When making decisions, humans can observe many kinds of information about others' activities, but their effects on performance are not well understood. We investigated social learning strategies using a simple problem-solving task in which participants search a complex space, and each can view and imitate others' solutions. Results showed that participants combined multiple sources of information to guide learning, including payoffs of peers' solutions, popularity of solution elements among peers, similarity of peers' solutions to their own, and relative payoffs from (...) individual exploration. Furthermore, performance was positively associated with imitation rates at both the individual and group levels. When peers' payoffs were hidden, popularity and similarity biases reversed, participants searched more broadly and randomly, and both quality and equity of exploration suffered. We conclude that when peers' solutions can be effectively compared, imitation does not simply permit scrounging, but it can also facilitate propagation of good solutions for further cumulative exploration. (shrink)

The resurgence of interest in collective behavior is in large part due to tools recently made available for conducting laboratory experiments on groups, statistical methods for analyzing large data sets reflecting social interactions, the rapid growth of a diverse variety of online self‐organized collectives, and computational modeling methods for understanding both universal and scenario‐specific social patterns. We consider case studies of collective behavior along four attributes: the primary motivation of individuals within the group, kinds of interactions among individuals, typical dynamics (...) that result from these interactions, and characteristic outcomes at the group level. With this framework, we compare the collective patterns of noninteracting decision makers, bee swarms, groups forming paths in physical and abstract spaces, sports teams, cooperation and competition for resource usage, and the spread and extension of innovations in an online community. Some critical issues surrounding collective behavior are then reviewed, including the questions of “Does group behavior always reduce to individual behavior?”“Is ‘group cognition’ possible?” and “What is the value of formal modeling for understanding group behavior?”. (shrink)

The very expertise with which psychologists wield their tools for achieving laboratory control may have had the unwelcome effect of blinding psychologists to the possibilities of discovering principles of behavior without conducting experiments. When creatively interrogated, a diverse range of large, real-world data sets provides powerful diagnostic tools for revealing principles of human judgment, perception, categorization, decision-making, language use, inference, problem solving, and representation. Examples of these data sets include patterns of website links, dictionaries, logs of group interactions, collections of (...) images and image tags, text corpora, history of financial transactions, trends in twitter tag usage and propagation, patents, consumer product sales, performance in high-stakes sporting events, dialect maps, and scientific citations. The goal of this issue is to present some exemplary case studies of mining naturally existing data sets to reveal important principles and phenomena in cognitive science, and to discuss some of the underlying issues involved with conducting traditional experiments, analyses of naturally occurring data, computational modeling, and the synthesis of all three methods. (shrink)

Options are often presented incidentally in a sequence, but does serial position impact choice after delay, and if so, how? We address this question in a consequential real-world choice domain. Using 25 years of citation data, and a unique identification strategy, we examine the relationship between article order and citation count. Results indicate that mere serial position affects the prominence that research achieves: Earlier-listed articles receive more citations. Furthermore, our identification strategy allows us to cast doubt on alternative explanations and (...) instead indicate that the effect is driven by psychological processes of attention and memory. These findings deepen the understanding of how presentation order impacts choice, suggest that subtle presentation factors can bias an important scientific metric, and shed light on how psychological processes shape collective outcomes. (shrink)

Standard models in quantitative linguistics assume that word usage follows a fixed frequency distribution, often Zipf's law or a close relative. This view, however, does not capture the near daily variations in topics of conversation, nor the short-term dynamics of language change. In order to understand the dynamics of human language use, we present a corpus of daily word frequency variation scraped from online news sources every 20 min for more than 2 years. We construct a simple time-varying model with (...) a latent state, which is observed via word frequency counts. We use Bayesian techniques to infer the parameters of this model for 20,000 words, allowing us to convert complex word-frequency trajectories into low-dimensional parameters in word usage. By analyzing the inferred parameters of this model, we quantify the relative mobility and drift of words on a day-to-day basis, while accounting for sampling error. We quantify this variation and show evidence against “rich-get-richer” models of word use, which have been previously hypothesized to explain statistical patterns in language. (shrink)

The behavioral sciences have flourished by studying how traditional and/or rational behavior has been governed throughout most of human history by relatively well-informed individual and social learning. In the online age, however, social phenomena can occur with unprecedented scale and unpredictability, and individuals have access to social connections never before possible. Similarly, behavioral scientists now have access to “big data” sets – those from Twitter and Facebook, for example – that did not exist a few years ago. Studies of human (...) dynamics based on these data sets are novel and exciting but, if not placed in context, can foster the misconception that mass-scale online behavior is all we need to understand, for example, how humans make decisions. To overcome that misconception, we draw on the field of discrete-choice theory to create a multiscale comparative “map” that, like a principal-components representation, captures the essence of decision making along two axes: aneast–westdimension that represents the degree to which an agent makes a decision independently versus one that is socially influenced, and anorth–south dimensionthat represents the degree to which there is transparency in the payoffs and risks associated with the decisions agents make. We divide the map into quadrants, each of which features a signature behavioral pattern. When taken together, the map and its signatures provide an easily understood empirical framework for evaluating how modern collective behavior may be changing in the digital age, including whether behavior is becoming more individualistic, as people seek out exactly what they want, or more social, as people become more inextricably linked, even “herdlike,” in their decision making. We believe the map will lead to many new testable hypotheses concerning human behavior as well as to similar applications throughout the social sciences. (shrink)