{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:28:49Z","timestamp":1777854529256,"version":"3.51.4"},"reference-count":51,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2016,7,11]],"date-time":"2016-07-11T00:00:00Z","timestamp":1468195200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Science"],"published-print":{"date-parts":[[2016,12]]},"abstract":"<jats:p>Although various approaches have been proposed throughout history, information, as one of the most fundamental elements in the world, does not have a general definition or theory that is acceptable to all disciplines. The biggest challenge is the unification of objective and subjective views, because they represent very different characteristics of information which are difficult to integrate into a single framework. We argue that the key to bridging the gap between objective and subjective views of information is a proper understanding of intelligence, because it gives rise to subjective experiences and assigns meaning to things. The purpose of this research is to explore possibilities and implications of applying neuroscience theory in the discussion of information. By incorporating the memory\u2013prediction framework of intelligence developed by Jeff Hawkins, we propose causality to be the general definition of information, and the combination of \u2018Physical Representations of Mental Patterns\u2019 and \u2018Physical Representations of Physical Patterns\u2019 to be the restricted definition in social contexts. With both general and restricted definitions clarified, we then discuss a few cases of information use and the implications of our approach.<\/jats:p>","DOI":"10.1177\/0165551515612662","type":"journal-article","created":{"date-parts":[[2015,10,28]],"date-time":"2015-10-28T22:49:44Z","timestamp":1446072584000},"page":"821-832","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Information as causality: An approach to a general theory of information"],"prefix":"10.1177","volume":"42","author":[{"given":"Tao","family":"Luo","sequence":"first","affiliation":[{"name":"Graduate School of Techno Design, Kookmin University, Seoul, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Younghwan","family":"Pan","sequence":"additional","affiliation":[{"name":"Graduate School of Techno Design, Kookmin University, Seoul, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2016,7,11]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"309","volume-title":"Feynman and Computation: Exploring the Limits of Computers","author":"Wheeler JA","year":"1999","unstructured":"Wheeler JA. 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