Inferring on the Intentions of Others by Hierarchical Bayesian Learning

Inferring on others'' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to “player” or “adviser” roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser''s incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players'' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers'' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser''s intentions were presently stable. Finally, our model of the players'' inference predicted the players'' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers'' helpfulness and the advisers'' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition.     

Reading the Mind in the Eyes or Reading between the Lines? Theory of Mind Predicts Collective Intelligence Equally Well Online and Face-To-Face

Recent research with face-to-face groups found that a measure of general group effectiveness (called “collective intelligence”) predicted a group’s performance on a wide range of different tasks. The same research also found that collective intelligence was correlated with the individual group members’ ability to reason about the mental states of others (an ability called “Theory of Mind” or “ToM”). Since ToM was measured in this work by a test that requires participants to “read” the mental states of others from looking at their eyes (the “Reading the Mind in the Eyes” test), it is uncertain whether the same results would emerge in online groups where these visual cues are not available. Here we find that: (1) a collective intelligence factor characterizes group performance approximately as well for online groups as for face-to-face groups; and (2) surprisingly, the ToM measure is equally predictive of collective intelligence in both face-to-face and online groups, even though the online groups communicate only via text and never see each other at all. This provides strong evidence that ToM abilities are just as important to group performance in online environments with limited nonverbal cues as they are face-to-face. It also suggests that the Reading the Mind in the Eyes test measures a deeper, domain-independent aspect of social reasoning, not merely the ability to recognize facial expressions of mental states.     

Competition over Personal Resources Favors Contribution to Shared Resources in Human Groups

Members of social groups face a trade-off between investing selfish effort for themselves and investing cooperative effort to produce a shared group resource. Many group resources are shared equitably: they may be intrinsically non-excludable public goods, such as vigilance against predators, or so large that there is little cost to sharing, such as cooperatively hunted big game. However, group members'' personal resources, such as food hunted individually, may be monopolizable. In such cases, an individual may benefit by investing effort in taking others'' personal resources, and in defending one''s own resources against others. We use a game theoretic “tug-of-war” model to predict that when such competition over personal resources is possible, players will contribute more towards a group resource, and also obtain higher payoffs from doing so. We test and find support for these predictions in two laboratory economic games with humans, comparing people''s investment decisions in games with and without the options to compete over personal resources or invest in a group resource. Our results help explain why people cooperatively contribute to group resources, suggest how a tragedy of the commons may be avoided, and highlight unifying features in the evolution of cooperation and competition in human and non-human societies.     

Making Smart Social Judgments Takes Time: Infants' Recruitment of Goal Information When Generating Action Predictions

Previous research has shown that young infants perceive others'' actions as structured by goals. One open question is whether the recruitment of this understanding when predicting others'' actions imposes a cognitive challenge for young infants. The current study explored infants'' ability to utilize their knowledge of others'' goals to rapidly predict future behavior in complex social environments and distinguish goal-directed actions from other kinds of movements. Fifteen-month-olds (N = 40) viewed videos of an actor engaged in either a goal-directed (grasping) or an ambiguous (brushing the back of her hand) action on a Tobii eye-tracker. At test, critical elements of the scene were changed and infants'' predictive fixations were examined to determine whether they relied on goal information to anticipate the actor''s future behavior. Results revealed that infants reliably generated goal-based visual predictions for the grasping action, but not for the back-of-hand behavior. Moreover, response latencies were longer for goal-based predictions than for location-based predictions, suggesting that goal-based predictions are cognitively taxing. Analyses of areas of interest indicated that heightened attention to the overall scene, as opposed to specific patterns of attention, was the critical indicator of successful judgments regarding an actor''s future goal-directed behavior. These findings shed light on the processes that support “smart” social behavior in infants, as it may be a challenge for young infants to use information about others'' intentions to inform rapid predictions.     

Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data

Use of socially generated “big data” to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society''s reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between “real time monitoring” and “early predicting” remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.     

Intention Understanding in Autism

When we observe a motor act (e.g. grasping a cup) done by another individual, we extract, according to how the motor act is performed and its context, two types of information: the goal (grasping) and the intention underlying it (e.g. grasping for drinking). Here we examined whether children with autistic spectrum disorder (ASD) are able to understand these two aspects of motor acts. Two experiments were carried out. In the first, one group of high-functioning children with ASD and one of typically developing (TD) children were presented with pictures showing hand-object interactions and asked what the individual was doing and why. In half of the “why” trials the observed grip was congruent with the function of the object (“why-use” trials), in the other half it corresponded to the grip typically used to move that object (“why-place” trials). The results showed that children with ASD have no difficulties in reporting the goals of individual motor acts. In contrast they made several errors in the why task with all errors occurring in the “why-place” trials. In the second experiment the same two groups of children saw pictures showing a hand-grip congruent with the object use, but within a context suggesting either the use of the object or its placement into a container. Here children with ASD performed as TD children, correctly indicating the agent''s intention. In conclusion, our data show that understanding others'' intentions can occur in two ways: by relying on motor information derived from the hand-object interaction, and by using functional information derived from the object''s standard use. Children with ASD have no deficit in the second type of understanding, while they have difficulties in understanding others'' intentions when they have to rely exclusively on motor cues.     

