1. Neuroscience
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Invariant representations of mass in the human brain

  1. Sarah Schwettmann  Is a corresponding author
  2. Joshua B Tenenbaum
  3. Nancy Kanwisher
  1. Massachusetts Institute of Technology, United States
Research Article
Cite this article as: eLife 2019;8:e46619 doi: 10.7554/eLife.46619
5 figures and 1 additional file

Figures

Stimuli and tasks from Experiments 1 and 2.

(a) Toppling tower task (adapted from Fischer et al., 2016) used as a localizer for all experiments. Still frames show an example tower from two different viewpoints during the 360° pan video. Participants were asked in different blocks to determine which side the tower would fall toward (red versus green), or whether the stimulus contained more blue or yellow blocks. (b) Stills extracted from example mass inference videos used Experiments 1 and 2 (top is extracted from early in video, bottom from later). Stills from ‘splash’ and ‘pillow’ videos show a heavy object; stills from the ‘blow' condition depict a light object. (c) Event-related scanning paradigm in Experiment 1. Each run (4 per subject) presented 36 videos in randomized order (144 total trials with each video presented 4 times), each followed by a 1 s response period (‘Light or Heavy?”) then a rest period of variable duration (mean 6 s). (d) Experiment 2 used a block design to compare decoding during physics and color blocks. Each run (6 per subject) consisted of 5 color blocks, 5 physics blocks, and 4 (12s) rest blocks. 6 videos were shown in each block (360 total trials with each video presented 5 times in a physics block and 5 times in a color block).

Experiment 3 design.

(a) Schematic of stimulus design and ramp scenario. To test the invariance of the mass representation to other physical dimensions, this design was chosen to unconfound mass from dimensions of friction, motion, and material (though it was not possible to unconfound these dimensions from each other). (b) Still frames from stimulus videos with examples of 3 material types and 3 possible line locations. Rows (1: lego, 2: cork, 3: aluminum) represent individual videos.

Main findings from all participants in all experiments.

(a) Group random effects map for the physics >color contrast in the localizer task based on all subjects (n = 32, two runs per subject p<0.0001 FDR), replicating the pattern reported by Fischer et al. (2016). (b) Group parcels and random effects map from all subjects in Fischer et al. (2016). Group parcels for the physics >color contrast computed using one run per subject (n = 12; left-out data from the other run used for validation); random effects map for the physics >color contrast based on all data (two runs per subject). Significant voxels in the group random effects analysis generally fall within the parcels identified in the parcel-based analysis, but not necessarily vice versa (the random effects map may underestimate the extent of the cortex engaged by the task due to anatomical variability across subjects). (c) Mean accuracies decoding mass from candidate physics fROIs in each subject across the three experiments. Decoding analyses were carried out on data from all parcels. A two-way ANOVA did not reveal a significant effect of L or R hemisphere (p=0.54) or frontal or parietal parcel (p=0.86) on decoding accuracy.

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