs. other-experienced) jealousy condition, activity was greater, inter alia, in the fronto-striato-thalamo-frontal circuit, a network implicated in habit formation & obsessive-compulsive disorder

From 2019... The obsessions of the green-eyed monster: jealousy and the female brain. Nadine Steis et al. Sexual and Relationship Therapy, May 21 2019.

Abstract: The present brain-imaging study assessed neural correlates of romantic jealousy in women who had suffered real infidelity by their partner. We predicted to find activation across different brain structures associated with the processing of negative emotions and cognitive processes as well as obsessive-compulsive behavior. FMRI scans were administered while participants listened to descriptions of their own or another person’s experience of infidelity and jealousy, or to nonsense words. In the self-experienced (vs. other-experienced) jealousy condition, activity was greater in areas commonly associated with the interaction between different negative emotions (i.e., insula, anterior cingulate cortex, medial prefrontal cortex) such as fear, anger, sadness and cognitive processes like rumination. Enhanced activity was also found in the fronto-striato-thalamo-frontal circuit, a network implicated in habit formation and obsessive-compulsive disorder. Activation in the above networks was not enhanced when participants listened to other-experienced infidelity reports, as indicated by comparisons with the neutral condition. We discuss implications for the understanding and treatment of jealousy.

Keywords: Jealousy, brain imaging, fMRI, obsessive-compulsive disorder (OCD), infidelity

Fear of receiving compassion from others, and related fears, are potentially important factors in the persistence of depression, stress disorders, and eating disorders; they may play a role in anxiety and related difficulties

Scared of compassion: Fear of compassion in anxiety, mood, and non‐clinical groups. Olivia A. Merritt, Christine L. Purdon. British Journal of Clinical Psychology, Volume 59 Issue 3 (September 2020), Pages i-iv, 277-460.

Objectives: Fear of receiving compassion from others, expressing compassion to others, and being compassionate towards oneself have been identified as potentially important factors in the persistence of depression, stress disorders, and eating disorders. There is good reason to expect that these fears may play a role in anxiety and related difficulties, but there is little available information on the extent to which they are present and associated with symptom severity.
Methods: This study compared the severity of the three fears of compassion (receiving, expressing to others, and showing to oneself) in those with a principal diagnosis of depression (n = 34), obsessive–compulsive disorder (OCD; n = 27), social anxiety disorder (SAD; n = 91), generalized anxiety disorder (GAD, n = 43), and a control sample with no mental health difficulties (n = 212).
Results: Those with depression, OCD, SAD, and GAD exhibited greater fear of receiving compassion and fear of self‐compassion than controls, and the differences between anxious and control groups remained significant even when controlling for depressed mood. Whereas fears of compassion did not predict symptom severity over and above depressed mood in people with GAD, fear of receiving compassion uniquely predicted SAD symptom severity, and fear of expressing compassion for others uniquely predicted OCD symptom severity in those high on fear of self‐compassion.
Conclusions: Fear of compassion is higher in those with anxiety and related disorders than non‐anxious controls. Although further research is needed, clinicians may benefit from assessing fear of compassion and addressing it in treatment.
Practitioner points: Those with anxiety and related disorders may fear receiving compassion from others or expressing compassion for themselves, even when controlling for depression. It may be informative to assess for fear of compassion and incorporate discussions about these fears into treatment, as these fears may interfere with treatment progress.

Seeing the Whole Picture? Avoided Negative Affect and Processing of Others’ Suffering

Seeing the Whole Picture? Avoided Negative Affect and Processing of Others’ Suffering. Birgit Koopmann-Holm et al. Personality and Social Psychology Bulletin, February 11, 2020.

Abstract: Noticing someone’s pain is the first step to a compassionate response. While past research suggests that the degree to which people want to avoid feeling negative (“avoided negative affect”; ANA) shapes how people respond to someone’s suffering, the present research investigates whether ANA also predicts how people process others’ suffering. In two studies, using complex photographs containing negative aspects (i.e., suffering), we found that the higher people’s ANA, the fewer details of negative aspects they correctly recognized, and the fewer negative words they used in their image descriptions. However, when asked to process negative content, the higher people’s ANA, the more negatively they rated that content. In Study 3, we report cultural differences in people’s sensitivity to notice suffering in an ambiguous image. ANA mediated these cultural differences. Implications for research on compassion are discussed.

Keywords: emotion, avoided negative affect, information processing, suffering, culture

(How) Do You Regret Killing One to Save Five? Affective and Cognitive Regret Differ After Utilitarian and Deontological Decisions

(How) Do You Regret Killing One to Save Five? Affective and Cognitive Regret Differ After Utilitarian and Deontological Decisions. Jacob Goldstein-Greenwood et al. Personality and Social Psychology Bulletin, January 28, 2020.

