Dynamics of Political Polarization Special Feature: Polarized information ecosystems can reorganize social networks via information cascades

The precise mechanisms by which the information ecosystem polarizes society remain elusive. Focusing on political sorting in networks, we develop a computational model that examines how social network structure changes when individuals participate in information cascades, evaluate their behavior, and potentially rewire their connections to others as a result. Individuals follow proattitudinal information sources but are more likely to first hear and react to news shared by their social ties and only later evaluate these reactions by direct reference to the coverage of their preferred source. Reactions to news spread through the network via a complex contagion. Following a cascade, individuals who determine that their participation was driven by a subjectively “unimportant” story adjust their social ties to avoid being misled in the future. In our model, this dynamic leads social networks to politically sort when news outlets differentially report on the same topic, even when individuals do not know others’ political identities. Observational follow network data collected on Twitter support this prediction: We find that individuals in more polarized information ecosystems lose cross-ideology social ties at a rate that is higher than predicted by chance. Importantly, our model reveals that these emergent polarized networks are less efficient at diffusing information: Individuals avoid what they believe to be “unimportant” news at the expense of missing out on subjectively “important” news far more frequently. This suggests that “echo chambers”—to the extent that they exist—may not echo so much as silence.

By standard measures, political polarization in the American mass public is at its highest point in nearly 50 y (1). The consequences of this fundamental and growing societal divide are potentially severe: High levels of polarization reduce policy responsiveness and have been associated with decreased social trust (2), acceptance of and dissemination of misinformation (3), democratic erosion (4), and in extreme cases even violence (5). While policy divides have traditionally been thought to drive political polarization, recent research suggests that political identity may play a stronger role (6, 7). Yet people’s political identities may be increasingly less visible to those around them: Many Americans avoid discussing and engaging with politics and profess disdain for partisanship (8), and identification as “independent” from the two major political parties is higher than at any point since the 1950s (9). Taken together, these conflicting patterns complicate simple narratives about the mechanisms underlying polarization. Indeed, how macrolevel divisions relate to the preferences, perceptions, and interpersonal interactions of individuals remains a significant puzzle.A solution to this puzzle is particularly elusive given that many Americans, increasingly wary of political disagreement, avoid signaling their politics in discussions and self-presentation and thus lack direct information about the political identities of their social connections (10). However, regardless of individuals’ perceptions about each other, the information ecosystem around them—the collection of news sources available to society—reflects, at least to some degree, the structural divides of the political and economic system (11, 12). Traditional accounts of media-driven polarization have emphasized a direct mechanism: Individuals are influenced by the news they consume (13) but also tend to consume news from outlets that align with their politics (14, 15), thereby reinforcing their views and shifting them toward the extremes (16, 17). However, large-scale behavioral studies have offered mixed evidence of these mechanisms (18, 19), including evidence that many people encounter a significant amount of counter-attitudinal information online (20–22). Furthermore, instead of directly tuning into news sources, individuals often look to their immediate social networks to guide their attention to the most important issues (23–27). Therefore, it is warranted to investigate how the information ecosystem may impact society beyond direct influence on individual opinions.Here, we examine media-driven polarization as a social process (28) and propose a mechanism—information cascades—by which a polarized information ecosystem can indirectly polarize society by causing individuals to self-sort into emergent homogeneous social networks even when they do not know others’ political identities. Information cascades, in which individuals observe and adopt the behavior of others, allow the actions of a few individuals to quickly propagate through a social network (29, 30). Found in social systems ranging from fish schools (31) and insect swarms (32) to economic markets (33) and popular culture (29), information cascades are a widespread social phenomenon that can greatly impact collective behavior such as decision making (34). Online social media platforms are especially prone to information cascades since the primary affordances of these services involve social networking and information sharing (35–38): For example, users often see and share posts of social connections without ever reading the source material (e.g., a shared news article) (39). In addition to altering beliefs and behavior, information cascades can also affect social organization: For instance, retweet cascades on Twitter lead to bursts of unfollowing and following activity (40) that indicate sudden shifts in social connections as a direct result of information spreading through the social network. While research so far has been agnostic as to the content of the information shared during a cascade, it is plausible that information from partisan news outlets could create substantial changes in networks of individuals.We therefore propose that the interplay between network-altering cascades and an increasingly polarized information ecosystem could result in politically sorted social networks, even in the absence of partisan cues. While we do not argue that this mechanism is the only driver of political polarization—a complex phenomenon likely influenced by several factors—we do argue that the interplay between information and social organization could be one driver that is currently overlooked in discussions of political polarization. We explore this proposition by developing a general theoretical model. After presenting the model, we use Twitter data to probe some of its predictions. Finally, we use the model to explore how the emergence of politically sorted networks might alter information diffusion.     

Link recommendation algorithms and dynamics of polarization in online social networks

The level of antagonism between political groups has risen in the past years. Supporters of a given party increasingly dislike members of the opposing group and avoid intergroup interactions, leading to homophilic social networks. While new connections offline are driven largely by human decisions, new connections on online social platforms are intermediated by link recommendation algorithms, e.g., “People you may know” or “Whom to follow” suggestions. The long-term impacts of link recommendation in polarization are unclear, particularly as exposure to opposing viewpoints has a dual effect: Connections with out-group members can lead to opinion convergence and prevent group polarization or further separate opinions. Here, we provide a complex adaptive–systems perspective on the effects of link recommendation algorithms. While several models justify polarization through rewiring based on opinion similarity, here we explain it through rewiring grounded in structural similarity—defined as similarity based on network properties. We observe that preferentially establishing links with structurally similar nodes (i.e., sharing many neighbors) results in network topologies that are amenable to opinion polarization. Hence, polarization occurs not because of a desire to shield oneself from disagreeable attitudes but, instead, due to the creation of inadvertent echo chambers. When networks are composed of nodes that react differently to out-group contacts, either converging or polarizing, we find that connecting structurally dissimilar nodes moderates opinions. Overall, our study sheds light on the impacts of social-network algorithms and unveils avenues to steer dynamics of radicalization and polarization in online social networks.

Online social networks are increasingly used to access political information (1), engage with political elites, and discuss politics (2). These new communication platforms can benefit democratic processes in several ways: They reduce barriers to information and, subsequently, increase citizen engagement, allow individuals to voice their concerns, help debunk false information, and improve accountability and transparency in political decision-making (3). In principle, individuals can use social media to access ideologically diverse viewpoints and make better-informed decisions (4, 5).At the same time, internet and online social networks reveal a dark side. There are mounting concerns over possible linkages between social media and affective polarization (6, 7). Other than healthy political deliberation, social networks can foster so-called “echo chambers” (8, 9) and “information cocoons” (3, 10) where individuals are only exposed to like-minded peers and homogeneous sources of information, which polarizes attitudes (for counterevidence, see ref. 5). As a result, social media can trigger political sectarianism (6, 7, 11–13) and fuel misinformation (14, 15). Averting the risks of online social networks for political institutions, and potentiating their advantages, requires multidisciplinary approaches and novel methods to understand long-term dynamics on social platforms.That is not an easy task. As pointed out by Woolley and Howard, “to understand contemporary political communication we must now investigate the politics of algorithms and automation” (16). While traditional media outlets are curated by humans, online social media resorts to computer algorithms to personalize contents through automatic filtering. To understand information dynamics in online social networks, one needs to take into account the interrelated subtleties of human decision making [e.g., only share specific contents (17), actively engage with other users, follow or befriend particular individuals, interact offline] and the outcomes of automated decisions (e.g., news sorting and recommendation systems) (18, 19). In this regard, much attention has been placed on the role of news filters and sorting (1, 18, 19). Shmargad and Klar (20) provide evidence that algorithms sorting news impact the way users engage with and evaluate political news, likely exacerbating political polarization. Likewise, Levy (21) notes that social media algorithms can substantially affect users’ news consumption habits.While past studies have examined how algorithms may affect which information appears on a person’s newsfeed, and subsequent polarization, social matching (22) or link recommendation (23) algorithms [also called user, contact, or people recommender systems (24, 25)] constitute another class of algorithms that can affect the way users engage in (and with) online social networks (examples of such systems in SI Appendix, Fig. S13). These algorithms are implemented to recommend new online connections—“friends” or “followees”—to social network users, based on supposed offline familiarity, likelihood of establishing a future relation, similar interests, or the potential to serve as a source of useful information. Current data provide evidence that link recommendation algorithms impact network topologies and increase network clustering: Daly et al. (26) show that an algorithm recommending friends-of-friends, in an IBM internal social network platform, increases clustering and network modularity. Su et al. (27) analyzed the Twitter graph before and after this platform implemented link recommendation algorithms and show that the “Who To Follow” feature led to a sudden increase in edge growth and the network clustering coefficient. Similarly, Zignani et al. (28) show that, on a small sample of the Facebook graph, the introduction of the “People You May Know” (PYMK) feature led to a sudden increase in the number of links and triangles [i.e., motifs comprising three nodes (A, B, C) where the links AB, AC, and BC exist] in the network. The fact that PYMK is responsible for a significant fraction of link creations is alluded to in other works (29). Furthermore, recent work shows, through experiments with real social media users (30) and simulations (31), that link recommendation algorithms can effectively be used as an intervention mechanism to increase networks’ structural diversity (30, 31) and minimize disagreements (32). It is thereby relevant to understand, 1) How do algorithmic link recommendations interplay with opinion formation? and 2) What are the long-term impacts of such algorithms on opinion polarization?Here, we tackle the previous questions from a complex adaptive–systems perspective (33), designing and analyzing a simple model where individuals interact in a dynamic social network. While several models explain the emergence of polarization through link formation based on opinion similarity (34–41) and information exchange (42), here we focus instead on rewiring based on “structural similarity,” which is defined as similarity based on common features that exclusively depend on the network structure (43). This contrasts with the broader concept of homophily, which typically refers to similarity based on common characteristics besides network properties (e.g., opinions, taste, age, background). Compared with rewiring based on homophily—which can also contribute to network fragmentation—rewiring based on structural similarity can be less restrictive in contexts where information about opinions and beliefs is not readily available to individuals before the connection is established. Furthermore, rewiring based on structural similarity is a backbone of link recommendation algorithms [e.g., “People you may know” or “Whom to follow” (25) suggestions], which rely on link prediction methods to suggest connections to users (43, 44). Importantly, our model combines three key ingredients: 1) Links are formed according to structural similarity, based on common neighbors, which is one of the simplest link prediction methods (43); this way, we do not assume a priori that individuals with similar opinions are likely to become connected [as recent works underline, sorting can be incidental to politics (45, 46)]. 2) Then, to examine opinion updating, we adapt a recent model that covers the interplay of social reinforcement and issue controversy to promote radicalization on social networks (39). 3) Last, we explicitly consider that nodes can react differently to out-group links, either converging in their opinions (10, 47) or polarizing further (48–50).We find that establishing links based on structural similarity alone [a process that is likely to be reinforced by link recommendation algorithms—see SI Appendix, Fig. S10 and previous work pointing that such algorithms affect a social network topology and increase their clustering coefficient (26–28)] contributes to opinion polarization. While our model sheds light on the effect of link recommendation algorithms on opinion formation and polarization dynamics, we also offer a justification for polarization to emerge through structural similarity-based rewiring, in the absence of explicit opinion-similarity rewiring (34, 36, 39, 51), confidence-bounds (37, 38, 40), or rewiring based on concordant messages (42).^* Second, we find that the effects of structural similarity-based rewiring are exacerbated if even moderate opinions have high social influence. Finally, we combine nodes that react differently to out-group contacts: “converging” nodes, which converge if exposed to different opinions (10, 21, 52), and “polarizing” nodes, which diverge when exposed to different viewpoints (48–50). We observe that the coexistence of both types of nodes can contribute to moderate opinions. Polarizing nodes develop radical opinions, and converging nodes, influenced by opposing viewpoints, yield more temperate ones. However, again, link recommendation algorithms impact this process: Given the existence of communities isolated to a greater degree through link recommendation, converging nodes may find it harder to access diverse viewpoints, which, in general, contributes to increasing the adoption of extreme opinions.     

