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10.1016/j.tics.2015.08.008 http://scihub22266oqcxt.onion/10.1016/j.tics.2015.08.008 C4636429!4636429!26440124     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Trends+Cogn+Sci 2015 ; 19 (11): 677-87 Nephropedia Template TP {{tp|p=26440124|t=2015. Understanding What We See: How We Derive Meaning From Vision |pdf=|usr=}}{{26440124}} gab.com Text Understanding What We See: How We Derive Meaning From Vision JO: Trends Cogn Sci http://www.kidney.de/pget.php?&tw=1&pmid=26440124 #FOAvip Recognising objects goes beyond vision, and requires models that incorporate different aspects of meaning. Most models focus on superordinate categories (e.g., animals, tools) which do not capture the richness of conceptual knowledge. We argue that object recognition must be seen as a dynamic process of transformation from low-level visual input through categorical organisation to specific conceptual representations. Cognitive models based on large normative datasets are well-suited to capture statistical regularities within and between concepts, providing both category structure and basic-level individuation. We highlight recent research showing how such models capture important properties of the ventral visual pathway. This research demonstrates that significant advances in understanding conceptual representations can be made by shifting the focus from studying superordinate categories to basic-level concepts. Twit Text FOAVip Understanding What We See: How We Derive Meaning From Vision ????Trends Cogn Sci http://www.kidney.de/pget.php?&tw=1&pmid=26440124 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26440124 #FOAvip English Wikipedia <ref>{{Cite pmid|26440124}}</ref> Understanding What We See: How We Derive Meaning From Vision #MMPMID26440124 Clarke A; Tyler LK Trends Cogn Sci 2015[Nov]; 19 (11): 677-87 PMID26440124show ga Recognising objects goes beyond vision, and requires models that incorporate different aspects of meaning. Most models focus on superordinate categories (e.g., animals, tools) which do not capture the richness of conceptual knowledge. We argue that object recognition must be seen as a dynamic process of transformation from low-level visual input through categorical organisation to specific conceptual representations. Cognitive models based on large normative datasets are well-suited to capture statistical regularities within and between concepts, providing both category structure and basic-level individuation. We highlight recent research showing how such models capture important properties of the ventral visual pathway. This research demonstrates that significant advances in understanding conceptual representations can be made by shifting the focus from studying superordinate categories to basic-level concepts. äUnderstanding What We See: How We Derive Meaning From Vision (epmc).pdf DeepDyve Pubget Overpricing