Artificial intelligence and illusions of understanding in scientific research.

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists' visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community's ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.

The dysfunctional immune response in renal cell carcinoma correlates with changes in the metabolic landscape of ccRCC during disease progression.

Renal cell carcinoma is an immunogenic tumour with a prominent dysfunctional immune cell infiltrate, unable to control tumour growth. Although tyrosine kinase inhibitors and immunotherapy have improved the outlook for some patients, many individuals are non-responders or relapse despite treatment. The hostile metabolic environment in RCC affects the ability of T-cells to maintain their own metabolic programme constraining T-cell immunity in RCC. We investigated the phenotype, function and metabolic capability of RCC TILs correlating this with clinicopathological features of the tumour and metabolic environment at the different disease stages. Flow cytometric analysis of freshly isolated TILs showed the emergence of exhausted T-cells in advanced disease based on their PD-1high and CD39 expression and reduced production of inflammatory cytokines upon in vitro stimulation. Exhausted T-cells from advanced stage disease also displayed an overall phenotype of metabolic insufficiency, characterized by mitochondrial alterations and defects in glucose uptake. Nanostring nCounter cancer metabolism assay on RNA obtained from 30 ccRCC cases revealed significant over-expression of metabolic genes even at early stage disease (pT1-2), while at pT3-4 and the locally advanced thrombi stages, there was an overall decrease in differentially expressed metabolic genes. Notably, the gene PPARGC1A was the most significantly down-regulated gene from pT1-2 to pT3-4 RCC which correlated with loss of mitochondrial function in tumour-infiltrating T-cells evident at this tumour stage. Down-regulation of PPARGC1A into stage pT3-4 may be the 'tipping-point' in RCC disease progression, modulating immune activity in ccRCC and potentially reducing the efficacy of immunotherapies in RCC and poorer patient outcomes.

Vaccine Nationalism Counterintuitively Erodes Public Trust in Leaders.

Global access to resources like vaccines is key for containing the spread of infectious diseases. However, wealthy countries often pursue nationalistic policies, stockpiling doses rather than redistributing them globally. One possible motivation behind vaccine nationalism is a belief among policymakers that citizens will mistrust leaders who prioritize global needs over domestic protection. In seven experiments (total N = 4,215 adults), we demonstrate that such concerns are misplaced: Nationally representative samples across multiple countries with large vaccine surpluses (Australia, Canada, United Kingdom, and United States) trusted redistributive leaders more than nationalistic leaders-even the more nationalistic participants. This preference generalized across different diseases and manifested in both self-reported and behavioral measures of trust. Professional civil servants, however, had the opposite intuition and predicted higher trust in nationalistic leaders, and a nonexpert sample also failed to predict higher trust in redistributive leaders. We discuss how policymakers' inaccurate intuitions might originate from overestimating others' self-interest.

Changing the culture of peer review for a more inclusive and equitable psychological science.

Peer review is a core component of scientific practice. Although peer review ideally improves research and promotes rigor, it also has consequences for what types of research are published and cited and who wants to (and is able to) advance in research-focused careers. Despite these consequences, few reviewers or editors receive training or oversight to ensure their feedback is helpful, professional, and culturally sensitive. Here, we critically examine the peer-review system in psychology and neuroscience at multiple levels, from ideas to institutions, interactions, and individuals. We highlight initiatives that aim to change the normative negativity of peer review and provide authors with constructive, actionable feedback that is sensitive to diverse identities, methods, topics, and environments. We conclude with a call to action for how individuals, groups, and organizations can improve the culture of peer review. We provide examples of how changes in the peer-review system can be made with an eye to diversity (increasing the range of identities and experiences constituting the field), equity (fair processes and outcomes across groups), and inclusion (experiences that promote belonging across groups). These changes can improve scientists' experience of peer review, promote diverse perspectives and identities, and enhance the quality and impact of science. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Algorithm-mediated social learning in online social networks.