Out-group animosity drives engagement on social media

There has been growing concern about the role social media plays in political polarization. We investigated whether out-group animosity was particularly successful at generating engagement on two of the largest social media platforms: Facebook and Twitter. Analyzing posts from news media accounts and US congressional members (n = 2,730,215), we found that posts about the political out-group were shared or retweeted about twice as often as posts about the in-group. Each individual term referring to the political out-group increased the odds of a social media post being shared by 67%. Out-group language consistently emerged as the strongest predictor of shares and retweets: the average effect size of out-group language was about 4.8 times as strong as that of negative affect language and about 6.7 times as strong as that of moral-emotional language—both established predictors of social media engagement. Language about the out-group was a very strong predictor of “angry” reactions (the most popular reactions across all datasets), and language about the in-group was a strong predictor of “love” reactions, reflecting in-group favoritism and out-group derogation. This out-group effect was not moderated by political orientation or social media platform, but stronger effects were found among political leaders than among news media accounts. In sum, out-group language is the strongest predictor of social media engagement across all relevant predictors measured, suggesting that social media may be creating perverse incentives for content expressing out-group animosity.

According to a recent article in the Wall Street Journal, a Facebook research team warned the company in 2018 that their “algorithms exploit the human brain’s attraction to divisiveness.” This research was allegedly shut down by Facebook executives, and Facebook declined to implement changes proposed by the research team to make the platform less divisive (1). This article is consistent with concerns that social media might be incentivizing the spread of polarizing content. For instance, Twitter CEO Jack Dorsey has expressed concern about the popularity of “dunking” (i.e., mocking or denigrating one’s enemies) on the platform (2). These concerns have become particularly relevant as social media rhetoric appears to have incited real-world violence, such as the recent storming of the US Capital (3). We sought to investigate whether out-group animosity was associated with increased virality on two of the largest social media platforms: Facebook and Twitter.A growing body research has examined the potential role of social media in exacerbating political polarization (4, 5). A large portion of this work has centered on the position that social media sorts us into “echo chambers” or “filter bubbles” that selectively expose people to content that aligns with their preexisting beliefs (6–11). However, some recent scholarship questions whether the “echo chamber” narrative has been exaggerated (12, 13). Some experiments suggest that social media can indeed increase polarization. For example, temporarily deactivating Facebook can reduce polarization on policy issues (14). However, other work suggests that polarization has grown the most among older demographic groups, who are the least likely to use social media (15), albeit the most likely to vote. As such, there is an open debate about the role of social media in political polarization and intergroup conflict.Other research has examined the features of social media posts that predict “virality” online. Much of the literature focuses on the role of emotion in social media sharing. High-arousal emotions, whether they are positive (e.g., awe) or negative (e.g., anger or outrage), contribute to the sharing of content online (16–20). Tweets expressing moral and emotional content are more likely to be retweeted within online political conversations, especially by members of one’s political in-group (21, 22). On Facebook, posts by politicians that express “indignant disagreement” receive more likes and shares (23), and negative news tends to spread farther on Twitter (24). Moreover, false rumors spread farther and faster on Twitter than true ones, especially in the domain of politics, possibly because they are more likely to express emotions such as surprise and fear (25).Yet, to our knowledge, little research has investigated how social identity motives contribute to online virality. Group identities are hypersalient on social media, especially in the context of online political or moral discussions (26). For example, an analysis of Twitter accounts found that people are increasingly categorizing themselves by their political identities in their Twitter bios over time, providing a public signal of their social identity (27). Additionally, since sharing behavior is public, it can reflect self-conscious identity presentation (28, 29). According to social identity theory (30) and self-categorization theory (31), when group identities are highly salient, this can lead individuals to align themselves more with their fellow in-group members, facilitating in-group favoritism and out-group derogation in order to maintain a positive sense of group distinctiveness (32). Thus, messages that fulfill group-based identity motives may receive more engagement online. As an anecdotal example, executives at the website Buzzfeed, which specializes in creating viral content, reportedly noticed that identity-related content contributed to virality and began creating articles appealing to specific group identities (33).People may process information in a manner that is consistent with their partisan identities, prior beliefs, and motivations, a process known as motivated cognition (34–37). Scholars noted early on that the degree to which individuals identify with their political party “raises a perceptual screen through which the individual tends to see what is favorable to his [or her] partisan orientation” (38). Partisan motivations have been hypothesized to influence online behavior, such as the sharing of true and false news online (39, 40). Accordingly, we suggest that just as people engage in motivated cognition—processing information in a way that supports their beliefs—people may also engage in motivated tweeting (or sharing, liking, or retweeting), selectively interacting with and attending to content that aligns with their partisan identity motivations. There is already evidence suggesting that people selectively follow (41) and retweet (10, 42) in-group members at much higher rates than out-group members.In polarized political contexts, out-group animosity may be a more successful strategy for expressing one’s partisan identity and generating engaging content than in-group favoritism. Political polarization has been growing rapidly in the United States over the past few decades. Affective polarization, which reflects dislike of people in the opposing political party as compared to one’s own party, has most strikingly increased (43), and ideological polarization may have increased as well (though this is still a topic of debate) (44). This growth in affective polarization is driven primarily by increasing out-party animosity (rather than increasing in-party warmth)—a phenomenon known as “negative partisanship” (45). According to recently released American National Election Studies data, affective polarization grew particularly steeply from 2016 to 2020, reaching its highest point in 40 y. Out-party animosity, more so than in-party warmth, has also become a more powerful predictor of important behaviors, such as voting behavior (46) and the sharing of political fake news (39). When out-party animosity is strong, partisans are motivated to distinguish themselves from the out-party (by, for instance, holding opinions that are distinct from the out-party) (47). While some research suggests that out-group cues might be more powerful than in-group cues (48), there is still debate about the extent to which partisan belief and behavior is driven by in-group favoritism versus out-group derogation (49). A limitation of prior research is that much of it is based on self-report surveys, and so it remains unknown how expressions of in-group favoritism or out-group animosity play out in a social media context—or whether one might be a more powerful contributor to virality than the other.We investigated the role that political in-group and out-group language, as well as emotional language, play in predicting online engagement in a large sample of posts from news media accounts and US congressional members (n = 2,730,215). We sought to examine this on both Facebook and Twitter since they are two of the world’s largest and most influential social media companies and constitute around three billion users out of nearly four billion total social media users worldwide (50). Specifically, we were interested in 1) how political in-group and out-group language compared to other established predictors of social media engagement, 2) whether in-group or out-group language was a better predictor of shares and retweets, and 3) whether out-group terms were associated with negative emotions (as measured by the six Facebook “reactions”), and whether in-group terms were associated with positive emotions, reflecting patterns of out-party derogation and in-group favoritism. Finally, 4) we wanted to see if these findings applied to both news sources and political leaders, who often have an outsized influence on social discourse as well as policy change.     

The spontaneous emergence of conventions: An experimental study of cultural evolution

