Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
The extent to which distracting information influences decisions can be informative about the nature of the underlying cognitive and perceptual processes. In a recent paper, a response time-based measure for quantifying the degree of interference (or facilitation) from distracting information termed resilience was introduced. Despite using a statistical measure, the analysis was limited to qualitative comparisons between different model predictions. In this paper, we demonstrate how statistical procedures from workload capacity analysis can be applied to the new resilience functions. In particular, we present an approach to null-hypothesis testing of resilience functions and a method based on functional principal components analysis for analyzing differences in the functional form of the resilience functions across participants and conditions.
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Source |
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http://dx.doi.org/10.3758/s13428-016-0784-3 | DOI Listing |
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