visual representation of gravity

General Relativity

• Released Wednesday, May 29th, 2019

This is a collection of media resources available on the Scientific Visualization Studio website relating to Einstein's general theory of relativity. More information and media can be found at: NASA's Blueshift Blog 100 Years of General Relativity How Scientists Captured the First Image of a Black Hole

Gravitational Lensing & Microlensing

First gamma-ray measurement of a gravitational lens.

Astronomers using NASA's Fermi observatory have made the first gamma-ray measurements of a gravitational lens, a kind of natural telescope formed when a rare cosmic alignment allows the gravity of a massive object to bend and amplify light from a more distant source.The opportunity arose in September 2012, when Fermi's Large Area Telescope (LAT) detected a series of bright gamma-ray flares from a source known as B0218+357, located 4.35 billion light-years away in the constellation Triangulum. These powerful outbursts in a known gravitational lens provided the key to making the measurement. Astronomers classify B0218+357 as a blazar, a type of active galaxy noted for intense outbursts. At the blazar's heart is a supersized black hole with a mass millions to billions of times that of the sun. As matter spirals toward this black hole, some of it blasts outward as jets of particles traveling near the speed of light in opposite directions.Long before light from B0218+357 reaches us, it passes directly through a spiral galaxy – one much like our own – located 4.03 billion light-years away. The galaxy's gravity bends the light into different paths, so astronomers see the background blazar as dual images. But these paths aren't the same length, which means that when one image flares, there's a delay of many days before the other does.While radio and optical telescopes can resolve and monitor the individual blazar images, Fermi's LAT cannot. Instead, the Fermi team exploited the playback delay between the images. In September 2012, when the blazar's flaring activity made it the brightest gamma-ray source outside of our own galaxy, Fermi scientists took advantage of the opportunity by using a week of dedicated LAT time to hunt for delayed flares. Three episodes of flares showing playback delays of 11.46 days were found, with the strongest evidence in a sequence of flares captured during the week-long LAT observations. ||

A Black Hole Visits Baltimore

A visualization of a black hole passing across Baltimore's Inner Harbor || baltimore_lensed-example_frame-1920x1080.png (1920x1080) [2.5 MB] || baltimore_lensed-example_frame-1920x1080.jpg (1920x1080) [509.5 KB] || baltimore_lensed-example_frame-1920x1080_searchweb.png (320x180) [108.6 KB] || baltimore_lensed-example_frame-1920x1080_thm.png (80x40) [6.7 KB] || baltimore_lensed-b-1920x1080.m4v (1920x1080) [23.3 MB] || baltimore_lensed-b-1920x1080.wmv (1920x1080) [24.0 MB] || baltimore_lensed-b-1280x720.m4v (1280x720) [14.2 MB] || baltimore_lensed-b-1920x1080.webm (1920x1080) [23.3 MB] || baltimore_lensed-b-1280x720.wmv (1280x720) [14.7 MB] || baltimore_lensed-b-1920x1080p30.mov (1920x1080) [295.7 MB] || baltimore_lensed-b-30688.key [28.4 MB] || baltimore_lensed-b-30688.pptx [25.8 MB] ||

Gravitational Microlensing Animation

Animation illustrating how gravitational microlensing works. 4k resolution. || Lensing_00789_print.jpg (1024x576) [60.5 KB] || Lensing_00789.png (3840x2160) [7.1 MB] || Lensing_00789_searchweb.png (320x180) [54.6 KB] || Lensing_00789_thm.png (80x40) [4.4 KB] || WFIRST_Microlensing_H264_1080p.mov (1920x1080) [57.6 MB] || WFIRST_Microlensing_H264_1080p.webm (1920x1080) [3.7 MB] || WFIRST_Microlensing_4k_ProRes.mov (3840x2160) [2.2 GB] || frames/3840x2160_16x9_30p/ (3840x2160) [64.0 KB] || WFIRST_Microlensing_H264_4k.mov (3840x2160) [76.0 MB] || WFIRST_Microlensing.key [60.0 MB] || WFIRST_Microlensing.pptx [59.7 MB] ||

Dark Matter Gravitational Lensing Animation

Animation illustrating light from a cluster of galaxies being lensed by dark matter. || GravLens_fr_00168_print.jpg (1024x576) [59.0 KB] || GravLens_fr_00168.png (3840x2160) [3.1 MB] || GravLens_fr_00168_searchweb.png (320x180) [43.7 KB] || GravLens_fr_00168_thm.png (80x40) [4.4 KB] || GravLens_H2641080p.mov (1920x1080) [27.1 MB] || GravLens_H2641080p.webm (1920x1080) [1.7 MB] || GravLens_4k_ProRes.mov (3840x2160) [1.4 GB] || frames/3840x2160_16x9_30p/ (3840x2160) [32.0 KB] || GravLens_H264_4K.mov (3840x2160) [35.4 MB] ||

NICER Lensing

The Neutron star Interior Composition Explorer (NICER) mission will study neutron stars, the densest known objects in the cosmos. These neutron star animations and graphics highlight some of their unique characteristics.For more information about NICER visit: nasa.gov/nicer. ||

Microlensing Study: Most Common Outer Planets Likely Neptune-mass

A new statistical study of planets found by the gravitational microlensing technique suggests that Neptune-mass planets may be the most common worlds in the outer reaches of planetary systems. Credit: NASA's Goddard Space Flight CenterMusic: "Hurricanes Wrap My Heart" from Stockmusic.netWatch this video on the NASA Goddard YouTube channel.Complete transcript available. || MOA_II_Still_print.jpg (1024x576) [117.4 KB] || MOA_II_Still.png (3356x1888) [8.3 MB] || 12425_Microlensing_Neptunes_ProRes_1920x1080_2997.mov (1920x1080) [3.3 GB] || 12425_Microlensing_Neptunes_FINAL_youtube_hq.mov (1920x1080) [821.9 MB] || 12425_Microlensing_Neptunes_H264_Good_1080.mov (1920x1080) [369.1 MB] || 12425_Microlensing_Neptunes_FINAL_HD.wmv (1920x1080) [167.7 MB] || 12425_Microlensing_Neptunes_H264_1080.m4v (1920x1080) [246.3 MB] || 12425_Microlensing_Neptunes_FINAL_appletv.m4v (1280x720) [124.2 MB] || 12425_Microlensing_Neptunes_Compatible_540.m4v (960x540) [94.7 MB] || 12425_Microlensing_Neptunes_ProRes_1920x1080_2997.webm (1920x1080) [24.6 MB] || 12425_Microlensing_Neptunes_FINAL_appletv_subtitles.m4v (1280x720) [124.4 MB] || Microlensing_Neptunes_SRT_Captions.en_US.srt [4.5 KB] || Microlensing_Neptunes_SRT_Captions.en_US.vtt [4.5 KB] || 12425_Microlensing_Neptunes_FINAL_ipod_sm.mp4 (320x240) [42.6 MB] ||

Black Holes

SVS Black Hole Gallery

New Simulation Sheds Light on Spiraling Supermassive Black Holes

Gas glows brightly in this computer simulation of supermassive black holes only 40 orbits from merging. Models like this may eventually help scientists pinpoint real examples of these powerful binary systems. Credit: NASA's Goddard Space Flight Center/Scott Noble; simulation data, d'Ascoli et al. 2018Music: "Games Show Sphere 01" from Killer TracksWatch this video on the NASA Goddard YouTube channel.Complete transcript available. || SMBH_Sim_Still_1.jpg (1920x1080) [333.8 KB] || SMBH_Sim_Still_1_print.jpg (1024x576) [138.8 KB] || SMBH_Sim_Still_1_searchweb.png (320x180) [69.3 KB] || SMBH_Sim_Still_1_thm.png (80x40) [6.4 KB] || 13043_SMBH_Simulation_ProRes_1920x1080_2997.mov (1920x1080) [2.0 GB] || 13043_SMBH_Simulation_1080.mp4 (1920x1080) [202.8 MB] || 13043_SMBH_Simulation_1080.webm (1920x1080) [17.4 MB] || SMBH_SRT_Captions.en_US.srt [2.0 KB] || SMBH_SRT_Captions.en_US.vtt [1.9 KB] ||

Hubble Detects a Rogue Supermassive Black Hole

The Hubble Space Telescope captured an image of a quasar named 3C 186 that is offset from the center of its galaxy. Astronomers hypothesize that this supermassive black hole was jettisoned from the center of its galaxy by the recoil from gravitational waves produced by the merging of two supermassive black holes. Read the press release here - https://www.nasa.gov/feature/goddard/2017/feature/gravitational-wave-kicks-monster-black-hole-out-of-galactic-coreDownload the Hubble images here - http://hubblesite.org/news_release/news/2017-12Read the science paper here - http://imgsrc.hubblesite.org/hvi/uploads/science_paper/file_attachment/231/3c186.pdf ||

Massive Black Hole Shreds Passing Star (Animation Only)

A star approaching too close to a massive black hole is torn apart by tidal forces, as shown in this artist's rendering. Filaments containing much of the star's mass fall toward the black hole. Eventually these gaseous filaments merge into a smooth, hot disc glowing brightly in X-rays. As the disk forms, it's central region heats up tremendously, which drives a flow of material, called a wind, away from the disk.Credit: NASA's Goddard Space Flight Center/CI LabWatch this video on the NASA Goddard YouTube channel.For complete transcript, click here. || BlackHoleAnimation.1675_print.jpg (1024x576) [119.5 KB] || BlackHoleAnimation.1675_searchweb.png (320x180) [88.0 KB] || BlackHoleAnimation.1675_thm.png (80x40) [5.9 KB] || 20228_Swift_Tidal_ProRes_1920x1080_5994.webm (1920x1080) [4.8 MB] || frames/1920x1080_16x9_60p/ (1920x1080) [256.0 KB] || 20228_Swift_Tidal_ProRes_1920x1080_5994.mov (1920x1080) [1.4 GB] || 20228_Swift_Tidal_H264_1920x1080_5994.mov (1920x1080) [813.8 MB] ||

Black Hole Accretion Disc Energies

A black hole is a massive object whose gravitational field is so intense that nothing - not even light (electromagnetic radiation) — can escape from within its so-called event horizon. Accretion disks of hot material encircle many black holes, and this material emits X-rays and other forms of energy. Gas closer to the black hole is hotter and emits more energetic radiation. Gas at the innermost stable orbit tells astronomers whether the black hole is spinning because a rotating black hole can host material in stable orbits much closer to its event horizon. Oppositely directed jets of gas often form in the innermost zone of black hole accretion disks. ||

Gravitational Waves

Doomed neutron stars create blast of light and gravitational waves.

This animation captures phenomena observed over the course of nine days following the neutron star merger known as GW170817, detected on Aug. 17, 2017. They include gravitational waves (pale arcs), a near-light-speed jet that produced gamma rays (magenta), expanding debris from a kilonova that produced ultraviolet (violet), optical and infrared (blue-white to red) emission, and, once the jet directed toward us expanded into our view from Earth, X-rays (blue). Credit: NASA's Goddard Space Flight Center/CI LabMusic: "Exploding Skies" from Killer TracksWatch this video on the NASA Goddard YouTube channel.Complete transcript available. || Neutron_Star_Merger_Still_2_new_1080.png (1920x1080) [2.5 MB] || Neutron_Star_Merger_Still_2_new_1080.jpg (1920x1080) [167.3 KB] || Neutron_Star_Merger_Still_2_new_print.jpg (1024x576) [50.4 KB] || Neutron_Star_Merger_Still_2_new.png (3840x2160) [7.7 MB] || Neutron_Star_Merger_Still_2_new.jpg (3840x2160) [1.0 MB] || Neutron_Star_Merger_Still_2_new_searchweb.png (320x180) [51.4 KB] || Neutron_Star_Merger_Still_2_new_thm.png (80x40) [4.4 KB] || 12740_NS_Merger_Update_H264_1080.mp4 (1920x1080) [96.9 MB] || 12740_NS_Merger_Update_1080p.mov (1920x1080) [101.9 MB] || 12740_NS_Merger_Update_1080.m4v (1920x1080) [50.3 MB] || 12740_NS_Merger_4k_Update_ProRes_3840x2160_5994.mov (3840x2160) [5.1 GB] || 12740_NS_Merger_4k_Update_H264.mov (3840x2160) [516.7 MB] || 12740_NS_Merger_4k_Update_H264.mp4 (3840x2160) [254.9 MB] || NS_Merger_SRT_Captions.en_US.srt [417 bytes] || NS_Merger_SRT_Captions.en_US.vtt [399 bytes] || 12740_NS_Merger_4k_Update.webm (3840x2160) [10.0 MB] ||

Gravitational Waves from Black Holes

A gravitational wave is a theoretical fluctuation in the curvature of spacetime caused by the movement of incredibly massive objects. In this animation, two massive black holes orbit each other, creating gravitational waves. ||

