Abstract cognitive maps of social network structure aid adaptive inference.

Social navigation-such as anticipating where gossip may spread, or identifying which acquaintances can help land a job-relies on knowing how people are connected within their larger social communities. Problematically, for most social networks, the space of possible relationships is too vast to observe and memorize. Indeed, people's knowledge of these social relations is well known to be biased and error-prone. Here, we reveal that these biased representations reflect a fundamental computation that abstracts over individual relationships to enable principled inferences about unseen relationships. We propose a theory of network representation that explains how people learn inferential cognitive maps of social relations from direct observation, what kinds of knowledge structures emerge as a consequence, and why it can be beneficial to encode systematic biases into social cognitive maps. Leveraging simulations, laboratory experiments, and "field data" from a real-world network, we find that people abstract observations of direct relations (e.g., friends) into inferences of multistep relations (e.g., friends-of-friends). This multistep abstraction mechanism enables people to discover and represent complex social network structure, affording adaptive inferences across a variety of contexts, including friendship, trust, and advice-giving. Moreover, this multistep abstraction mechanism unifies a variety of otherwise puzzling empirical observations about social behavior. Our proposal generalizes the theory of cognitive maps to the fundamental computational problem of social inference, presenting a powerful framework for understanding the workings of a predictive mind operating within a complex social world.

Emotion prediction errors guide socially adaptive behaviour.

People make decisions based on deviations from expected outcomes, known as prediction errors. Past work has focused on reward prediction errors, largely ignoring violations of expected emotional experiences-emotion prediction errors. We leverage a method to measure real-time fluctuations in emotion as people decide to punish or forgive others. Across four studies (N = 1,016), we reveal that emotion and reward prediction errors have distinguishable contributions to choice, such that emotion prediction errors exert the strongest impact during decision-making. We additionally find that a choice to punish or forgive can be decoded in less than a second from an evolving emotional response, suggesting that emotions swiftly influence choice. Finally, individuals reporting significant levels of depression exhibit selective impairments in using emotion-but not reward-prediction errors. Evidence for emotion prediction errors potently guiding social behaviours challenge standard decision-making models that have focused solely on reward.

Cognitive maps of social features enable flexible inference in social networks.

In order to navigate a complex web of relationships, an individual must learn and represent the connections between people in a social network. However, the sheer size and complexity of the social world makes it impossible to acquire firsthand knowledge of all relations within a network, suggesting that people must make inferences about unobserved relationships to fill in the gaps. Across three studies (n = 328), we show that people can encode information about social features (e.g., hobbies, clubs) and subsequently deploy this knowledge to infer the existence of unobserved friendships in the network. Using computational models, we test various feature-based mechanisms that could support such inferences. We find that people's ability to successfully generalize depends on two representational strategies: a simple but inflexible similarity heuristic that leverages homophily, and a complex but flexible cognitive map that encodes the statistical relationships between social features and friendships. Together, our studies reveal that people can build cognitive maps encoding arbitrary patterns of latent relations in many abstract feature spaces, allowing social networks to be represented in a flexible format. Moreover, these findings shed light on open questions across disciplines about how people learn and represent social networks and may have implications for generating more human-like link prediction in machine learning algorithms.

Crowdsourcing punishment: Individuals reference group preferences to inform their own punitive decisions.

Justice systems delegate punishment decisions to groups in the belief that the aggregation of individuals' preferences facilitates judiciousness. However, group dynamics may also lead individuals to relinquish moral responsibility by conforming to the majority's preference for punishment. Across five experiments (N = 399), we find Victims and Jurors tasked with restoring justice become increasingly punitive (by as much as 40%) as groups express a desire to punish, with every additional punisher augmenting an individual's punishment rates. This influence is so potent that knowing about a past group's preference continues swaying decisions even when they cannot affect present outcomes. Using computational models of decision-making, we test long-standing theories of how groups influence choice. We find groups induce conformity by making individuals less cautious and more impulsive, and by amplifying the value of punishment. However, compared to Victims, Jurors are more sensitive to moral violation severity and less readily swayed by the group. Conformity to a group's punitive preference also extends to weightier moral violations such as assault and theft. Our results demonstrate that groups can powerfully shift an individual's punitive preference across a variety of contexts, while additionally revealing the cognitive mechanisms by which social influence alters moral values.

