| Title |
Identifying and individuating cognitive systems: a task-based distributed cognition alternative to agent-based extended cognition
|
|---|---|
| Published in |
Cognitive Processing, March 2016
|
| DOI | 10.1007/s10339-016-0759-4 |
| Pubmed ID | |
| Authors |
Jim Davies, Kourken Michaelian |
| Abstract |
This article argues for a task-based approach to identifying and individuating cognitive systems. The agent-based extended cognition approach faces a problem of cognitive bloat and has difficulty accommodating both sub-individual cognitive systems ("scaling down") and some supra-individual cognitive systems ("scaling up"). The standard distributed cognition approach can accommodate a wider variety of supra-individual systems but likewise has difficulties with sub-individual systems and faces the problem of cognitive bloat. We develop a task-based variant of distributed cognition designed to scale up and down smoothly while providing a principled means of avoiding cognitive bloat. The advantages of the task-based approach are illustrated by means of two parallel case studies: re-representation in the human visual system and in a biomedical engineering laboratory. |
X Demographics
The data shown below were collected from the profiles of 2 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 | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Unknown | 38 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 15% |
| Student > Ph. D. Student | 6 | 15% |
| Student > Master | 4 | 10% |
| Professor > Associate Professor | 3 | 8% |
| Student > Doctoral Student | 2 | 5% |
| Other | 8 | 21% |
| Unknown | 10 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 7 | 18% |
| Psychology | 7 | 18% |
| Linguistics | 3 | 8% |
| Social Sciences | 3 | 8% |
| Philosophy | 2 | 5% |
| Other | 7 | 18% |
| Unknown | 10 | 26% |
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 05 April 2016.
All research outputs
#13,464,540
of 22,860,626 outputs
Outputs from Cognitive Processing
#146
of 337 outputs
Outputs of similar age
#146,867
of 301,001 outputs
Outputs of similar age from Cognitive Processing
#3
of 8 outputs
Altmetric has tracked 22,860,626 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 337 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.3. This one has gotten more attention than average, scoring higher than 53% of its peers.
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We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.