The present study discusses findings that replicate and extend the original work of Burns and Vollmeyer (2002), which showed that performance in problem solving tasks was more accurate when people were engaged in a non-specific goal than in a specific goal. The main innovation here was to examine the goal specificity effect under both observation-based and conventional action-based learning conditions. The findings show that goal specificity affects the accuracy of problem solving in the same way, both when the learning stage (...) of the task is observationbased and when it is action-based. Additionally, the findings show that, when instructions do not promote goal specificity, observation-based problem solving is as effective as action-based problem solving. (shrink)

How can we help people develop judgement and decision skills? One approach is to teach formal methods such as decision analyses, but these are difficult to apply in ill-structured settings, and the methods are unworkable when one is under time pressure and uncertain conditions. If we regard these skills as types of expertise that can be developed, then in a given domain we may attempt to define the cues, patterns, and strategies used by experts, and develop a programme to teach (...) people how to think like experts. However, in many settings this can be impractical. A different approach to decision skills training is to teach people how to learn like experts. By identifying a set of strategies used by experts to develop their proficiency at decision making, we can develop a programme aimed at helping people become reflective practitioners. This article describes such a training programme. (shrink)

Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision‐making process: instance‐based knowledge, recognition‐based retrieval, adaptive strategies, necessity‐based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision‐making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, (...) adapt their judgment strategies from heuristic‐based to instance‐based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT's learning mechanisms have been implemented in an ACT‐R cognitive model. Through a series of experiments, this paper shows how the IBLT's learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments. (shrink)

In problem solving research insights into the relationship between monitoring and control in the transfer of complex skills remain impoverished. To address this, in four experiments participants solved two complex control tasks that were identical in structure but varied in presentation format. Participants learnt either to solve the second task, based on their original learning phase from the first task, or learnt to solve the second task, based on another participant’s learning phase. Experiment 1 showed that, under conditions in which (...) participants’ learning phase was experienced twice, performance deteriorated in the second task. In contrast, when the learning phases in the first and second tasks differed, performance improved in the second task. Experiment 2 introduced instructional manipulations that induced the same response patterns as Experiment 1. In Experiment 3 further manipulations were introduced that biased the way participants evaluated the learning phase in the second task. In Experiment 4, judgments of self-efficacy were shown to track control performance. The implications of these findings for theories of complex skill acquisition are discussed. (shrink)

Given the privileged status claimed for active learning in a variety of domains (visuo-motor learning, causal induction, problem solving, education, skill learning), the present study examines whether action-based learning is a necessary, or a sufficient, means of acquiring the relevant skills needed to perform a task typically described as requiring active learning. To achieve this, the present study compared the effects of action-based and observation-based learning on controlling a complex dynamic task environment. Both action- and observationbased learners either learnt by (...) describing the changes in the environment in the form of a conditional statement, or not. The findings show that observational learners are sensitive to the instructional manipulations pursued during learning, in ways that are comparable to the active learning conditions. For both, advantages in performance, accuracy in knowledge of the task, and self-insight were found when learning was based on inducing rules from the task environment. (shrink)

We review the progress of naturalistic decision making in the decade since the first conference on the subject in 1989. After setting out a brief history of NDM we identify its essential characteristics and consider five of its main contributions: recognition-primed decisions, coping with uncertainty, team decision making, decision errors, and methodology. NDM helped identify important areas of inquiry previously neglected, it introduced new models, conceptualizations, and methods, and recruited applied investigators into the field. Above all, NDM contributed a new (...) perspective on how decisions are made. NDM still faces significant challenges, including improvement of the quantity and rigor of its empirical research, and confirming the validity of its prescriptive models. Copyright © 2001 John Wiley & Sons, Ltd. (shrink)

Previous research suggests that early performance of amnesic individuals in a probabilistic category learning task is relatively unimpaired. When combined with impaired declarative knowledge, this is taken as evidence for the existence of separate implicit and explicit memory systems. The present study contains a more fine-grained analysis of learning than earlier studies. Using a dynamic lens model approach with plausible learning models, we found that the learning process is indeed indistinguishable between an amnesic and control group. However, in contrast to (...) earlier findings, we found that explicit knowledge of the task structure is also good in both the amnesic and the control group. This is inconsistent with a crucial prediction from the multiple-systems account. The results can be explained from a single system account and previously found differences in later categorization performance can be accounted for by a difference in learning rate. (shrink)

Multiple cue probability learning studies have typically focused on stationary environments. We present three experiments investigating learning in changing environments. A fine-grained analysis of the learning dynamics shows that participants were responsive to both abrupt and gradual changes in cue-outcome relations. We found no evidence that participants adapted to these types of change in qualitatively different ways. Also, in contrast to earlier claims that these tasks are learned implicitly, participants showed good insight into what they learned. By fitting formal learning (...) models, we investigated whether participants learned global functional relationships or made localized predictions from similar experienced exemplars. Both a local (the Associative Learning Model) and a global learning model (the novel Bayesian Linear Filter) fitted the data of the first two experiments. However, the results of Experiment 3, which was specifically designed to discriminate between local and global learning models, provided more support for global learning models. Finally, we present a novel model to account for the cue competition effects found in previous research and displayed by some of our participants. (shrink)