How do shared conventions emerge in complex decentralized social systems? This question engages fields as diverse as linguistics, sociology, and cognitive science. Previous empirical attempts to solve this puzzle all presuppose that formal or informal institutions, such as incentives for global agreement, coordinated leadership, or aggregated information about the population, are needed to facilitate a solution. Evolutionary theories of social conventions, by contrast, hypothesize that such institutions are not necessary in order for social conventions to form. However, empirical tests of this hypothesis have been hindered by the difficulties of evaluating the real-time creation of new collective behaviors in large decentralized populations. Here, we present experimental results—replicated at several scales—that demonstrate the spontaneous creation of universally adopted social conventions and show how simple changes in a population’s network structure can direct the dynamics of norm formation, driving human populations with no ambition for large scale coordination to rapidly evolve shared social conventions.Social conventions are the foundation for social and economic life (1–7), However, it remains a central question in the social, behavioral, and cognitive sciences to understand how these patterns of collective behavior can emerge from seemingly arbitrary initial conditions (2–4, 8, 9). Large populations frequently manage to coordinate on shared conventions despite a continuously evolving stream of alternatives to choose from and no a priori differences in the expected value of the options (1, 3, 4, 10). For instance, populations are able to produce linguistic conventions on accepted names for children and pets (11), on common names for colors (12), and on popular terms for novel cultural artifacts, such as referring to junk email as “SPAM” (13, 14). Similarly, economic conventions, such as bartering systems (2), beliefs about fairness (3), and consensus regarding the exchangeability of goods and services (15), emerge with clear and widespread agreement within economic communities yet vary broadly across them (3, 16).Prominent theories of social conventions suggest that institutional mechanisms—such as centralized authority (14), incentives for collective agreement (15), social leadership (16), or aggregated information (17)—can explain global coordination. However, these theories do not explain whether, or how, it is possible for conventions to emerge when social institutions are not already in place to guide the process. A compelling alternative approach comes from theories of social evolution (2, 18–20). Social evolutionary theories maintain that networks of locally interacting individuals can spontaneously self-organize to produce global coordination (21, 22). Although there is widespread interest in this approach to social norms (6, 7, 14, 18, 23–26), the complexity of the social process has prevented systematic empirical insight into the thesis that these local dynamics are sufficient to explain universally adopted conventions (27, 28).Several difficulties have limited prior empirical research in this area. The most notable of these limitations is scale. Although compelling experiments have successfully shown the creation of new social conventions in dyadic and small group interactions (29–31), the results in small group settings can be qualitatively different from the dynamics in larger groups (Model), indicating that small group experiments are insufficient for demonstrating whether or how new conventions endogenously form in larger populations (32, 33). Important progress on this issue has been made using network-based laboratory experiments on larger groups (15, 24). However, this research has been restricted to studying coordination among players presented with two or three options with known payoffs. Natural convention formation, by contrast, is significantly complicated by the capacity of individuals to continuously innovate, which endogenously expands the “ecology” of alternatives under evaluation (23, 29, 31). Moreover, prior experimental studies have typically assumed the existence of either an explicit reward for universal coordination (15) or a mechanism that aggregates and reports the collective state of the population (17, 24), which has made it impossible to evaluate the hypothesis that global coordination is the result of purely local incentives.More recently, data science approaches to studying norms have addressed many of these issues by analyzing behavior change in large online networks (34). However, these observational studies are limited by familiar problems of identification that arise from the inability to eliminate the confounding influences of institutional mechanisms. As a result, previous empirical research has been unable to identify the collective dynamics through which social conventions can spontaneously emerge (8, 34–36).We addressed these issues by adopting a web-based experimental approach. We studied the effects of social network structure on the spontaneous evolution of social conventions in populations without any resources to facilitate global coordination (9, 37). Participants in our study were rewarded for coordinating locally, however they had neither incentives nor information for achieving large scale agreement. Further, to eliminate any preexisting bias in the evolutionary process, we studied the emergence of arbitrary linguistic conventions, in which none of the options had any a priori value or advantage over the others (3, 23). In particular, we considered the prototypical problem of whether purely local interactions can trigger the emergence of a universal naming convention (38, 39).     

Social experiences switch states of memory engrams through regulating hippocampal Rac1 activity

In pathological or artificial conditions, memory can be formed as silenced engrams that are unavailable for retrieval by presenting conditioned stimuli but can be artificially switched into the latent state so that natural recall is allowed. However, it remains unclear whether such different states of engrams bear any physiological significance and can be switched through physiological mechanisms. Here, we show that an acute social reward experience switches the silent memory engram into the latent state. Conversely, an acute social stress causes transient forgetting via turning a latent memory engram into a silent state. Such emotion-driven bidirectional switching between latent and silent states of engrams is mediated through regulation of Rac1 activity–dependent reversible forgetting in the hippocampus, as stress-activated Rac1 suppresses retrieval, while reward recovers silenced memory under amnesia by inhibiting Rac1. Thus, data presented reveal hippocampal Rac1 activity as the basis for emotion-mediated switching between latent and silent engrams to achieve emotion-driven behavioral flexibility.

Animals are required to flexibly retrieve memories according to their emotional states for achieving optimal behaviors in an ever-changing environment. To understand the underlying mechanisms of such regulation, extensive effort has been devoted to studying the impact of emotion on memory processes (1–5). For instance, stress can block memory retrieval through hormones, neuroinflammation, or depression of synapses in both human and animal models (6–12), while reward and novelty can facilitate both the formation and maintenance of memories (13, 14). However, how emotion might directly impact the memory engram remains elusive. The proposed theory and experimental demonstrations have revealed the presence of multiple states of memory engrams, such as silent, latent, and active states (15–17). In the silent state, only artificial activation of engram cells is capable of inducing memory expression, whereas latent engram cells can be activated by a natural conditioned stimulus to drive the engram into the active state for memory retrieval. It is intriguing to note that training either mouse models of Alzheimer’s disease or protein synthesis-inhibited mice are reported to yield only silent engrams, and such silent engrams could be turned into a latent state (18–22) through artificial manipulations of either optical stimulation–induced long-term potentiation (LTP) or virus-driven overexpression of activated PAK1. However, the physiological significance of the different states of engrams, particularly the silent engram, remains unclear. In the course of studying the functions of reversible forgetting (23, 24), we became interested in testing the idea that the emotional impact on memory retrieval could be mediated through switching the engram between latent and silent states, while reversible forgetting may play a role in making such switching.To investigate this idea, we tested the effects of acute social reward (SR) and social stress (SS) on memory retrieval. Since mating and fighting are widely perceived and used as behavioral stimuli for evoking feelings of reward and stress, respectively (25–27), we adopted a modified short procedure for evoking acute emotion through social interactions. For SR treatment, a single experimental male mouse is exposed to two females brought from different home cages for 10 min. Such a subtle positive experience is sufficient to enhance retrieval of contextual fear memory (28). For SS treatment, a single experimental male mouse is exposed to a group of five male littermates for 10 min. This is a hostile social environment in which the experimental mouse fights with other littermates during this time window. Such a subtle stressful experience is sufficient to significantly reduce 24-h contextual fear memory (28). Based on these two paradigms of acute social experiences, we investigated how emotion affects memory retrieval through alterations in engram states.     

Algorithmic amplification of politics on Twitter

Content on Twitter’s home timeline is selected and ordered by personalization algorithms. By consistently ranking certain content higher, these algorithms may amplify some messages while reducing the visibility of others. There’s been intense public and scholarly debate about the possibility that some political groups benefit more from algorithmic amplification than others. We provide quantitative evidence from a long-running, massive-scale randomized experiment on the Twitter platform that committed a randomized control group including nearly 2 million daily active accounts to a reverse-chronological content feed free of algorithmic personalization. We present two sets of findings. First, we studied tweets by elected legislators from major political parties in seven countries. Our results reveal a remarkably consistent trend: In six out of seven countries studied, the mainstream political right enjoys higher algorithmic amplification than the mainstream political left. Consistent with this overall trend, our second set of findings studying the US media landscape revealed that algorithmic amplification favors right-leaning news sources. We further looked at whether algorithms amplify far-left and far-right political groups more than moderate ones; contrary to prevailing public belief, we did not find evidence to support this hypothesis. We hope our findings will contribute to an evidence-based debate on the role personalization algorithms play in shaping political content consumption.

Political content is a major part of the public conversation on Twitter. Politicians, political organizations, and news outlets engage large audiences on Twitter. At the same time, Twitter employs algorithms that learn from data to sort content on the platform. This interplay of algorithmic content curation and political discourse has been the subject of intense scholarly debate and public scrutiny (1–15). When first established as a service, Twitter used to present individuals with content from accounts they followed, arranged in a reverse chronological feed. In 2016, Twitter introduced machine learning algorithms to render tweets on this feed called Home timeline based on a personalized relevance model (16). Individuals would now see older tweets deemed relevant to them, as well as some tweets from accounts they did not directly follow.Personalized ranking prioritizes some tweets over others on the basis of content features, social connectivity, and user activity. There is evidence that different political groups use Twitter differently to achieve political goals (17–20). What has remained a matter of debate, however, is whether or not any ranking advantage falls along established political contours, such as the left or right (2, 7), the center or the extremes (1, 3), specific parties (2, 7), or news sources of a certain political inclination (21). In this work, we provide systematic quantitative insights into this question based on a massive-scale randomized experiment on the Twitter platform.     

Two kinds of memory signals in neurons of the human hippocampus

Prior studies of the neural representation of episodic memory in the human hippocampus have identified generic memory signals representing the categorical status of test items (novel vs. repeated), whereas other studies have identified item specific memory signals representing individual test items. Here, we report that both kinds of memory signals can be detected in hippocampal neurons in the same experiment. We recorded single-unit activity from four brain regions (hippocampus, amygdala, anterior cingulate, and prefrontal cortex) of epilepsy patients as they completed a continuous recognition task. The generic signal was found in all four brain regions, whereas the item-specific memory signal was detected only in the hippocampus and reflected sparse coding. That is, for the item-specific signal, each hippocampal neuron responded strongly to a small fraction of repeated words, and each repeated word elicited strong responding in a small fraction of neurons. The neural code was sparse, pattern-separated, and limited to the hippocampus, consistent with longstanding computational models. We suggest that the item-specific episodic memory signal in the hippocampus is fundamental, whereas the more widespread generic memory signal is derivative and is likely used by different areas of the brain to perform memory-related functions that do not require item-specific information.