Millisecond Pulsar with Gravitational Waves

A pulsar is generally believed to be a rapidly rotating neutron star that emits pulses of radiation (such as x-rays and radio waves) at known regular intervals. A millisecond pulsar is one with a rotational period in the range of 1-10 milliseconds. As the pulsar picks up speed through accretion, it distorts due to subtle changes in the crust. Such slight distortion is enough to produce gravitational waves. Material flowing onto the pulsar surface from its companion star tends to quicken the spin, but the loss of energy to gravitational waves tends to slow the spin. This competition between forces may reach an equilibrium, setting a natural speed limit for millisecond pulsars beyond which they cannot spin faster. ||

NASA's Fermi Preps to Narrow Down Gravitational Wave Sources

Fermi's GBM saw a fading X-ray flash at nearly the same moment LIGO detected gravitational waves from a black hole merger in 2015. This movie shows how scientists can narrow down the location of the LIGO source on the assumption that the burst is connected to it. In this case, the LIGO search area is reduced by two-thirds. Greater improvements are possible in future detections.Credit: NASA's Goddard Space Flight Center Watch this video on the NASAgovVideo YouTube channel. || LIGO_GBM_Common_only_Earth.png (1920x1080) [4.2 MB] || LIGO_GBM_Common_only_Earth_print.jpg (1024x576) [168.3 KB] || LIGO_GBM_Common_only_Earth_searchweb.png (320x180) [97.0 KB] || LIGO_GBM_Common_only_Earth_web.png (320x180) [97.0 KB] || LIGO_GBM_Common_only_Earth_thm.png (80x40) [6.6 KB] || Fermi_LIGO_GBM_localizations_H264_YouTube_1080p.mp4 (1920x1080) [82.8 MB] || Fermi_LIGO_GBM_localizations_H264_720p.mp4 (1280x720) [35.4 MB] || Fermi_LIGO_GBM_localizations_ProRes_1920x1080_30.mov (1920x1080) [431.3 MB] || Fermi_LIGO_GBM_localizations_H264_720p.webm (1280x720) [2.3 MB] || 12216_Fermi_LIGO_Localization_ProRes_7282x4096_30.mov (7282x4096) [6.0 GB] || 12216_Fermi_LIGO_Localization_4K.m4v (3840x2160) [140.3 MB] || 12216_Fermi_LIGO_Localization_4K.mov (4096x2304) [90.6 MB] ||

Black Hole Binary Creates Gravity Waves

When smaller black holes orbit around a supermassive black hole, Einstein's theory of general relativity predicts that they will emit gravitational radiation. These ripples of space-time cause the orbits to shrink and gradually brings the black holes closer enough together to merge. ||

Merging Black Holes

A black hole is a massive object whose gravitational field is so intense that no light (electromagnetic radiation) can escape it. When two orbiting black holes merge, a massive amount of energy is released in the form of jets. Meanwhile, the movement of these massive bodies disturbs the fabric of space-time around them, sending ripples of gravitational waves radiating outward. These waves are predicted by Einstein's theory of general relativity, but have yet to be directly detected. ||

Atomic Interferometry

Einstein predicted gravity waves in his general theory of relativity, but to date these ripples in the fabric of space-time have never been observed. Now a scientific research technique called Atomic Interferometry is trying to re-write the canon. In conjunction with researchers at Stanford University, scientists at NASA Goddard are developing a system to measure the faint gravitational vibrations generated by movement of massive objects in the universe. The scientific payoff could be important, helping better clarify key issues in our understanding of cosmology. But application payoff could be substantial, too, with the potential to develop profound advances in fields like geolocation and timekeeping. In this video we examine how the system would work, and the scientific underpinnings of the research effort. ||

Merging Neutron Stars

Neutron star merge.

Binary systems containing neutron stars are born when the cores of two orbiting stars collapse in supernova explosions. Neutron stars pack the mass of our sun into the size of a city. They are so dense and packed so tightly that the boundaries atoms nuclei disappear. In such systems, Einstein's theory of general relativity predicts that neutron stars emit gravitational radiation, ripples of space-time. This causes the orbits to shrink and gradually brings the neutron stars closer together. Shown here is such a system after about 1 billion years, when two equal-mass neutron whirl around each other at 60,000 times a minute. The stars merge in a few milliseconds, sending out a burst of gravitational waves and a brief, intense gamma-ray burst. ||

Star Collision

Light bursts from the collision of two neutron stars. || Neutron_Star_Merger_Still_1_1024x576.jpg (1024x576) [148.9 KB] || Neutron_Star_Merger_Still_1.jpg (3840x2160) [2.4 MB] || Neutron_Star_Merger_Still_1_searchweb.png (320x180) [88.4 KB] || Neutron_Star_Merger_Still_1_thm.png (80x40) [7.3 KB] ||

Crash And Burst

Imagine a dead star the size of a city and with more mass than our sun. Now imagine two of these ultra-heavy spheres smashing into each other, generating a blast bright enough to outshine an entire galaxy. Scientists have recreated just that using supercomputers to model what happens during the collision of two neutron stars. The entire process unfolds in just 35 thousandths of a second, but what this new analysis reveals is how the tangled magnetic field lines of the collapsed neutron stars restructure around a black hole, focusing a narrow stream of particles that jet into space at 99.995 percent the speed of light. Scientists believe events like this are one source of gamma-ray bursts, the powerful flashes of light from beyond the Milky Way that were first detected by satellites in the late 1960s. Watch the visualization below to see this lightning-fast cosmic wreck evolve in super-slow motion. ||

Einstein's Cosmic Speed Limit

In its first year of operations, NASA's Fermi Gamma-ray Space Telescope has mapped the entire sky with unprecedented resolution and sensitivity in gamma-rays, the highest-energy form of light. On May 10, 2009 a pair of gamma-ray photons reached Fermi only 900 milliseconds apart after traveling for 7 billion years. Fermi's measurement gives us rare experimental evidence that space-time is smooth as Einstein predicted, and has shut the door on several approaches to gravity where space-time is foamy enough to interfere strongly with light.Watch this video on the NASAexplorer YouTube channel.For complete transcript, click here. || Einsteins_Cosmic_Speed_Limit_512x288_web.png (320x180) [223.5 KB] || Einsteins_Cosmic_Speed_Limit_512x288_thm.png (80x40) [16.5 KB] || Einsteins_Cosmic_Speed_Limit_Thumbnail.jpg (346x260) [107.4 KB] || Einsteins_Cosmic_Speed_Limit_AppleTV.webmhd.webm (960x540) [82.4 MB] || Einsteins_Cosmic_Speed_Limit_AppleTV.m4v (960x540) [208.4 MB] || Einsteins_Cosmic_Speed_Limit_1280x720_H264.mov (1280x720) [433.5 MB] || Einsteins_Cosmic_Speed_Limit_1280x720_ProRes.mov (1280x720) [5.2 GB] || Einsteins_Cosmic_Speed_Limit_640x480_ipod.m4v (640x360) [68.6 MB] || Einsteins_Cosmic_Speed_Limit_512x288.mpg (512x288) [38.3 MB] || Einsteins_Cosmic_Speed_Limit_320x240.mp4 (320x180) [26.5 MB] || GSFC_20091029_EinsteinsCosmicSpeedLimit.wmv (346x236) [38.4 MB] ||

LISA Pathfinder & LISA

Lisa detects gravitational waves.

The Laser Interferometer Space Antenna (LISA) consists of three spacecraft orbiting the sun in a triangular configuration. The LISA mission will study the mergers of supermassive black holes, test Einstein's theory of general relativity, probe the early Universe, and search for gravitational waves. As these passing waves ripple space and time, they will alter the lasers shining between the spacecraft, offering a different perspective on the Universe. LISA is scheduled for launch in 2015. ||

LISA Pathfinder Spaceflight Experiment a Rousing Success

The LISA Pathfinder mission is an ESA-led effort to demonstrate technologies for a future gravitational wave observatory in space. NASA Goddard astrophysicist Ira Thorpe, a member of the team, discusses the mission and its spectacular results so far. Credit: NASA's Goddard Space Flight CenterWatch this video on the NASA Goddard YouTube channel.Complete transcript available. || LPF_Still.png (1920x1080) [3.1 MB] || LPF_Still_print.jpg (1024x576) [110.1 KB] || LPF_Still_searchweb.png (320x180) [98.0 KB] || LPF_Still_thm.png (80x40) [9.8 KB] || 12264_LISA_Pathfinder_Final_ProRes_1920x1080_2997.mov (1920x1080) [3.6 GB] || YOUTUBE_HQ_12264_LISA_Pathfinder_Final_youtube_hq.mov (1920x1080) [1.2 GB] || 12264_LISA_Pathfinder_Final-HD_1080p.mov (1920x1080) [409.0 MB] || 12264_LISA_Pathfinder_Final-Apple_Devices_HD_Best.m4v (1920x1080) [272.7 MB] || 12264_LISA_Pathfinder_Final_appletv.m4v (1280x720) [138.6 MB] || 12264_LISA_Pathfinder_Final_large.mp4 (1920x1080) [278.0 MB] || 12264_LISA_Pathfinder_Final_appletv_subtitles.m4v (1280x720) [138.7 MB] || 12264_LISA_Pathfinder_Final_appletv.webm (1280x720) [24.4 MB] || 12264_LISA_Pathfinder_SRT_Captions.en_US.srt [5.6 KB] || 12264_LISA_Pathfinder_SRT_Captions.en_US.vtt [5.6 KB] ||

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1.2: Visualizing Gravity: the Gravitational Field

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• Page ID 1625

• Michael Fowler
• University of Virginia

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Introduction

Let’s begin with the definition of gravitational field:

The gravitational field at any point P in space is defined as the gravitational force felt by a tiny unit mass placed at P.

So, to visualize the gravitational field, in this room or on a bigger scale such as the whole Solar System, imagine drawing a vector representing the gravitational force on a one kilogram mass at many different points in space, and seeing how the pattern of these vectors varies from one place to another (in the room, of course, they won’t vary much!). We say “a tiny unit mass” because we don’t want the gravitational field from the test mass itself to disturb the system. This is clearly not a problem in discussing planetary and solar gravity.

To build an intuition of what various gravitational fields look like, we’ll examine a sequence of progressively more interesting systems, beginning with a simple point mass and working up to a hollow spherical shell, this last being what we need to understand the Earth’s own gravitational field, both outside and inside the Earth.

Field from a Single Point Mass

This is of course simple: we know this field has strength GM / r 2 , and points towards the mass—the direction of the attraction. Let’s draw it anyway, or, at least, let’s draw in a few vectors showing its strength at various points:

This is a rather inadequate representation: there’s a lot of blank space, and, besides, the field attracts in three dimensions, there should be vectors pointing at the mass in the air above (and below) the paper. But the picture does convey the general idea.

A different way to represent a field is to draw “field lines”, curves such that at every point along the curve’s length, its direction is the direction of the field at that point. Of course, for our single mass, the field lines add little insight:

The arrowheads indicate the direction of the force, which points the same way all along the field line. A shortcoming of the field lines picture is that although it can give a good general idea of the field, there is no precise indication of the field’s strength at any point. However, as is evident in the diagram above, there is a clue: where the lines are closer together, the force is stronger. Obviously, we could put in a spoke-like field line anywhere, but if we want to give an indication of field strength, we’d have to have additional lines equally spaced around the mass.

Gravitational Field for Two Masses

The next simplest case is two equal masses. Let us place them symmetrically above and below the x -axis:

Recall Newton’s Universal Law of Gravitation states that any two masses have a mutual gravitational attraction \( \dfrac {Gm_1m_2}{r^2} \). A point mass m = 1 at P will therefore feel gravitational attraction towards both masses M , and a total gravitational field equal to the vector sum of these two forces , illustrated by the red arrow in the figure.

The Principle of Superposition

The fact that the total gravitational field is just given by adding the two vectors together is called the Principle of Superposition . This may sound really obvious, but in fact it isn’t true for every force found in physics: the strong forces between elementary particles don’t obey this principle, neither do the strong gravitational fields near black holes. But just adding the forces as vectors works fine for gravity almost everywhere away from black holes, and, as you will find later, for electric and magnetic fields too. Finally, superposition works for any number of masses, not just two: the total gravitational field is the vector sum of the gravitational fields from all the individual masses. Newton used this to prove that the gravitational field outside a solid sphere was the same as if all the mass were at the center by imagining the solid sphere to be composed of many small masses—in effect, doing an integral, as we shall discuss in detail later. He also invoked superposition in calculating the orbit of the Moon precisely, taking into account gravity from both the Earth and the Sun.

Exercise : For the two mass case above, sketch the gravitational field vector at some other points: look first on the x -axis, then away from it. What do the field lines look like for this two mass case? Sketch them in the neighborhood of the origin .

Field Strength at a Point Equidistant from the Two Masses

It is not difficult to find an exact expression for the gravitational field strength from the two equal masses at an equidistant point P .

Choose the x , y axes so that the masses lie on the y -axis at (0, a ) and (0,- a ).

By symmetry, the field at P must point along the x -axis, so all we have to do is compute the strength of the x -component of the gravitational force from one mass, and double it.

