Noise is a major challenge for the analysis of fMRI data in general and for connectivity analyses in particular. As researchers develop increasingly sophisticated tools to model statistical dependence between the fMRI signal in different brain regions, there is a risk that these models may increasingly capture artifactual relationships between regions, that are the result of noise. Thus, choosing optimal denoising methods is a crucial step to maximize the accuracy and reproducibility of connectivity models. Most comparisons between denoising methods require knowledge of the ground truth: of what is the ‘real signal’. For this reason, they are usually based on simulated fMRI data. However, simulated data may not match the statistical properties of real data, limiting the generalizability of the conclusions. In this article, we propose an approach to evaluate denoising methods using real (non-simulated) fMRI data. First, we introduce an intersubject version of multivariate pattern dependence (iMVPD) that computes the statistical dependence between a brain region in one participant, and another brain region in a different participant. iMVPD has the following advantages: 1) it is multivariate, 2) it trains and tests models on independent partitions of the real fMRI data, and 3) it generates predictions that are both between subjects and between regions. Since whole-brain sources of noise are more strongly correlated within subject than between subjects, we can use the difference between standard MVPD and iMVPD as a ‘discrepancy metric’ to evaluate denoising techniques (where more effective techniques should yield smaller differences). As predicted, the difference is the greatest in the absence of denoising methods. Furthermore, a combination of removal of the global signal and CompCorr optimizes denoising (among the set of denoising options tested).

People make fast and reasonable predictions about the physical behavior of everyday objects. To do so, people may be using principled approximations, similar to models developed by engineers for the purposes of real-time physical simulations. We hypothesize that people use simplified object approximations for tracking and action (the "body" representation), as opposed to fine-grained forms for recognition (the "shape" representation). We used three classic psychophysical tasks (causality perception, collision detection, and change detection) in novel settings that dissociate body and shape. People's behavior across tasks indicates that they rely on approximate bodies for physical reasoning, and that this approximation lies between convex hulls and fine-grained shapes.

People make fast and reasonable predictions about the physical behavior of everyday objects. To do so, people may use principled mental shortcuts, such as object simplification, similar to models developed by engineers for real-time physical simulations. We hypothesize that people use simplified object approximations for tracking and action (the body representation), as opposed to fine-grained forms for visual recognition (the shape representation). We used three classic psychophysical tasks (causality perception, time-to-collision, and change detection) in novel settings that dissociate body and shape. People's behavior across tasks indicates that they rely on coarse bodies for physical reasoning, which lies between convex hulls and fine-grained shapes. Our empirical and computational findings shed light on basic representations people use to understand everyday dynamics, and how these representations differ from those used for recognition. Public Significance StatementPeople interact with objects in the world in real-time, which requires mental shortcuts in physical reasoning. We propose that a key physical mental shortcut is the simplification of fine-grained shapes into coarser bodies. Such simplified bodies explain novel results across several psychophysical tasks, including judgments of causality, time-to-collision, and change detection.