The Effects of Prediction on the Perception for Own-Race and Other-Race Faces

Human beings do not passively perceive important social features about others such as race and age in social interactions. Instead, it is proposed that humans might continuously generate predictions about these social features based on prior similar experiences. Pre-awareness of racial information conveyed by others'' faces enables individuals to act in “culturally appropriate” ways, which is useful for interpersonal relations in different ethnicity groups. However, little is known about the effects of prediction on the perception for own-race and other-race faces. Here, we addressed this issue using high temporal resolution event-related potential techniques. In total, data from 24 participants (13 women and 11 men) were analyzed. It was found that the N170 amplitudes elicited by other-race faces, but not own-race faces, were significantly smaller in the predictable condition compared to the unpredictable condition, reflecting a switch to holistic processing of other-race faces when those faces were predictable. In this respect, top-down prediction about face race might contribute to the elimination of the other-race effect (one face recognition impairment). Furthermore, smaller P300 amplitudes were observed for the predictable than for unpredictable conditions, which suggested that the prediction of race reduced the neural responses of human brains.     

When Does Reward Maximization Lead to Matching Law?

What kind of strategies subjects follow in various behavioral circumstances has been a central issue in decision making. In particular, which behavioral strategy, maximizing or matching, is more fundamental to animal''s decision behavior has been a matter of debate. Here, we prove that any algorithm to achieve the stationary condition for maximizing the average reward should lead to matching when it ignores the dependence of the expected outcome on subject''s past choices. We may term this strategy of partial reward maximization “matching strategy”. Then, this strategy is applied to the case where the subject''s decision system updates the information for making a decision. Such information includes subject''s past actions or sensory stimuli, and the internal storage of this information is often called “state variables”. We demonstrate that the matching strategy provides an easy way to maximize reward when combined with the exploration of the state variables that correctly represent the crucial information for reward maximization. Our results reveal for the first time how a strategy to achieve matching behavior is beneficial to reward maximization, achieving a novel insight into the relationship between maximizing and matching.     

Within- and Cross-Modal Distance Information Disambiguate Visual Size-Change Perception

Perception is fundamentally underconstrained because different combinations of object properties can generate the same sensory information. To disambiguate sensory information into estimates of scene properties, our brains incorporate prior knowledge and additional “auxiliary” (i.e., not directly relevant to desired scene property) sensory information to constrain perceptual interpretations. For example, knowing the distance to an object helps in perceiving its size. The literature contains few demonstrations of the use of prior knowledge and auxiliary information in combined visual and haptic disambiguation and almost no examination of haptic disambiguation of vision beyond “bistable” stimuli. Previous studies have reported humans integrate multiple unambiguous sensations to perceive single, continuous object properties, like size or position. Here we test whether humans use visual and haptic information, individually and jointly, to disambiguate size from distance. We presented participants with a ball moving in depth with a changing diameter. Because no unambiguous distance information is available under monocular viewing, participants rely on prior assumptions about the ball''s distance to disambiguate their -size percept. Presenting auxiliary binocular and/or haptic distance information augments participants'' prior distance assumptions and improves their size judgment accuracy—though binocular cues were trusted more than haptic. Our results suggest both visual and haptic distance information disambiguate size perception, and we interpret these results in the context of probabilistic perceptual reasoning.     

