Related Experiment Video
Updated: May 11, 2026
13:04
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Published on: September 19, 2012
A mixture approach to vagueness and ambiguity
1Faculty of Psychology and Educational Sciences, University of Leuven, Leuven, Belgium. steven.verheyen@ppw.kuleuven.be
Plos One
|May 14, 2013
Summary
Individual differences in natural language categorization arise from ambiguity (different criteria) and vagueness (different cut-offs). A mixture model revealed distinct subgroups with varying criteria and response tendencies.
Area of Science:
- Cognitive Science
- Natural Language Processing
- Computational Linguistics
Background:
- Individuals exhibit variability when classifying items into natural language categories.
- This inter-individual variation in semantic categorization is a key area of linguistic research.
Purpose of the Study:
- To investigate the sources of disagreement in semantic categorization.
- To model how ambiguity and vagueness contribute to differing category judgments.
- To identify distinct groups of categorizers based on their semantic criteria.
Main Methods:
- Application of a mixture model to categorization data for eight natural language categories.
- Analysis of inter-individual differences in item classification.
- Identification of latent categorizer groups.
Main Results:
- The mixture model successfully identified latent groups of categorizers.
- These groups differed in their criteria for category membership (ambiguity).
- Within groups, categorizers varied in their threshold for assigning membership (vagueness).
Conclusions:
- Inter-individual differences in semantic categorization stem from both ambiguity and vagueness.
- Distinct subgroups of categorizers exist, each emphasizing different attributes.
- The findings provide a computational framework for understanding semantic variability.
More Related Videos
Related Concept Videos
00:59Uncertainty: Overview
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
00:59Propagation of Uncertainty from Random Error
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
01:20Frustration and Conflict: Approach-Approach, Approach-Avoidance
Frustration occurs when people are obstructed or prevented from achieving a desired goal or fulfilling a perceived need. For example, when someone's input is ignored in a discussion, it can lead to feelings of frustration. Conflict, however, arises from opposing interests, goals, or actions. Conflicts can take various forms based on the nature of these opposing desires or goals.
One common type of conflict is the Approach–Approach Conflict. In this case, a person faces two desirable options,...
One common type of conflict is the Approach–Approach Conflict. In this case, a person faces two desirable options,...
01:10Propagation of Uncertainty from Systematic Error
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
01:28Accuracy, limits, and approximation
Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
01:13What is Matter?
The substance of the universe—from a grain of sand to a star—is called matter. Scientists define matter as anything that occupies space and has mass. An object’s mass and its weight are related concepts, but not quite the same. An object’s mass is the amount of matter contained in the object and is the same whether that object is on Earth or in the zero-gravity environment of outer space. An object’s weight, on the other hand, is its mass as affected by the pull of gravity. Where gravity...