Invariant representations of mass in the human brain

An intuitive understanding of physical objects and events is critical for successfully interacting with the world. Does the brain achieve this understanding by running simulations in a mental physics engine, which represents variables such as force and mass, or by analyzing patterns of motion without encoding underlying physical quantities? To investigate, we scanned participants with fMRI while they viewed videos of objects interacting in scenarios indicating their mass. Decoding analyses in brain regions previously implicated in intuitive physical inference revealed mass representations that generalized across variations in scenario, material, friction, and motion energy. These invariant representations were found during tasks without action planning, and tasks focusing on an orthogonal dimension (object color). Our results support an account of physical reasoning where abstract physical variables serve as inputs to a forward model of dynamics, akin to a physics engine, in parietal and frontal cortex.

[1]  J. S. Guntupalli,et al.  Decoding neural representational spaces using multivariate pattern analysis. , 2014, Annual review of neuroscience.

[2]  Jody C Culham,et al.  fMRI Repetition Suppression for Familiar But Not Arbitrary Actions with Tools , 2012, The Journal of Neuroscience.

[3]  Chenfanfu Jiang,et al.  Probabilistic Simulation Predicts Human Performance on Viscous Fluid-Pouring Problem , 2016, CogSci.

[4]  John G. Mikhael,et al.  Functional neuroanatomy of intuitive physical inference , 2016, Proceedings of the National Academy of Sciences.

[5]  Rob Fergus,et al.  Learning Physical Intuition of Block Towers by Example , 2016, ICML.

[6]  Jessica B. Hamrick,et al.  Simulation as an engine of physical scene understanding , 2013, Proceedings of the National Academy of Sciences.

[7]  W. Krieg Functional Neuroanatomy , 1953, Springer Series in Experimental Entomology.

[8]  Susan J. Hespos,et al.  PSYCHOLOGICAL SCIENCE Research Article Five-Month-Old Infants Have Different Expectations for Solids and Liquids , 2022 .

[9]  R. Baillargeon,et al.  Infants use compression information to infer objects' weights: examining cognition, exploration, and prospective action in a preferential-reaching task. , 2012, Child development.

[10]  Joshua B. Tenenbaum,et al.  Humans predict liquid dynamics using probabilistic simulation , 2015, CogSci.

[11]  Jack L. Gallant,et al.  Encoding and decoding in fMRI , 2011, NeuroImage.

[12]  Jonathan S. Cant,et al.  Object Ensemble Processing in Human Anterior-Medial Ventral Visual Cortex , 2012, The Journal of Neuroscience.

[13]  J. Randall Flanagan,et al.  Representation of Object Weight in Human Ventral Visual Cortex , 2014, Current Biology.

[14]  Marcel Adam Just,et al.  Neural Representations of Physics Concepts , 2016, Psychological science.

[15]  Joshua B. Tenenbaum,et al.  Physical predictions over time , 2013, CogSci.

[16]  Philippe A. Chouinard,et al.  Role of the Primary Motor and Dorsal Premotor Cortices in the Anticipation of Forces during Object Lifting , 2005, The Journal of Neuroscience.

[17]  Jessica B. Hamrick,et al.  Inferring mass in complex scenes by mental simulation , 2016, Cognition.

[18]  G. Goldenberg,et al.  Tool use and mechanical problem solving in apraxia , 1998, Neuropsychologia.

[19]  Jacqueline C. Snow,et al.  Preserved Object Weight Processing after Bilateral Lateral Occipital Complex Lesions , 2018, Journal of Cognitive Neuroscience.

[20]  G. Goldenberg,et al.  The neural basis of tool use. , 2009, Brain : a journal of neurology.

[21]  Noah D. Goodman,et al.  Learning physical parameters from dynamic scenes , 2018, Cognitive Psychology.

[22]  Haim Sompolinsky,et al.  Implications of Neuronal Diversity on Population Coding , 2006, Neural Computation.

[23]  Bastiaan R Bloem,et al.  Weight-Specific Anticipatory Coding of Grip Force in Human Dorsal Premotor Cortex , 2012, The Journal of Neuroscience.

[24]  J. Tenenbaum,et al.  An integrative computational architecture for object-driven cortex , 2019, Current Opinion in Neurobiology.

[25]  Louise P. Kirsch,et al.  Information about the Weight of Grasped Objects from Vision and Internal Models Interacts within the Primary Motor Cortex , 2010, The Journal of Neuroscience.

[26]  J. Randall Flanagan,et al.  Coding and use of tactile signals from the fingertips in object manipulation tasks , 2009, Nature Reviews Neuroscience.

[27]  Susan J. Hespos,et al.  Five-Month-Old Infants Have General Knowledge of How Nonsolid Substances Behave and Interact , 2016, Psychological science.

[28]  Afra Wohlschläger,et al.  The Neural Correlates of Planning and Executing Actual Tool Use , 2014, The Journal of Neuroscience.

[29]  David D. Cox,et al.  Untangling invariant object recognition , 2007, Trends in Cognitive Sciences.

[30]  W. T. Thach,et al.  Motor mechanisms of the CNS: cerebrocerebellar interrelations. , 1969, Annual review of physiology.

[31]  Kevin A. Smith,et al.  Consistent physics underlying ballistic motion prediction , 2013, CogSci.

[32]  Jody C Culham,et al.  Decoding the neural mechanisms of human tool use , 2013, eLife.