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Smaller = denser, and the brain knows it: natural statistics of object density shape weight expectations.

Peters MA, Balzer J, Shams L - PLoS ONE (2015)

Bottom Line: Interestingly, this "smaller is denser" relationship does not hold for natural or unliftable objects, suggesting some ideal density range for objects designed to be lifted.These results indicate that the human brain represents the statistics of everyday objects and that this representation can be quantitatively abstracted and applied to novel objects.Finally, that the brain possesses and can use precise knowledge of the nonlinear association between size and weight carries important implications for implementation of forward models of motor control in artificial systems.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, University of California Los Angeles, 1285 Franz Hall, Box 951563, Los Angeles, California, 90095-1563, United States of America.

ABSTRACT
If one nondescript object's volume is twice that of another, is it necessarily twice as heavy? As larger objects are typically heavier than smaller ones, one might assume humans use such heuristics in preparing to lift novel objects if other informative cues (e.g., material, previous lifts) are unavailable. However, it is also known that humans are sensitive to statistical properties of our environments, and that such sensitivity can bias perception. Here we asked whether statistical regularities in properties of liftable, everyday objects would bias human observers' predictions about objects' weight relationships. We developed state-of-the-art computer vision techniques to precisely measure the volume of everyday objects, and also measured their weight. We discovered that for liftable man-made objects, "twice as large" doesn't mean "twice as heavy": Smaller objects are typically denser, following a power function of volume. Interestingly, this "smaller is denser" relationship does not hold for natural or unliftable objects, suggesting some ideal density range for objects designed to be lifted. We then asked human observers to predict weight relationships between novel objects without lifting them; crucially, these weight predictions quantitatively match typical weight relationships shown by similarly-sized objects in everyday environments. These results indicate that the human brain represents the statistics of everyday objects and that this representation can be quantitatively abstracted and applied to novel objects. Finally, that the brain possesses and can use precise knowledge of the nonlinear association between size and weight carries important implications for implementation of forward models of motor control in artificial systems.

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Comparison between environmental object data and observers’ data.Overlay of natural log-transformed environmental and observers’ expected (a) weight (EW) ratios and (b) density ratios as a function of volume ratios for the three object types shows agreement between environmental data and participants’ predictions of objects’ weight (and thus density) relationships. Error bars denote standard deviation across participants’ responses.
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pone.0119794.g005: Comparison between environmental object data and observers’ data.Overlay of natural log-transformed environmental and observers’ expected (a) weight (EW) ratios and (b) density ratios as a function of volume ratios for the three object types shows agreement between environmental data and participants’ predictions of objects’ weight (and thus density) relationships. Error bars denote standard deviation across participants’ responses.

Mentions: Next we computed the true small/large ratio for weight (wS/wL) and density (dS/dL) for each of these small-large object pairs, and, due to the identified power function relationships, computed their natural log transforms. Linear trends to the log environmental object weight ratio (WR) and density ratio (DR) data were fitted as a function of log volume ratios (VR) (WR = .613VR + .114, DR = -.387VR + .114) (Fig. 5). These linear trends were subsequently used to calculate the average log weight and density ratios for each of the volume ratios used in the perceptual experiment (Tables 2 and 3).


Smaller = denser, and the brain knows it: natural statistics of object density shape weight expectations.

Peters MA, Balzer J, Shams L - PLoS ONE (2015)

Comparison between environmental object data and observers’ data.Overlay of natural log-transformed environmental and observers’ expected (a) weight (EW) ratios and (b) density ratios as a function of volume ratios for the three object types shows agreement between environmental data and participants’ predictions of objects’ weight (and thus density) relationships. Error bars denote standard deviation across participants’ responses.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4358826&req=5

pone.0119794.g005: Comparison between environmental object data and observers’ data.Overlay of natural log-transformed environmental and observers’ expected (a) weight (EW) ratios and (b) density ratios as a function of volume ratios for the three object types shows agreement between environmental data and participants’ predictions of objects’ weight (and thus density) relationships. Error bars denote standard deviation across participants’ responses.
Mentions: Next we computed the true small/large ratio for weight (wS/wL) and density (dS/dL) for each of these small-large object pairs, and, due to the identified power function relationships, computed their natural log transforms. Linear trends to the log environmental object weight ratio (WR) and density ratio (DR) data were fitted as a function of log volume ratios (VR) (WR = .613VR + .114, DR = -.387VR + .114) (Fig. 5). These linear trends were subsequently used to calculate the average log weight and density ratios for each of the volume ratios used in the perceptual experiment (Tables 2 and 3).

Bottom Line: Interestingly, this "smaller is denser" relationship does not hold for natural or unliftable objects, suggesting some ideal density range for objects designed to be lifted.These results indicate that the human brain represents the statistics of everyday objects and that this representation can be quantitatively abstracted and applied to novel objects.Finally, that the brain possesses and can use precise knowledge of the nonlinear association between size and weight carries important implications for implementation of forward models of motor control in artificial systems.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, University of California Los Angeles, 1285 Franz Hall, Box 951563, Los Angeles, California, 90095-1563, United States of America.

ABSTRACT
If one nondescript object's volume is twice that of another, is it necessarily twice as heavy? As larger objects are typically heavier than smaller ones, one might assume humans use such heuristics in preparing to lift novel objects if other informative cues (e.g., material, previous lifts) are unavailable. However, it is also known that humans are sensitive to statistical properties of our environments, and that such sensitivity can bias perception. Here we asked whether statistical regularities in properties of liftable, everyday objects would bias human observers' predictions about objects' weight relationships. We developed state-of-the-art computer vision techniques to precisely measure the volume of everyday objects, and also measured their weight. We discovered that for liftable man-made objects, "twice as large" doesn't mean "twice as heavy": Smaller objects are typically denser, following a power function of volume. Interestingly, this "smaller is denser" relationship does not hold for natural or unliftable objects, suggesting some ideal density range for objects designed to be lifted. We then asked human observers to predict weight relationships between novel objects without lifting them; crucially, these weight predictions quantitatively match typical weight relationships shown by similarly-sized objects in everyday environments. These results indicate that the human brain represents the statistics of everyday objects and that this representation can be quantitatively abstracted and applied to novel objects. Finally, that the brain possesses and can use precise knowledge of the nonlinear association between size and weight carries important implications for implementation of forward models of motor control in artificial systems.

Show MeSH