Bettina von Helversen

The mapping model: A cognitive theory of quantitative estimation

How do people make quantitative estimations, such as estimating a cars selling price? Traditionally, linear-regression-type models have been used to answer this question. These models assume that people weight and integrate all information available to estimate a criterion. The authors propose an alternative cognitive theory for quantitative estimation. The mapping model, inspired by the work of N. R. Brown and R. S. Siegler (1993) on metrics and mappings, offers a heuristic approach to decision making. The authors test this model against established alternative models of estimation, namely, linear regression, an exemplar model, and a simple estimation heuristic. With 4 experimental studies the authors compare the models under different environmental conditions. The mapping model proves to be a valid model to predict peoples estimates.

Learning to Choose: Cognitive Aging and Strategy Selection Learning in Decision Making

Decision makers often have to learn from experience. In these situations, people must use the available feedback to select the appropriate decision strategy. How does the ability to select decision strategies on the basis of experience change with age? We examined younger and older adults strategy selection learning in a probabilistic inference task using a computational model of strategy selection learning. Older adults showed poorer decision performance compared with younger adults. In particular, older adults performed poorly in an environment favoring the use of a more cognitively demanding strategy. The results suggest that the impact of cognitive aging on strategy selection learning depends on the structure of the decision environment.

Are stripes beneficial? Dazzle camouflage influences perceived speed and hit rates.

In the animal kingdom, camouflage refers to patterns that help potential prey avoid detection. Mostly camouflage is thought of as helping prey blend in with their background. In contrast, disruptive or dazzle patterns protect moving targets and have been suggested as an evolutionary force in shaping the dorsal patterns of animals. Dazzle patterns, such as stripes and zigzags, are thought to reduce the probability with which moving prey will be captured by impairing predators perception of speed. We investigated how different patterns of stripes (longitudinal—i.e., parallel to movement direction–and vertical–i.e., perpendicular to movement direction) affect the probability with which humans can hit moving objects and if differences in hitting probability are caused by a misperception of speed. A first experiment showed that longitudinally striped objects were hit more often than unicolored objects. However, vertically striped objects did not differ from unicolored objects. A second study examining the link between perceived speed and hitting probability showed that longitudinally and vertically striped objects were both perceived as moving faster and were hit more often than unicolored objects. In sum, our results provide evidence that striped patterns disrupt the perception of speed, which in turn influences how often objects are hit. However, the magnitude and the direction of the effects depend on additional factors such as speed and the task setup.

Performance benefits of depression: Sequential decision making in a healthy sample and a clinically depressed sample.

Previous research reported conflicting results concerning the influence of depression on cognitive task performance. Whereas some studies reported that depression enhances performance, other studies reported negative or null effects. These discrepant findings appear to result from task variation, as well as the severity and treatment status of participant depression. To better understand these moderating factors, we study the performance of individuals-in a complex sequential decision task similar to the secretary problem-who are nondepressed, depressed, and recovering from a major depressive episode. We find that depressed individuals perform better than do nondepressed individuals. Formal modeling of participants decision strategies suggested that acutely depressed participants had higher thresholds for accepting options and made better choices than either healthy participants or those recovering from depression.

Ecological Rationality: A Framework for Understanding and Aiding the Aging Decision Maker

The notion of ecological rationality sees human rationality as the result of the adaptive fit between the human mind and the environment. Ecological rationality focuses the study of decision making on two key questions: First, what are the environmental regularities to which people’s decision strategies are matched, and how frequently do these regularities occur in natural environments? Second, how well can people adapt their use of specific strategies to particular environmental regularities? Research on aging suggests a number of changes in cognitive function, for instance, deficits in learning and memory that may impact decision-making skills. However, it has been shown that simple strategies can work well in many natural environments, which suggests that age-related deficits in strategy use may not necessarily translate into reduced decision quality. Consequently, we argue that predictions about the impact of aging on decision performance depend not only on how aging affects decision-relevant capacities but also on the decision environment in which decisions are made. In sum, we propose that the concept of the ecological rationality is crucial to understanding and aiding the aging decision maker.