If the distance from the point P to one of the masses is s , the gravitational force towards that mass has strength \( \dfrac{GM}{s^2} \). This force has a component along the x -axis equal to, \( \dfrac {GM}{s^2}cos \alpha \) where \( \alpha \) is the angle between the line from P to the mass and the x -axis, so the total gravitational force on a small unit mass at P is \( \dfrac {2GM}{s^2}cos\alpha \) directed along the x -axis.

From the diagram, \( cos \alpha = \dfrac {x}{s} \), so the force on a unit mass at P from the two masses M is \[F = - \dfrac {2GMx}{(x^2 + a^2)^{3/2}} \]

in the x -direction. Note that the force is exactly zero at the origin, and everywhere else it points towards the origin.

Gravitational Field from a Ring of Mass

Now, as long as we look only on the x -axis, this identical formula works for a ring of mass 2 M in the y , z plane! It’s just a three-dimensional version of the argument above, and can be visualized by rotating the two-mass diagram above around the x -axis, to give a ring perpendicular to the paper, or by imagining the ring as made up of many beads, and taking the beads in pairs opposite each other.

Bottom line : the field from a ring of total mass M , radius a , at a point P on the axis of the ring distance x from the center of the ring is \[F = - \dfrac {2GMx}{(x^2 + a^2)^{3/2}} \].

*Field Outside a Massive Spherical Shell

This is an optional section: you can safely skip to the result on the last line. In fact, you will learn an easy way to derive this result using Gauss’s Theorem when you do Electricity and Magnetism. I just put this section in so you can see that this result can be derived by the straightforward, but quite challenging, method of adding the individual gravitational attractions from all the bits making up the spherical shell.

What about the gravitational field from a hollow spherical shell of matter? Such a shell can be envisioned as a stack of rings .

To find the gravitational field at the point P , we just add the contributions from all the rings in the stack.

In other words, we divide the spherical shell into narrow “zones”: imagine chopping an orange into circular slices by parallel cuts, perpendicular to the axis—but of course our shell is just the skin of the orange! One such slice gives a ring of skin, corresponding to the surface area between two latitudes, the two parallel lines in the diagram above. Notice from the diagram that this “ring of skin” will have radius \( \alpha \, sin \theta \), therefore circumference \( 2\pi \alpha \, sin \theta \) and breadth \(\alpha \, d \theta\), where we’re taking \( d \theta \) to be very small . This means that the area of the ring of skin is \[ length * breath = 2\pi \alpha sin \theta * \alpha d \theta \].

So, if the shell has mass \( \rho \) per unit area, this ring has mass \(2 \pi \alpha^2 \rho sin \theta d \theta \), and the gravitational force at P from this ring will be \[F = - \dfrac {2Gx \pi \alpha^2 \rho sin \theta d \theta}{(x^2 + \alpha^2 )^{3/2}} \].

Now, to find the total gravitational force at P from the entire shell we have to add the contributions from each of these “rings” which, taken together, make up the shell. In other words, we have to integrate the above expression in \( \theta \, from \space \theta = 0 \, to \, \theta = \pi \).

So the gravitational field is: \[ F = - \int \limits_ {0}^{\pi} \dfrac {2Gx \pi \alpha^2 \rho sin \theta d \theta}{(x^2 + \alpha^2 )^{3/2}} = - \int \limits_{0}^{\pi} \dfrac {2Gx \pi \alpha^2 \rho sin \theta d \theta}{(x^2 + \alpha^2 )^{3/2}} \].

In fact, this is quite a tricky integral: \(\theta \), x and s are all varying! It turns out to be is easiest done by switching variables from \(\theta\) to s .

Label the distance from P to the center of the sphere by r . Then, from the diagram, \(s^2 = r^2 + \alpha^2 - 2\alpha r cos\theta \), and a , r are constants, so \( sds=\alpha r sin \theta d \theta \), and \[ F = - \int \limits_ {0}^{\pi} \dfrac {2Gx \pi \alpha^2 \rho sin \theta d \theta}{s^3} = - \int \limits_{\gamma - \alpha}^{\gamma + \alpha} \dfrac {2Gx \pi \alpha^2 \rho}{s^3} \cdot \dfrac {sds}{ar} = - \dfrac {2Ga^2 \rho \pi}{ar} \int \limits_{\gamma - \alpha}^{\gamma + \alpha} \dfrac {xds}{s^2} \]

Now \(x = s cos \alpha \), and from the diagram \( \alpha^2 = s^2 + r^2 - 2 sr cos \alpha \), so \( x = \dfrac {s^2 + r^2 - \alpha^2 }{2r} \),

and, writing \( 4\pi \alpha^2 \rho = M. \).

\[F = \dfrac{GM}{4\alpha r^2} \int \limits_{\gamma - \alpha}^{\gamma + \alpha} \left( 1 + \dfrac {r^2 - \alpha^2}{s^2} \right)ds = \dfrac{GM}{4\alpha r^2} \left(2\alpha + (r^2 - \alpha^2)\left( \dfrac{1}{r-\alpha} - \dfrac{1}{r+\alpha} \right) \right) = \dfrac{GM}{r^2} \]

The derivation was rather lengthy, but the answer is simple:

The gravitational field outside a uniform spherical shell is GM / r 2 towards the center.

And, there’s a bonus: for the ring, we only found the field along the axis , but for the spherical shell, once we’ve found it in one direction, the whole problem is solved —for the spherical shell, the field must be the same in all directions.

Field Outside a Solid Sphere

Once we know the gravitational field outside a shell of matter is the same as if all the mass were at a point at the center, it’s easy to find the field outside a solid sphere: that’s just a nesting set of shells, like spherical Russian dolls. Adding them up,

The gravitational field outside a uniform sphere is GM / r 2 towards the center.

There’s an added bonus: since we found this result be adding uniform spherical shells, it is still true if the shells have different densities , provided the density of each shell is the same in all directions. The inner shells could be much denser than the outer ones—as in fact is the case for the Earth.

Field Inside a Spherical Shell

This turns out to be surprisingly simple! We imagine the shell to be very thin, with a mass density \( \rho \) kg per square meter of surface. Begin by drawing a two-way cone radiating out from the point P , so that it includes two small areas of the shell on opposite sides: these two areas will exert gravitational attraction on a mass at P in opposite directions. It turns out that they exactly cancel .

This is because the ratio of the areas A 1 and A 2 at distances r 1 and r 2 are given by \( \dfrac {A_1}{A_2} = \dfrac {r_1^2}{r_2^2} \): since the cones have the same angle, if one cone has twice the height of the other, its base will have twice the diameter, and therefore four times the area . Since the masses of the bits of the shell are proportional to the areas, the ratio of the masses of the cone bases is also \( \dfrac {r_1^2}{r_2^2} \). But the gravitational attraction at P from these masses goes as \( \dfrac {GM}{r^2} \), and that r 2 term cancels the one in the areas, so the two opposite areas have equal and opposite gravitational forces at P .

In fact, the gravitational pull from every small part of the shell is balanced by a part on the opposite side—you just have to construct a lot of cones going through P to see this. (There is one slightly tricky point—the line from P to the sphere’s surface will in general cut the surface at an angle. However, it will cut the opposite bit of sphere at the same angle , because any line passing through a sphere hits the two surfaces at the same angle, so the effects balance, and the base areas of the two opposite small cones are still in the ratio of the squares of the distances r 1 , r 2 .)

Field Inside a Sphere: How Does g Vary on Going Down a Mine?

This is a practical application of the results for shells. On going down a mine, if we imagine the Earth to be made up of shells, we will be inside a shell of thickness equal to the depth of the mine, so will feel no net gravity from that part of the Earth. However, we will be closer to the remaining shells, so the force from them will be intensified.

Suppose we descend from the Earth’s radius r E to a point distance r from the center of the Earth. What fraction of the Earth’s mass is still attracting us towards the center? Let’s make life simple for now and assume the Earth’s density is uniform , call it \( \rho \) kg per cubic meter.

Then the fraction of the Earth’s mass that is still attracting us (because it’s closer to the center than we are—inside the red sphere in the diagram) is \( \dfrac {V_{red}}{V_blue} = \dfrac {4}{3} \pi r^3 / \dfrac {4}{3} \pi r_E^3 = \dfrac {r^3}{r_E^3} \).

The gravitational attraction from this mass at the bottom of the mine, distance r from the center of the Earth, is proportional to mass/ r 2 . We have just seen that the mass is itself proportional to r 3 , so the actual gravitational force felt must be proportional to \( \dfrac {r^3}{r^2} = r \).

That is to say, the gravitational force on going down inside the Earth is linearly proportional to distance from the center . Since we already know that the gravitational force on a mass m at the Earth’s surface \( r = r_E \) is mg , it follows immediately that in the mine the gravitational force must be \[F = \dfrac {mgr}{r_E} \].

So there’s no force at all at the center of the Earth—as we would expect, the masses are attracting equally in all directions.

Contributors

• Michael Fowler  (Beams Professor,  Department of Physics ,  University of Virginia)

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• Published: 19 May 2022

Modulation of biological motion perception in humans by gravity

• Ying Wang   ORCID: orcid.org/0000-0002-5756-2480 1 , 2 , 3 ,
• Xue Zhang   ORCID: orcid.org/0000-0003-2573-4740 1 , 2 , 3 , 4 ,
• Chunhui Wang 5 ,
• Weifen Huang 5 ,
• Qian Xu 1 , 2 , 3 ,
• Dong Liu 1 , 2 , 3 ,
• Wen Zhou   ORCID: orcid.org/0000-0001-6730-2116 1 , 2 , 3 ,
• Shanguang Chen 5 , 6 &
• Yi Jiang   ORCID: orcid.org/0000-0002-5746-7301 1 , 2 , 3 , 7  

Nature Communications volume  13 , Article number:  2765 ( 2022 ) Cite this article

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The human visual perceptual system is highly sensitive to biological motion (BM) but less sensitive to its inverted counterpart. This perceptual inversion effect may stem from our selective sensitivity to gravity-constrained life motion signals and confer an adaptive advantage to creatures living on Earth. However, to what extent and how such selective sensitivity is shaped by the Earth’s gravitational field is heretofore unexplored. Taking advantage of a spaceflight experiment and its ground-based analog via 6° head-down tilt bed rest (HDTBR), we show that prolonged microgravity/HDTBR reduces the inversion effect in BM perception. No such change occurs for face perception, highlighting the particular role of gravity in regulating kinematic motion analysis. Moreover, the reduced BM inversion effect is associated with attenuated orientation-dependent neural responses to BM rather than general motion cues and correlated with strengthened functional connectivity between cortical regions dedicated to visual BM processing (i.e., pSTS) and vestibular gravity estimation (i.e., insula). These findings suggest that the neural computation of gravity may act as an embodied constraint, presumably implemented through visuo-vestibular interaction, to sustain the human brain’s selective tuning to life motion signals.

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Introduction.

As inhabitants of the Earth, humans have evolved under the constant influence of gravity. Without even noticing it, we keep our head aligned with the gravitational up and our feet pulled down towards the center of the globe. Indeed, almost everything we encounter or interact with is subject to the same gravitational constraint. This constraint, among the various environmental invariants, has infiltrated into our mental representation of the external world, formalizing psychological principles that mimic the physical laws to advance our cognitive, perceptual, and motor capabilities 1 , 2 , 3 , 4 .

Numerous studies have revealed a robust effect of representational gravity on visual memory, showing that the memorized position of a moving or an unsupported object was shifted downward, i.e., towards the direction of implied gravity 5 . Meanwhile, influences of the gravitational force were evident in the manual interception and time-to-collision estimation. In contrast to people’s deficient ability to intercept randomly accelerated objects or estimate arbitrary accelerations 6 , the timing of interceptive responses to free-falling balls was sufficiently precise and barely affected by visual deprivation 7 , 8 , 9 . Moreover, observers tended to trigger the catching movement earlier when the balls came from above instead of below, indicating that they applied a prior assumption that downward motion is accelerated by gravity 10 .

Intriguingly, the human brain’s selective tuning to gravity-constrained visual events also extends to the perception of biological motion (BM), the distinctive movement patterns initiated by living organisms that carry the signals of life. Human beings, like various other terrestrial animals, are endowed with the ability to readily extract messages transmitted by the BM signals, especially those from their conspecifics 11 , 12 , 13 , 14 , 15 , 16 , 17 . However, turning the stimulus upside-down severely impeded the detection and recognition of BM 18 , 19 , 20 , 21 , 22 , 23 , 24 , reduced the neural activity associated with BM representation 25 , and abolished the innate preference for BM in neonates and newly hatched chicks 26 , 27 , 28 , establishing the existence of a prominent inversion effect in visual BM processing. Remarkably, such inversion effect even occurred for an unusual body form orientation, e.g., waking on hands 29 , or for spatially scrambled BM stimuli devoid of any familiar shape 24 , 30 , suggesting that it may involve a brain mechanism devoted primarily to the analysis of kinematic cues. In particular, upright BM, especially the locomotion of articulated animals, consists of ballistic or pendular motion with acceleration profiles compatible with the effect of gravity. Thus, the inversion effect in visual BM perception is ascribed to a predisposed gravity bias in humans and other legged vertebrates, which may serve as a perceptual filter to enable efficient detection and appropriate interpretation of life motion signals 24 , 28 .