Social Network Analysis Shows Direct Evidence for Social Transmission of Tool Use in Wild Chimpanzees

Social network analysis methods have made it possible to test whether novel behaviors in animals spread through individual or social learning. To date, however, social network analysis of wild populations has been limited to static models that cannot precisely reflect the dynamics of learning, for instance, the impact of multiple observations across time. Here, we present a novel dynamic version of network analysis that is capable of capturing temporal aspects of acquisition—that is, how successive observations by an individual influence its acquisition of the novel behavior. We apply this model to studying the spread of two novel tool-use variants, “moss-sponging” and “leaf-sponge re-use,” in the Sonso chimpanzee community of Budongo Forest, Uganda. Chimpanzees are widely considered the most “cultural” of all animal species, with 39 behaviors suspected as socially acquired, most of them in the domain of tool-use. The cultural hypothesis is supported by experimental data from captive chimpanzees and a range of observational data. However, for wild groups, there is still no direct experimental evidence for social learning, nor has there been any direct observation of social diffusion of behavioral innovations. Here, we tested both a static and a dynamic network model and found strong evidence that diffusion patterns of moss-sponging, but not leaf-sponge re-use, were significantly better explained by social than individual learning. The most conservative estimate of social transmission accounted for 85% of observed events, with an estimated 15-fold increase in learning rate for each time a novice observed an informed individual moss-sponging. We conclude that group-specific behavioral variants in wild chimpanzees can be socially learned, adding to the evidence that this prerequisite for culture originated in a common ancestor of great apes and humans, long before the advent of modern humans.     

Selfish Play Increases during High-Stakes NBA Games and Is Rewarded with More Lucrative Contracts

High-stakes team competitions can present a social dilemma in which participants must choose between concentrating on their personal performance and assisting teammates as a means of achieving group objectives. We find that despite the seemingly strong group incentive to win the NBA title, cooperative play actually diminishes during playoff games, negatively affecting team performance. Thus team cooperation decreases in the very high stakes contexts in which it is most important to perform well together. Highlighting the mixed incentives that underlie selfish play, personal scoring is rewarded with more lucrative future contracts, whereas assisting teammates to score is associated with reduced pay due to lost opportunities for personal scoring. A combination of misaligned incentives and psychological biases in performance evaluation bring out the “I” in “team” when cooperation is most critical.     

Bridging the Mechanical and the Human Mind: Spontaneous Mimicry of a Physically Present Android

The spontaneous mimicry of others'' emotional facial expressions constitutes a rudimentary form of empathy and facilitates social understanding. Here, we show that human participants spontaneously match facial expressions of an android physically present in the room with them. This mimicry occurs even though these participants find the android unsettling and are fully aware that it lacks intentionality. Interestingly, a video of that same android elicits weaker mimicry reactions, occurring only in participants who find the android “humanlike.” These findings suggest that spontaneous mimicry depends on the salience of humanlike features highlighted by face-to-face contact, emphasizing the role of presence in human-robot interaction. Further, the findings suggest that mimicry of androids can dissociate from knowledge of artificiality and experienced emotional unease. These findings have implications for theoretical debates about the mechanisms of imitation. They also inform creation of future robots that effectively build rapport and engagement with their human users.     

Quantifying Collective Attention from Tweet Stream

Online social media are increasingly facilitating our social interactions, thereby making available a massive “digital fossil” of human behavior. Discovering and quantifying distinct patterns using these data is important for studying social behavior, although the rapid time-variant nature and large volumes of these data make this task difficult and challenging. In this study, we focused on the emergence of “collective attention” on Twitter, a popular social networking service. We propose a simple method for detecting and measuring the collective attention evoked by various types of events. This method exploits the fact that tweeting activity exhibits a burst-like increase and an irregular oscillation when a particular real-world event occurs; otherwise, it follows regular circadian rhythms. The difference between regular and irregular states in the tweet stream was measured using the Jensen-Shannon divergence, which corresponds to the intensity of collective attention. We then associated irregular incidents with their corresponding events that attracted the attention and elicited responses from large numbers of people, based on the popularity and the enhancement of key terms in posted messages or “tweets.” Next, we demonstrate the effectiveness of this method using a large dataset that contained approximately 490 million Japanese tweets by over 400,000 users, in which we identified 60 cases of collective attentions, including one related to the Tohoku-oki earthquake. “Retweet” networks were also investigated to understand collective attention in terms of social interactions. This simple method provides a retrospective summary of collective attention, thereby contributing to the fundamental understanding of social behavior in the digital era.     

Defectors Cannot Be Detected during“Small Talk” with Strangers

To account for the widespread human tendency to cooperate in one-shot social dilemmas, some theorists have proposed that cooperators can be reliably detected based on ethological displays that are difficult to fake. Experimental findings have supported the view that cooperators can be distinguished from defectors based on “thin slices” of behavior, but the relevant cues have remained elusive, and the role of the judge''s perspective remains unclear. In this study, we followed triadic conversations among unacquainted same-sex college students with unannounced dyadic one-shot prisoner''s dilemmas, and asked participants to guess the PD decisions made toward them and among the other two participants. Two other sets of participants guessed the PD decisions after viewing videotape of the conversations, either with foreknowledge (informed), or without foreknowledge (naïve), of the post-conversation PD. Only naïve video viewers approached better-than-chance prediction accuracy, and they were significantly accurate at predicting the PD decisions of only opposite-sexed conversation participants. Four ethological displays recently proposed to cue defection in one-shot social dilemmas (arms crossed, lean back, hand touch, and face touch) failed to predict either actual defection or guesses of defection by any category of observer. Our results cast doubt on the role of “greenbeard” signals in the evolution of human prosociality, although they suggest that eavesdropping may be more informative about others'' cooperative propensities than direct interaction.     

