Abstract
Computers help neuroscientists to analyze experimental results by automating the application of statistics; however, computer-aided experiment planning is far less common, due to a lack of similar quantitative formalisms for systematically assessing evidence and uncertainty. While ontologies and other Semantic Web resources help neuroscientists to assimilate required domain knowledge, experiment planning requires not only ontological but also epistemological (e.g., methodological) information regarding how knowledge was obtained. Here, we outline how epistemological principles and graphical representations of causality can be used to formalize experiment planning toward causal discovery. We outline two complementary approaches to experiment planning: one that quantifies evidence per the principles of convergence and consistency, and another that quantifies uncertainty using logical representations of constraints on causal structure. These approaches operationalize experiment planning as the search for an experiment that either maximizes evidence or minimizes uncertainty. Despite work in laboratory automation, humans must still plan experiments and will likely continue to do so for some time. There is thus a great need for experiment-planning frameworks that are not only amenable to machine computation but also useful as aids in human reasoning.
Highlights
Much of the work in neuroscience involves planning experiments to identify causal mechanisms; neuroscientists do not use computers to plan future experiments as effectively as they use them to analyze past experiments
This paper presents our perspective on computer-aided experiment planning and the role of graphical representations in formalizing causal discovery
This paper addresses what we see to be a large asymmetry in this process: Scientists have robust statistical methods for validating observational assertions on the basis of experiments; scientists lack similar quantitative formalisms for justifying hypotheses on the basis of domain knowledge
Summary
Much of the work in neuroscience involves planning experiments to identify causal mechanisms; neuroscientists do not use computers to plan future experiments as effectively as they use them to analyze past experiments. Instead, when neuroscientists search for relevant information, they routinely rely on serendipity and their incomplete memory of publications When they synthesize evidence, neuroscientists often use unspecified methods, based mostly on implicit strategies that are accumulated through years of training. It applies to much of biology, this problem is worrisome in neuroscience as researchers in this field often integrate information across multiple diverse disciplines, including molecular, cellular, systems, behavioral, and cognitive neuroscience. This paper presents our perspective on computer-aided experiment planning and the role of graphical representations in formalizing causal discovery.
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