scholarly journals Studying science denial with a complex problem-solving task

2021 ◽  
Author(s):  
Justin Sulik ◽  
Ryan McKay
Keyword(s):  
Problem Solving ◽  
Cognitive Style ◽  
Political Ideology ◽  
Data Gathering ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Multi Stage ◽  
Individual Cognition ◽  
Problem Solving Task ◽  
Hypothesis Evaluation

Explanations of science denial rooted in individual cognition tend to focus on general trait-like factors such as cognitive style, conspiracist ideation or delusional ideation. However, we argue that this focus typically glosses over the concrete, mechanistic elements of belief formation, such as hypothesis generation, data gathering, or hypothesis evaluation. We show, empirically, that such elements predict variance in science denial not accounted for by cognitive style, even after accounting for social factors such as political ideology. We conclude that a cognitive account of science denial would benefit from the study of complex (i.e., open-ended, multi-stage) problem solving that incorporates these mechanistic elements.

scholarly journals The effect of conceptual complexity on information search in a complex problem-solving task

1967 ◽  
Vol 7 (4) ◽  
pp. 137-138 ◽  
Author(s):  
Marvin Karlins ◽  
Thomas Coffman ◽  
Helmut Lamm ◽  
Harold Schroder
Keyword(s):  
Problem Solving ◽  
Information Search ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Problem Solving Task

Training to Reduce the Disruptive Effects of Interruptions

1994 ◽  
Vol 38 (18) ◽  
pp. 1173-1177 ◽  
Author(s):  
Stephen M. Hess ◽  
Mark C. Detweiler
Keyword(s):  
Problem Solving ◽  
Complex Problem ◽  
Main Task ◽  
Complex Problem Solving ◽  
Excellent Performance ◽  
The Third ◽  
Processing Demands ◽  
Problem Solving Task

Two multi-session experiments are described in which a complex problem-solving task was interrupted at different stages of practice. In Experiment 1, subjects practiced the main problem-solving task for three sessions, with intermittent interruptions during each session. By the end of Session 3, interruptions which were similar to the main task, in terms of type of material processed and processing demands, no longer disrupted performance as they had in Sessions 1 and 2. In Experiment 2, subjects practiced the same problem-solving task for two sessions without interruptions. The same types of interruptions used in Experiment 1 were introduced in Session 3. Although the main task was well learned by the third session, the interruptions disrupted subjects' main-task accuracies dramatically. These results suggest that training tasks under uninterrupted conditions can lead to excellent performance, but may not allow subjects to develop the kinds of strategies needed to flexibly recover from interruptions when they occur.

How to Accumulate Empirical Engineering Knowledge in the Complex Problem-Solving Process for Novice Engineers

2021 ◽  
Vol 17 (1) ◽  
pp. 52-71
Author(s):  
Lijun Liu ◽  
Zuhua Jiang
Keyword(s):  
Problem Solving ◽  
Practical Experience ◽  
Complex Problem ◽  
Engineering Problem ◽  
Empirical Knowledge ◽  
Complex Problem Solving ◽  
Knowledge Accumulation ◽  
Multi Stage ◽  
The Difference ◽  
Novel Concept

Complex problem solving is recognized as an important resource of empirical knowledge accumulation. Learners can acquire and consolidate their empirical knowledge based on practical experience in the complex problem solving. Although many studies on complex problem solving have documented the effects of problem solving on knowledge accumulation, few have addressed the detailed process of empirical knowledge accumulation in the complex problem solving. The purpose of this study is to explore the process of novices' empirical knowledge accumulation in solving complex problem. A multi-stage experiment based on a real-world engineering problem is presented in the study. By means of analysis of the difference between novice and experts, a novel concept of “inflection point” and a novel “four period novices' empirical knowledge accumulation curve” are presented, which can be used to explain the novices' empirical knowledge accumulation process in solving complex problem.

Complex Problem Solving and Worked Examples

2009 ◽  
Vol 23 (2) ◽  
pp. 129-138 ◽  
Author(s):  
Florian Schmidt-Weigand ◽  
Martin Hänze ◽  
Rita Wodzinski
Keyword(s):  
Problem Solving ◽  
Cognitive Load ◽  
Learning Experience ◽  
Worked Examples ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Strategic Learning ◽  
Learning Behavior ◽  
Worked Example ◽  
8Th Grade

How can worked examples be enhanced to promote complex problem solving? N = 92 students of the 8th grade attended in pairs to a physics problem. Problem solving was supported by (a) a worked example given as a whole, (b) a worked example presented incrementally (i.e. only one solution step at a time), or (c) a worked example presented incrementally and accompanied by strategic prompts. In groups (b) and (c) students self-regulated when to attend to the next solution step. In group (c) each solution step was preceded by a prompt that suggested strategic learning behavior (e.g. note taking, sketching, communicating with the learning partner, etc.). Prompts and solution steps were given on separate sheets. The study revealed that incremental presentation lead to a better learning experience (higher feeling of competence, lower cognitive load) compared to a conventional presentation of the worked example. However, only if additional strategic learning behavior was prompted, students remembered the solution more correctly and reproduced more solution steps.

The Prediction of Problem-Solving Assessed Via Microworlds

2016 ◽  
Vol 32 (4) ◽  
pp. 298-306 ◽  
Author(s):  
Samuel Greiff ◽  
Katarina Krkovic ◽  
Jarkko Hautamäki
Keyword(s):  
Working Memory ◽  
Problem Solving ◽  
Complex Problem ◽  
Two Dimensions ◽  
Assessment Instruments ◽  
Complex Problem Solving ◽  
Visual Spatial ◽  
Rule Knowledge ◽  
Rule Application ◽  
Fluid Reasoning

Abstract. In this study, we explored the network of relations between fluid reasoning, working memory, and the two dimensions of complex problem solving, rule knowledge and rule application. In doing so, we replicated the recent study by Bühner, Kröner, and Ziegler (2008) and the structural relations investigated therein [ Bühner, Kröner, & Ziegler, (2008) . Working memory, visual-spatial intelligence and their relationship to problem-solving. Intelligence, 36, 672–680]. However, in the present study, we used different assessment instruments by employing assessments of figural, numerical, and verbal fluid reasoning, an assessment of numerical working memory, and a complex problem solving assessment using the MicroDYN approach. In a sample of N = 2,029 Finnish sixth-grade students of which 328 students took the numerical working memory assessment, the findings diverged substantially from the results reported by Bühner et al. Importantly, in the present study, fluid reasoning was the main source of variation for rule knowledge and rule application, and working memory contributed only a little added value. Albeit generally in line with previously conducted research on the relation between complex problem solving and other cognitive abilities, these findings directly contrast the results of Bühner et al. (2008) who reported that only working memory was a source of variation in complex problem solving, whereas fluid reasoning was not. Explanations for the different patterns of results are sought, and implications for the use of assessment instruments and for research on interindividual differences in complex problem solving are discussed.

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