In one word: Not from experience

Brehmer, Berndt

doi:10.1016/0001-6918(80)90034-7

Cited by 416 publications

(226 citation statements)

References 20 publications

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“…People can generally detect linear, positive correlations given enough trials -if the outcome feedback is accurate enough. However, people have great difficulty in the presence of random error, nonlinearity, and negative correlations, often never discovering the true relationship (Brehmer 1980).…”

Section: D-4428mentioning

confidence: 99%

Learning in and about complex systems

Sterman¹

1994

System Dynamics Review

918

238

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Change is accelerating, and the complexity of the systems in which we live is growing.Increasingly change is the result of humanity itself. As complexity grows so do the unanticipated side effects of human action, further increasing complexity in a vicious cycle.Many scholars call for the development of 'systems thinking' to improve our ability to manage wisely. But how do people learn in and about complex dynamic systems? Learning is a feedback process in which our decisions alter the real world, we receive information feedback about the world, and using the new information we revise the decisions we make and the mental models that motivate those decisions. Unfortunately, in the world of social action various impediments slow or prevent these learning feedbacks from functioning, allowing erroneous and harmful behaviors and beliefs to persist. The barriers to learning include the dynamic complexity of the systems themselves, inadequate and ambiguous outcome feedback, systematic 'misperceptions of feedback' where our cognitive maps omit important feedback processes, delays, stocks and flows, and nonlinearities that characterize complex systems, inability to simulate mentally the dynamics of our cognitive maps, poor interpersonal and organizational inquiry skills, and poor scientific reasoning skills. To be successful methods to enhance learning about complex systems must address all these impediments. Effective methods for learning in and about complex dynamic systems must include (1) tools to elicit participant knowledge, articulate and reframe perceptions, and create maps of the feedback structure of a problem from those perceptions; (2) simulation tools and management flight simulators to assess the dynamics of those maps and test new policies; and (3) methods to improve scientific reasoning skills, strengthen group process and overcome defensive routines for individuals and teams. D-4428

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Section: D-4428mentioning

confidence: 99%

Learning in and about complex systems

Sterman¹

1994

System Dynamics Review

918

238

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“…There has been extensive research on the conditions under which people are able to master such tasks (Brehmer, 1980;Doherty & Balzer, 1988;Klayman, 1988). The main findings are that people perform well when the cues are few in number and linearly related to the outcome, when the content of the problem is meaningful, and when they receive sufficient trials and appropriate feedback.…”

mentioning

confidence: 99%

Insight and strategy in multiple-cue learning.

Lagnado

Newell

Kahan

et al. 2006

Journal of Experimental Psychology: General

114

167

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In multiple-cue learning (also known as probabilistic category learning) people acquire information about cue-outcome relations and combine these into predictions or judgments. Previous researchers claimed that people can achieve high levels of performance without explicit knowledge of the task structure or insight into their own judgment policies. It has also been argued that people use a variety of suboptimal strategies to solve such tasks. In three experiments the authors reexamined these conclusions by introducing novel measures of task knowledge and self-insight and using "rolling regression" methods to analyze individual learning. Participants successfully learned a four-cue probabilistic environment and showed accurate knowledge of both the task structure and their own judgment processes. Learning analyses suggested that the apparent use of suboptimal strategies emerges from the incremental tracking of statistical contingencies in the environment.Keywords: multiple-cue learning, self-insight, strategy, rolling regression, implicit versus explicit learning A fundamental goal of cognition is to predict the future on the basis of past experience. In an uncertain world this involves learning about the probabilistic relations that hold between the available information and an outcome of interest and integrating this information into a singular judgment. Thus, a stockbroker draws on various market indicators to predict the future price of a share, and a race-course pundit uses factors such as form and track condition to pick the likely winner of a race.The underlying structure of this kind of prediction is captured in the multiple-cue learning framework (Brunswik, 1943;Hammond, 1955), which focuses on how people learn from repeated exposure to probabilistic information. This paradigm, also known as probabilistic category learning, has been applied in numerous areas of psychology, including human judgment (Brehmer, 1979;

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“…This may also be true of the subjects of Column 3. That change could not have been predicted from the work of Brehmer (1980), who claimed that it is rare for subjects to improve judgments as a result of experience.…”

Section: Selection Of Cells Before and After Decision Makingmentioning

confidence: 89%

The judgment of covariation between binary variables: Some conditions that influence the process

Seggie

1987

Memory & Cognition

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Two hundred subjects made decisions based on the data of two binary variables, ethnic origin and employment status. They were asked to specify the information necessary and sufficient for making the decision before doing so. Variables studied were: (1) Type of question. Five questions were used, four of which focused on one of the four instances involved (e.g., Which information is necessary to find out ifthere is a connection between Asian immigrants and unemployment?). Question five was unbiased. (2) Type of variable. Half of the subjects found two symmetric variables, and half found one symmetric and one asymmetric variable. (3) Level of correlation. Five different correlations were used, three favoring Asians, one favoring Europeans, and one representing a zero correlation. Subjects made logical decisions that were not influenced by any of the experimental conditions. The selection of information before and after making choices was influenced by all variables except level of correlation. The biased questions produced deviations from normative reasoning; the two symmetrical variables and the process of making decisions encouraged normative reasoning.

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In one word: Not from experience

Cited by 416 publications

References 20 publications

Learning in and about complex systems

Learning in and about complex systems

Insight and strategy in multiple-cue learning.

The judgment of covariation between binary variables: Some conditions that influence the process

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