Arguably, the alleged gravity bias in life motion perception confers an adaptive advantage to all living creatures on Earth, including human beings. However, what gives rise to such perceptual bias, especially to what extent and how it is shaped by the Earth’s gravity field, remains largely unknown. It is possible that the inversion effect in BM perception, like that in face perception, is primarily built on our visual experience gained from long-term interaction with other tellurian animals throughout evolution. Alternatively, considering that our selective sensitivity to gravity-compatible BM signals has emerged and evolved in the Earth’s gravity environment, it is conceivable that this particular gravity environment (1 g) provides an indispensable, ongoing source for the BM inversion effect. On Earth, we achieve a continuous estimate of gravity’s orientation based on the real-time computation of vestibular and other bodily cues 31 . More particularly, the posterior part of the insular cortex, a core region responsible for vestibular processing, can respond to visually presented gravitational motion in an orientation-dependent manner. Such neural computation may provide a gravitational field vector along which gravitational acceleration could be identified during visual motion analysis, thereby acting as an online constraint to cultivate a selective sensitivity towards gravity-compatible BM patterns. If this is the case, we should expect to observe a stable BM inversion effect in 1 g gravity but a reduction of such inversion effect under reduced gravity conditions, mediated by a recalibration of the neural computation in the visual-vestibular network.

To examine these possibilities, we carried out a space experiment in which we assessed the BM inversion effect in astronauts exposed to microgravity during spaceflight (Fig.  1 , Spaceflight). We also conducted ground-based control experiments in two groups of observers, one in an isolation environment (i.e., a simulated space capsule), one in a regular lab environment, to investigate the potential influences of non-gravity-related confounding factors such as test environment and practice effect (Fig.  1 , Control). To support the behavioral results obtained from the space experiment and further investigate the neural responses in the human brain, we conducted a ground-based spaceflight analog experiment using the 6° head-down tilt bed rest (Fig.  1 , HDTBR). Prolonged HDTBR can lead to the elimination of Gz gravitational stimuli on the body (i.e., head to toe G-stress) and the lack of work against the force of gravity by the bone, muscle, and cardiovascular systems in the vertical direction, which may cause changes in physiological responses 32 , 33 and vestibular neural processing 34 , 35 similar to what occurs with long-term spaceflight. It has been widely used by NASA and other international space agencies, in combination with the spaceflight recently 36 , to investigate the long-term influence of microgravity on humans.

Participants performed a biological motion (BM) perception task before, during, and after the spaceflight/HDTBR, or throughout the control experiments. Besides, participants in the HDTBR experiment completed a face perception task in addition to the BM task during each test session and underwent two fMRI scanning sessions before and after the bed rest.

If our superior sensitivity to upright relative to inverted BM is actively shaped by the neural computation of gravity, we predict that the BM inversion effect (i.e., the gravity bias) will decrease from pre- to in-flight in the astronaut group, get reduced with accumulating time in the HDTBR group, but yield no significant change in the control groups. We also assume that the neural activity associated with orientation-dependent BM representation will decrease after the HDTBR. Furthermore, if the vestibular gravity estimate is essential to the observed perceptual changes, we expect to observe altered functional connectivity in the visual-vestibular network underlying the perceptual changes.

Space experiment

The astronauts performed a BM perception task (Fig.  2a ) before (PreFL), during (InFL), and after the spaceflight (PostFL). We divided the accuracy difference between the upright and inverted conditions by their sum to obtain a normalized BM inversion effect for each participant (Fig.  2b ). A repeated measures ANOVA of the BM inversion effect revealed a significant main effect of the test phase ( F (2, 8) = 8.39, p  = 0.011). Remarkably, the inversion effect was largely diminished after about one week of spaceflight (InFL vs. PreFL: p  = 0.048, adjusted for multiple comparisons by Bonferroni correction). Such impact was reduced but not eliminated half to one month after the astronauts returned to Earth (PostFL vs. PreFL: p  = 0.073, Bonferroni corrected, also refer to supplementary results and Fig. S 1 for more details). These results demonstrate a profound influence of microgravity exposure on the BM inversion effect, suggesting that the Earth’s gravity plays a pivotal role in sustaining the visual system’s orientation-dependent tuning to BM signals.

a Participants judged the walking direction of an upright or inverted point-light walker (rendered in blue for illustration only) embedded in a dynamic mask. The mask consisted of scrambled walkers with balanced left and right walking direction cues. Blue and white arrows represent the walking directions indicated by the target walker and the noise mask, respectively. Demos of sample BM stimuli are provided as supplementary information. b The normalized BM inversion effect (BMIE) and the task performance for the upright (BMUpr) and inverted (BMInv) conditions obtained before (PreFL), during (InFL), and following (PostFL) the spaceflight. Diamonds show individual data ( n  = 5). * p  = 0.048 (two-tailed paired t -test, Bonferroni corrected). c Results obtained from the isolation control experiment where participants performed the BM perception task before (the first session), during (the second to the fourth session), and after (the fifth session) 30-day isolation. Blue dots represent individual data ( n  = 2). Error bars indicate ±1 SEM. d Results obtained from the regular control experiment where participants performed the BM perception task repeatedly in five test sessions over more than one month (35 days on average). Blue dots represent individual data ( n  = 22). Error bars indicate ±1 SEM. Source data are provided as a Source Data file.

Further analysis revealed that spaceflight caused distinct change patterns in BM perception performances for the upright and inverted conditions (Fig.  2b ). Overall, the test phase had a significant main effect on inverted BM perception ( F (2, 8) = 5, p  = 0.039) but less influenced upright BM perception ( F (2, 8) = 2.74, p  = 0.124). More specifically, for upright BM stimuli, the response accuracy declined slightly during spaceflight and returned to the normal level after the astronauts returned to Earth. By contrast, for inverted BM stimuli, the accuracy tended to increase during the flight and maintained at a relatively high level until half to one month after the flight (see also supplementary results and Fig. S 1 for more details). The clear contrast between the two conditions suggests that microgravity intervenes with BM perception through an orientation-sensitive rather than a general adaptation mechanism.

Control experiments

To further examine whether the test environment or practice effect could account for the observations from the space experiment, we administered the BM perception tasks to participants in two ground-based control experiments (Fig.  1 , Control). For participants who were isolated in a simulated space capsule for 30 days, repeated tests caused no systematic change in the BM inversion effect or the separate performance on upright and inverted BM perception over time (Fig.  2c ; Friedman’s Two-way Analysis of Variance on the inversion effect: χ 2 (4) = 2.8, p  = 0.592; upright: χ 2 (4) = 3.6, p  = 0.463; inverted: χ 2 (4) = 4.82, p  = 0.306). We obtained similar results from another group of participants who performed the same task in a regular lab environment repeatedly over more than one month (Fig.  2d ; repeated measures ANOVA on the inversion effect: F (4, 84) = 0.83, p  = 0.512; upright: F (4, 84) = 0.76, p  = 0.557; inverted: F (4, 84) = 1.55, p  = 0.194). Results from these control experiments consistently suggest that non-gravity-related environmental factors or practice effects cannot account for the reduction of the inversion effect observed in the space microgravity environment.

HDTBR experiment

To support the behavioral results obtained from the space experiment and further elucidate the neural mechanisms associated with the perceptual changes, we conducted a ground-based spaceflight analog experiment where a group of healthy participants completed a BM perception task (Fig.  3a, b ) before, during, and after 45-day HDTBR. The behavioral results were highly consistent with the findings from the space experiment. The perceptual inversion effect changed significantly throughout the test sessions (Fig.  3c ; F (4, 44) = 3.82, p  = 0.009). There was an evident decrease of the inversion effect at the end of the HDTBR period (BR43 vs. BR-1: p  = 0.038, Bonferroni corrected), which partially recovered 10 days after the bed rest (BR + 10 vs. BR-1: p  = 0.284, Bonferroni corrected). Also, in agreement with the space experiment, the participants’ perceptual performance for the upright BM stimuli was rather stable across test sessions ( F (4, 44) = 1.13, p  = 0.353), while their performance for the inverted BM stimuli increased significantly due to the bed rest ( F (4, 44) = 4.12, p  = 0.006). Compared with the pre-bedrest baseline, the perception of inverted BM was marginally higher at the end of the bed rest period (BR43 vs. BR-1: p  = 0.053, Bonferroni corrected), whereas such influence was no longer evident 10 days after the bed rest (BR + 10 vs. BR-1: p  = 0.159, Bonferroni corrected).

a We measured the BM and face inversion effects, which were respectively related to both motion and form processing and form processing alone. b Procedures of the BM and face perception tasks in which participants indicated whether two successively presented walkers/faces were the same. c The change of the perceptual inversion effect along five test sessions before (day −1), during (day 13, 27, & 43), and after the bed rest (day + 10) for the BM (blue line, n  = 12) and the face (green line, n  = 15) perception tasks. Error bars indicate ±1 SEM. Source data are provided as a Source Data file.

To explore whether the current findings were specific to BM processing, we further compared the inversion effect in BM perception with the inversion effect in face perception (Fig.  3a, b ). Faces, akin to BM, are of great biological significance and involve domain-specific neural mechanisms genetically wired in the human brain 37 , 38 , 39 , 40 . However, although robust face inversion effects occurred at both behavioral and neural levels 41 , 42 , such effects rely on the encoding of configural rather than kinematic information 43 . Therefore, if gravity specifically influences the visual sensitivity to gravity-constrained motion signals, we should expect a significant impact of HDTBR on the BM but not on the face inversion effect. In support of this assumption, prolonged HDTBR did not exert a reliable influence on the face inversion effect across the test sessions (Fig.  3c ; F (4, 56) = 1.65, p  = 0.174). Moreover, by looking into the pattern of change over time, we noticed that the face inversion effect did not show a decreasing trend from the second test session to the end of the HDTBR (linear trend test for the effect from BR13 to BR 43: F (1,14) = 0.01, p  = 0.941), standing in stark contrast to the gradual change of the BM inversion effect during the same test period (linear trend test: F (1, 11) = 32.38, p  < 0.001).

In addition to the behavioral measures, we acquired the participants’ neural responses to BM stimuli before and after the bed rest stage using functional magnetic resonance imaging (fMRI). As controls, participants also viewed face and house images during scanning. In line with the literature 25 , 42 , at the pre-bedrest stage, brain areas selectively tuned to BM and face signals showed higher responses to upright than inverted stimuli (Fig.  4a ). Upright BM stimuli, relative to their inverted counterparts, activated the posterior superior temporal sulcus (pSTS) ( t (11) = 4.95, p  = 4×10 −4 ); and upright faces, compared with the inverted ones, was associated with higher activation in the fusiform face area (FFA) ( t (14) = 4.80, p  = 3 × 10 −4 ). For houses, there was no evident inversion effect ( t (15) = 1.10, p  = 0.29) in the parahippocampal place area (PPA) 44 . Crucially, after bed rest, the orientation-dependent responses to BM stimuli in the right pSTS decreased to a significant extent ( t (11) = −2.75, p  = 0.019), while no significant change was found for faces ( t (14) = −0.38, p  = 0.71) or houses ( t (15) = −0.67, p  = 0.516) in the corresponding cortical regions (Fig.  4a ). These results, in accord with our behavioral findings, provide substantial evidence that HDTBR specifically reduces the gravity bias in visual BM processing.

a The bar charts show the BM inversion effect in the pSTS, hMT + , and FBA, the face inversion effect in the FFA, and the house inversion effect in the PPA, averaged across participants. Colored dots represent individual data. Error bars indicate ±1 SEM. *: p  = 0.019 (two-tailed paired t-test). A summary of the ROIs is displayed in a single participant. b The enhancements of resting-state functional connectivity (RSFC) between the pSTS and two insula ROIs (Ri: retroinsula; pIns: posterior insula) after vs. before the bed rest were significantly correlated with the change of the inversion effect in BM perception (BMIE). No reliable correlation was observed between such RSFC change and the change of the inversion effect in face perception (FIE), or between the Ins-FFA RSFC change and the change of perceptual FIE. Note that in a and b, we present data obtained from participants who showed valid performances in the corresponding behavioral task to facilitate the further correlation analysis (see methods for details; n  = 12 in the BM condition for pSTS and MT + , n  = 9 for FBA since no cluster could be identified in three participants due to noisy signals; n  = 15 in the face condition for FFA, and n  = 16 in the house condition for PPA). Additional results for ROI analysis based on data from all participants ( n  = 16) are shown in Fig. S 1 . Source data are provided as a Source Data file.

Visual BM conveys kinematic and configural cues that are analyzed in distinct pathways converging at the pSTS 45 , 46 . It raises the question of whether neural activity in the motion-selective or body-form-selective areas could account for the decrease of the inversion effect observed at the pSTS. To address this issue, we extracted neural activity from the human motion complex (hMT+) and the fusiform body area (FBA) 47 , 48 , which respectively engage in the visual processing of body motion and body form information. The differences of neural signals in response to the upright and inverted BM were comparable before and after the bed rest in both the hMT+( t (11) = −0.88, p  = 0.40) and the FBA ( t (8) = 0.34, p  = 0.739), indicating that the interaction between gravity and BM information analyses may not be completed before the stage where motion and form information are integrated into a coherent BM representation at the pSTS.