Human social learning is increasingly occurring on online social platforms, such as Twitter, Facebook, and TikTok. On these platforms, algorithms exploit existing social-learning biases (i.e., towards prestigious, ingroup, moral, and emotional information, or 'PRIME' information) to sustain users' attention and maximize engagement. Here, we synthesize emerging insights into 'algorithm-mediated social learning' and propose a framework that examines its consequences in terms of functional misalignment. We suggest that, when social-learning biases are exploited by algorithms, PRIME information becomes amplified via human-algorithm interactions in the digital social environment in ways that cause social misperceptions and conflict, and spread misinformation. We discuss solutions for reducing functional misalignment, including algorithms promoting bounded diversification and increasing transparency of algorithmic amplification.

Overperception of moral outrage in online social networks inflates beliefs about intergroup hostility.

As individuals and political leaders increasingly interact in online social networks, it is important to understand the dynamics of emotion perception online. Here, we propose that social media users overperceive levels of moral outrage felt by individuals and groups, inflating beliefs about intergroup hostility. Using a Twitter field survey, we measured authors' moral outrage in real time and compared authors' reports to observers' judgements of the authors' moral outrage. We find that observers systematically overperceive moral outrage in authors, inferring more intense moral outrage experiences from messages than the authors of those messages actually reported. This effect was stronger in participants who spent more time on social media to learn about politics. Preregistered confirmatory behavioural experiments found that overperception of individuals' moral outrage causes overperception of collective moral outrage and inflates beliefs about hostile communication norms, group affective polarization and ideological extremity. Together, these results highlight how individual-level overperceptions of online moral outrage produce collective overperceptions that have the potential to warp our social knowledge of moral and political attitudes.

Neural representations of vicarious rewards are linked to interoception and prosocial behaviour.

Every day we constantly observe other people receiving rewards. Theoretical accounts posit that vicarious reward processing might be linked to people's sensitivity to internal body states (interoception) and facilitates a tendency to act prosocially. However, the neural processes underlying the links between vicarious reward processing, interoception, and prosocial behaviour are poorly understood. Previous research has linked vicarious reward processing to the anterior cingulate gyrus (ACCg) and the anterior insula (AI). Can we predict someone's propensity to be prosocial or to be aware of interoceptive signals from variability in how the ACCg and AI process rewards? Here, participants monitored rewards being delivered to themselves or a stranger during functional magnetic resonance imaging. Later, they performed a task measuring their willingness to exert effort to obtain rewards for others, and a task measuring their propensity to be aware and use interoceptive respiratory signals. Using multivariate similarity analysis, we show that people's willingness to be prosocial is predicted by greater similarity between self and other representations in the ACCg. Moreover, greater dissimilarity in self-other representations in the AI is linked to interoceptive propensity. These findings highlight that vicarious reward is linked to bodily signals in AI, and foster prosocial tendencies through the ACCg.

A computational account of how individuals resolve the dilemma of dirty money.

Money can be tainted when it is associated with direct or indirect harm to others. Deciding whether to accept "dirty money" poses a dilemma because money can be used to help others, but accepting dirty money has moral costs. How people resolve the dilemma of dirty money remains unknown. One theory casts the dilemma as a valuation conflict that can be resolved by integrating the costs and benefits of accepting dirty money. Here, we use behavioral experiments and computational modeling to test the valuation conflict account and unveil the cognitive computations employed when deciding whether to accept or reject morally tainted cash. In Study 1, British participants decided whether to accept "dirty" money obtained by inflicting electric shocks on another person (versus "clean" money obtained by shocking oneself). Computational models showed that the source of the money (dirty versus clean) impacted decisions by shifting the relative valuation of the money's positive and negative attributes, rather than imposing a uniform bias on decision-making. Studies 2 and 3 replicate this finding and show that participants were more willing to accept dirty money when the money was directed towards a good cause, and observers judged such decisions to be more praiseworthy than accepting dirty money for one's own profit. Our findings suggest that dirty money can be psychologically "laundered" through charitable activities and have implications for understanding and preventing the social norms that can justify corrupt behavior.

Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence.

