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Inferring the causes of illness is a culturally universal example of causal thinking. We tested the hypothesis that making causal inferences about biological processes (e.g. illness) depends on the animacy semantic network. Participants (n=20) undergoing fMRI read two-sentence vignettes that elicited implicit causal inferences across sentences, either about the emergence of illness or about the mechanical breakdown of inanimate objects, in addition to noncausal control vignettes. All vignettes were about people and were linguistically matched. The same participants performed localizer tasks: language, logical reasoning, and mentalizing. Inferring illness causes, relative to all control conditions, selectively engaged a portion of the precuneus (PC) previously implicated in the semantic representation of animates (e.g. people, animals). Neural responses to causal inferences about illness were adjacent to but distinct from responses to mental state inferences, suggesting a neural mind/body distinction. We failed to find evidence for domain-general responses to causal inference. Causal inference is supported by content-specific semantic networks that encode causal knowledge.
Pubmed ID: 41221997
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A Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run. Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. This Python-based, cross-platform, open-source software toolbox software toolbox for the application of classifier-based analysis techniques to fMRI datasets makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages.
View all literature mentionsOpen source application to allow the presentation of stimuli and collection of data for a wide range of neuroscience, psychology and psychophysics experiments. It is intended as a free, powerful alternative to Presentation or e-Prime.
View all literature mentionsAn independent federal agency created by Congress to promote the progress of science; to advance the national health, prosperity, and welfare; to secure the national defense They are the funding source for approximately 20 percent of all federally supported basic research conducted by America''s colleges and universities. In many fields such as mathematics, computer science and the social sciences, NSF is the major source of federal backing. NSF leadership has two major components: a director who oversees NSF staff and management responsible for program creation and administration, merit review, planning, budget and day-to-day operations; and a 24-member National Science Board (NSB) of eminent individuals that meets six times a year to establish the overall policies of the foundation.The director and all Board members serve six year terms. Each of them, as well as the NSF deputy director, is appointed by the President of the United States and confirmed by the U.S. Senate. At present, NSF has a total workforce of about 2,100 at its Arlington, Va., headquarters, including approximately 1,400 career employees, 200 scientists from research institutions on temporary duty, 450 contract workers and the staff of the NSB office and the Office of the Inspector General. NSF is the only federal agency whose mission includes support for all fields of fundamental science and engineering, except for medical sciences. They are tasked with keeping the United States at the leading edge of discovery in areas from astronomy to geology to zoology. So, in addition to funding research in the traditional academic areas, the agency also supports high-risk, high pay-off ideas, novel collaborations and numerous projects that may seem like science fiction today, but which the public will take for granted tomorrow. And in every case, they ensure that research is fully integrated with education so that today''s revolutionary work will also be training tomorrow''s top scientists and engineers NSF''s task of identifying and funding work at the frontiers of science and engineering is not a top-down process.
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