How does prolonged exposure to microgravity modulate the orientation specificity in visual BM representation? In the Earth’s environment, the human vestibular organs located in the inner ears monitor the linear and rotational accelerations of the head as well as the constant gravitational acceleration. The vestibular system further encodes head orientation relative to gravity and head motion and infers the position of the head in three-dimensional space, which contributes to a multitude of brain functions ranging from oculomotor and postural control, motion perception, spatial orientation, navigation to bodily self-consciousness 49 , 50 . The versatility of the vestibular system has been attributed to its multisensory nature. Unlike other sensory systems, there is no cortical area dedicated exclusively to the vestibular sense, and the vestibular inputs are integrated with the visual, motor, proprioceptive, and somatosensory signals throughout the thalamocortical vestibular pathways 49 , 51 . Within this multimodal network, the posterior part of the insular cortex is considered a core region since it can be activated by both vestibular stimulation and visually presented motion that carries gravitational acceleration cues 31 . This makes it a promising candidate to be responsible for the microgravity-induced recalibration of the gravity bias in visual BM representation. To test this possibility, we analyzed the resting-state functional connectivity (RSFC) between the pSTS and the insular cortex, which showed strengthened connectivity between these regions after prolonged HDTBR (Fig.  4b ). Specifically, using the individual pSTS as a seed, we could identify two clusters in the right insula (retroinsula: Ri, voxel coordinates of peak activation: x = 41, y = −30, z = 21; posterior insula: pIns, voxel coordinates of peak activation: x = 38, y = −2, z = 16) that showed enhanced connection with the pSTS after the bed rest ( p  < 0.05 at the group level, uncorrected). These locations were coherent with the cortical regions critically involved in the computation of the gravity cue in 1 g motion 31 . More importantly, the elevated RSFC between the pSTS and these insula regions (defined as a Ri ROI and a pIns ROI for each participant) could well predict the decrement in the perceptual BM inversion effect (Fig.  4b ; Ri: r  = −0.84, p  < 0.001; pIns: r  = −0.61, p  = 0.034), suggesting a key functional role of the insula-pSTS connectivity in the observed change of the gravity bias in BM perception. By contrast, there were no significant correlations between the enhancement of the pSTS-Insula connectivity and the change of the perceptual face inversion effect (Fig.  4b ; Ri: r  = −0.01, p  = 0.972; pIns: r  = 0.05, p  = 0.878) and between the change of the face inversion effect and that of the FFA-Insula connectivity (Fig.  4b ; Ri: r  = −0.06, p  = 0.825; pIns: r  = −0.29, p  = 0.322). Taken together, these results convergently suggest that the altered functional connectivity between cortical regions dedicated to visual BM representation and vestibular gravity estimation specifically underlies the attenuation of the gravity bias in BM perception.

Immersed in the planet’s gravitational field, humans are equipped with sophisticated cognitive capacities to cope with the effects of gravity. The rule that objects are attracted by the gravity of Earth has been so deeply ingrained that it distorts the visual representation of an object’s location 5 , facilitates the timing of free-falling motion 9 , 10 , and is thought to underlie the orientation specificity of visual BM perception 24 , 28 . Here we investigated how the gravity environment shapes our brain’s responses to visual BM signals, capitalizing on the manned spaceflight and HDTBR techniques. Specifically, we demonstrated that the BM perceptual inversion effect diminished after two weeks’ exposure to microgravity in space, and decreased gradually throughout a 45-day spaceflight analog using HDTBR. Moreover, we found an attenuated inversion effect in the neural representation of BM after the HDTBR. These findings provide substantial evidence that the Earth’s gravity facilitates the orientation-dependent visual perception of BM information.

In contrast to the significant change of the BM inversion effect, no such pattern emerged for the face inversion effect or the corresponding neural activity under simulated microgravity. These results are in line with the finding that the face inversion effect persisted in space despite impaired learning and recognition performances 52 . They are also consistent with the observation that the encoding of BM but not that of faces is affected by stimulus inversion relative to the gravitational frame of reference 53 . While the face inversion effect can be largely accounted for by compromised form processing 43 , 54 , the BM inversion effect involves a shape-independent component driven by the disruption of gravity-compatible motion invariants 24 , 55 . Hence, the reduced BM inversion effect following microgravity exposure probably results from the reduced gravitational influence on visual motion processing rather than form processing. In the 1 g environment, the internal representation of gravity may facilitate body kinematics analysis in an orientation-dependent manner. Prolonged microgravity exposure may reduce this influence and therefore diminish the BM inversion effect.

These findings link the physical effect of gravity to the mental constraints imposed on visual motion analysis, providing fresh insights into the origins of our abilities to process visual BM signals. The superior sensitivity to the movement of living creatures is an animal instinct 11 , 12 , 13 , 14 , 15 , 16 , 17 that emerges from the first days or even the first hours of life 26 , 27 , 28 , 30 . Recent studies provide corroborative evidence for the existence of an innate mechanism for the visual analysis of BM by showing that individual differences in BM perception are under domain-specific genetic influences 37 , 38 . Here we further propose that the Earth’s gravity may play a vital part in the evolutionarily old mechanism underlying BM perception. More specifically, constant exposure to the planet’s gravitational field may exert selective pressure on its permanent inhabitants, thereby fostering the development of an ability to spot animated motions compatible with the effect of gravity.

The current study also extends our knowledge regarding the adaptive plasticity of the human perceptual system in the microgravity environment. Despite the solid evidence for potential health issues caused by microgravity exposure, far less clear is the impact of microgravity on perceptual and cognitive functions 56 , 57 , 58 . More specifically, there is little consensus about whether microgravity exposure would disturb the orientation-dependent effects in visual form perception 52 , 59 , 60 , 61 . The current study has identified a potent influence of microgravity on a specific aspect of visual information processing, i.e., BM perception, and indicated a mechanism by which the visual analysis of gravity-constrained kinematic rather than form cues adapts to the microgravity environment. Further studies are warranted to elucidate whether such mechanism is specific to BM processing or can be generalized to the visual analysis of non-biological gravitational motion. In addition, recent studies tracking the recovery trajectories of spaceflight-related neurocognitive changes reveal that most of these changes are (at least partially) reversible, albeit it may take weeks to months for some effects to recover to the baseline level 34 , 62 . Consistent with these results, our finding that the BM inversion effect did not fully recover several weeks after the spaceflight suggests that re-adaptation of brain functioning to the Earth’s gravitational field is a cumulative rather than an immediate process. The complete time course of such gravity-related neuroplasticity is a topic worthy of further research.

How did microgravity exposure modulate BM processing in the brain? The fMRI experiment showed a decrease of the inversion effect in the BM-selective region (i.e., the pSTS), but not in cortical areas responding to biological forms (i.e., faces and bodies) or general motion cues, suggesting that prolonged HDTBR specifically modulates the neural representation of BM information. Moreover, we found strengthened resting-state functional connectivity between the pSTS and the retroinsula and the posterior insula, with such altered visual-vestibular connectivity predicting the reduction of the perceptual inversion effect. Under the microgravity condition, the most prominent change in the sensory system is the lack of graviceptive stimulation, resulting in significantly different vestibular afferent signals relative to that under the normal gravity condition. It has been posited that the insular cortex in the vestibular system stores an internal model for analyzing visual gravitational motion based on graviceptive information 31 . This model represents a prior expectation about the effects of gravity, which could regulate online sensory information analysis (visual, vestibular, tactile, or proprioceptive) to assist in visual perception and manual interceptions 63 . In our experiments, prolonged HDTBR may induce adaptation of this internal model or modulate its implementation during visual BM analysis through the reweighting of sensory inputs 35 , thereby reducing the disparity between the visual responses to upright and inverted BM signals. Possibly, sensory reweighting that occurs in both the spaceflight and the HDTBR conditions leads to the similar perceptual outcomes observed in the current study 34 , 35 . However, since HDTBR is not identical to the spaceflight regarding the gravitational stimulation, future research on the spaceflight-induced changes of neural responses to BM stimuli in astronauts is needed to verify this assumption.

In sum, the current findings reveal that the human brain adaptively utilizes gravity as an embodied constraint to facilitate the perception of life motion signals. Throughout our evolutionary history, humans tend to incorporate the gravity force acting on the movements of other biological organisms and their own bodies into visual motion analysis. Such an embodied constraint can lead to superior perceptual processing of gravity-compatible kinematic cues and sustain the brain’s orientation-dependent tuning to life motion signals, conferring an evolutionary advantage to tellurian animals. Nevertheless, escaping from the Earth’s gravity may remove such a constraint through recalibrating the visual-vestibular connectivity, which provides an adaptive mechanism to help us better accommodate altered gravity environments.

Participants

Six astronauts (two females, mean age ± SD = 42 ± 6.9 years) participated in the space experiment. They were exposed to microgravity conditions for 13 or 15 days during the manned flight missions of China’s Shenzhou program. All but one (male) of these astronauts completed the tasks in all test sessions and were included in the data analysis. All participants gave informed consent in accordance with protocols approved by the Institutional Review Board of the China Astronaut Research and Training Center.

Stimuli and procedure

Participants were trained on the task prior to the experiment and tested before, during, and after the spaceflight. Table  1 showed the comprehensive test schedules for all participants. Each participant performed the task at least once and up to twice (in which case results were averaged) within each test session. Since there was no significant difference between the two post-flight sessions, we combined them into a PostFL condition in the main text and showed data from the post-flight1 and post-flight2 sessions in the supplementary results.

Each test trial began with an intact point-light walker embedded in a scrambled mask presented at the center of the screen for 1 s. Participants were required to judge the locomotion direction of the target walker (left or right, counterbalanced across trials) by pressing one of two keys. The target walker was composed of 15 white dots located at the head and critical joints of a human figure walking on a treadmill without translational motion 64 . The scrambled mask was generated by randomizing the initial positions of the dots constituting the intact walker. Half of the dots in the mask were made of scrambled walkers in the same direction as the target walker, and the other half in the opposite direction, to eliminate the potential effects of directional cues conveyed by the mask (see 37 Experiment 4 for details and supplementary files for demos of sample BM stimuli). There were 20 trials for each of the two experimental conditions, target upright and target inverted. For the upright condition, the number of dots in the mask was set to 30/75. For the inverted condition, the mask contained 15/30 dots. The noise level of the mask was counterbalanced across trials. The experiment programs were written and compiled in VB and VC.

Data analysis

We calculated a normalized perceptual BM inversion effect (BMIE) for each participant by dividing the accuracy (i.e., percent of correct responses) difference between the upright and inverted conditions by their sum. To reduce any potential interference, further analysis was only conducted with participants who exhibited a perceptual inversion effect (larger than 0) in the baseline/preflight session. The same rules applied to the analysis of all behavioral data obtained from the current study (including both the BM and face conditions) to facilitate comparisons across experiments and conditions. In the space experiment, all five participants showed a BMIE before the spaceflight and were subject to further analysis. Analysis of the behavioral data obtained from this study was performed by using SPSS20.

Ground-based control experiments

Two healthy volunteers (2 males, mean age±SD = 35 ± 4.2 years) from the China Astronaut Research and Training Center participated in the 30-day isolation control experiment. Another twenty-four volunteers (12 females, mean age±SD = 22 ± 2.8 years) recruited among college students took part in the regular control experiment and got monetary payment for their participation. All participants had normal or corrected-to-normal vision and provided informed consent in accordance with protocols approved by the Institutional Review Board of the China Astronaut Research and Training Center (the isolation control experiment) and the Institutional Review Board of the Institute of Psychology, Chinese Academy of Sciences (the regular control experiment).

In two control experiments, participants performed the same BM perception task as in the space experiment but under the normal gravity (1 g) condition. In the isolation control experiment, participants executed tasks similar to what astronauts do for a space mission in a simulated space capsule on the ground for 30 days. Meanwhile, they performed the BM perception tests before, during, and after the isolation 14 times in total. The time intervals between any two successive tests were 2–6 days. To improve the reliability of the results and facilitate comparisons across experiments, data obtained from adjacent time points were averaged and combined into five test sessions: −10 to −1 day before the isolation (T1/baseline, with 2 tests), the 1 st , 2 nd , and 3 rd 10-day during the isolation (T2-T4, with 3 to 4 tests for each), and +1 to +10 day after the isolation (T5, with 2 tests). In the regular control experiment, participants performed the same task 6 times over more than one month (35 days on overage) in the lab. The intervals between any two successive test sessions were 6–9 days. Results from the first two tests were combined to achieve a stable baseline session (T1), followed by another four test sessions (T2-T4, with 1 test in each).

We calculated the BMIE for each participant in the same way as that in the space experiment. Also consistent with the space experiment, only participants who showed a stable inversion effect in the baseline session were subject to further analysis: 2 (in 2) for the isolation control experiment and 22 (in 24) for the regular control experiment.

