You might wonder why we call our tools 'databases'. Well, the simple answer is: we did not come up with a decent alternative. You could also argument that Metaneva relies on a database holding detailed data that need to be evaluated, analyzed and computed. Nevertheless, feel free to call our databases 'components', 'scripts' or even 'tools'. Untill inspiration hands out a perfect alternative, we will use 'databases' instead.
Evaluation of Future Experiments
- Our evaluative tool provides the cognitive sciences with a data storage where one can test a future experiment and match it with related experiments. This allows to query a certain type of paradigm as an explorative yet thorough query of current research.
- Additionaly one can evaluate the likelyhood of brain activity and effect size.
- Finally the tool allows a relevancy report, providing a detailed report of brain activity given the paradigm as an indication what areas will be activated and what areas are poorly studied using this particular paradigm.
Conceptual Analysis: Defining a Taxonomy
- A conceptual analysis allows to (re-)define the concepts used throughout the cognitive sciences, This allows to describe a function (e.g. decision) in its experimental components (e.g. paradigms).
- With a conceptual analysis we enable the development of a neural functional taxonomy.
- Using the concepts and their definitions, we are able to link neural (functional) systems throught their experimental components (regardless of their semantic similarity).
Meta-analysis
We develop a brain area and a functional meta-analysis. The entire history for the particular meta-analysis will be stored, allowing a re-analysis using identical criteria or to modify the analysis slightly in order to reproduce or test the reported meta-analysis.
- Brain Area Meta-Analysis.
- Reports all relevant paradigms and functions for that particular brain area.
- Enables the study of overlapping neural mechanisms, detailed reports of the connection between functions and brain areas.
- Allows the researcher to formulate advice on which paradigms need to be investigated since current research does not provide sufficient data for the analysis.
- Functional meta-analysis
- Reports all involved functions within a particular brain area.
- Studies the variety of brain area activity.
- Formulates suggestions for further research.
Computational Analysis: Dynamic Neural Networks
The most challenging of all is the computational analysis, where one uses a meta-analysis to link it with detailed anatomical data. The advantage of such detailed data lies in the possibilities it creates for computing neural networks. Detailed data about the anatomical connections allows computing dynamic neural networks. Dynamic neural networks are embedded neural networks, where one links different networks with each other.