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 http://www.cognitiveatlas.org), and outline how this project has the potential to drive novel discoveries about both mind and brain.
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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 http://www.cognitiveatlas.org). To date, cognitive ontologies are in the build-up, but a definition of sub-processes involved in ToM is still missing. "
[Show abstract][Hide abstract] ABSTRACT: We review nine current neurocognitive theories of how theory of mind (ToM) is implemented in the brain and evaluate them based on the results from a recent meta-analysis by Schurz et al. (2014), where we identified six types of tasks that are the most frequently used in imaging research on ToM. From theories about cognitive processes being associated with certain brain areas, we deduce predictions about which areas should be engaged by the different types of ToM tasks. We then compare these predictions with the observed activations in the meta-analysis, and identify a number of unexplained findings in current theories. These can be used to revise and improve future neurocognitive accounts of ToM.
Frontiers in Psychology 10/2015; 6(360). DOI:10.3389/fpsyg.2015.01610 · 2.80 Impact Factor
"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. "
[Show abstract][Hide abstract] ABSTRACT: Successful cooperation requires that humans can flexibly adjust choices to their partner's behaviour. This, in turn, presupposes a representation of a partner's past decisions in working memory. The aim of the current study was to investigate the role of working memory processes in cooperation. For that purpose, we tested the effects of working memory updating (Experiment 1) and working memory maintenance demands (Experiments 2 and 3) on cooperative behaviour in the Prisoner's dilemma game. We found that demands on updating, but not maintenance, of working memory contents impaired strategy use in the Prisoner's dilemma. Thus, our data show that updating a partner's past behaviour in working memory represents an important precondition for strategy use in cooperation.
Psychological Research 02/2015; DOI:10.1007/s00426-015-0651-3 · 2.47 Impact Factor
"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. "
[Show abstract][Hide abstract] 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.
Frontiers in Neuroscience 12/2013; 7(7):240. DOI:10.3389/fnins.2013.00240 · 3.66 Impact Factor