| Title |
Visual re-identification of individual objects: a core problem for organisms and AI
|
|---|---|
| Published in |
Cognitive Processing, October 2015
|
| DOI | 10.1007/s10339-015-0736-3 |
| Pubmed ID | |
| Authors |
Chris Fields |
| Abstract |
Two open questions about the visual re-identification of individual objects over extended time periods are briefly reviewed: (1) How much a priori information about the nature of objects, identity and time is required to support robust individual object re-identification abilities? and (2) how do epistemic feelings, such as the feeling of familiarity, contribute both to object re-identification and to the perception of opportunities and risks associated with individual objects and their affordances? The ongoing interplay between experiments that can be carried out with human subjects and experiments made possible with robotic systems is examined. It is suggested that developmental robotics, including virtual-reality simulations of robot-environment interactions, may provide the best route to understanding both the implementation of epistemic feelings in humans and their functional contribution to the identification of persistent individual objects. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 25% |
| India | 1 | 25% |
| Germany | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
| Science communicators (journalists, bloggers, editors) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 34 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 24% |
| Student > Ph. D. Student | 5 | 15% |
| Researcher | 2 | 6% |
| Student > Doctoral Student | 1 | 3% |
| Unspecified | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 15 | 44% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 6 | 18% |
| Computer Science | 3 | 9% |
| Engineering | 2 | 6% |
| Sports and Recreations | 2 | 6% |
| Nursing and Health Professions | 1 | 3% |
| Other | 5 | 15% |
| Unknown | 15 | 44% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 09 October 2015.
All research outputs
#14,287,279
of 23,342,092 outputs
Outputs from Cognitive Processing
#168
of 338 outputs
Outputs of similar age
#140,824
of 279,369 outputs
Outputs of similar age from Cognitive Processing
#3
of 6 outputs
Altmetric has tracked 23,342,092 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 338 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.5. This one has gotten more attention than average, scoring higher than 50% of its peers.
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We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.