What is urgently needed is an informatics resource that can solve the problems listed above. Such a resource would provide the ability to identify the particular usage of terms, to allow browsing for related concepts, and to allow the identification of relevant evidence from the literature that is related to these concepts. This would allow intelligent aggregation of research findings, which could help overcome the information overload that currently afflicts researchers. We propose that this challenge can be best addressed through the development and widespread implementation of an ontology for cognitive neuroscience. In philosophy, “ontology” refers to the study of existence or being. However, in bioinformatics the term is increasingly used in the sense defined by Gruber (1993
) as an “explicit specification of a conceptualization,” or a structured knowledge base meant to support the sharing of knowledge as well as automated reasoning about that knowledge. Ontologies have also provided the basis for effective knowledge accumulation in molecular biology and genomics (Bard and Rhee, 2004
). One of the best known examples is the Gene Ontology2
(GO; Ashburner et al., 2000
). This ontology provides consistent descriptors for gene products, including cellular components (e.g., “ribosome”), biological processes (e.g., “signal transduction”), and molecular functions (e.g., “catalytic activity”). GO provides the basis on which to annotate datasets regarding their function, which prevents the common problem of different researchers using different names to describe the same biological structure or process across different organisms. It also provides the ability to traverse the ontology in order to discover larger-scale regularities by expanding the search to include the subordinate terms in the ontology. There are increasingly powerful tools that are built around ontologies such as GO; given a dataset (such as a gene expression pattern), these tools provide a broad range of functions such as the comparison of genetic datasets based on the similarity of their GO annotation patterns (Ruths et al., 2009
) and the extraction of novel biological facts from the text of articles (Müller et al., 2004
). Ontologies have also been developed in a number of other domains in neuroscience (Martone et al., 2004
); most relevant to cognitive neuroscience, there are well-developed ontologies of brain structure (Bowden and Dubach, 2003
A large body of research in cognitive science has developed detailed domain-specific theories of mental processes, but there has been very little work to systematically characterize how these processes are defined and how they fit together into a larger structure. In part this likely reflects the functionalist character of modern psychology, which arose in reaction to the structuralist approach of the nineteenth century (e.g., as seen in the so-called “faculty psychology” that was employed by phrenologists; Boring, 1950
). There have been some attempts at larger-scale “unified theories of cognition” such as Anderson’s ACT-R (Anderson et al., 2004
) and Newell’s SOAR (Laird et al., 1987
), but these approaches have primarily focused on the development of general unifying computational principles rather than on a systematic characterization of the broad range of cognitive processes.
Other extant vocabularies, such as the medical subject headings (MeSH), contain some content relevant to cognitive neuroscience, but suffer from serious limitations. For example, the MeSH hierarchy for “Cognition” includes just the following concepts: Awareness, Cognitive Dissonance, Comprehension, Consciousness, Imagination, and Intuition. These terms possess no meaningful relation to the current conceptual framework of cognitive science. In addition, the MeSH terms are a mixture of mental processes (e.g., “comprehension”), experimental phenomena (e.g., “illusions”), and experimental procedures (e.g., “maze learning”), along with outdated terms such as “neurolinguistic programming” (which is best characterized as a pseudoscience). Given that MeSH is the lexicon used for indexing articles and expanding queries in PubMed, this suggests that searches of this literature could be greatly improved through the use of vocabularies that better reflect current thinking.
The development of formal ontologies of cognition faces a distinct challenge in comparison to other domains in biology, such as neuroanatomy or cellular function: There is precious little consensus across the field regarding the basic units of mental function. Given that a formal ontology is generally meant to express the shared ontological commitments of a group, this poses a difficult challenge to the development of an ontology of mental processes. There are two alternatives in this case. The first would be to forge ahead and develop a single ontology based on the consensus obtained within a small group of individuals. This would have the benefit of providing an ontology approved by consensus of its architects, but it would be useless to anyone who did not share the group’s ontological commitments. An alternative approach, which we adhere to in the present work, is to allow and capture disagreement, in order to represent the range of views that are present in the field. Our approach to this issue is inspired by the success of social collaborative knowledge building projects such as Wikipedia, which allow discussion and the expression of divergent views in service of developing a broader consensus, and one that can be modified flexibly over time as new knowledge emerges.