Related Experiment Video
Updated: Jul 11, 2026

12:59
Barnes Maze Testing Strategies with Small and Large Rodent Models
Published on: February 26, 2014
Expertise-based differences in search and option-generation strategies.
Markus Raab1, Joseph G Johnson
1University of Flensburg, Institute for Movement Science and Sport, Auf dem Campus, Flensburg, Germany. raab@uni-flensburg.de
Journal of Experimental Psychology. Applied
|October 11, 2007
Summary
Expert athletes generate higher quality options than novices, even when the number of options is similar. This study used eye-tracking and verbal protocols in handball to model decision-making strategies.
Area of Science:
- Sports Science
- Cognitive Psychology
- Human Decision Making
Background:
- Previous research explored option generation in expert decision-making.
- Understanding how expertise influences option generation and selection is crucial for performance.
Purpose of the Study:
- To investigate how athletes of varying expertise levels generate and select options in a realistic task.
- To verify decision strategies using eye-tracking data and verbal protocols.
- To elaborate a model of option generation, deliberation, and selection.
Main Methods:
- Longitudinal study over 2 years with handball athletes.
- Utilized eye-tracking to verify decision strategies.
- Employed verbal protocols to identify the option-generation process.
- Analyzed option quality based on athlete expertise levels.
Main Results:
- Athletes of varying expertise generated a similar average number of options.
- Option quality differed significantly between expert, near-expert, and nonexpert athletes.
- Eye-tracking data corroborated inferred decision strategies.
- Initial and final choices reflected differences in option quality.
Conclusions:
- Expertise significantly impacts the quality of generated options, not just the quantity.
- Decision-making models can be refined by incorporating expertise-level differences in option generation and selection.
- Eye-tracking provides objective verification for cognitive strategy research in sports.
Related Concept Videos
Problem-Solving
Effective problem-solving consists of two steps: 1. identifying the problem and 2. selecting the appropriate problem-solving strategy (i.e., a plan of action used to find a solution). Humans use four problem-solving strategies:
Trial and Error and Algorithm
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light bulb,...
Decision Making: Traditional Method
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Methods of Medium Optimization
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Heuristics
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
The Availability Heuristic
A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
