Nicholas Gaspelin
Biographic Data
| ID | 5710300 |
|---|---|
| NAME | Nicholas Gaspelin |
| GIVEN NAMES | Nicholas |
| FAMILY NAME | Gaspelin |
| SIGNATURE | GASPELIN N |
| AFFILIATIONS | Binghamton University |
| ORCID | 0000-0002-1182-0632 |
| VERIFIED | Yes |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 9 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2012 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
What can a half-million saccades tell us about distractor suppression
Signal suppression 2.0
The role of salience in the suppression of distracting stimuli
No identification of abrupt onsets that capture attention
Prior target locations attract overt attention during search
The Role of Inhibition in Avoiding Distraction by Salient Stimuli
How to get statistically significant effects in any ERP experiment (and why you shouldn't)
ERP experiments generate massive datasets, often containing thousands of values for each participant, even after averaging. The richness of these datasets can be very useful in testing sophisticated hypotheses, but this richness also creates many opportunities to obtain effects that are statistically significant but do not reflect true differences among groups or conditions (bogus effects). The purpose of this paper is to demonstrate how common a…
Attentional capture in driving displays
Drivers face frequent distraction on the roadways, but little is known about situations placing them at risk of misallocating visual attention. To investigate this issue, we asked participants to search for a red target embedded within simulated driving scenes (photographs taken from inside a car) in three experiments. Distraction was induced by presenting, via a GPS unit, red or green distractors positioned in an irrelevant location at which the…
Perception of facial attractiveness requires some attentional resources
No prominent works on this page.
Perception of facial attractiveness requires some attentional resources
How to get statistically significant effects in any ERP experiment (and why you shouldn't)
ERP experiments generate massive datasets, often containing thousands of values for each participant, even after averaging. The richness of these datasets can be very useful in testing sophisticated hypotheses, but this richness also creates many opportunities to obtain effects that are statistically significant but do not reflect true differences among groups or conditions (bogus effects). The purpose of this paper is to demonstrate how common a…
Attentional capture in driving displays
Drivers face frequent distraction on the roadways, but little is known about situations placing them at risk of misallocating visual attention. To investigate this issue, we asked participants to search for a red target embedded within simulated driving scenes (photographs taken from inside a car) in three experiments. Distraction was induced by presenting, via a GPS unit, red or green distractors positioned in an irrelevant location at which the…
The Role of Inhibition in Avoiding Distraction by Salient Stimuli
Prior target locations attract overt attention during search
No identification of abrupt onsets that capture attention
The role of salience in the suppression of distracting stimuli
Signal suppression 2.0
What can a half-million saccades tell us about distractor suppression
Cognitive psychology (7 works) · Psychology (7 works) · Neural and Behavioral Psychology Studies (6 works) · Cognition (4 works) · Neuroscience (4 works) · Salient (4 works) · Visual perception and processing mechanisms (4 works) · Computer Science (3 works) · Distraction (3 works) · Artificial Intelligence (2 works)