The hippocampus is essential for the formation of declarative (conscious) memory (1, 2), including both episodic memory (memory for events) and semantic memory (factual knowledge). Episodic memories represent the “what, when, and where” information about remembered events (3). Here, we focus on the neural representation of episodic memory for events, specifically words presented and later repeated in a continuous recognition memory format (4).Bilateral hippocampal lesions result in substantial anterograde amnesia for new events, whether memory is tested by recall or recognition (5). By contrast, bilateral lesions to a more anterior medial temporal lobe structure―the amygdala―have no such effect (6). One might therefore expect to find single-unit activity associated with episodic memory in the hippocampus but not in the amygdala. Yet, the earliest single-neuron studies failed to detect hippocampal neurons that fired differentially to recently presented test items vs. novel items. This was true in studies with humans (7, 8) and monkeys (9–11). One early study with monkeys identified a few such neurons in the hippocampus (12), and other studies found them in areas other than the hippocampus (e.g., inferomedial temporal cortex or inferotemporal temporal cortex) (9–11, 13, 14). Overall, this was not the pattern anticipated from lesion studies.Subsequent studies successfully detected some memory-related neural activity (15–17), observing that ∼10% of hippocampal neurons exhibited differential firing rates based on item status, with some firing more for repeated items and others firing more for novel items. Surprisingly, similar “memory-selective” neurons were also reliably detected in the amygdala at approximately the same frequency. Yet, these memory-selective neurons responded differentially to the generic, categorical status of test items (repeated vs. novel) and thus are not episodic memory signals (i.e., signals representing memory for specific events). According to neurocomputational models dating back to Marr (18), episodic memory representations in the hippocampus are supported by sparse neural codes (19–21). If memories for individual items are sparsely coded in largely nonoverlapping (pattern-separated) neural assemblies, it should be possible to find neurons that respond to particular repeated items, rather than to an item’s generic status. Two recent single-unit studies with humans detected such neurons in the hippocampus, but not in the amygdala (22, 23), apparently reflecting sparsely coded episodic memories. In the present study, we tested 1) whether the generic and the item-specific signals coexist in neural firing patterns recorded during the same memory task, and 2) whether the two kinds of signals are present exclusively in the hippocampus or are also evident in other brain regions.During a continuous recognition memory procedure, neurons were simultaneously recorded from four brain regions: hippocampus, amygdala, anterior cingulate cortex, and prefrontal cortex. Altogether, 55 continuous recognition memory sessions were completed by 34 epilepsy patients who had implanted clinical depth electrodes with microwires measuring single-unit activity (SUA) and multiunit activity bilaterally (24). We limited the present analyses to SUA. Words were presented consecutively and repeated once after varying lags; patients judged each word as either “novel” or “repeated.” Thus, repeated words differed from their earlier presentations as novel words only with respect to their combined “what, when, and where” episodic status (3).     

Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion

Coordination among social animals requires rapid and efficient transfer of information among individuals, which may depend crucially on the underlying structure of the communication network. Establishing the decision-making circuits and networks that give rise to individual behavior has been a central goal of neuroscience. However, the analogous problem of determining the structure of the communication network among organisms that gives rise to coordinated collective behavior, such as is exhibited by schooling fish and flocking birds, has remained almost entirely neglected. Here, we study collective evasion maneuvers, manifested through rapid waves, or cascades, of behavioral change (a ubiquitous behavior among taxa) in schooling fish (Notemigonus crysoleucas). We automatically track the positions and body postures, calculate visual fields of all individuals in schools of ∼150 fish, and determine the functional mapping between socially generated sensory input and motor response during collective evasion. We find that individuals use simple, robust measures to assess behavioral changes in neighbors, and that the resulting networks by which behavior propagates throughout groups are complex, being weighted, directed, and heterogeneous. By studying these interaction networks, we reveal the (complex, fractional) nature of social contagion and establish that individuals with relatively few, but strongly connected, neighbors are both most socially influential and most susceptible to social influence. Furthermore, we demonstrate that we can predict complex cascades of behavioral change at their moment of initiation, before they actually occur. Consequently, despite the intrinsic stochasticity of individual behavior, establishing the hidden communication networks in large self-organized groups facilitates a quantitative understanding of behavioral contagion.The social transmission of behavioral change is central to collective animal behavior. For many mobile groups, such as schooling fish and flocking birds, social contagion can be fast, resulting in dramatic waves of response (1–6). Such waves are evident in particular when individuals are under threat of attack from predators (1). Despite the ubiquity and importance of behavioral contagion, and the fact that survival depends on how individual interactions scale to collective properties (2), we still know very little about the sensory basis and mechanism of such coordinated collective response.In the early 20th century, Edmund Selous proposed that rapid waves of turning in large flocks of birds resulted from a direct transference of thoughts among animals: “They must think collectively, all at the same time… a flash out of so many brains” (3). By the mid- 1950s, however, attention had turned from telepathy to synchrony arising from the rapid transmission of local behavioral response to neighbors, with some of the first experimental studies of cascading behavioral change undertaken by Dimitrii Radakov (4). Radakov (4) hand-traced the paths of each fish, frame-by-frame, revealing that the speed of the “wave of agitation” could propagate much faster than the maximum swim speed of individuals. Using similar methodology, Treherne and Foster (5) studied rapid waves of escape response in marine skaters, describing what they saw as “the Trafalgar effect” in reference to the speed of communication, via signaling flags, among ships in the British Navy’s fleet at the battle of Trafalgar in 1805. Signals observable at a distance allowed information to travel much faster than the ships could move themselves. Since these studies, similar behavioral cascades have been found in many other organisms (2, 6–8).Describing general “macroscopic” properties, such as the speed or direction of behavioral waves, is relatively straightforward. Revealing the nature of social interactions by which information propagates among individuals, however, has proven much more difficult. In many situations, such as when a predator attacks a group (1) or when artificial stimuli are used, it is not possible to differentiate between the propagation of behavior via social contagion and the propagation of behavior resulting from direct response to the stimulus, or some combination of both. For example, the sound of an object dropped into the water (9) creates a near-instantaneous acoustic cue typically available to all individuals. This problem is further exacerbated by the fact that response latency associated with direct behavioral response increases with distance to the stimulus (10); thus, the null expectation for asocial response by members of a group to a stimulus would be a fast wave of response (appearing to travel via contagion) from the stimulus outward.In previous studies, therefore, it has not been possible to isolate the social component of rapid collective response. Although simulations can qualitatively reproduce phenomena reminiscent of such waves (6, 11), the underlying assumptions made may be incorrect. For example, a predominant paradigm has been to consider individuals as “self-propelled particles,” which (inspired by collective processes in physical systems) interact with neighbors through social “forces” (12–15). In such models, it is usually assumed that they do so with neighbors within a fixed distance [a “metric range” (12, 13)] or with a fixed number of near-neighbors regardless of their distance [a “topological range” (15)]. These assumptions, although mathematically convenient, do not necessarily represent what is convenient, or appropriate, for neural sensing and decision making. Furthermore, it has been shown that these representations poorly reflect the sensory information used during social response in schooling golden shiner fish (16), the focal species of the present work.A major challenge in the study of collective animal behavior is that the pathways of communication are not directly observable. In the study of isolated organisms, it has long been realized that mapping the physical and functional connectivity of neural networks is essential to developing a quantitative and predictive science of how individual behavior is generated. By contrast, in the study of mobile animal groups, the analogous issue of determining the structure of the sensory networks by which interactions, and the resulting group behavior, are mediated remains to be explored. The structure and heterogeneity of networks are known to have a profound impact on contagious processes in general, from spreading neural electrical activity (17), innovations (18), disease (19), or power grid (20) failure. In all such scenarios, predicting the magnitude of contagion and identifying influential nodes (either in terms of their capacity to instigate or inhibit widespread contagion) are crucial.Several measures have become prominent predictors of influence in networks, including an individual’s degree (number of connections) (21) and betweenness-centrality (the number of shortest paths that pass through a focal individual) (22). In the study of contagious disease, those individuals who have a large number of social contacts (a high degree), yet whose contacts do not form a tight clique [i.e., have a low clustering coefficient (19)], have the capacity to be “superspreaders,” allowing infection to spread extensively (23). Although disease transmission and social contagion are similar in some respects, there are important differences. Whereas disease transmission can follow contact with a single infected individual (simple contagion), in many social processes, behavioral change depends on reinforcement via multiple contacts [complex contagion (24)].Here, we focus on studying rapid waves of behavioral change in the context of collective evasion, using strongly schooling fish (golden shiners) as an experimental study system. To uncover the process by which this behavioral change spreads, we exploit the fact that shiners, like other fish, exhibit “fast-start” behavior when they perceive an aversive stimulus (e.g., via the visual, acoustic, or mechanosensory system) (25), and occasionally do so in the absence of any external stimulus. Studying fast-start evasion resulting from spontaneous startle events, instead of presenting a stimulus visible to multiple individuals, offers us the opportunity to identify the initiator of escape waves unambiguously and to avoid confounding social and asocial factors.Because fast-start is mediated by a reflex circuit involving a pair of giant neurons, the Mauthner cells (26), it may be expected that individual fish would be unable to establish the causal factor for escape in others (i.e., whether it resulted from a real threat or not). We first test this hypothesis by comparing evasion response resulting from spontaneous startles with evasion response resulting from an experimentally controlled alarming mechanosensory stimulus, and find no difference in response. This result suggests that when responding to fast-start behavior, golden shiners do not differentiate between threat-induced and spontaneous startles, and is consistent with previous experiments on birds (27), and also with theoretical predictions suggesting that the risk of predation makes it simply too costly for vulnerable organisms, like golden shiners, to wait to determine if the escape motion of others is associated with real danger (28).To investigate the mechanism of transmission of evasion behavior, we performed a detailed analysis of 138 spontaneous evasion maneuvers in schools of 150 ± 4 freely swimming fish (body length of  ≈  4.5–5 cm, spontaneous evasion experiments minimally interfered with schooling behavior). Due to the importance of visual cues in this species (16, 29), we reconstruct a planar representation of each fish’s visual field using ray casting to approximate the pathways of light onto the retina, based on automated estimation of the body posture and eye position of each individual (SI Appendix). This representation reveals the underlying visual information available to each fish. Because we can determine unambiguously the initiator and first responder (the first and second individuals to startle) of any behavioral cascade, and only social cues are present, we can investigate the nature of social contagion in this system by asking what sensory information is predictive of whether or not an individual will be the first to respond.This approach allows us to study how individuals translate sensory information to motor response (evasion) and, consequently, to reveal the social cues that inform individual decision making in this behavioral context. Knowing these cues then allows us to reconstruct quantitative interaction networks by which evasion behavior propagates across groups. We address a key question: From the structural properties of the network alone, is it possible to predict whether a given individual’s startle will result in a behavioral cascade, and of what magnitude? We also reveal the general nature of this contagion process and the relationship between spatial position and social influence, and susceptibility to social influence, in large, mobile animal groups.     

Chemotactic self-caging in active emulsions

A common feature of biological self-organization is how active agents communicate with each other or their environment via chemical signaling. Such communications, mediated by self-generated chemical gradients, have consequences for both individual motility strategies and collective migration patterns. Here, in a purely physicochemical system, we use self-propelling droplets as a model for chemically active particles that modify their environment by leaving chemical footprints, which act as chemorepulsive signals to other droplets. We analyze this communication mechanism quantitatively both on the scale of individual agent–trail collisions as well as on the collective scale where droplets actively remodel their environment while adapting their dynamics to that evolving chemical landscape. We show in experiment and simulation how these interactions cause a transient dynamical arrest in active emulsions where swimmers are caged between each other’s trails of secreted chemicals. Our findings provide insight into the collective dynamics of chemically active particles and yield principles for predicting how negative autochemotaxis shapes their navigation strategy.