Ground-based HDTBR experiment

Sixteen healthy male volunteers (mean age ± SD = 26.6 ± 4.2 years) were recruited and paid for their participation in the HDTBR experiment. They underwent the 6° head-down tilt bed rest for 45 days, preceded by a 10-day adaptive phase to get familiar with the environment and tasks and complete the pre-bedrest test, followed by a 10-day recovery phase for the post-bedrest test. All participants had normal vision, with no family history of genetic diseases, and were free of neurological, psychiatric, or chronic health disorders. They provided informed consent in accordance with protocols approved by the Institutional Review Board of the China Astronaut Research and Training Center.

Behavioral assessments

Participants received training on the behavioral tasks prior to the tests. They were tested at five time points throughout the experiments, including one day before bed rest (BR-1), three different days during the bed rest (BR13, BR27, BR43), and ten days after bed rest (BR + 10). During the HDTBR period, participants lied on a specifically-made bed (rotated 6° relative to the horizontal plane) all the time to help them stick to the head-down tilt posture. They did all the daily activities and tests on the bed and were allowed to turn over to the prone position but never left the bed. At the pre- and post-bedrest stages, participants were free to move about but had to lie on the bed (with head and feet at the same height) when doing the perceptual tasks. The task procedure was kept consistent for all the test sessions.

Each test session consisted of four blocks to assess the perception of upright BM, inverted BM, upright face and inverted face stimuli, respectively. In the BM task, participants were required to judge whether or not two successively presented point-light walkers (1 s each, with a blank interval of an average of 500 ms) had the same walking direction by pressing one of two mouse buttons. The walkers were centrally located on the screen and subtended approximately 8.4 deg in height. The walking direction could be one of five angles: 10° left/right, 5° left/right, or 0° deviated from the vector pointing toward the viewer. Each direction was replicated 8 times for the walker displayed in the first interval, with a balanced number of same and different trials, resulting in a total of 40 trials within each block. The order of these trials was completely randomized. The procedure of the face perception task was similar to that of the BM task, except that a pair of same-gender face images were displayed (200 ms for each, to the left and right of the central fixation respectively) in each trial and participants had to indicate whether the faces had the same or different identities. The stimulus set consisted of 20 male and 20 female face images with the neutral expression, which were selected from the Chinese Affective Pictures System 65 . The gender (male/female) and location of the first face (left/right) were counterbalanced within each block. Visual stimuli were displayed on an LCD screen (1024 × 768 resolution; refresh rate: 60 Hz) hung above the head of the participants with a viewing distance of about 60 cm. Stimulus presentation and experimental manipulation were carried out using Matlab 2014b with the PsychToolbox-3 extensions 66 .

fMRI experiment

Participants underwent two fMRI scanning sessions, one prior to (BR-3) and the other following (BR + 3) the bed rest phase. The scanning protocol consisted of one resting state scan, two functional runs (with visual tasks) and an anatomical scan. During the task runs, upright and inverted visual stimuli from 3 categories, including BM, face, and house, were back-projected onto a screen inside the magnet bore. Participants viewed these stimuli via a mirror mounted on the head coil. The stimuli contained a set of point-light walkers 67 facing toward 8 directions equally distributed between 0 and 180 degrees, 8 grayscale neutral face images from the NimStim face stimulus set 68 , and 8 house pictures. Each run was comprised of 18 blocks with 3 repetitions for the 6 conditions. The blocks were run in a pseudo-random order, interleaved with 2-s fixation intervals. Within each 10-s block, 10 exemplars selected randomly from the same category were displayed for 500 ms per item with 500-ms fixation intervals. Participants were required to perform a 1-back task within each block, i.e., press a button whenever the present stimulus was identical to the preceding one, to help maintain attention to the stimuli.

Behavioral data analysis

We calculated the BMIE and FIE for each participant in the same way as that in the space and the ground-based control experiments. Also consistent with those experiments, the analysis of behavioral data was based on results obtained from the participants who exhibited a perceptual inversion effect in the baseline (pre-bedrest) conditions, both for the BM perception task ( N  = 12) and the face perception task ( N  = 15). The same participants were included in the analysis of the fMRI data to calculate the correlation between the HDTBR-induced changes in behavioral performances and neural responses (see the result section in the main text). In addition, we also analyzed the fMRI data based on all 16 participants and presented the results in the supplementary file (see supplementary results and Fig. S 2 ).

fMRI data acquisition and analysis

Data acquisition.

The fMRI data were collected on a 3T Siemens Trio scanner equipped with a 12-channel phased-array head coil. High-resolution anatomical images were acquired using a 3D T1-weighted magnetization-prepared rapid-acquisition gradient echo (MPRAGE) sequence (1 × 1 × 1.33 mm 3 resolution; 144 slices, no gap; TR/TE = 2530/3.39 ms, flip angle = 7°). Task and resting-state fMRI data were acquired using a 2D T2-weighted echo-planar imaging (EPI) sequence (3.125 × 3.125 mm 2 in-plane resolution; image matrix = 64 × 64; 33 slices, 3.5 mm thickness, 0.7 mm gap; TR/TE = 2000/30 ms; flip angle = 90°).

Data preprocessing

Preprocessing and statistical analyses of fMRI data were performed using the Analysis of Functional NeuroImages (AFNI 17.0.11) package 69 and MATLAB 2014b. The first two volumes of functional data from each run were discarded to allow for magnetization equilibrium. Spike noise in the signals was removed from the remaining volumes through interpolation. The functional data were corrected for slice timing and realigned to the volume acquired closest in time to the anatomical scan to correct for head movements. Low-frequency drifts were removed using a high-pass filter with a cut-off frequency of 1/128 Hz. The functional images and two structural images were registered to the average image of the two structural images and transformed into Talairach coordinate space using the TT_N27 template. The functional images were resampled to 3 × 3 × 3 mm 3 resolution with a grey matter mask. The data were normalized with respect to the average signal of the entire run for each voxel, and modeled using multiple linear regression analysis for each condition and for each participant. Six motion parameters obtained from head motion correction were included as nuisance regressors.

ROI Analysis

The ROIs include the posterior superior temporal sulcus (pSTS), the fusiform face area (FFA), the parahippocampal place area (PPA), the fusiform body area (FBA), and the human motion complex (hMT+). We localized each of these ROIs for each participant based on the β values estimated for all conditions from all 4 functional runs, to avoid biases towards any test session or any orientation condition. The pSTS and FBA were identified with the contrast of (BM_upr+BM_inv) vs. (House_upr + House_inv); the FFA with (Face_upr + Face_inv) vs. (House_upr + House_inv); the PPA with (House_upr + House_inv) vs. (Face_upr + Face_inv); and the hMT+ with BM_inv vs. House_inv. Each ROI consisted of the most activated contiguous voxels ( q  < 0.05, FDR corrected, cluster size between about 135–675 mm 3 , i.e., 5–25 voxels) within the corresponding anatomical location reported in the literature 25 , 42 , 44 , 47 . Given the right lateralization of BM and face processing, only ROIs in the right hemisphere were considered for further analyses. No FBA cluster could be identified in three participants due to the large noise in the signals. Average peak MNI coordinates (x, y, z) for each ROI (valid participants only) were: pSTS (52, −44, 8), FFA (42, −44, −24), PPA (29, −33, −15), FBA (43, −39, −23), hMT + (51, −65, −1). For each participant, run, and condition, the raw time course of the fMRI signals was converted into a time course of percent signal change, relative to the average signal intensity for houses in the pSTS, FBA, hMT + , FFA, and that for faces in the PPA. Time courses of these BOLD signals were extracted from the most activated voxels of each ROI and corrected for baseline differences using the average signals of −2 and 0 s. BOLD responses from 4 to 10 s were averaged for the ROI analysis. The inversion effect was defined as the difference of neural activation between the upright and inverted conditions, following Kanwisher’s study on the face inversion effect 42 .

Resting-state functional connectivity

All preprocessing steps of resting-state functional data were consistent with those of the task-related fMRI data except for additional spatial smoothing with Gaussian kernel of 4 mm FWHM. The effects of head motion were removed through multiple linear regression. Voxel-wise mean and standard deviation of motion parameters (12 estimates) were included as nuisance regressors, and the residuals were considered clean resting-state fMRI signals. For resting-state data obtained before and after bed rest, the residuals were averaged across all voxels within each seed ROI. The functional connectivity between the seed and the whole brain was measured by Pearson’s correlation, with the r values transformed into Fisher’s Z scores. For each participant, a retroinsula (Ri) and a posterior insula (pIns) ROIs were localized respectively as a set of contiguous voxels within the posterior part of the right insula in TT_Daemon atlas showing enhanced connectivity with the pSTS after bed rest ( p  < 0.05, uncorrected, except that for one participant the pIns was located at a p value = 0.1). The average peak MNI coordinates (x, y, z) were: Ri (44, −28, 16); pIns (42, −3, 8). The change of functional connectivity strength after bed rest was calculated as the difference between the post-bedrest and pre-bedrest connectivity strengths divided by the sum of these values. Data within 3 standard deviations from the mean were included in further analysis to calculate the Pearson’s correlation coefficient between the change of functional connectivity and that of behavioral performances.

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this article.

Data availability

The data generated in this study have been deposited in the Institutional Knowledge Repository of the Institute of Psychology, Chinese Academy of Sciences ( http://ir.psych.ac.cn/handle/311026/42020 ) and other relevant materials are available from the corresponding authors upon request. Individual data for astronauts were shown in Fig.  2 . & Fig. S 1 and not uploaded to an open source platform following the information security protocols of the China Astronaut Research and Training Center.  Source data are provided with this paper.

Code availability

The custom code used in the study for fMRI data processing with AFNI is publicly available via the Institutional Knowledge Repository of the Institute of Psychology, Chinese Academy of Sciences at: http://ir.psych.ac.cn/handle/311026/42020 .

Hubbard, T. L. Environmental invariants in the representation of motion: Implied dynamics and representational momentum, gravity, friction, and centripetal force. Psychon. Bull. Rev. 2 , 322–338 (1995).

Article   CAS   PubMed   Google Scholar  

McIntyre, J., Zago, M., Berthoz, A. & Lacquaniti, F. Does the brain model Newton’s laws? Nat. Neurosci. 4 , 693–694 (2001).

Shepard, R. N. Ecological constraints on internal representation: resonant kinematics of perceiving, imagining, thinking, and dreaming. Psychol. Rev. 91 , 417–447 (1984).

Shepard, R. N. Perceptual-cognitive universals as reflections of the world. Psychon. Bull. Rev. 1 , 2–28 (1994).

Hubbard, T. L. Representational gravity: Empirical findings and theoretical implications. Psychon. Bull. Rev. 27 , 36–55 (2020).

Article   PubMed   Google Scholar  

Port, N. L., Lee, D., Dassonville, P. & Georgopoulos, A. P. Manual interception of moving targets .1. Performance and movement initiation. Exp. Brain Res. 116 , 406–420 (1997).

Brenner, E., Driesen, B. & Smeets, J. B. J. Precise timing when hitting falling balls. Front. Hum. Neurosci . 8 , 342 (2014).

Article   PubMed   PubMed Central   Google Scholar  

Lacquaniti, F. & Maioli, C. Adaptation to Suppression of Visual Information during Catching. J. Neurosci. 9 , 149–159 (1989).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Zago, M. et al. Internal models of target motion: Expected dynamics overrides measured kinematics in timing manual interceptions. J. Neurophysiol. 91 , 1620–1634 (2004).

Senot, P., Zago, M., Lacquaniti, F. & McIntyre, J. Anticipating the effects of gravity when intercepting moving objects: Differentiating up and down based on nonvisual cues. J. Neurophysiol. 94 , 4471–4480 (2005).

Blake, R. Cats perceive biological motion. Psychol. Sci. 4 , 54–57 (1993).

Article   Google Scholar  

Dittrich, W. H., Lea, S. E. G., Barrett, J. & Gurr, P. R. Categorization of natural movements by pigeons: Visual concept discrimination and biological motion. J. Exp. Anal. Behav. 70 , 281–299 (1998).

Herman, L. M., Morrel-Samuels, P. & Pack, A. A. Bottlenosed dolphin and human recognition of veridical and degraded video displays of an artificial gestural language. J. Exp. Psychol. Gen. 119 , 215–230 (1990).

Johansson, G. Visual perception of biological motion and a model for its analysis. Percept. Psychophys. 14 , 195–204 (1973).

Omori, E. & Watanabe, S. Discrimination of Johansson’s stimuli in pigeons. Int. J. Comp. Psychol. 9 , 92 (1996).

Google Scholar  

Oram, M. & Perrett, D. Integration of form and motion in the anterior superior temporal polysensory area (STPa) of the macaque monkey. J. Neurophysiol. 76 , 109–129 (1996).

Regolin, L., Tommasi, L. & Vallortigara, G. Visual perception of biological motion in newly hatched chicks as revealed by an imprinting procedure. Anim. Cogn. 3 , 53–60 (2000).

Barclay, C. D., Cutting, J. E. & Kozlowski, L. T. Temporal and spatial factors in gait perception that influence gender recognition. Percept. Psychophys. 23 , 145–152 (1978).

