Opening the cuebox: the information children and young adults generate and rely on when making inferences from memory.

We used a cue-generation and a cue-selection paradigm to investigate the cues children (9- to 12-year-olds) and young adults (17-year-olds) generate and select for a range of inferences from memory. We found that children generated more cues than young adults, who, when asked why they did not generate some particular cues, responded that they did not consider them relevant for the task at hand. On average, the cues generated by children were more perceptual but as informative as the cues generated by young adults. When asked to select the most informative of two cues, both children and young adults tended to choose a hidden (i.e., not perceptual) cue. Our results suggest a developmental change in the cuebox (i.e., the set of cues used to make inferences from memory): New cues are added to the cuebox as more cues are learned, and some old, perceptual cues, although informative, are replaced with hidden cues, which, by both children and young adults, are generally assumed to be more informative than perceptual cues.

Heuristics: foundations for a novel approach to medical decision making.

Medical decision-making is a complex process that often takes place during uncertainty, that is, when knowledge, time, and resources are limited. How can we ensure good decisions? We present research on heuristics-simple rules of thumb-and discuss how medical decision-making can benefit from these tools. We challenge the common view that heuristics are only second-best solutions by showing that they can be more accurate, faster, and easier to apply in comparison to more complex strategies. Using the example of fast-and-frugal decision trees, we illustrate how heuristics can be studied and implemented in the medical context. Finally, we suggest how a heuristic-friendly culture supports the study and application of heuristics as complementary strategies to existing decision rules.

Make your own kinds of cues: when children make more accurate inferences than adults.

In everyday decision making, we do not always have the luxury of using certain knowledge but often need to rely on cues, that is, pieces of information that can aid reasoning. We ask whether and under what circumstances children can focus on informative cues and make accurate inferences in real-world problems. We tested second-, third-, and fifth-grade children and young adults on two problems: which of two real cars is more expensive and which of two real cities has more inhabitants. We manipulated whether cues were given to the participants or the participants needed to generate their own cues. The main result was that when generating their own cues, younger children matched older children and young adults in accuracy or even outperformed them. On the other hand, when cues were given, children were less accurate than young adults. A possible explanation for this result is that children, on their own, tend to generate "perceptual" cues (e.g., "Which car is longer?") that are informative in the problems we studied. However, children are not able to recognize the most informative cues in a set that is given to them because they are not familiar with all cues (e.g., non-perceptual cues such as which car has more horsepower).

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.

The robust beauty of ordinary information.

Heuristics embodying limited information search and noncompensatory processing of information can yield robust performance relative to computationally more complex models. One criticism raised against heuristics is the argument that complexity is hidden in the calculation of the cue order used to make predictions. We discuss ways to order cues that do not entail individual learning. Then we propose and test the thesis that when orders are learned individually, people's necessarily limited knowledge will curtail computational complexity while also achieving robustness. Using computer simulations, we compare the performance of the take-the-best heuristic--with dichotomized or undichotomized cues--to benchmarks such as the naïve Bayes algorithm across 19 environments. Even with minute sizes of training sets, take-the-best using undichotomized cues excels. For 10 environments, we probe people's intuitions about the direction of the correlation between cues and criterion. On the basis of these intuitions, in most of the environments take-the-best achieves the level of performance that would be expected from learning cue orders from 50% of the objects in the environments. Thus, ordinary information about cues--either gleaned from small training sets or intuited--can support robust performance without requiring Herculean computations.

American and German students' knowledge, perceptions, and behaviors with respect to over-the-counter pain relievers.

OBJECTIVE: To better understand the knowledge base and perceptions involved in the decision to buy and use over-the-counter pain relievers (OTCPRs) by taking into account the environment in which these decisions are made. The authors expected that the differences in access and marketing would affect knowledge and decision making related to OTCPRs in the United States and Germany. DESIGN: A survey was given to 108 undergraduate university students in the United States and Germany (58 and 50 participants, respectively). RESULTS: The authors found that significantly more Americans than Germans take OTCPRs and that they also take significantly more OTCPRs. Americans exhibited less knowledge about side effects than their German counterparts. When asked when they consulted package labels, Americans reported they were more likely to do so before buying a product, whereas Germans reported consulting labels before taking OTCPRs for the first time. Package labels affected more Americans' decisions to purchase OTCPRs; Americans were also less likely to consult a doctor when feeling pain but more likely to take OTCPRs. Finally, Americans viewed OTCPRs as riskier after their status changed from prescription only to over the counter, whereas Germans believed they posed less risk. CONCLUSION: This study analyzed health-related behavior by looking at how environmental factors shape decision processes related to over-the-counter drug use. The results indicate that looking at environmental factors does help to explain differences in knowledge, perceptions, and behaviors among German and American students.

"A 30% chance of rain tomorrow": how does the public understand probabilistic weather forecasts?

The weather forecast says that there is a "30% chance of rain," and we think we understand what it means. This quantitative statement is assumed to be unambiguous and to convey more information than does a qualitative statement like "It might rain tomorrow." Because the forecast is expressed as a single-event probability, however, it does not specify the class of events it refers to. Therefore, even numerical probabilities can be interpreted by members of the public in multiple, mutually contradictory ways. To find out whether the same statement about rain probability evokes various interpretations, we randomly surveyed pedestrians in five metropolises located in countries that have had different degrees of exposure to probabilistic forecasts--Amsterdam, Athens, Berlin, Milan, and New York. They were asked what a "30% chance of rain tomorrow" means both in a multiple-choice and a free-response format. Only in New York did a majority of them supply the standard meteorological interpretation, namely, that when the weather conditions are like today, in 3 out of 10 cases there will be (at least a trace of) rain the next day. In each of the European cities, this alternative was judged as the least appropriate. The preferred interpretation in Europe was that it will rain tomorrow "30% of the time," followed by "in 30% of the area." To improve risk communication with the public, experts need to specify the reference class, that is, the class of events to which a single-event probability refers.

Risk attitude reversals in drivers' route choice when range of travel time information is provided.

Automobile drivers were recently found to be risk averse when choosing among routes that had an average travel time shorter than the certain travel time of a route considered as a reference. Conversely, drivers were found to be risk seeking when choosing among routes that had an average travel time longer than the certain travel time of the reference route. In a driving simulation study in which the reference route had a range of travel times, this pattern was replicated when thereference range was smaller than the ranges of the available routes. However, the pattern was reversed when the reference range was larger than the ranges of the available routes. We recently proposed a simple heuristic model that fit the relatively complex data quite well. Actual or potential applications of this research include the design of variable message signs and of route choice support systems.

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. Copyright 2001 Academic Press.