Motile microorganisms have evolved to sense their environment and react to external chemical or physical cues via taxis. Specifically, organisms respond to a gradient in the concentration field of a chemical species by chemotaxis (1) or autochemotaxis when the gradient is generated by the organisms themselves (2). In microorganisms, chemotaxis and autochemotaxis guide many collective processes, such as colony migration (3, 4), aggregation (5, 6), or biofilm formation (7), where the emergent complex behavior is governed by the interplay of physical effects and biological processes. Many aggregatory, quorum-sensing (8) behaviors are based on attractive signaling (i.e., positive autochemotaxis). Repulsive signaling (negative autochemotaxis) is of practical importance to efficient space exploration (e.g., when ant colonies forage using mutual avoidance) (9).Complex collective behavior can result from intricate biological mechanisms but also, can be solely caused by nonequilibrium dynamics (refs. 10–14 have such examples), such that there is a need to untangle physics and biology. To this end, current research in artificial active matter aims to design and develop synthetic microswimmers that can mimic strategies like chemotaxis by purely physicochemical means (15). Self-phoretic particles, which propel via a self-generated local chemical gradient (16–18), are widely studied in theory and experiment. Suspensions of these particles exhibit nontrivial dynamics influenced by autochemotaxis (19–24). Specifically, self-propelling droplets (25) provide an experimental model for repulsive chemical signaling (12, 26–28). Along their way, the droplets shed a persistent trail of depleted fuel, which acts as a chemorepellent to other droplets. Hence, the motion of such a droplet is affected by the previous passage of another droplet.In this study, we show that in an active emulsion, droplets remodel their chemical environment while adapting their dynamics to that evolving resource landscape. In spirit, this resembles Pseudomonas aeruginosa organizing their interactions by shedding attractive trails (7, 29), with the difference that droplet trails are chemorepulsive. We start by a quantitative analysis of individual “delayed collision” events in quasi–two-dimensional (quasi-2D) confinement; we directly visualize and map the chemical footprints of droplets and measure the diffusion coefficient of the constituent chemicals. We use these results to fit a generic analytical model in 2D based on time delay, angle of impact, and chemical coupling strength. We show that these parameters determine whether a droplet crosses a chemical trail or rebounds from it. We then proceed to the collective dynamics, comparing experimental data with simulations using our single-event fits. We demonstrate how such individual binary collisions cause autochemotactic arrest in ensembles of chemically active droplets, a kind of “history caging,” where swimmers are transiently trapped in an evolving network of repulsive trails. We finally address the question of whether this type of caging is possible in three dimensions (3D) via experiments in density-matched bulk media.     

Empirical social triad statistics can be explained with dyadic homophylic interactions

The remarkable robustness of many social systems has been associated with a peculiar triangular structure in the underlying social networks. Triples of people that have three positive relations (e.g., friendship) between each other are strongly overrepresented. Triples with two negative relations (e.g., enmity) and one positive relation are also overrepresented, and triples with one or three negative relations are drastically suppressed. For almost a century, the mechanism behind these very specific (“balanced”) triad statistics remained elusive. Here, we propose a simple realistic adaptive network model, where agents tend to minimize social tension that arises from dyadic interactions. Both opinions of agents and their signed links (positive or negative relations) are updated in the dynamics. The key aspect of the model resides in the fact that agents only need information about their local neighbors in the network and do not require (often unrealistic) higher-order network information for their relation and opinion updates. We demonstrate the quality of the model on detailed temporal relation data of a society of thousands of players of a massive multiplayer online game where we can observe triangle formation directly. It not only successfully predicts the distribution of triangle types but also explains empirical group size distributions, which are essential for social cohesion. We discuss the details of the phase diagrams behind the model and their parameter dependence, and we comment on to what extent the results might apply universally in societies.

Recognizing the fundamental role of triadic interactions in shaping social structures, Heider (1) introduced the notion of balanced and unbalanced triads. A triad (triangle) of individuals is balanced if it includes zero or two negative links; otherwise, it is unbalanced. Heider (1) hypothesized that social networks have a tendency to reduce the number of unbalanced triangles over time such that balanced triads would dominate in a stationary situation. This theory of “social balance” has been confirmed empirically in many different contexts, such as schools (2), monasteries (3), social media (4), or computer games (5). Social balance theory and its generalizations (6–8) have been studied extensively for more than a half century for their importance in understanding polarization of societies (9), global organization of social networks (10), evolution of the network of international relations (11), opinion formation (12, 13), epidemic spreading (14, 15), government formation (16), and decision-making processes (17).Following Heider’s intuition (18–41), current approaches toward social balance often account for the effect of triangles on social network formation in one way or another. For example, the models in refs. 22 and 23 consider a reduction of the number of unbalanced triads either in the neighborhood of a node or in the whole network. The latter process sometimes leads to imbalance due to the existence of so-called jammed states (42). In order to reach social balance, individuals can also update their links according to their relations to common neighbors (18–21) or adjust link weights via opinion updates (24, 25) or via a minimization of social stress based on triadic interactions (37–44). These works not only ignore the difficulty of individuals to know the social interactions beyond their direct neighbors in reality, so far, they also have not considered the detailed statistical properties of the over- or underrepresentation of the different types of triads, such as those reported in refs. 4 and 5, with the exception of refs. 43 and 44.It is generally believed that the similarity of individuals plays a crucial role in the formation of social ties in social networks, something that has been called homophily (45–48). This means that to form a positive or negative tie with another person, people compare only pairwise overlaps in their individual opinions (dyadic interaction). It has also been argued that social link formation takes into account a tendency in people to balance their local interaction networks in the sense that they introduce friends to each other, that they do give up friendships if two mutual friends have negative attitudes toward each other, and that they tend to avoid situations where everyone feels negatively about the others. This is the essence of social balance theory (1). Obviously, link formation following social balance is cognitively much more challenging than homophily-based link formation since in the former, one has to keep in mind the many mutual relations between all your neighbors in a social network. While social balance–driven link formation certainly occurs in the context of close friendships, it is less realistic to assume that this mechanism is at work in social link formation in general. In Fig. 1, we schematically show the situation in a portion of a social network. It is generally hard for node i to know all the relations between his neighbors j, k, and l.Open in a separate windowFig. 1.Schematic view of opinion and link updates in a society. Every individual has an opinion vector whose components represent (binary) opinions on G=5 different subjects. Red (blue) links denote positive (negative) relationships. The question marks denote unknown relationships between i’s neighbors. As an agent i flips one of its opinions (red circle), si1, from 1 to –1, i can either decrease or increase its individual stress, H(i), depending on the value of the parameter α (Eq. 1). For instance, H(i) would increase if α=1 but would decrease for α=0. For high “rationality” values of individuals w.r.t. social stress, as quantified by β, the latter is more likely to be accepted, resulting in a reduction of the number of unbalanced triads in i’s neighborhood.Here, assuming that it is generally unrealistic for individuals to know their social networks at the triadic level, we aim to understand the emergence and the concrete statistics of balanced triads on the basis of dyadic or one-to-one interactions. Therefore, we use a classic homophily rule (45, 46) to define a “stress level” between any pair of individuals based on the similarity (or overlap) of their individual opinions. Here, the opinions of an individual i are represented by a vector with G components, si, that we show in Fig. 1. Homophily implies that i and j tend to become friends if the overlap (e.g., scalar product of their opinion vectors) is positive, and they become enemies if the overlap is negative. Such a specification of homophily is often referred to as an attraction–repulsion or assimilation–differentiation rule (49, 50). Assuming that, generally, social relations rearrange such as to minimize individual social stress on average, we will show that balanced triads naturally emerge from purely dyadic homophilic interactions without any explicit selection mechanisms for specific triads. We formulate the opinion link dynamics leading to social balance within a transparent physics-inspired framework. In particular, we observe a dynamic transition between two different types of balanced steady states that correspond to different compositions of balanced triads.Explaining the empirical statistics of triangles in social systems is a challenge. Early works considered groups of a few monks in a monastery (3) or a few students in classrooms (51). The studies suffered from limited data and small network sizes. Large-scale studies were first performed in online platforms (4) and in the society of players of the massive multiplayer online game (MMOG) Pardus. Players in Pardus engage in a form of economic life, such as trade and mining, and in social activities, such as communication on a number of channels, forming friendships and enmities (details are in refs. 5, 52, and 53). In the social networks of this game, balanced triads were once more confirmed to be overrepresented compared with what is expected by chance. Similar patterns of triad statistics were also observed in Epinion, Slashdot, and Wikipedia (4). More details on the Pardus society are in Materials and Methods. This dataset gives us the unique possibility to validate the model and compare the predictions with actual triangle statistics and formation of positively connected groups that are foundational to social cohesion.     

