2004
Volume 111, Issue 3
  • ISSN: 0002-5275
  • E-ISSN: 2352-1244

Abstract

Abstract

Meta-analysis is a crucial research tool in cognitive neuroscience. For meta-analysis to succeed, it is important that studies that are grouped together investigate the same cognitive process and that studies that investigate different cognitive processes are grouped apart. After all, comparing apples and oranges makes no sense. Studies’ comparability depends on the cognitive tasks employed. Yet current meta-analyses, especially when automated (e.g. Neurosynth, BrainMap), select and group studies based on cognitive labels (e.g. ‘working memory’, ‘self-reflection’). Unfortunately, labels are often applied inconsistently to tasks: different tasks may receive the same cognitive label, leading to a comparison of apples and oranges during meta-analysis; and the same task may receive different labels, leading to meta-analyses that fail to include all apples, so to speak. I propose conceptual review as a method for overcoming this problem. A conceptual review analyzes the conceptual implications of task choices made in a cognitive neuroscience subfield. It applies philosophical ways of uncovering and analyzing implicit assumptions to the methodological choices neuroscientists make. I explicate how this works and discuss several ways in which conceptual review would benefit cognitive neuroscience. Conceptual review could be combined with neuro-informatics to improve the quality of automated versions of meta-analysis and thereby provide an important contribution to progress in cognitive neuroscience.

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2019-10-01
2021-06-24
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