Deliberation’s Blindsight How Cognitive Load Can Improve Judgments

Multitasking poses a major challenge in modern work environments by putting the worker under cognitive load. Performance decrements often occur when people are under high cognitive load because they switch to less demanding—and often less accurate—cognitive strategies. Although cognitive load disturbs performance over a wide range of tasks, it may also carry benefits. In the experiments reported here, we showed that judgment performance can increase under cognitive load. Participants solved a multiple-cue judgment task in which high performance could be achieved by using a similarity-based judgment strategy but not by using a more demanding rule-based judgment strategy. Accordingly, cognitive load induced a shift to a similarity-based judgment strategy, which consequently led to more accurate judgments. By contrast, shifting to a similarity-based strategy harmed judgments in a task best solved by using a rule-based strategy. These results show how important it is to consider the cognitive strategies people rely on to understand how people perform in demanding work environments.

Adult age differences in categorization and multiple-cue judgment

We often need to infer unknown properties of objects from observable ones, just like detectives must infer guilt from observable clues and behavior. But how do inferential processes change with age? We examined young and older adults reliance on rule-based and similarity-based processes in an inference task that can be considered either a categorization or a multiple-cue judgment task, depending on the nature of the criterion (binary vs. continuous). Both older and young adults relied on rule-based processes in the multiple-cue judgment task. In the categorization task, however, the majority of older adults relied on rule-based processes while young adults preferred similarity-based processes. Moreover, older adults who relied on rule-based processes performed poorly compared with young adults who relied on the same process, suggesting that aging is associated with deficits in applying rule-based processes.

Models of quantitative estimations : Rule-based and exemplar-based processes compared

The cognitive processes underlying quantitative estimations vary. Past research has identified task-contingent changes between rule-based and exemplar-based processes (P. Juslin, L. Karlsson, & H. Olsson, 2008). B. von Helversen and J. Rieskamp (2008), however, proposed a simple rule-based model-the mapping model-that outperformed the exemplar model in a task thought to promote exemplar-based processing. This raised questions about the assumptions of rule-based versus exemplar-based models that underlie the notion of task contingency of cognitive processes. Rule-based models, such as the mapping model, assume the abstraction of explicit task knowledge. In contrast, exemplar models should profit if storage and activation of the exemplars is facilitated. Two studies tested the importance of the two models assumptions. When knowledge about cues existed, the rule-based mapping model predicted quantitative estimations best. In contrast, when knowledge about the cues was difficult to gain, participants estimations were best described by an exemplar model. The results emphasize the task contingency of cognitive processes.

Do children profit from looking beyond looks? From similarity-based to cue abstraction processes in multiple-cue judgment

The authors investigated the ability of 9- to 11-year-olds and of adults to use similarity-based and rule-based processes as a function of task characteristics in a task that can be considered either a categorization task or a multiple-cue judgment task, depending on the nature of the criterion (binary vs. continuous). Both children and adults relied on similarity-based processes in the categorization task. However, adults relied on cue abstraction in the multiple-cue judgment task, whereas the majority of children continued to rely on similarity-based processes. Reliance on cue abstraction resulted in better judgments for adults but not for children in the multiple-cue judgment task. This suggests that 9- to 11-year-olds may have defaulted to similarity-based processes because they were not able to employ a cue abstraction process efficiently.

Predicting sentencing for low-level crimes: comparing models of human judgment.

Laws and guidelines regulating legal decision making are often imposed without taking the cognitive processes of the legal decision maker into account. In the case of sentencing, this raises the question of whether the sentencing decisions of prosecutors and judges are consistent with legal policy. Especially in handling low-level crimes, legal personnel suffer from high case loads and time pressure, which can make it difficult to comply with the often complex rulings of the law. To understand the cognitive processes underlying sentencing decisions, an analysis of trial records in cases of larceny, fraud, and forgery was conducted. Applying a Bayesian approach, five models of human judgment were tested against each other to predict the sentencing recommendations of the prosecution and to identify the crucial factors influencing sentencing decisions. The factors influencing sentencing were broadly consistent with the penal code. However, the prosecutors considered only a limited number of factors and neglected factors that were legally relevant and rated as highly important. Furthermore, testing the various cognitive judgment models against each other revealed that the sentencing process was apparently not consistent with the judgment policy recommended by the legal literature. Instead, the results show that prosecutors sentencing recommendations were best described by the mapping model, a heuristic model of quantitative estimation. According to this model, sentencing recommendations rely on a categorization of cases based on the cases characteristics.