Local generation of multineuronal spike sequences in the hippocampal CA1 region

Sequential activity of multineuronal spiking can be observed during theta and high-frequency ripple oscillations in the hippocampal CA1 region and is linked to experience, but the mechanisms underlying such sequences are unknown. We compared multineuronal spiking during theta oscillations, spontaneous ripples, and focal optically induced high-frequency oscillations (“synthetic” ripples) in freely moving mice. Firing rates and rate modulations of individual neurons, and multineuronal sequences of pyramidal cell and interneuron spiking, were correlated during theta oscillations, spontaneous ripples, and synthetic ripples. Interneuron spiking was crucial for sequence consistency. These results suggest that participation of single neurons and their sequential order in population events are not strictly determined by extrinsic inputs but also influenced by local-circuit properties, including synapses between local neurons and single-neuron biophysics.A hypothesized hallmark of cognition is self-organized sequential activation of neuronal assemblies (1). Self-organized neuronal sequences have been observed in several cortical structures (2–5) and neuronal models (6–7). In the hippocampus, sequential activity of place cells (8) may be induced by external landmarks perceived by the animal during spatial navigation (9) and conveyed to CA1 by the upstream CA3 region or layer 3 of the entorhinal cortex (10). Internally generated sequences have been also described in CA1 during theta oscillations in memory tasks (4, 11), raising the possibility that a given neuronal substrate is responsible for generating sequences at multiple time scales. The extensive recurrent excitatory collateral system of the CA3 region has been postulated to be critical in this process (4, 7, 12, 13).The sequential activity of place cells is “replayed” during sharp waves (SPW) in a temporally compressed form compared with rate modulation of place cells (14–20) and may arise from the CA3 recurrent excitatory networks during immobility and slow wave sleep. The SPW-related convergent depolarization of CA1 neurons gives rise to a local, fast oscillatory event in the CA1 region (“ripple,” 140–180 Hz; refs. 8 and 21). Selective elimination of ripples during or after learning impairs memory performance (22–24), suggesting that SPW ripple-related replay assists memory consolidation (12, 13). Although the local origin of the ripple oscillations is well demonstrated (25, 26), it has been tacitly assumed that the ripple-associated, sequentially ordered firing of CA1 neurons is synaptically driven by the upstream CA3 cell assemblies (12, 15), largely because excitatory recurrent collaterals in the CA1 region are sparse (27). However, sequential activity may also emerge by local mechanisms, patterned by the different biophysical properties of CA1 pyramidal cells and their interactions with local interneurons, which discharge at different times during a ripple (28–30). A putative function of the rich variety of interneurons is temporal organization of principal cell spiking (29–32). We tested the “local-circuit” hypothesis by comparing the probability of participation and sequential firing of CA1 neurons during theta oscillations, natural spontaneous ripple events, and “synthetic” ripples induced by local optogenetic activation of pyramidal neurons.     

Group discussion improves lie detection

Groups of individuals can sometimes make more accurate judgments than the average individual could make alone. We tested whether this group advantage extends to lie detection, an exceptionally challenging judgment with accuracy rates rarely exceeding chance. In four experiments, we find that groups are consistently more accurate than individuals in distinguishing truths from lies, an effect that comes primarily from an increased ability to correctly identify when a person is lying. These experiments demonstrate that the group advantage in lie detection comes through the process of group discussion, and is not a product of aggregating individual opinions (a “wisdom-of-crowds” effect) or of altering response biases (such as reducing the “truth bias”). Interventions to improve lie detection typically focus on improving individual judgment, a costly and generally ineffective endeavor. Our findings suggest a cheap and simple synergistic approach of enabling group discussion before rendering a judgment.Detecting deception is difficult. Accuracy rates in experiments are only slightly greater than chance, even among trained professionals (1–4). This meager accuracy rate appears driven by a modest ability to detect truths rather than lies. In one meta-analysis, individuals accurately identified 61% of truths, but only 47% of lies (5). These results have led researchers to develop costly training programs targeting individual lie detectors to increase accuracy (6–10). We test a different strategy: asking individuals to detect lies as a group.There are three reasons that groups might detect deception better than individuals. First, because individuals have some skill in distinguishing truths from lies, statistically aggregating individual judgments could increase accuracy (a “wisdom-of-crowds” effect) (11, 12). If individuals detect truths better than lies, aggregating individual judgments would increase truth detection more than lie detection.Second, individuals show a reliable “truth bias,” assuming others are truthful unless given cause for suspicion (5, 13). If groups are less trusting than individuals (14–15), then they could detect lies more accurately because they guess someone is lying more often.Finally, group deliberation could increase accuracy by providing useful information that individuals lack otherwise (16–18). This predicts that group discussion alters how individuals evaluate a given statement to increase accuracy. Because individuals already possess some accuracy in detecting truths, unique improvement from group discussion would increase accuracy in detecting lies.We know of only two inconclusive experiments that test a group advantage in lie detection. In one experiment, participants first made an individual judgment before group discussion, making the independent influence of the subsequent group discussion unclear (17). Although groups were no more accurate than individuals overall, they were marginally better (0.05 < P < 0.10) detecting lies. In the other experiment, groups were no more accurate than individuals (19), but this experiment sampled only two targets, leaving open the possibility of stimulus-specific confounds.We therefore designed four experiments to directly test whether groups could detect liars better than individuals, and, if so, why. Existing research demonstrates that increasing incentives for accuracy among lie detectors does not increase accuracy, but that increasing incentives for effective deception among lie tellers can make lies easier to detect (5). We therefore did not manipulate lie detectors’ incentives to detect truths vs. lies accurately, but instead asked participants to detect truths vs. lies in low-stakes (experiments 1, 2, and 4) and high-stakes contexts (experiment 3) for the lie tellers.     

Metapopulations with habitat modification

Across the tree of life, organisms modify their local environment, rendering it more or less hospitable for other species. Despite the ubiquity of these processes, simple models that can be used to develop intuitions about the consequences of widespread habitat modification are lacking. Here, we extend the classic Levins metapopulation model to a setting where each of n species can colonize patches connected by dispersal, and when patches are vacated via local extinction, they retain a “memory” of the previous occupant—modeling habitat modification. While this model can exhibit a wide range of dynamics, we draw several overarching conclusions about the effects of modification and memory. In particular, we find that any number of species may potentially coexist, provided that each is at a disadvantage when colonizing patches vacated by a conspecific. This notion is made precise through a quantitative stability condition, which provides a way to unify and formalize existing conceptual models. We also show that when patch memory facilitates coexistence, it generically induces a positive relationship between diversity and robustness (tolerance of disturbance). Our simple model provides a portable, tractable framework for studying systems where species modify and react to a shared landscape.

Many interactions between species are realized indirectly, through effects on a shared environment. For example, consumers compete indirectly by altering resource availability (1, 2). However, the ways that species affect and are affected by their environment extend far beyond the consumption of resources. Across the tree of life and over a tremendous range of spatial scales, organisms make complex and sometimes substantial changes to the physical and chemical properties of their local environment (3–6). Many species also impact local biotic factors; for example, plant–soil feedbacks are often driven by changes in soil microbiome composition (4, 7–9).Numerous studies have recognized and discussed the ways that such changes can mediate interactions between species, as well as the obstacles to modeling these complex, indirect interactions (5, 7, 10–12). In some instances, the effects of environmental modification by one species on another can be accounted for implicitly in models of direct interactions (2, 13, 14) or within the well-established framework of resource competition (12, 15). But in many other cases, new modeling approaches are necessary.Because the range of ecosystems where interactions are driven by environmental modification is wide and varied, many parallel strands of theory have developed for them. Examples include “traditional” ecosystem engineers (16–20), plant–soil feedbacks (4, 7, 21), and chemically mediated interactions between microbes (5, 12). Similar dynamics underlie Janzen–Connell effects, where individuals (e.g., tropical trees) modify their local environment by supporting high densities of natural enemies (8, 22–24), and immune-mediated pathogen competition, where pathogen strains modify their hosts by inducing specific immunity (25–28). These last two examples highlight that environmental modification might be “passive,” in the sense that it is generated by the environment itself.While each of these systems has attracted careful study, it is difficult to elucidate general principles for the dynamics of environmentally mediated interactions without a simple, shared theoretical framework. Are there generic conditions for the coexistence of many species in these systems? What are typical relationships between diversity and ecosystem productivity or robustness? We especially lack theoretical expectations for high-diversity communities, as most existing models focus on the dynamics of one or two species (4, 7, 16, 17).To begin answering these questions, we introduce and analyze a flexible model for species interactions mediated by environmental modification. Two essential features of these interactions—which underlie the difficulty integrating them into standard ecological theory—are that environmental modifications are localized in space and persistent in time (10). To capture these aspects, we adopt the metapopulation framework, introduced by Levins (29), which provides a minimal model for population dynamics with distinct local and global scales. Metapopulation models underpin a productive and diverse body of theory in ecology (30, 31), including various extensions to study multispecies communities (32, 33). Here, we adopt the simplest such extension, by assuming zero-sum dynamics and an essentially horizontal community (34, 35). Our modeling framework accommodates lasting environmental modification by introducing a versatile notion of “patch memory,” in which the state of local sites depends on past occupants.In line with evidence from a range of systems, we find that patch memory can support the robust coexistence of any number of species, even in an initially homogeneous landscape. We derive quantitative conditions for species’ coexistence and show how they connect to existing conceptual models. Importantly, these conditions apply even as several model assumptions are relaxed. We also investigate an emergent relationship between species diversity and robustness, demonstrating that our modeling framework can provide insight for a variety of systems characterized by localized environmental feedbacks.     

Sleep,Brain, and Cognition Special Feature: Multiple traces and altered signal-to-noise in systems consolidation: Evidence from the 7T fMRI Natural Scenes Dataset

The brain mechanisms of memory consolidation remain elusive. Here, we examine blood-oxygen-level-dependent (BOLD) correlates of image recognition through the scope of multiple influential systems consolidation theories. We utilize the longitudinal Natural Scenes Dataset, a 7-Tesla functional magnetic resonance imaging human study in which ∼135,000 trials of image recognition were conducted over the span of a year among eight subjects. We find that early- and late-stage image recognition associates with both medial temporal lobe (MTL) and visual cortex when evaluating regional activations and a multivariate classifier. Supporting multiple-trace theory (MTT), parts of the MTL activation time course show remarkable fit to a 20-y-old MTT time-dynamical model predicting early trace intensity increases and slight subsequent interference (R² > 0.90). These findings contrast a simplistic, yet common, view that memory traces are transferred from MTL to cortex. Next, we test the hypothesis that the MTL trace signature of memory consolidation should also reflect synaptic “desaturation,” as evidenced by an increased signal-to-noise ratio. We find that the magnitude of relative BOLD enhancement among surviving memories is positively linked to the rate of removal (i.e., forgetting) of competing traces. Moreover, an image-feature and time interaction of MTL and visual cortex functional connectivity suggests that consolidation mechanisms improve the specificity of a distributed trace. These neurobiological effects do not replicate on a shorter timescale (within a session), implicating a prolonged, offline process. While recognition can potentially involve cognitive processes outside of memory retrieval (e.g., re-encoding), our work largely favors MTT and desaturation as perhaps complementary consolidative memory mechanisms.