Abstract: Sacrificial moral dilemmas, in which opting to kill one person will save multiple others, are definitionally suboptimal: Someone dies either way. Decision-makers, then, may experience regret about these decisions. Past research distinguishes affective regret, negative feelings about a decision, from cognitive regret, thoughts about how a decision might have gone differently. Classic dual-process models of moral judgment suggest that affective processing drives characteristically deontological decisions to reject outcome-maximizing harm, whereas cognitive deliberation drives characteristically utilitarian decisions to endorse outcome-maximizing harm. Consistent with this model, we found that people who made or imagined making sacrificial utilitarian judgments reliably expressed relatively more affective regret and sometimes expressed relatively less cognitive regret than those who made or imagined making deontological dilemma judgments. In other words, people who endorsed causing harm to save lives generally felt more distressed about their decision, yet less inclined to change it, than people who rejected outcome-maximizing harm.

Keywords: moral dilemmas, regret, affective regret, cognitive regret, dual-process model

In cognitive diagnosis models, the condensation rule reflects how latent attributes influence individuals’ observed item responses; in practice, multiple condensation rules may be involved in an item simultaneously

Zhan, Peida. 2020. “Deterministic-inputs, Noisy Mixed Modeling for Identifying Coexisting Condensation Rules.” PsyArXiv. February 12. doi:10.31234/

Abstract: In cognitive diagnosis models (CDMs), the condensation rule reflects how latent attributes influence individuals’ observed item responses. In practice, multiple condensation rules may be involved in an item simultaneously, which indicates that the contribution of required attributes to the correct item response probability follows multiple condensation rules with different proportions. To consider the coexisting condensation rules while keeping the interpretability of model parameters, this study proposed the deterministic-inputs, noisy mixed (DINMix) model. Two simulation studies were conducted to evaluate the psychometric properties of the proposed model. The results indicate that the model parameters for the DINMix model can be well recovered, and the DINMix model can accurately identify coexisting condensation rules. An empirical example was also analyzed to illustrate the applicability and advantages of the proposed model.

6. Summary and Discussion
The condensation rule describes the logical relationship between the required attributes and the item response. When an item contains coexisting condensation rules, it means that the contribution of required attributes to the correct item response probability follows multiple condensation rules with different proportions. Coexisting condensation rules reflect the complexity of cognitive processes in problem-solving. To take into account coexisting condensation rules while keeping the interpretability of model parameters, this study proposed the DINMix model. Two simulation studies were conducted to evaluate the psychometric properties of the proposed model. The simulation results indicate that (a) the model parameters for the DINMix model can be well recovered, especially in the conditions with a larger sample, longer test length, and higher item quality; (b) the DINMix model can adaptively and accurately identify coexisting condensation rules, either existing simultaneously in an item or existing separately in multiple items. An empirical example was also analyzed to illustrate the applicability and advantages of the proposed model.

As aforementioned, the DINMix model can be viewed as a constraint model from the GDINA model after some parameter transformations. Thus, the number of item parameters of the DINMix model is larger than that of the reduced models but smaller than that of the general models. For example, in the simulated condition in simulation Study 2, there were 60, 60, 60, 100, and 140 items parameters for the DINA, DINO, DINR, DINMix, and GDINA models, respectively. To explore the differences between the performance of the DINMix and GDINA models, we also used the GDINA model to conduct a simple analysis of the data in simulation Study 2, based on the GDINA package (Ma & de la Torre, 2020) in R software. The results (see Tables S4 and S5 in online supplements) indicate that the performance of the DINMix and GDINA models was almost identical in the recovery of attributes and item parameters. Specifically, the DINMix and GDINA models have almost the same diagnostic capabilities, but the former is more concise and easier to be interpreted.

The work represented in this article is an initial attempt to simultaneously consider multiple condensation rules in a single CDM. Despite promising results, some limitations still exist. First, the utilized model framework (see Equations 1 and 5) models the aberrant responses at the item level. However, in practice, such aberrant responses may occur at the attribute rather than item level, such as the noisy inputs, deterministic, ‘and’ gate model (Junker & Sijtsma, 2001). Ways to incorporate attribute-level aberrant responses into the proposed model are worthy of further research, as Equation 11 in de la Torre (2011) seems to give us a reference. Second, within-item characteristic dependency (Zhan, Jiao, Liao et al., 2019), which means that the dependency exists between the guessing and slip parameters within an item, was not considered in the proposed model. It can be incorporated into the proposed model to increase the estimation accuracy of the item parameters in a future study. Third, only the dichotomous scoring item and dichotomous attribute were modeled in the proposed model. It would be meaningful and practical to extend the current model to consider polytomous scoring items (e.g., Ma & de la Torre, 2016) and polytomous attributes (e.g., Zhan et al., 2020). Fourth, in recent years, some studies have focused on the Q-matrix validation or estimation (Chen et al., 2018; de la Torre & Chiu, 2016) and the multiple strategies for problem-solving (Ma & Guo, 2019), which are not covered in current study. Fifth, notably, the generalizability of the findings of this study is dependent upon the limitations of the design of the simulation studies, such as a fixed number of attributes and assuming the Q-matrix is correct. To further generalize these findings, a wider range of simulated conditions should be considered in future studies.