Social media really is making us more morally outraged

Charlotte Hu

Popular Science

updated 13 AUG 21

Here is an excerpt:

The most interesting finding for the team was that some of the more politically moderate people tended to be the ones who are influenced by social feedback the most. “What we know about social media now is that a lot of the political content we see is actually produced by a minority of users—the more extreme users,” Brady says. 

One question that’s come out of this study is: what are the conditions under which moderate users either become more socially influenced to conform to a more extreme tone, as opposed to just get turned off by it and leave the platform, or don’t engage any more? “I think both of these potential directions are important because they both imply that the average tone of conversation on the platform will get increasingly extreme.”

Social media can exploit base human psychology

Moral outrage is a natural tendency. “It’s very deeply ingrained in humans, it happens online, offline, everyone, but there is a sense that the design of social media can amplify in certain contexts this natural tendency we have,” Brady says. But moral outrage is not always bad. It can have important functions, and therefore, “it’s not a clear-cut answer that we want to reduce moral outrage.”

“There’s a lot of data now that suggest that negative content does tend to draw in more engagement on the average than positive content,” says Brady. “That being said, there are lots of contexts where positive content does draw engagement. So it’s definitely not a universal law.” 

It’s likely that multiple factors are fueling this trend. People could be attracted to posts that are more popular or go viral on social media, and past studies have shown that we want to know what the gossip is and what people are doing wrong. But the more people engage with these types of posts, the more platforms push them to us. 

Jonathan Nagler, a co-director of NYU Center for Social Media and Politics, who was not involved in the study, says it’s not shocking that moral outrage gets rewarded and amplified on social media. 

The info is here.

Meta-analysis of human prediction error for incentives, perception, cognition, and action

Corlett, P.R., Mollick, J.A. & Kober, H.

Neuropsychopharmacol. (2022). 

https://doi.org/10.1038/s41386-021-01264-3

Abstract

Prediction errors (PEs) are a keystone for computational neuroscience. Their association with midbrain neural firing has been confirmed across species and has inspired the construction of artificial intelligence that can outperform humans. However, there is still much to learn. Here, we leverage the wealth of human PE data acquired in the functional neuroimaging setting in service of a deeper understanding, using an MKDA (multi-level kernel-based density) meta-analysis. Studies were identified with Google Scholar, and we included studies with healthy adult participants that reported activation coordinates corresponding to PEs published between 1999–2018. Across 264 PE studies that have focused on reward, punishment, action, cognition, and perception, consistent with domain-general theoretical models of prediction error we found midbrain PE signals during cognitive and reward learning tasks, and an insula PE signal for perceptual, social, cognitive, and reward prediction errors. There was evidence for domain-specific error signals––in the visual hierarchy during visual perception, and the dorsomedial prefrontal cortex during social inference. We assessed bias following prior neuroimaging meta-analyses and used family-wise error correction for multiple comparisons. This organization of computation by region will be invaluable in building and testing mechanistic models of cognitive function and dysfunction in machines, humans, and other animals. Limitations include small sample sizes and ROI masking in some included studies, which we addressed by weighting each study by sample size, and directly comparing whole brain vs. ROI-based results.

Discussion

There appeared to be regionally compartmentalized PEs for primary and secondary rewards. Primary rewards elicited PEs in the dorsal striatum and amygdala, while secondary reward PEs were in ventral striatum. This is consistent with the representational transition that occurs with learning. We also found separable PEs for valence domains: caudal regions of the caudate-putamen are involved in the learning of safety signals and avoidance learning, more anterior striatum is selective for rewards, while more posterior is selective for losses. We found posterior midbrain aversive PE, consistent with preclinical findings that dopamine neurons––which respond to negative valence––are located more posteriorly in the midbrain and project to medial prefrontal regions. Additionally, we found both appetitive and aversive PEs in the amygdala, consistent with animal studies. The presence of both appetitive and aversive PE signals in the amygdala is consistent with its expanding role regulating learning based on surprise and uncertainty rather than fear per se. 

Perhaps conspicuous in its absence, given preclinical work, is the hippocampus, which is often held to be a nexus for reward PE, memory PE, and perceptual PE. This may be because the hippocampus is constantly and commonly engaged throughout task performance. Its PEs may not be resolved by the sluggish BOLD response, which is based on local field potentials and may represent the projections into a region (and therefore the striatal PE signals we observed may be the culmination of the processing in CA1, CA3, and subiculum). Furthermore, we have only recently been able to image subfields of the hippocampus (with higher field strengths and more rapid sequences); as higher resolution PE papers accrue we will revisit the meta-analysis of PEs.

The research is here.

On the ethics of algorithmic decision-making in healthcare

Grote T, Berens P
Journal of Medical Ethics 
2020;46:205-211.

Abstract

In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare.

From the Conclusion

In this paper, we aimed at examining which opportunities and pitfalls machine learning potentially provides to enhance of medical decision-making on epistemic and ethical grounds. As should have become clear, enhancing medical decision-making by deferring to machine learning algorithms requires trade-offs at different levels. Clinicians, or their respective healthcare institutions, are facing a dilemma: while there is plenty of evidence of machine learning algorithms outsmarting their human counterparts, their deployment comes at the costs of high degrees of uncertainty. On epistemic grounds, relevant uncertainty promotes risk-averse decision-making among clinicians, which then might lead to impoverished medical diagnosis. From an ethical perspective, deferring to machine learning algorithms blurs the attribution of accountability and imposes health risks to patients. Furthermore, the deployment of machine learning might also foster a shift of norms within healthcare. It needs to be pointed out, however, that none of the issues we discussed presents a knockout argument against deploying machine learning in medicine, and our article is not intended this way at all. On the contrary, we are convinced that machine learning provides plenty of opportunities to enhance decision-making in medicine.

The article is here.

Information Processing Biases in the Brain: Implications for Decision-Making and Self-Governance

Sali, A.W., Anderson, B.A. & Courtney, S.M.
Neuroethics (2018) 11: 259.
https://doi.org/10.1007/s12152-016-9251-1

Abstract

To make behavioral choices that are in line with our goals and our moral beliefs, we need to gather and consider information about our current situation. Most information present in our environment is not relevant to the choices we need or would want to make and thus could interfere with our ability to behave in ways that reflect our underlying values. Certain sources of information could even lead us to make choices we later regret, and thus it would be beneficial to be able to ignore that information. Our ability to exert successful self-governance depends on our ability to attend to sources of information that we deem important to our decision-making processes. We generally assume that, at any moment, we have the ability to choose what we pay attention to. However, recent research indicates that what we pay attention to is influenced by our prior experiences, including reward history and past successes and failures, even when we are not aware of this history. Even momentary distractions can cause us to miss or discount information that should have a greater influence on our decisions given our values. Such biases in attention thus raise questions about the degree to which the choices that we make may be poorly informed and not truly reflect our ability to otherwise exert self-governance.

Here is part of the Conclusion:

In order to consistently make decisions that reflect our goals and values, we need to gather the information necessary to guide these decisions, and ignore information that is irrelevant. Although the momentary acquisition of irrelevant information will not likely change our goals, biases in attentional selection may still profoundly influence behavioral outcomes, tipping the balance between competing options when faced with a single goal (e.g., save the least competent swimmer) or between simultaneously competing goals (e.g., relieve drug craving and withdrawal symptoms vs. maintain abstinence). An important component of self-governance might, therefore, be the ability to exert control over how we represent our world as we consider different potential courses of action.