Systems consolidation refers to the reorganization of a memory trace with prolonged time and experience across large-scale neuronal networks (1). The precise mechanisms underlying this process remain unclear, but the end result includes the stabilization of certain memories, the equally vital forgetting of nonessential information (2), and the transformation of some memories into more behaviorally adaptive or gist-like representations (3). Influential theories of systems-level consolidation are largely built upon the seminal observations that varying medial temporal lobe (MTL) damage causes an inverse memory effect, whereby the ability to recall recently encoded memories is reduced while many older memories (weeks to years) remain intact (4).Theoretical approaches to explain these findings began with the standard consolidation theory (SCT), which proposed that MTL contributions to any memory trace diminish over time (5). Alternatively, multiple-trace theory (MTT), put forward in 1997, clarified inconsistencies of this standpoint with many experiments showing that MTL lesions caused more severe retrograde amnesia for episodic than for semantic memories (6, 7). For example, Bright et al. (8) showed limited retrograde amnesia for a variety of tests of public events and personalities (semantic memory), while for autobiographical episodes, a retrograde amnesia extended back further. Episodic memories contain elements often in the form of visual images (9) that are recollected within some overlaying context (10). MTT posited that an episodic memory must rely on the MTL, and on multiple content-relevant cortical modules, across its entire lifespan, not just the beginning. Early MTT developments emphasized that episodic memory reactivations—which occur during conscious recall or recognition but also during “offline” memory replays (11) within waking quiescence and sleep (12, 13)—lead to a rich distributed network of multiple, overlaid traces in the MTL over time. This process, coined as “trace expansion,” would presumably provide memory protection from partial lesions (14). Within the human functional magnetic resonance imaging (fMRI) literature, there are conflicting reports (14) showing both SCT-predicted decreases in hippocampus activity during recall (e.g., refs. 15–17) and MTT-predicted increases in hippocampus activity during recall (e.g., refs. 18–20). Most of this prior work has a limited time perspective (with only three or fewer timepoints), and brain measurements were not acquired with high-field fMRI. Moreover, while multiple time-dynamical analytic models of MTL trace intensity have been inspired by the nonlinear probability time curves of retrograde amnesia (21), to our knowledge, there has not yet been any application of these mathematical formulations to functional human neuroimaging data due to the paucity of timepoints and samples.The analysis of the connectivity between the MTL and the neocortex offers a crucial perspective of systems-level memory consolidation (3). Intracranial human studies are now establishing precise timing links between the hippocampus and content-relevant cortex necessary for memory retrieval (22–26). For instance, Norman et al. (26) investigated autobiographical memory remoteness spanning days, weeks, and months. They demonstrated that hippocampal ripples—high-frequency (∼80 to 100 Hz in humans) oscillatory events in hippocampal local field potentials—correlate with memory remoteness and promote communication across large-scale networks. According to the authors, their findings “support theories that emphasize richer hippocampal representations of remote memories (e.g., the MTT)” (26), which conflicts with SCT. SCT emphasizes that the MTL’s role should be diminished over time. While SCT does not posit that MTL traces are entirely removed, a simplistic but common narrative derives itself from SCT: fully consolidated memories (episodic or semantic) may completely lose their dependency on the hippocampus (12, 27, 28), which we refer to hereon as “trace transfer.” The validity of these viewpoints—MTT, SCT, and trace transfer—remains unclear.Mechanistic underpinnings of systems consolidation may rely on an increased signal-to-noise ratio of traces, although this has not been explicitly addressed by either SCT or MTT. Specifically, because most learning involves strengthening synaptic connections throughout the brain, intense learning is poised to increase cellular needs for energy and supplies, move synapses close to saturation, and decrease signal-to-noise ratios (2). Sleep is the principal mechanism that renormalizes net synaptic strength and restores cellular homeostasis while maintaining certain memory traces (2, 29). In this regard, retaining memories—through sleep or other consolidation mechanisms—may result in the reorganization of the synaptic landscape to promote desaturation and, thus, improve signal-to-noise ratios of surviving traces at the systems level. Simulation models and recent studies in mice have, indeed, supported this perspective (30–32). However, more evidence is necessary to advance this hypothesis.Here, we utilize the recently acquired, publicly available Natural Scenes Dataset (NSD), an unprecedented resource to study memory consolidation (33). Over 300 d, eight subjects participated in weekly 7-Tesla (7T) fMRI scans while exposed to the NSD; ∼135,000 trials (∼2/3 of total trials) involved subjects seeing an image that was previously presented in the experiment. We first examined the relevant memory consolidation models in describing trace evolution. Does natural scene image recognition, which we presume to be episodic in nature, continually rely on the MTL over time as MTT suggests, or are these traces transferred to cortex as suggested by the trace transfer thesis? Furthermore, in regard to MTT, can MTL time dynamics be explained by a precise mathematical model formulated in the early MTT literature? And can the timescale (days vs. min) of trace evolution be distinguished from different mathematical frameworks? In the latter part of this work, we investigated the hypothesis that increased signal-to-noise ratio of brain traces would occur over time. Specifically, we tested whether the relative blood-oxygen-level-dependent (BOLD) enhancement of surviving traces over time is linked to the concomitant deletion of other traces (i.e., forgetting). Finally, because the MTL is proposed to bind content-relevant cortical modules, we assessed whether the specific MTL connectivity changes according to specific image-feature content.     

Responses to social and environmental stress are attenuated by strong male bonds in wild macaques

In humans and obligatory social animals, individuals with weak social ties experience negative health and fitness consequences. The social buffering hypothesis conceptualizes one possible mediating mechanism: During stressful situations the presence of close social partners buffers against the adverse effects of increased physiological stress levels. We tested this hypothesis using data on social (rate of aggression received) and environmental (low temperatures) stressors in wild male Barbary macaques (Macaca sylvanus) in Morocco. These males form strong, enduring, and equitable affiliative relationships similar to human friendships. We tested the effect of the strength of a male’s top three social bonds on his fecal glucocorticoid metabolite (fGCM) levels as a function of the stressors’ intensity. The attenuating effect of stronger social bonds on physiological stress increased both with increasing rates of aggression received and with decreasing minimum daily temperature. Ruling out thermoregulatory and immediate effects of social interactions on fGCM levels, our results indicate that male Barbary macaques employ a tend-and-befriend coping strategy in the face of increased environmental as well as social day-to-day stressors. This evidence of a stress-ameliorating effect of social bonding among males under natural conditions and beyond the mother–offspring, kin or pair bond broadens the generality of the social buffering hypothesis.Strong affiliative social relationships exert powerful beneficial effects on an individual’s health and fitness in both humans and nonhuman animals (1–5). One well-studied mediating mechanism, conceptualized in the social buffering hypothesis, is that the presence of a close social partner attenuates the reactivity of the hypothalamic–pituitary–adrenal (HPA) axis (apart from other positive effects on physiological responses) and thus buffers against the potentially adverse effects of physiological stress (4, 6, 7). Evidence for the social buffering hypothesis rests primarily on experimental studies exposing subjects to stressful situations when a close social partner is present or absent (6–8). In that sense, previous studies on the social buffering effect captured an interaction effect of social bonding and a stressor, usually via exposure to a novel environment or, in humans, psychological stress on the stress response (4).The individual functioning as a social buffer against stress is usually a pair-bonded partner [in humans and nonhuman animals (6, 8–11)] or mother [in infant nonhuman animals (11–13)]. The “tend-and-befriend” stress-coping-mechanism (i.e., turning to close affiliates and kin), when under stress, has been linked to the attachment–caregiving system partly regulated by the oxytocinergic system (14–16). Possibly as a direct consequence of this, humans exhibit a strong sex difference in behavioral coping mechanisms to perceived stressful events; women are more likely to seek social support in stressful situations compared with men (ref. 17, but see ref. 18). Stress alleviation via social support has also been shown in nonhuman primates where females with stronger bonds or a tighter social network showed an attenuated response to stressors compared with those with weaker social ties (19, 20). For example, the death of a close female partner (catastrophic stressor), usually kin, in baboons led to increased physiological stress, and the bereaved partner attempted to alleviate this response by strengthening existing bonds (21). After a conflict event in chimpanzees (Pan troglodytes) and bonobos (Pan paniscus) closely bonded bystanders can actively console recipients of aggression, thereby reducing behavioral measures of stress (22–24). Many nonhuman primate females live in closely interwoven matrilineal networks of mutual affiliation and support (25–27) that generate strong fitness advantages in terms of increased reproductive rates and survival (1, 28, 29).Because most males compete for opportunities to fertilize females (30) the focus of studies investigating correlates of male physiological stress have historically been on reproductive competition and hierarchical status (31–33). Nevertheless, recent and increasing evidence has shown that males of some vertebrate species also form strong social bonds that can enhance their fitness (refs. 34–38 and reviewed in ref. 39). However, to date social buffering effects on acute HPA responses in adult male vertebrates have been investigated predominantly in pair-living species (or pair-housed animals) in response to the female pair partner’s presence (reviewed in ref. 6). It remains to be shown whether the human sex difference in behavioral stress-coping mechanisms is exhibited by other mammals as well or whether males, like females, experience social buffering responses under stress when they have strong social ties to other same-sex individuals in their group.Similar to philopatric female baboons and male chimpanzees (38, 40, 41) macaque males of some species, including Barbary macaques (Macaca sylvanus), form strong social relationships with a few male partners (35, 36, 39, 42) that are stable over consecutive years and characterized by equitability in exchanges of affiliation (37). The mechanisms guiding partner selection for the formation of social bonds in male macaques are currently unknown. Parallel dispersal has been observed (43), and in large provisioned groups maternal relatedness partly drives agonistic support (44), but the strength of male social bonds is not decreased in maternally unrelated males in the wild (35). Males vary in the number and strength of social bonds they form (37), which may partly be guided by age (36, 45) and may additionally be affected by personality (46, 47).Barbary macaque males frequently experience noncatastrophic stressful situations in their daily lives that may be social or environmental. Within-group conflicts resulting in aggression represent a social stressor that is positively correlated to glucocorticoid levels (a measure of physiological stress) across many primates (19, 48–51). Within-group aggressive conflicts also vary between individuals (50, 52) and between seasons, with peaks during the mating season (36). An annually recurring environmental stressor in the study population of Barbary macaques is cold stress during the winter months. Winter survival probability was found to be predicted by the number of affiliative relationships an individual formed (53). In baboons temperature stress is associated with increased glucocorticoid levels (54, 55).Here we took advantage of this macaque system of strong male bonding and the occurrence of several stressors in an individual’s daily life to test the social buffering hypothesis in a natural situation and within the male sex. As the buffering hypothesis proposes that social support or bonding is related to well-being only during stressful situations (4), we predicted an interaction effect: As stressor intensity increases (i.e., rate of aggression received increases or minimum temperature declines), the attenuating effect of an individual''s social bond strength on faecal glucocorticoid metabolite (fGCM) levels becomes stronger. We also controlled for an alternative, not mutually exclusive, hypothesis, the “immediate effects hypothesis,” stating that affiliative social behavior directly alleviates physiological stress irrespective of the social relationship the partners feature (20, 56, 57). For this, we tested the proximate effects of rates of grooming given and received by all group members, grooming with the top three male partners, or frequency of male–infant–male triadic interactions on fGCMs.     

