Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
98%
921
2 minutes
20
A key question in the field of scene perception is what information people use when making decisions about images of scenes. A significant body of evidence has indicated the importance of global properties of a scene image. Ideally, well-controlled, real-world images would be used to examine the influence of these properties on perception. Unfortunately, real-world images are generally complex and impractical to control. In the current research, we elicit ratings of naturalness and openness from a large number of subjects using Amazon Mechanic Turk. Subjects were asked to indicate which of a randomly chosen pair of scene images was more representative of a global property. A score and rank for each image was then estimated based on those comparisons using the Bradley-Terry-Luce model. These ranked images offer the opportunity to exercise control over the global scene properties in stimulus set drawn from complex real-world images. This will allow a deeper exploration of the relationship between global scene properties and behavioral and neural responses.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.3758/s13428-018-1053-4 | DOI Listing |