Bertenthal, B. & Pinto, J. Global processing of biological motions. Psychol. Sci. 5 , 221–225 (1994).

Dittrich, W. H. Action categories and the perception of biological motion. Perception 22 , 15–22 (1993).

Ikeda, H., Blake, R. & Watanabe, K. Eccentric perception of biological motion is unscalably poor. Vis. Res. 45 , 1935–1943 (2005).

Pavlova, M. & Sokolov, A. Orientation specificity in biological motion perception. Percept. Psychophys. 62 , 889–899 (2000).

Sumi, S. Upside-down presentation of the Johansson moving light-spot pattern. Perception 13 , 283–286 (1984).

Troje, N. F. & Westhoff, C. The inversion effect in biological motion perception: Evidence for a “life detector”? Curr. Biol. 16 , 821–824 (2006).

Grossman, E. D. & Blake, R. Brain activity evoked by inverted and imagined biological motion. Vis. Res. 41 , 1475–1482 (2001).

Fox, R. & McDaniel, C. The perception of biological motion by human infants. Science 218 , 486–487 (1982).

Article   ADS   CAS   PubMed   Google Scholar  

Simion, F., Regolin, L. & Bulf, H. A predisposition for biological motion in the newborn baby. Proc. Natl Acad. Sci. USA 105 , 809–813 (2008).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Vallortigara, G. & Regolin, L. Gravity bias in the interpretation of biological motion by inexperienced chicks. Curr. Biol. 16 , R279–R280 (2006).

Shipley, T. F. The effect of object and event orientation on perception of biological motion. Psychol. Sci. 14 , 377–380 (2003).

Bardi, L., Regolin, L. & Simion, F. The first time ever I saw your feet: inversion effect in newborns’ sensitivity to biological motion. Dev. Psychol. 50 , 986–993 (2014).

Indovina, I. et al. Representation of visual gravitational motion in the human vestibular cortex. Science 308 , 416–419 (2005).

Hargens, A. R. & Vico, L. Long-duration bed rest as an analog to microgravity. J. Appl. Physiol. 120 , 891–903 (2016).

Pavy-Le Traon, A., Heer, M., Narici, M. V., Rittweger, J. & Vernikos, J. From space to Earth: advances in human physiology from 20 years of bed rest studies (1986–2006). Eur. J. Appl. Physiol. 101 , 143–194 (2007).

Hupfeld, K. E. et al. Brain and behavioral evidence for reweighting of vestibular inputs with long-duration spaceflight. Cereb. Cortex. 32 , 755–769 (2022).

Yuan, P. et al. Vestibular brain changes within 70 days of head down bed rest. Hum. Brain Mapp. 39 , 2753–2763 (2018).

Koppelmans, V. et al. Study protocol to examine the effects of spaceflight and a spaceflight analog on neurocognitive performance: extent, longevity, and neural bases. BMC Neurol. 13 , 205 (2013).

Wang, Y. et al. Heritable aspects of biological motion perception and its covariation with autistic traits. Proc. Natl Acad. Sci. USA 115 , 1937–1942 (2018).

Wang, Y., Wang, L., Xu, Q., Liu, D. & Jiang, Y. Domain-specific genetic influence on visual-ambiguity resolution. Psychol. Sci. 25 , 1600–1607 (2014).

Wilmer, J. B. et al. Human face recognition ability is specific and highly heritable. Proc. Natl Acad. Sci. USA 107 , 5238–5241 (2010).

Zhu, Q. et al. Heritability of the specific cognitive ability of face perception. Curr. Biol. 20 , 137–142 (2010).

Yin, R. K. Looking at Upside-down Faces. J. Exp. Psychol. 81 , 141 (1969).

Yovel, G. & Kanwisher, N. The neural basis of the behavioral face-inversion effect. Curr. Biol. 15 , 2256–2262 (2005).

Farah, M. J., Tanaka, J. W. & Drain, H. M. What causes the face inversion effect? J. Exp. Psychol. Hum. Percept. Perform. 21 , 628–634 (1995).

Epstein, R. & Kanwisher, N. A cortical representation of the local visual environment. Nature 392 , 598–601 (1998).

Jastorff, J. & Orban, G. A. Human functional magnetic resonance imaging reveals separation and integration of shape and motion cues in biological motion processing. J. Neurosci. 29 , 7315–7329 (2009).

Vangeneugden, J., Peelen, M. V., Tadin, D. & Battelli, L. Distinct neural mechanisms for body form and body motion discriminations. J. Neurosci. 34 , 574–585 (2014).

Peelen, M. V. & Downing, P. E. Selectivity for the human body in the fusiform gyrus. J. Neurophysiol. 93 , 603–608 (2005).

Peelen, M. V., Wiggett, A. J. & Downing, P. E. Patterns of fMRI activity dissociate overlapping functional brain areas that respond to biological motion. Neuron 49 , 815–822 (2006).

Lopez, C. & Blanke, O. The thalamocortical vestibular system in animals and humans. Brain. Res. Rev. 67 , 119–146 (2011).

Pfeiffer, C., Serino, A. & Blanke, O. The vestibular system: a spatial reference for bodily self-consciousness. Front. Integr. Neurosci. 8 , 31 (2014).

Angelaki, D. E. & Cullen, K. E. Vestibular system: The many facets of a multimodal sense. Annu. Rev. Neurosci. 31 , 125–150 (2008).

de Schonen, S., Leone, G. & Lipshits, M. The face inversion effect in microgravity: is gravity used as a spatial reference for complex object recognition? Acta Astronaut. 42 , 287–301 (1998).

Article   ADS   PubMed   Google Scholar  

Chang, D. H., Harris, L. R. & Troje, N. F. Frames of reference for biological motion and face perception. J. Vis. 10 , 22 (2010).

Freire, A., Lee, K. & Symons, L. A. The face-inversion effect as a deficit in the encoding of configural information: Direct evidence. Perception 29 , 159–170 (2000).

Chang, D. H. & Troje, N. F. Acceleration carries the local inversion effect in biological motion perception. J. Vis. 9 , 19 (2009).

Fowler, B., Comfort, D. & Bock, O. A review of cognitive and perceptual-motor performance in space. Aviat. Space Envir. Md. 71 , A66–A68 (2000).

CAS   Google Scholar  

Manzey, D. & Lorenz, B. Mental performance during short-term and long-term spaceflight. Brain. Res. Rev. 28 , 215–221 (1998).

White, R. J. & Averner, M. Humans in space. Nature 409 , 1115–1118 (2001).

Lathan, C., Wang, Z. Y. & Clement, G. Changes in the vertical size of a three-dimensional object drawn in weightlessness by astronauts. Neurosci. Lett. 295 , 37–40 (2000).

Leone, G., de Schonen, S. & Lipshits, M. Prolonged weightlessness, reference frames and visual symmetry detection. Acta Astronaut. 42 , 281–286 (1998).

Lipshits, M., McIntyre, J., Zaoui, M., Gurfinkel, V. & Berthoz, A. Does gravity play an essential role in the asymmetrical visual perception of vertical and horizontal line length? Acta Astronaut. 49 , 123–130 (2001).

Harris, L. R., Jenkin, M., Jenkin, H., Zacher, J. E. & Dyde, R. T. The effect of long-term exposure to microgravity on the perception of upright. NPJ Microgravity 3 , 3 (2017).

Lacquaniti, F. et al. Visual gravitational motion and the vestibular system in humans. Front. Integr. Neurosci. 7 , 101 (2013).

Troje, N. F. Decomposing biological motion: A framework for analysis and synthesis of human gait patterns. J. Vis. 2 , 371–387 (2002).

Gong, X., Huang, Y.-X., Wang, Y. & Luo, Y.-J. Revision of the Chinese facial affective picture system. Chin. Ment. Health J . (2011).

Brainard, D. H. The Psychophysics Toolbox. Spat. Vis. 10 , 433–436 (1997).

Vanrie, J. & Verfaillie, K. Perception of biological motion: A stimulus set of human point-light actions. Behav. Res. Methods Instrum. Comput. 36 , 625–629 (2004).

Tottenham, N. et al. The NimStim set of facial expressions: judgments from untrained research participants. Psychiatry Res. 168 , 242–249 (2009).

Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29 , 162–173 (1996).

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Acknowledgements

The authors thank Yi Xiao, Yu Tian for their help with the organization and implementation of the spaceflight experiment, Xiaoping Chen for her help with the organization and implementation of the bed rest experiment, and Bogeng Song for his help with collecting data of the regular control experiment. This research was supported by grants from the Ministry of Science and Technology of China (2021ZD0203800 to Y.J. and 2011CB711000 to S.C.), the National Natural Science Foundation of China (31830037 to Y.J. and 32171059 to Y.W.), the Youth Innovation Promotion Association (2018116 to Y.W.), the Strategic Priority Research Program (XDB32010300 to Y.J.), the Key Research Program of Frontier Sciences (QYZDB-SSW-SMC030 to Y.J.) of the Chinese Academy of Sciences, and the Fundamental Research Funds for the Central Universities.

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Department of Psychology, University of Chinese Academy of Sciences, Beijing, China

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National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China

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Y.W. and Y.J. designed the experiments as part of a project devised and organized by S.C.; C.W. and W.H. contributed to the implementation of and provided technical supports to the study; Q.X. and D.L. collected the data for the bed rest experiment; Y.W. and X.Z. analyzed the data; Y.W., X.Z., W.Z., and Y.J. wrote the manuscript.

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The influence of representational gravity on spatial orientation: an eye movement study

• Published: 27 November 2023
• Volume 43 , pages 14485–14493, ( 2024 )

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• Tianqi Yang 1 ,
• Yaning Guo 1 ,
• Xianyang Wang 2 ,
• Shengjun Wu 2 ,
• Xiuchao Wang 3 ,
• Hui Wang 1 &
• Xufeng Liu 1  

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Spatial orientation is a fundamental subject in aviation psychology. The influence of representational gravity can lead to systematic errors during uniform linear motion. However, it remains unclear whether representational gravity during motion can affect spatial orientation. In this study, college students from Xi’an, China were recruited to participate in an experiment based on the Spatial Visualization Dynamic Test. We compared the accuracy of spatial orientation estimation and eye movement indices when the main direction of spatial orientation was in the lower right versus when it was in the upper right. The results revealed that individuals were prone to overestimate the adjustment angle when the main direction of spatial orientation was in the lower right, and underestimate the adjustment angle when the main direction of spatial orientation was in the upper right; the average pupil size was significantly larger when the main direction of spatial orientation was in the lower right than that when the main direction of spatial orientation was in the upper right. In conclusion, spatial orientation in motion was influenced by representational gravity, and when representational gravity aligned with the main direction of spatial orientation, it led to increased cognitive resource consumption.

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Tilted frames of reference have similar effects on the perception of gravitational vertical and the planning of vertical saccadic eye movements, the visual representations of motion and of gravity are functionally independent: evidence of a differential effect of smooth pursuit eye movements.

Spatial Effect of Target Display on Visual Search

Data availability.

The datasets presented in this article are not readily available because the datasets involve unfinished research projects. If necessary, requests to access the datasets should be directed to the corresponding author.

Angelaki, D. E., Ng, J., Abrego, A. M., Cham, H. X., Asprodini, E. K., Dickman, J. D., & Laurens, J. (2020). A gravity-based three-dimensional compass in the mouse brain. Nature Communications, 11 (1), 1855. https://doi.org/10.1038/s41467-020-15566-5

Article   PubMed   PubMed Central   Google Scholar  

Asadi, A., Saeedpour-Parizi, M. R., Aiken, C. A., Jahanbani, Z., Abadi, H. S., & D., Simpson, T., & Marchant, D. (2022). Effects of attentional focus and cognitive load on novice dart throwing: Evidence from quiet eye duration and pupillary responses. Human Movement Science, 86 , 103015. https://doi.org/10.1016/j.humov.2022.103015

Article   PubMed   Google Scholar  

Asato, M. R., Nawarawong, N., Hermann, B., Crumrine, P., & Luna, B. (2011). Deficits in oculomotor performance in pediatric epilepsy. Epilepsia, 52 (2), 377–385. https://doi.org/10.1111/j.1528-1167.2010.02787.x

Bastin, J., Lebranchu, P., Jerbi, K., Kahane, P., Orban, G., Lachaux, J. P., & Berthoz, A. (2012). Direct recordings in human cortex reveal the dynamics of gamma-band [50-150 Hz] activity during pursuit eye movement control. NeuroImage, 63 (1), 339–347. https://doi.org/10.1016/j.neuroimage.2012.07.011

Biondi, F. N., Saberi, B., Graf, F., Cort, J., Pillai, P., & Balasingam, B. (2023). Distracted worker: Using pupil size and blink rate to detect cognitive load during manufacturing tasks. Applied Ergonomics, 106 , 103867. https://doi.org/10.1016/j.apergo.2022.103867

Bles, W. (2008). Spatial disorientation training–demonstration and avoidance . Netherlands: The Research and Technology Organisation of North Atlantic Treaty Organisation Technical Report.