Norms and the Flexibility of Moral Action.

A complex web of social and moral norms governs many everyday human behaviors, acting as the glue for social harmony. The existence of moral norms helps elucidate the psychological motivations underlying a wide variety of seemingly puzzling behavior, including why humans help or trust total strangers. In this review, we examine four widespread moral norms: Fairness, altruism, trust, and cooperation, and consider how a single social instrument-reciprocity-underpins compliance to these norms. Using a game theoretic framework, we examine how both context and emotions moderate moral standards, and by extension, moral behavior. We additionally discuss how a mechanism of reciprocity facilitates the adherence to, and enforcement of, these moral norms through a core network of brain regions involved in processing reward. In contrast, violating this set of moral norms elicits neural activation in regions involved in resolving decision conflict and exerting cognitive control. Finally, we review how a reinforcement mechanism likely governs learning about morally normative behavior. Together, this review aims to explain how moral norms are deployed in ways that facilitate flexible moral choices.

Anti-Inflammatory Effects of Curvularin-Type Metabolites from a Marine-Derived Fungal Strain Penicillium sp. SF-5859 in Lipopolysaccharide-Induced RAW264.7 Macrophages.

Chemical study on the extract of a marine-derived fungal strain Penicillium sp. SF-5859 yielded a new curvularin derivative (1), along with eight known curvularin-type polyketides (2-9). The structures of these metabolites (1-9) were established by comprehensive spectroscopic analyses, including 1D and 2D nuclear magnetic resonance (NMR) spectroscopy, and mass spectrometry (MS). In vitro anti-inflammatory effects of these metabolites were evaluated in lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages. Among these metabolites, 3-9 were shown to strongly inhibit LPS-induced overproduction of nitric oxide (NO) and prostaglandin E2 (PGE2) with IC50 values ranging from 1.9 µM to 18.1 µM, and from 2.8 µM to 18.7 µM, respectively. In the further evaluation of signal pathways involved in these effects, the most active compound, (10E,15S)-10,11-dehydrocurvularin (8) attenuated the expression of inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2) in LPS-stimulated RAW264.7 macrophages. Furthermore, compound 8 was shown to suppress the upregulation of pro-inflammatory mediators and cytokines via the inhibition of the nuclear factor-κB (NF-κB) signaling pathway, but not through the mitogen-activated protein kinase (MAPK) pathway. Based on the comparisons of the different magnitude of the anti-inflammatory effects of these structurally-related metabolites, it was suggested that the opening of the 12-membered lactone ring in curvularin-type metabolites and blocking the phenol functionality led to the significant decrease in their anti-inflammatory activity.

Penicillospirone from a marine isolate of Penicillium sp. (SF-5292) with anti-inflammatory activity.

Chemical investigation of the EtOAc extract of a marine-derived fungal isolate Penicillium sp. SF-5292 yielded a new polyketide-type metabolite, penicillospirone (1). The structure of 1 was determined by analysis of spectroscopic data such as 1D and 2D NMR spectra and MS data, and the final structure including absolute configuration was unambiguously established by single-crystal X-ray diffraction analysis. In the evaluation of its anti-inflammatory effects, 1 inhibited the overproduction of nitric oxide (NO) and prostaglandin E2 (PGE2) in lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages and BV2 microglia, and these inhibitory effects were correlated with the suppressive effect of 1 against overexpressions of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2). Furthermore, 1 also inhibited the production of pro-inflammatory cytokines such as tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß), IL-6, and IL-12. Overall, the anti-inflammatory effect of 1 was suggested to be mediated through the negative regulation of NF-κB pathway.