As Artificial Intelligence (AI) proliferates across important social institutions, many of the most powerful AI systems available are difficult to interpret for end-users and engineers alike. Here, we sought to characterize public attitudes towards AI interpretability. Across seven studies (N = 2475), we demonstrate robust and positive attitudes towards interpretable AI among non-experts that generalize across a variety of real-world applications and follow predictable patterns. Participants value interpretability positively across different levels of AI autonomy and accuracy, and rate interpretability as more important for AI decisions involving high stakes and scarce resources. Crucially, when AI interpretability trades off against AI accuracy, participants prioritize accuracy over interpretability under the same conditions driving positive attitudes towards interpretability in the first place: amidst high stakes and scarce resources. These attitudes could drive a proliferation of AI systems making high-impact ethical decisions that are difficult to explain and understand.

Distinguishing self-involving from self-serving choices in framing effects.

We distinguish two types of cases that have potential to generate quasi-cyclical preferences: self-involving choices where an agent oscillates between first- and third-person perspectives that conflict regarding their life-changing implications, and self-serving choices where frame-based reasoning can be "first-personally rational" yet "third-personally irrational." We argue that the distinction between these types of cases deserves more attention in Bermúdez's account.

Prosocial correlates of transformative experiences at secular multi-day mass gatherings.

Humans have long sought experiences that transcend or change their sense of self. By weakening boundaries between the self and others, such transformative experiences may lead to enduring changes in moral orientation. Here we investigated the psychological nature and prosocial correlates of transformative experiences by studying participants before (n = 600), during (n = 1217), 0-4 weeks after (n = 1866), and 6 months after (n = 710) they attended a variety of secular, multi-day mass gatherings in the US and UK. Observations at 6 field studies and 22 online followup studies spanning 5 years showed that self-reported transformative experiences at mass gatherings were common, increased over time, and were characterized by feelings of universal connectedness and new perceptions of others. Participants' circle of moral regard expanded with every passing day onsite-an effect partially mediated by transformative experience and feelings of universal connectedness. Generosity was remarkably high across sites but did not change over time. Immediately and 6 months following event attendance, self-reported transformative experience persisted and predicted both generosity (directly) and moral expansion (indirectly). These findings highlight the prosocial qualities of transformative experiences at secular mass gatherings and suggest such experiences may be associated with lasting changes in moral orientation.

Computational ethics.

Technological advances are enabling roles for machines that present novel ethical challenges. The study of 'AI ethics' has emerged to confront these challenges, and connects perspectives from philosophy, computer science, law, and economics. Less represented in these interdisciplinary efforts is the perspective of cognitive science. We propose a framework - computational ethics - that specifies how the ethical challenges of AI can be partially addressed by incorporating the study of human moral decision-making. The driver of this framework is a computational version of reflective equilibrium (RE), an approach that seeks coherence between considered judgments and governing principles. The framework has two goals: (i) to inform the engineering of ethical AI systems, and (ii) to characterize human moral judgment and decision-making in computational terms. Working jointly towards these two goals will create the opportunity to integrate diverse research questions, bring together multiple academic communities, uncover new interdisciplinary research topics, and shed light on centuries-old philosophical questions.

Psychological Selfishness.

Selfishness is central to many theories of human morality, yet its psychological nature remains largely overlooked. Psychologists often draw on classical conceptions of selfishness from evolutionary biology (i.e., selfish gene theory), economics (i.e., rational self-interest), and philosophy (i.e., psychological egoism), but such characterizations offer limited insight into the psychology of selfishness. To address this gap, we propose a novel framework in which selfishness is recast as a psychological construction. From this view, selfishness is perceived in ourselves and others when we detect a situation-specific desire to benefit the self that disregards others' desires and prevailing social expectations for the situation. We argue that detecting and deterring such psychological selfishness in both oneself and others is crucial in social life-facilitating the maintenance of social cohesion and close relationships. In addition, we show how using this psychological framework offers a richer understanding of the nature of human social behavior. Delineating a psychological construct of selfishness can promote coherence in interdisciplinary research on selfishness and provide insights for interventions to prevent or remediate the negative effects of selfishness.

The impact of a fellow on a regional robotic-assisted partial nephrectomy service.