Sleep,Brain, and Cognition Special Feature: Shaping overnight consolidation via slow-oscillation closed-loop targeted memory reactivation

Sleep constitutes a privileged state for new memories to reactivate and consolidate. Previous work has demonstrated that consolidation can be bolstered experimentally either via delivery of reminder cues (targeted memory reactivation [TMR]) or via noninvasive brain stimulation geared toward enhancing endogenous sleep rhythms. Here, we combined both approaches, controlling the timing of TMR cues with respect to ongoing slow-oscillation (SO) phases. Prior to sleep, participants learned associations between unique words and a set of repeating images (e.g., car) while hearing a prototypical image sound (e.g., engine starting). Memory performance on an immediate test vs. a test the next morning quantified overnight memory consolidation. Importantly, two image sounds were designated as TMR cues, with one cue delivered at SO UP states and the other delivered at SO DOWN states. A novel sound was used as a TMR control condition. Behavioral results revealed a significant reduction of overnight forgetting for words associated with UP-state TMR compared with words associated with DOWN-state TMR. Electrophysiological results showed that UP-state cueing led to enhancement of the ongoing UP state and was followed by greater spindle power than DOWN-state cueing. Moreover, UP-state (and not DOWN-state) cueing led to reinstatement of target image representations. Together, these results unveil the behavioral and mechanistic effects of delivering reminder cues at specific phases of endogenous sleep rhythms and mark an important step for the endeavor to experimentally modulate memories during sleep.

Memory consolidation (i.e., the stabilization and integration of newly acquired memories over time) benefits from postlearning sleep (1–4). Ignited by the finding of hippocampal “replay” in rodent sleep recordings (5–7), theoretical and computational models have highlighted the critical role of reactivation for effective systems consolidation. Specifically, reactivation of hippocampal learning representations is thought to gradually transfer memories to cortical sites for more permanent storage, and sleep—hallmarked by the absence of external distracters—constitutes a privileged state for this “hippocampal–cortical dialogue” (8–10). More recent work in humans has sought to capitalize on sleep as a window of opportunity to modulate memory consolidation experimentally. In particular, a seminal study linked learning materials to a particular environmental scent and showed that providing olfactory reminders to sleeping participants can slow down overnight forgetting (11). Known as targeted memory reactivation (TMR), a large body of work has since established the efficacy of presenting olfactory or auditory reminder cues for bolstering consolidation of recent learning experiences (reviewed in refs. 12 and 13).What are the underlying mechanisms governing reactivation and consolidation during sleep? During non-rapid eye movement (NREM) sleep, the scalp electroencephalogram (EEG) is dominated by two cardinal signatures: slow oscillations [SOs; <1-Hz high-amplitude EEG fluctuations (14, 15)] and sleep spindles [∼12- to 16-Hz waxing and waning bursts of 0.5- to 2-s duration (16, 17)]. Both these phenomena individually as well as their co-occurrence have been implicated in memory consolidation (18–23). SOs can be thought of as global pacemakers of brain activity during sleep, toggling between intervals of neuronal excitability (UP states) and inhibition (DOWN states). Importantly, SO UP states tend to group sleep spindles (22, 24–26), which in turn, have been linked to memory reprocessing and plasticity (27–32). Given their pivotal role for orchestrating brain processes and their relatively high signal to noise ratio (i.e., detectability against the background EEG), SOs have been targeted by efforts to experimentally boost memory consolidation via noninvasive brain stimulation. One particularly promising approach has been to entrain SOs, e.g., by transcranial direct current stimulation (33) or by applying auditory clicks in a closed-loop fashion (34). In the latter case, SOs are detected algorithmically in real time, and brief bursts of noise (clicks) are presented when an SO UP state occurs. This has been shown to prolong ongoing SO trains, elicit sleep spindles coupled to SOs, and enhance behavioral expressions of memory consolidation (34–37).In sum, both the delivery of reminder cues (TMR) and experimentally augmenting SOs have yielded promising results for the endeavor to strengthen overnight memory consolidation. This begs the question of whether the two approaches can be combined, i.e., is TMR more effective when cues are delivered at a particular phase of ongoing SOs?. Indeed, retrospective analysis of a TMR experiment suggested that the effects of memory cueing are modulated by the SO phase at which cues were presented, with the optimal presentation interval spanning the SO UP state (38). To date, only two studies have modulated consolidation by applying TMR at different SO phases. First, Shimuzi et al. (39) presented reminder cues associated with a spatial memory task during the DOWN to UP transition and found an improvement in memory performance compared with a condition without any intervention. However, the study also included a cueing phase during wakefulness embedded in an interference task, rendering it difficult to unambiguously link effects to sleep TMR. Moreover, no control condition was included in which TMR would be applied at a different SO phase. Second, Göldi et al. (40) examined the effects of TMR (linked to a vocabulary learning task) delivered at SO UP states vs. DOWN states. Results remained somewhat ambiguous, with an advantage of UP-state cued content over uncued items but no statistical difference of DOWN-state cueing compared with UP-state cueing or no cueing. Notably, none of these studies assessed whether the emergence of spindles and/or reactivation of target associations (28, 41) would vary as a function of UP- vs. DOWN-state delivery of TMR cues.In this study, we thus used a closed-loop protocol in which we experimentally controlled the SO phase at which different mnemonic reminder cues were delivered. We hypothesized that cues delivered during SO UP states would not only lead to enhanced behavioral expressions of memory consolidation compared with cues delivered during SO DOWN states but would also entrain stronger spindle activity and more effectively trigger reactivation of target representations. This would pave the way to maximizing the efficacy of experimental/therapeutic interventions seeking to control memory processes during sleep.     

Phase-transforming metamaterial with magnetic interactions

Solid–solid phase transformations can affect energy transduction and change material properties (e.g., superelasticity in shape memory alloys and soft elasticity in liquid crystal elastomers). Traditionally, phase-transforming materials are based on atomic- or molecular-level thermodynamic and kinetic mechanisms. Here, we develop elasto-magnetic metamaterials that display phase transformation behaviors due to nonlinear interactions between internal elastic structures and embedded, macroscale magnetic domains. These phase transitions, similar to those in shape memory alloys and liquid crystal elastomers, have beneficial changes in strain state and mechanical properties that can drive actuations and manage overall energy transduction. The constitutive response of the elasto-magnetic metamaterial changes as the phase transitions occur, resulting in a nonmonotonic stress–strain relation that can be harnessed to enhance or mitigate energy storage and release under high–strain-rate events, such as impulsive recoil and impact. Using a Landau free energy–based predictive model, we develop a quantitative phase map that relates the geometry and magnetic interactions to the phase transformation. Our work demonstrates how controllable phase transitions in metamaterials offer performance capabilities in energy management and programmable material properties for high-rate applications.

Soli d–solid phase transitions can enhance energy transduction to drive actuation. For example, in shape memory alloys, phase transitions from martensite to austenite lead to changes in the lattice displacement, which have been harnessed to cause movement (1, 2). Similarly, phase transitions in liquid crystal elastomers, from well-aligned nematic states to randomly aligned isotropic states, have been used to drive motion (3–8). Such phase transitions can also change the mechanical properties of the material system. For example, liquid crystal elastomers can enter in a state of “semisoft elasticity,” where strain is accommodated at near-constant stress as the material transitions from one phase to another (4, 9). Transformations such as these offer desirable control of energy conversion (10, 11), which can be beneficial in dissipating energy and protecting the system from damage. While solid–solid phase transitions provide promise for energy-management devices, predictably engineering these transitions and their impact on material properties is challenging.Mechanical metamaterials offer an opportunity to overcome this challenge (12, 13). Mechanical metamaterials use periodically arranged blocks, or “meta-atoms” (13), to mediate mechanical deformation, stress, and energy. They have been used to program stress–strain responses (14–16), modulate elastic wave propagation (17–21), and control energy dissipation (22, 23). Mechanical metamaterials typically rely upon internal geometric changes to introduce functionality, taking advantage of known nonlinear geometric mechanics for elastic materials (24, 25). This approach is widely adopted since analytical or numerical models can be readily derived to understand and predict the observed behaviors, thus providing pathways for systematic programming of the material''s response.Further advantages may be realized by combining additional fields beyond elasticity (26). Recently, the addition of field-responsive materials to metamaterials has been demonstrated to offer advantageous functionality (27–31). These demonstrations are impactful on their own, but more importantly, they introduce a broader paradigm with far-reaching implications. In particular, the combination of nonlinear fields, such as magnetic and electric, with orientationally dependent regimes of attraction and repulsion opens up the creation of “meta-atoms” that more closely reflect characteristics of atomic or molecular arrangements in materials phase structures (31). Accordingly, this counter play of nonlinear fields can be used to quantitatively define phase transitions in mechanical metamaterials composed of elastic and magnetic elements.Here, we demonstrate the power of this paradigm by developing elasto-magnetic metamaterials that undergo phase transitions. These phase transitions, similar to enabling ones in shape memory alloys and liquid crystal elastomers, have beneficial changes in strain state and mechanical properties that can drive actuations and manage overall energy transduction. The metamaterial changes its constitutive response concurrently as the phase transitions happen, resulting in a nonmonotonic stress–strain relation. The reversible shift between the two phases significantly enhances the metamaterials'' dynamic performance, improving the energy release in dynamic recoil and mitigating the impact loading. Importantly, we introduce a Landau free energy framework to model the phase transitions for the elasto-magnetic metamaterials, which can be extended to metamaterials with other field-responsive materials or ones that are purely mechanical (20, 21). This framework creates opportunities based on fundamental principles for using phase transitions to control engineering performance at high rates.