A feature integration theory of attention
Order ID 53563633773 Type Essay Writer Level Masters Style APA Sources/References 4 Perfect Number of Pages to Order 5-10 Pages Description/Paper Instructions
A feature integration theory of attention
Visual Search (Shapes)
Teaching Information
| Background | Results | Suggestions | References |
Background
How do we find what we’re looking for and ignore distracting information? The visual search task requires participants to determine whether a target (such as a particular letter, shape, or image) is present in an array of other stimuli. For example, a participant might be asked to determine whether a red letter is present in the following display:
Two major findings came out of early visual search studies (e.g., Treisman & Gelade, 1980). First, finding the target is sometimes so easy that the target is said to “pop out” from the array. Searches in which at least one feature always differentiates targets from distractors (in the above example, color) are called feature searches. In feature searches, the time to find the target is not affected by the number of irrelevant distractor stimuli: people are just as fast to spot the red letter whether there’s one blue letter or a dozen.
In conjunction searches, participants must consider at least two features. For example, a conjunction search would be finding a red X in an array of blue X’s and red T’s, meaning that the conjunction of two features (shape and color) is required to isolate the target. In conjunction searches, the number of distractors matter: search times increase linearly with the number of distractors. Thus, whereas feature searches are fast, parallel, and independent of distractor stimuli, conjunction searches are slow, serial, and depend on the number of distractors.
The feature integration theory is often used to explain these results (Treisman & Gelade, 1980). According to feature integration theory, the features or characteristics (e.g., shape, color, etc.) of objects we see are coded independently (but in parallel) early in visual processing. For example, at the earliest stages of processing a red X, independent, unconnected features are being activated for “red” and for “X”. Only later are those features “integrated” into a unified “red x” representation. In a feature search, features don’t need to be integrated to complete the search, so the search is rapid. Conjunction searches are slower because that integration has to happen in order to distinguish targets from distractors.
**Added by Craig** Article on the STM capacity for feature and conjunction searches (Luck & Vogel, 1997). And another frequently cited article (Wolfe, 1994).
Results
For the standard experiment, the dependent variable will usually be reaction time. The most likely independent variables will be the number of items in the study set, and the type of search (feature or conjunction). Based on past results (e.g., Treisman & Gelade, 1980), for feature searches, the search time is independent of set size. In contrast, for conjunction searches, reaction time should increase with set size. A repeated-measures ANOVA with search type (feature vs. conjunction) and set size as independent variables would be appropriate.
Suggestions
- Change the number of targets. Are feature searches always independent of set size, even when the number of possible targets is high?
- How do search times change when target colors or target letter shapes are very similar (or dissimilar) to the distractors.
References
Treisman, A., & Gelade, G. (1980). A feature integration theory of attention. Cognitive Psychology, 12, 97-136.
Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390(6657), 279–281. https://doi.org/10.1038/36846
Wolfe, J. M. (1994). Guided search 2.0: A revised model of visual search. Psychonomic Bulletin & Review, 1(2), 202–238. https://doi.org/10.3758/BF03200774
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