Creativity Through Connectedness: The Role of Closeness and Perspective Taking in Group Creativity

Achievements in various fields of creativity are resulting more and more from collaborative teams. This research investigated the role of interpersonal process variables, namely closeness and perspective taking in group creativity, with a 2 by 2 experimental design. Sixty-one 3-person groups assigned to 4 conditions (a: closeness and perspective taking, b: perspective taking, c: no closeness and no perspective taking, d: closeness). Group members collaboratively wrote stories that were rated by 3 independent expert judges. There was a positive main effect of closeness and negative main effect of perspective taking on group creativity scores. Moreover, the significant interaction between perspective taking and closeness displayed that combination of closeness with perspective taking negatively affect group creativity. These results indicate that closeness might be beneficial for group creativity only when it is not accompanied with perspective taking

When Does Diversity Trump Ability (and Vice Versa) in Group Decision Making? A Simulation Study

It is often unclear which factor plays a more critical role in determining a group's performance: the diversity among members of the group or their individual abilities. In this study, we addressed this “diversity vs. ability” issue in a decision-making task. We conducted three simulation studies in which we manipulated agents' individual ability (or accuracy, in the context of our investigation) and group diversity by varying (1) the heuristics agents used to search task-relevant information (i.e., cues); (2) the size of their groups; (3) how much they had learned about a good cue search order; and (4) the magnitude of errors in the information they searched. In each study, we found that a manipulation reducing agents' individual accuracy simultaneously increased their group's diversity, leading to a conflict between the two. These conflicts enabled us to identify certain conditions under which diversity trumps individual accuracy, and vice versa. Specifically, we found that individual accuracy is more important in task environments in which cues differ greatly in the quality of their information, and diversity matters more when such differences are relatively small. Changing the size of a group and the amount of learning by an agent had a limited impact on this general effect of task environment. Furthermore, we found that a group achieves its highest accuracy when there is an intermediate amount of errors in the cue information, regardless of the environment and the heuristic used, an effect that we believe has not been previously reported and warrants further investigation

Swarm Intelligence in Animal Groups: When Can a Collective Out-Perform an Expert?

An important potential advantage of group-living that has been mostly neglected by life scientists is that individuals in animal groups may cope more effectively with unfamiliar situations. Social interaction can provide a solution to a cognitive problem that is not available to single individuals via two potential mechanisms: (i) individuals can aggregate information, thus augmenting their ‘collective cognition’, or (ii) interaction with conspecifics can allow individuals to follow specific ‘leaders’, those experts with information particularly relevant to the decision at hand. However, a-priori, theory-based expectations about which of these decision rules should be preferred are lacking. Using a set of simple models, we present theoretical conditions (involving group size, and diversity of individual information) under which groups should aggregate information, or follow an expert, when faced with a binary choice. We found that, in single-shot decisions, experts are almost always more accurate than the collective across a range of conditions. However, for repeated decisions – where individuals are able to consider the success of previous decision outcomes – the collective's aggregated information is almost always superior. The results improve our understanding of how social animals may process information and make decisions when accuracy is a key component of individual fitness, and provide a solid theoretical framework for future experimental tests where group size, diversity of individual information, and the repeatability of decisions can be measured and manipulated

"If only I had taken the other road...": Regret, risk and reinforced learning in informed route-choice

This paper presents a study of the effect of regret on route choice behavior when both descriptional information and experiential feedback on choice outcomes are provided. The relevance of Regret Theory in travel behavior has been well demonstrated in non-repeated choice environments involving decisions on the basis of descriptional information. The relation between regret and reinforced learning through experiential feedbacks is less understood. Using data obtained from a simple route-choice experiment involving different levels of travel time variability, discrete-choice models accounting for regret aversion effects are estimated. The results suggest that regret aversion is more evident when descriptional information is provided ex-ante compared to a pure learning from experience condition. Yet, the source of regret is related more strongly to experiential feedbacks rather than to the descriptional information itself. Payoff variability is negatively associated with regret. Regret aversion is more observable in choice situations that reveal risk-seeking, and less in the case of risk-aversion. These results are important for predicting the possible behavioral impacts of emerging information and communication technologies and intelligent transportation systems on travelers' behavior. © 2012 Springer Science+Business Media, LLC

Formal requirements of Markov state models for paired associate learning

Discrete state learning models that make Markov assumptions are a powerful tool for the analysis and optimization of performance in paired associate tasks. We seek here to derive bounds on the complexity needed by such models in order to account for the critical effects of lag and retention intervals on paired associate learning. More specifically, after establishing that two different Markov chains are needed (one for describing the effects of trials where a paired associate is presented and one for describing the effects of trials where the paired associate is not presented), we determine the minimum number of states required in a Markov model with two chains. It is shown formally that, under certain psychologically plausible assumptions, more than three states are required. A model with two chains and four states is presented and it is shown empirically that it can account for the lag and retention effects in paired associate learning.</p

Experimental evaluation of policies for sequencing the presentation of associations

Two policies for sequencing the presentation of associations are compared to the standard policy of randomly cycling through the list of associations. According to the modified-dropout policy, on each trial an association is presented that has not been presented on the two most recent trials and on which the observed number of correct responses since the last error is minimum. The second policy is based on a Markov state model of learning: on each trial, an association is presented that maximizes an arithmetic function of Bayesian estimates of residence in model states, a function that approximately indexes how unlearned associations are. Retention is improved relative to the standard policy only for the model-based policy.</p

From Meehl to Fast and Frugal Heuristics (and Back): New Insights into How to Bridge the Clinical—Actuarial Divide

It is difficult to overestimate Paul Meehl's influence on judgment and decision-making research. His ‘disturbing little book’ (Meehl, 1986, p. 370) Clinical versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence (1954) is known as an attack on human judgment and a call for replacing clinicians with actuarial methods. More than 40 years later, fast and frugal heuristics—proposed as models of human judgment—were formalized, tested, and found to be surprisingly accurate, often more so than the actuarial models that Meehl advocated. We ask three questions: Do the findings of the two programs contradict each other? More generally, how are the programs conceptually connected? Is there anything they can learn from each other? After demonstrating that there need not be a contradiction, we show that both programs converge in their concern to develop (a) domain-specific models of judgment and (b) nonlinear process models that arise from the bounded nature of judgment. We then elaborate the differences between the programs and discuss how these differences can be viewed as mutually instructive: First, we show that the fast and frugal heuristic models can help bridge the clinical—actuarial divide, that is, they can be developed into actuarial methods that are both accurate and easy to implement by the unaided clinical judge. We then argue that Meehl's insistence on improving judgment makes clear the importance of examining the degree to which heuristics are used in the clinical domain and how acceptable they would be as actuarial tools. © 2008, SAGE Publications. All rights reserved

The framing of drivers\u27 route choices when travel time information is provided under varying degrees of cognitive load

In two experiments, participants chose between staying on a main route with a certain travel time and diverting to an alternative route that could take a range of travel times. In the first experiment, travel time information was displayed on a sheet of paper to participants seated at a desk. In the second experiment, the same information was displayed in a virtual environment through which participants drove. Overall, participants were risk-averse when the average travel time along the alternative route was shorter than the certain travel time of the main route but risk-seeking when the average travel time of the alternative route was longer than the certain travel time along the main route. In the second experiment, in which cognitive load was higher, participants simplified their decision-making strategies. A simple probabilistic model describes the risk-taking behavior and the load effects. Actual or potential applications of this research include the development of efficient travel time information systems for drivers.</p