The Cognitive Atlas: Toward a Knowledge Foundation for Cognitive Neuroscience

Imaging Research Center and Departments of Psychology and Neurobiology, University of Texas Austin, TX, USA.
Frontiers in Neuroinformatics (Impact Factor: 3.26). 09/2011; 5(17):17. DOI: 10.3389/fninf.2011.00017
Source: PubMed


Cognitive neuroscience aims to map mental processes onto brain function, which begs the question of what "mental processes" exist and how they relate to the tasks that are used to manipulate and measure them. This topic has been addressed informally in prior work, but we propose that cumulative progress in cognitive neuroscience requires a more systematic approach to representing the mental entities that are being mapped to brain function and the tasks used to manipulate and measure mental processes. We describe a new open collaborative project that aims to provide a knowledge base for cognitive neuroscience, called the Cognitive Atlas (accessible online at, and outline how this project has the potential to drive novel discoveries about both mind and brain.

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Available from: Robert Bilder
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    • "The key for such a deconstruction is to know – or to have a good hypothesis about – which are the underlying sub-processes to look at. One promising way to define the sub-processes of ToM would be a cognitive ontology, like the cognitive atlas (Poldrack et al., 2011; visit To date, cognitive ontologies are in the build-up, but a definition of sub-processes involved in ToM is still missing. "
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    • "The proposed role of WM updating in strategic decisionmaking in the PDG is supported also by neuroanatomical findings suggesting that WM updating and social cooperation are related to activity in the lateral prefrontal cortex (Rilling, Sanfey, Aronson, Nystrom, & Cohen, 2004b; Schumacher et al., 1996; Smith & Jonides, 1999). Since two tasks with overlapping neural correlates are likely to involve also common cognitive processes (Poldrack et al., 2011), these functional imaging results suggest a functional relationship between WM updating and social cooperation. "
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    • "to add annotations from several ontologies (not including CogPO) to determine if these markups would improve CogPO classification performance. The ontologies used for annotation were the Foundation Model of Anatomy (Rosse and Mejino, 2003), Cognitive Atlas (Poldrack et al., 2011), NIFSTD (Bug et al., 2008), and RadLex (Langlotz, 2006). The goal was to annotate the brain areas, other cognitive terms, or imaging methods that might have been mentioned in the abstract text. "
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    ABSTRACT: Identifying the experimental methods in human neuroimaging papers is important for grouping meaningfully similar experiments for meta-analyses. Currently, this can only be done by human readers. We present the performance of common machine learning (text mining) methods applied to the problem of automatically classifying or labeling this literature. Labeling terms are from the Cognitive Paradigm Ontology (CogPO), the text corpora are abstracts of published functional neuroimaging papers, and the methods use the performance of a human expert as training data. We aim to replicate the expert's annotation of multiple labels per abstract identifying the experimental stimuli, cognitive paradigms, response types, and other relevant dimensions of the experiments. We use several standard machine learning methods: naive Bayes (NB), k-nearest neighbor, and support vector machines (specifically SMO or sequential minimal optimization). Exact match performance ranged from only 15% in the worst cases to 78% in the best cases. NB methods combined with binary relevance transformations performed strongly and were robust to overfitting. This collection of results demonstrates what can be achieved with off-the-shelf software components and little to no pre-processing of raw text.
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