Multiple reward–cue contingencies favor expectancy over uncertainty in shaping the reward–cue attentional salience.
Saved in:
| Title: | Multiple reward–cue contingencies favor expectancy over uncertainty in shaping the reward–cue attentional salience. |
|---|---|
| Authors: | De Tommaso, Matteo (AUTHOR), Mastropasqua, Tommaso (AUTHOR), Turatto, Massimo (AUTHOR) |
| Source: | Psychological Research. Mar2019, Vol. 83 Issue 2, p332-346. 15p. 1 Color Photograph, 1 Diagram, 3 Graphs. |
| Subjects: | Uncertainty, Attention |
| Abstract: | Reward-predicting cues attract attention because of their motivational value. A debated question regards the conditions under which the cue's attentional salience is governed more by reward expectancy rather than by reward uncertainty. To help shedding light on this relevant issue, here, we manipulated expectancy and uncertainty using three levels of reward-cue contingency, so that, for example, a high level of reward expectancy (p =.8) was compared with the highest level of reward uncertainty (p =.5). In Experiment 1, the best reward–cue during conditioning was preferentially attended in a subsequent visual search task. This result was replicated in Experiment 2, in which the cues were matched in terms of response history. In Experiment 3, we implemented a hybrid procedure consisting of two phases: an omission contingency procedure during conditioning, followed by a visual search task as in the previous experiments. Crucially, during both phases, the reward–cues were never task relevant. Results confirmed that, when multiple reward-cue contingencies are explored by a human observer, expectancy is the major factor controlling both the attentional and the oculomotor salience of the reward–cue. [ABSTRACT FROM AUTHOR] |
| Copyright of Psychological Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
