You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Gaze movements and spatial working memory in collision avoidance: a traffic intersection task
|
|---|---|
| Published in |
Frontiers in Behavioral Neuroscience, January 2013
|
| DOI | 10.3389/fnbeh.2013.00062 |
| Pubmed ID | |
| Authors |
Gregor Hardiess, Sabrina Hansmann-Roth, Hanspeter A. Mallot |
| Abstract |
Street crossing under traffic is an everyday activity including collision detection as well as avoidance of objects in the path of motion. Such tasks demand extraction and representation of spatio-temporal information about relevant obstacles in an optimized format. Relevant task information is extracted visually by the use of gaze movements and represented in spatial working memory. In a virtual reality traffic intersection task, subjects are confronted with a two-lane intersection where cars are appearing with different frequencies, corresponding to high and low traffic densities. Under free observation and exploration of the scenery (using unrestricted eye and head movements) the overall task for the subjects was to predict the potential-of-collision (POC) of the cars or to adjust an adequate driving speed in order to cross the intersection without collision (i.e., to find the free space for crossing). In a series of experiments, gaze movement parameters, task performance, and the representation of car positions within working memory at distinct time points were assessed in normal subjects as well as in neurological patients suffering from homonymous hemianopia. In the following, we review the findings of these experiments together with other studies and provide a new perspective of the role of gaze behavior and spatial memory in collision detection and avoidance, focusing on the following questions: (1) which sensory variables can be identified supporting adequate collision detection? (2) How do gaze movements and working memory contribute to collision avoidance when multiple moving objects are present and (3) how do they correlate with task performance? (4) How do patients with homonymous visual field defects (HVFDs) use gaze movements and working memory to compensate for visual field loss? In conclusion, we extend the theory of collision detection and avoidance in the case of multiple moving objects and provide a new perspective on the combined operation of external (bottom-up) and internal (top-down) cues in a traffic intersection task. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 3% |
| Belgium | 2 | 3% |
| Spain | 1 | 1% |
| Canada | 1 | 1% |
| Unknown | 72 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 19% |
| Student > Master | 15 | 19% |
| Researcher | 9 | 12% |
| Student > Bachelor | 8 | 10% |
| Student > Doctoral Student | 7 | 9% |
| Other | 10 | 13% |
| Unknown | 14 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 12 | 15% |
| Psychology | 12 | 15% |
| Medicine and Dentistry | 8 | 10% |
| Computer Science | 8 | 10% |
| Neuroscience | 7 | 9% |
| Other | 15 | 19% |
| Unknown | 16 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 June 2013.
All research outputs
#15,272,977
of 22,711,645 outputs
Outputs from Frontiers in Behavioral Neuroscience
#2,211
of 3,148 outputs
Outputs of similar age
#181,510
of 280,737 outputs
Outputs of similar age from Frontiers in Behavioral Neuroscience
#92
of 165 outputs
Altmetric has tracked 22,711,645 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,148 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.3. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 280,737 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 165 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.