INTRODUCTION: Training a fellow has a cost in time and effort for the surgeon and their team. Their relative inexperience may also negatively affect the patient. The aim of this study was to determine and quantify the impact of a fellow on a regional robotic-assisted partial nephrectomy service and on perioperative outcomes. MATERIALS AND METHODS: We reviewed the prospectively collected data for 522 patients who had undergone robotic-assisted partial nephrectomy since 2015 during the tenure of six fellows. Perioperative outcomes for three groups were compared: group A (no fellow participation), group B (some participation) and group C (fellow completed entire operation). We also reviewed progression over 12 months. RESULTS: Demographics were similar in all groups apart from the percentage of men, which was lower in group C (p < 0.05). Operative time was 27 minutes longer for group B (p < 0.001). Warm ischaemia time was significantly shorter for group A but the difference was only four minutes (p < 0.001). Length of stay was slightly shorter for group C compared with the other groups (p < 0.01). Trifecta achievement was greatest for group A (p < 0.001). There were no perioperative deaths in any group and positive margins, complications and readmissions were low and similar in all groups. Towards the end of their fellowship, fellows performed more operations independently. CONCLUSION: There is a measurable, but small, negative impact of a fellow on a robotic-assisted partial nephrectomy service, which reduces with experience. With appropriate supervision and patient selection, a fellow can be taught robotic-assisted partial nephrectomy without affecting patient safety or treatment outcome.

Psychedelic science in post-COVID-19 psychiatry.

The medium- to long-term consequences of COVID-19 are not yet known, though an increase in mental health problems are predicted. Multidisciplinary strategies across socio-economic and psychological levels may be needed to mitigate the mental health burden of COVID-19. Preliminary evidence from the rapidly progressing field of psychedelic science shows that psilocybin therapy offers a promising transdiagnostic treatment strategy for a range of disorders with restricted and maladaptive habitual patterns of cognition and behaviour, notably depression, addiction and obsessive compulsive disorder. The COMPASS Pathways (COMPASS) phase 2b double-blind trial of psilocybin therapy in antidepressant-free, treatment-resistant depression (TRD) is underway to determine the safety, efficacy and optimal dose of psilocybin. Results from the Imperial College London Psilodep-RCT comparing the efficacy and mechanisms of action of psilocybin therapy to the selective serotonin reuptake inhibitor (SSRI) escitalopram will soon be published. However, the efficacy and safety of psilocybin therapy in conjunction with SSRIs in TRD is not yet known. An additional COMPASS study, with a centre in Dublin, will begin to address this question, with potential implications for the future delivery of psilocybin therapy. While at a relatively early stage of clinical development, and notwithstanding the immense challenges of COVID-19, psilocybin therapy has the potential to play an important therapeutic role for various psychiatric disorders in post-COVID-19 clinical psychiatry.

The MAD Model of Moral Contagion: The Role of Motivation, Attention, and Design in the Spread of Moralized Content Online.

With more than 3 billion users, online social networks represent an important venue for moral and political discourse and have been used to organize political revolutions, influence elections, and raise awareness of social issues. These examples rely on a common process to be effective: the ability to engage users and spread moralized content through online networks. Here, we review evidence that expressions of moral emotion play an important role in the spread of moralized content (a phenomenon we call moral contagion). Next, we propose a psychological model called the motivation, attention, and design (MAD) model to explain moral contagion. The MAD model posits that people have group-identity-based motivations to share moral-emotional content, that such content is especially likely to capture our attention, and that the design of social-media platforms amplifies our natural motivational and cognitive tendencies to spread such content. We review each component of the model (as well as interactions between components) and raise several novel, testable hypotheses that can spark progress on the scientific investigation of civic engagement and activism, political polarization, propaganda and disinformation, and other moralized behaviors in the digital age.

Adding diffusion ordered NMR spectroscopy (DOSY) to the arsenal for characterizing paramagnetic complexes.

Herein we report the development of diffusion ordered NMR spectroscopy (DOSY) for its use to characterize metal complexes containing paramagnetic first row transition metal elements. This technique is capable of assessing the purity and speciation of paramagnetic complexes, and also provides a convenient method to provide qualitative and sometimes quantitative molecular weight data.