The Effect of Framing and Normative Messages in Building Support for Climate Policies

Deep cuts in greenhouse gas emissions are required to mitigate climate change. However, there is low willingness amongst the public to prioritise climate policies for reducing emissions. Here we show that the extent to which Australians are prepared to reduce their country''s CO₂ emissions is greater when the costs to future national income are framed as a “foregone-gain”—incomes rise in the future but not by as much as in the absence of emission cuts—rather than as a “loss”—incomes decrease relative to the baseline expected future levels (Studies 1 & 2). The provision of a normative message identifying Australia as one of the world''s largest CO₂ emitters did not increase the amount by which individuals were prepared to reduce emissions (Study 1), whereas a normative message revealing the emission policy preferences of other Australians did (Study 2). The results suggest that framing the costs of reducing emissions as a smaller increase in future income and communicating normative information about others'' emission policy preferences are effective methods for leveraging public support for emission cuts.     

Familial Trends in Low Birth Weight

The reproductive performance of sisters and sisters-in-law of 185 women who had delivered “light-for-dates” and “premature expulsion” low birth weight infants was studied. Percentile birth weights were compared taking into account length of gestation, fetal sex, and the height, weight, parity, and smoking habits of the mother. Sisters of women who had delivered light-for-dates babies had lighter babies than the general population, their sisters-in-law, or the sisters of women in the premature expulsion group. These other groups, however, had the expected distribution of percentile birth weights. Data on familial trends in smoking habits and unknown gestation are also presented. The results are consistent with the theory that the mother''s own intrauterine experience affects her reproductive performance but could also be explained by shared family learning experience of as yet unidentified microsocial factors related to pregnancy performance.     

Plant intelligence: Why,why not or where?

The concept of plant intelligence, as proposed by Anthony Trewavas, has raised considerable discussion. However, plant intelligence remains loosely defined; often it is either perceived as practically synonymous to Darwinian fitness, or reduced to a mere decorative metaphor. A more strict view can be taken, emphasizing necessary prerequisites such as memory and learning, which requires clarifying the definition of memory itself. To qualify as memories, traces of past events have to be not only stored, but also actively accessed. We propose a criterion for eliminating false candidates of possible plant intelligence phenomena in this stricter sense: an “intelligent” behavior must involve a component that can be approximated by a plausible algorithmic model involving recourse to stored information about past states of the individual or its environment. Re-evaluation of previously presented examples of plant intelligence shows that only some of them pass our test.

“You were hurt?” Kumiko said, looking at the scar.Sally looked down. “Yeah.”“Why didn''t you have it removed?”“Sometimes it''s good to remember.”“Being hurt?”“Being stupid.”—(W. Gibson: Mona Lisa Overdrive)

Key words: intelligence, memory, learning, plant development, mathematical models, plant neurobiology, definition of terms     

What can experience add to early medical education? Consensus survey

Objective To provide a rationale for integrating experience into early medical education (“early experience”).Design Small group discussions to obtain stakeholders'' views. Grounded theory analysis with respondent, internal, and external validation.Setting Problem based, undergraduate medical curriculum that is not vertically integrated.Participants A purposive sample of 64 students, staff, and curriculum leaders from three university medical schools in the United Kingdom.Results Without early experience, the curriculum was socially isolating and divorced from clinical practice. The abruptness of students'' transition to the clinical environment in year 3 generated positive and negative emotions. The rationale for early experience would be to ease the transition; orientate the curriculum towards the social context of practice; make students more confident to approach patients; motivate them; increase their awareness of themselves and others; strengthen, deepen, and contextualise their theoretical knowledge; teach intellectual skills; strengthen learning of behavioural and social sciences; and teach them about the role of health professionals.Conclusion A rationale for early experience would be to strengthen and deepen cognitively, broaden affectively, contextualise, and integrate medical education. This is partly a process of professional socialisation that should start earlier to avoid an abrupt transition. “Experience” can be defined as “authentic human contact in a social or clinical context that enhances learning of health, illness or disease, and the role of the health professional.”