| Title |
Place recognition from distant landmarks: human performance and maximum likelihood model
|
|---|---|
| Published in |
Biological Cybernetics, February 2018
|
| DOI | 10.1007/s00422-018-0751-4 |
| Pubmed ID | |
| Authors |
Hanspeter A. Mallot, Stephan Lancier |
| Abstract |
We present a simple behavioral experiment on human place recognition from a configuration of four visual landmarks. Participants were asked to navigate several paths, all involving a turn at one specific point, and while doing so incidentally learned the position of that turning point. In the test phase, they were asked to return to the turning point in a reduced environment leaving only the four landmarks visible. Results are compared to two versions of a maximum likelihood model of place recognition using either view-based or depth-based cues for place recognition. Only the depth-based model is in good qualitative agreement with the data. In particular, it reproduces landmark configuration-dependent effects of systematic bias and statistical error distribution as well as effects of approach direction. The model is based on a place code (depth and bearing of the landmarks at target location) and an egocentric working memory of surrounding space including current landmark position in a local, map-like representation. We argue that these elements are crucial for human place recognition. |
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 % |
|---|---|---|
| Germany | 2 | 50% |
| United Kingdom | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
| Members of the public | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 21 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 29% |
| Student > Ph. D. Student | 5 | 24% |
| Student > Bachelor | 2 | 10% |
| Student > Doctoral Student | 2 | 10% |
| Student > Master | 2 | 10% |
| Other | 1 | 5% |
| Unknown | 3 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 8 | 38% |
| Neuroscience | 3 | 14% |
| Philosophy | 1 | 5% |
| Medicine and Dentistry | 1 | 5% |
| Computer Science | 1 | 5% |
| Other | 2 | 10% |
| Unknown | 5 | 24% |
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 27 February 2018.
All research outputs
#14,094,152
of 23,025,074 outputs
Outputs from Biological Cybernetics
#475
of 678 outputs
Outputs of similar age
#181,107
of 330,211 outputs
Outputs of similar age from Biological Cybernetics
#3
of 7 outputs
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