Boril, J., Smrz, V., Blasch, E., & Lone, M. (2020). Spatial disorientation impact on the precise approach in simulated flight. Aerospace Medicine and Human Performance, 91 (10), 767–775. https://doi.org/10.3357/AMHP.5591.2020

Bosco, G., Monache, S. D., Gravano, S., Indovina, I., La Scaleia, B., Maffei, V., Zago, M., & Lacquaniti, F. (2015). Filling gaps in visual motion for target capture. Frontiers in Integrative Neuroscience, 9 , 13. https://doi.org/10.3389/fnint.2015.00013

Cermeño-Aínsa, S. (2020). The cognitive penetrability of perception: A blocked debate and a tentative solution. Consciousness and Cognition, 77 , 102838. https://doi.org/10.1016/j.concog.2019.102838

Chen, T., Ding, J., Yue, G. H., Liu, H., Li, J., & Jiang, C. (2022). Global-local consistency benefits memory-guided tracking of a moving target. Brain and Behavior, 12 (1), e2444. https://doi.org/10.1002/brb3.2444

Chuntul, A. V., Lapa, V. V., & Davydov, V. V. (2015). Spatial orientation of pilot using a cockpit exterior surveillance system. Human Physiology, 41 , 728–731. https://doi.org/10.1134/S0362119715070038

Article   Google Scholar  

Cohen, J., LaRue, C., & Cohen, H. H. (2017). Attention interrupted: Cognitive distraction & workplace safety. Professional Safety, 62 (11), 28–34.

Google Scholar  

Coluccia, E., & Louse, G. (2004). Gender differences in spatial orientation: A review. Journal of Environmental Psychology, 24 (3), 329–340. https://doi.org/10.1016/j.jenvp.2004.08.006

De Sá, T. N. A. (2014). Fourier decomposition of spatial localization errors reveals an idiotropic dominance of an internal model of gravity. Vision Research, 105 , 177–188. https://doi.org/10.1016/j.visres.2014.10.024

De Sá Teixeira, N. A., & Hecht, H. (2014). The dynamic representation of gravity is suspended when the idiotropic vector is misaligned with gravity. Journal of Vestibular Research: Equilibrium & Orientation, 24 (4), 267–279. https://doi.org/10.3233/VES-140511

De Sá Teixeira, N. A., Hecht, H., & Oliveira, A. M. (2013). The representational dynamics of remembered projectile locations. Journal of Experimental Psychology, 39 (6), 1690–1699. https://doi.org/10.1037/a0031777

De Sá Teixeira, N. A., Bosco, G., DelleMonache, S., & Lacquaniti, F. (2019). The role of cortical areas hMT/V5+ and TPJ on the magnitude of representational momentum and representational gravity: A transcranial magnetic stimulation study. Experimental Brain Research, 237 (12), 3375–3390. https://doi.org/10.1007/s00221-019-05683-z

DelleMonache, S., Lacquaniti, F., & Bosco, G. (2017). Differential contributions to the interception of occluded ballistic trajectories by the temporoparietal junction, area hMT/V5+, and the intraparietal cortex. Journal of Neurophysiology, 118 (3), 1809–1823. https://doi.org/10.1152/jn.00068.2017

de’Sperati, C., & Thornton, I. M. (2019). Motion prediction at low contrast. Vision research , 154 , 85–96. https://doi.org/10.1016/j.visres.2018.11.004

Gibb, R., Ercoline, B., & Scharff, L. (2011). Spatial disorientation: Decades of pilot fatalities. Aviation, Space, and Environmental Medicine, 82 (7), 717–724. https://doi.org/10.3357/asem.3048.2011

Gilbert, C. D., & Li, W. (2013). Top-down influences on visual processing. Nature Reviews Neuroscience, 14 (5), 350–363. https://doi.org/10.1038/nrn3476

Hao, C., Cheng, L., Guo, L., Zhao, R., Wu, Y., Li, X., Chi, Z., Zhang, J., Liu, X., Ma, X., Wang, A., Dong, C., & Li, J. (2022). Detection of unrecognized spatial disorientation: A theoretical perspective. Technology and Health Care, 30 (S1), 469–480. https://doi.org/10.3233/THC-THC228043

Hogervorst, M. A., Brouwer, A. M., & van Erp, J. B. (2014). Combining and comparing EEG, peripheral physiology and eye-related measures for the assessment of mental workload. Frontiers in Neuroscience, 8 , 322. https://doi.org/10.3389/fnins.2014.00322

Horner, C. A., Schroeder, J. E., Mitroff, S. R., & Cain, M. S. (2019). The effect of extended target concealment on motion extrapolation. Journal of Vision, 19 (12), 17–22. https://doi.org/10.1167/19.10.12

Hubbard, T. L. (1990). Cognitive representation of linear motion: Possible direction and gravity effects in judged displacement. Memory & Cognition, 18 (3), 299–309. https://doi.org/10.3758/bf03213883

Hubbard, T. L. (1997). Target size and displacement along the axis of implied gravitational attraction: Effects of implied weight and evidence of representational gravity. Journal of Experimental Psychology, 23 , 1484–1493. https://doi.org/10.1037/0278-7393.23.6.1484

Hubbard, T. L. (2020). Representational gravity: Empirical findings and theoretical implications. Psychonomic Bulletin & Review, 27 (1), 36–55. https://doi.org/10.3758/s13423-019-01660-3

Huttunen, K. H., Keränen, H. I., Pääkkönen, R. J., PäivikkiEskelinen-Rönkä, R., & Leino, T. K. (2011). Effect of cognitive load on articulation rate and formant frequencies during simulator flights. The Journal of the Acoustical Society of America, 129 (3), 1580–1593. https://doi.org/10.1121/1.3543948

Indovina, I., Mazzarella, E., Maffei, V., Cesqui, B., Passamonti, L., & Lacquaniti, F. (2015). Sound-evoked vestibular stimulation affects the anticipation of gravity effects during visual self-motion. Experimental Brain Research, 233 (8), 2365–2371. https://doi.org/10.1007/s00221-015-4306-9

Jiang, S., Chen, W., & Kang, Y. (2021). Correlation Evaluation of pilots’ situation awareness in bridge simulations via eye-tracking technology. Computational Intelligence and Neuroscience, 2021 , 7122437. https://doi.org/10.1155/2021/7122437

Kantowitz, B. H., Hart, S. G., & Bortolussi, M. R. (1983). Measuring pilot workload in a moving-base simulator: I. asynchronous secondary choice-reaction task. Proceedings of the Human Factors Society Annual Meeting , 27 (4), 319–322. https://doi.org/10.1177/154193128302700413

Kok, E. M., & Jarodzka, H. (2017). Before your very eyes: The value and limitations of eye tracking in medical education. Medical Education, 51 (1), 114–122. https://doi.org/10.1111/medu.13066

Kowalczuk, K. P., Gazdzinski, S. P., Janewicz, M., Gąsik, M., Lewkowicz, R., & Wyleżoł, M. (2016). Hypoxia and Coriolis illusion in pilots during simulated flight. Aerospace Medicine and Human Performance, 87 (2), 108–113. https://doi.org/10.3357/AMHP.4412.2016

Lacquaniti, F., Bosco, G., Indovina, I., La Scaleia, B., Maffei, V., Moscatelli, A., & Zago, M. (2013). Visual gravitational motion and the vestibular system in humans. Frontiers in Integrative Neuroscience, 7 , 101. https://doi.org/10.3389/fnint.2013.00101

Lacquaniti, F., Bosco, G., Gravano, S., Indovina, I., La Scaleia, B., Maffei, V., & Zago, M. (2015). Gravity in the brain as a reference for space and time perception. Multisensory Research, 28 (5–6), 397–426. https://doi.org/10.1163/22134808-00002471

Ledegang, W. D., & Groen, E. L. (2018). Spatial disorientation influences on pilots’ visual scanning and flight performance. Aerospace Medicine and Human Performance, 89 (10), 873–882. https://doi.org/10.3357/AMHP.5109.2018

Lewkowicz, R., Stróżak, P., Bałaj, B., & Francuz, P. (2019). Audtory verbal working memory load effects on a simulator-induced spatial disorientation event. Aerospace Medicine and Human Performance, 90 (6), 531–539. https://doi.org/10.3357/AMHP.5277.2019

Macpherson, F. (2017). The relationship between cognitive penetration and predictive coding. Consciousness and Cognition, 47 , 6–16. https://doi.org/10.1016/j.concog.2016.04.001

Maffei, V., Mazzarella, E., Piras, F., Spalletta, G., Caltagirone, C., Lacquaniti, F., & Daprati, E. (2016). Processing of visual gravitational motion in the peri-sylvian cortex: Evidence from brain-damaged patients. Cortex, 78 , 55–69. https://doi.org/10.1016/j.cortex.2016.02.004

Marchi, F. (2017). Attention and cognitive penetrability: The epistemic consequences of attention as a form of metacognitive regulation. Consciousness and Cognition, 47 , 48–62. https://doi.org/10.1016/j.concog.2016.06.014

Matton, N., Paubel, P. V., & Puma, S. (2022). Toward the use of pupillary responses for pilot selection. Human Factors, 64 (3), 555–567. https://doi.org/10.1177/0018720820945163

Merz, S. (2022). Motion perception investigated inside and outside of the laboratory. Experimental Psychology, 69 (2), 61–74. https://doi.org/10.1027/1618-3169/a000545

Newen, A., & Vetter, P. (2017). Why cognitive penetration of our perceptual experience is still the most plausible account. Consciousness and Cognition, 47 , 26–37. https://doi.org/10.1016/j.concog.2016.09.005

Newman, R. L., & Rupert, A. H. (2020). The magnitude of the spatial disorientation problem in transport airplanes. Aerospace Medicine and Human Performance, 91 (2), 65–70. https://doi.org/10.3357/AMHP.5442.2020

Paas, F., Tuovinen, J., Tabbers, H. K., & Van Gerven, P. W. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38 , 63–71. https://doi.org/10.1207/S15326985EP3801_8

Peters, A. (2002). Examining neocortical circuits: Some background and facts. Journal of Neurocytology, 31 (3–5), 183–193. https://doi.org/10.1023/a:1024157522651

Poisson, R. J., & Miller, M. E. (2014). Spatial disorientation mishap trends in the U.S. Air force 1993–2013. Aviation, Space, and Environmental Medicine , 85 (9), 919–924. https://doi.org/10.3357/ASEM.3971.2014

Primativo, S., Clark, C., Yong, K. X. X., Firth, N. C., Nicholas, J., Alexander, D., Warren, J. D., Rohrer, J. D., & Crutch, S. J. (2017). Eyetracking metrics reveal impaired spatial anticipation in behavioural variant frontotemporal dementia. Neuropsychologia, 106 , 328–340. https://doi.org/10.1016/j.neuropsychologia.2017.10.014

Robison, M. K., & Unsworth, N. (2019). Pupillometry tracks fluctuations in working memory performance. Attention, Perception & Psychophysics, 81 (2), 407–419. https://doi.org/10.3758/s13414-018-1618-4

Shepard, R. N. (2001). Perceptual-cognitive universals as reflections of the world. The Behavioral and Brain Sciences, 24 (4), 581–671.

Stott, J. R. (2013). Orientation and disorientation in aviation. Extreme Physiology & Medicine, 2 (1), 2. https://doi.org/10.1186/2046-7648-2-2

Tribukait, A., & Eiken, O. (2017). Instrument failure, stress, and spatial disorientation leading to a fatal crash with a large aircraft. Aerospace Medicine and Human Performance, 88 (11), 1043–1048. https://doi.org/10.3357/AMHP.4885.2017

Unsworth, N., & Robison, M. K. (2018). Tracking working memory maintenance with pupillometry. Attention, Perception & Psychophysics, 80 (2), 461–484. https://doi.org/10.3758/s13414-017-1455-x

van der Wel, P., & van Steenbergen, H. (2018). Pupil dilation as an index of effort in cognitive control tasks: A review. Psychonomic Bulletin & Review, 25 (6), 2005–2015. https://doi.org/10.3758/s13423-018-1432-y

Verde, P., Piccardi, L., Bianchini, F., Guariglia, C., Carrozzo, P., Morgagni, F., Boccia, M., Di Fiore, G., & Tomao, E. (2015). Gender differences in navigational memory: pilots vs. nonpilots. Aerospace Medicine and Human Performance , 86 (2), 103–111. https://doi.org/10.3357/AMHP.4024.2015

Vicovaro, M., & Dalmaso, M. (2021). Is “heavy” up or down? Testing the vertical spatial representation of weight. Psychological Research Psychologische Forschung, 85 (3), 1183–1200. https://doi.org/10.1007/s00426-020-01309-0

Webb, C. M., Estrada, A., & Kelley, A. M. (2008). The effects of spatial disorientation on cognitive processing. The International Journal of Aviation Psychology, 22 (3), 224–241. https://doi.org/10.1080/10508414.2012.689211

Wolbers, T., & Hegarty, M. (2010). What determines our navigational abilities? Trends in Cognitive Sciences, 14 (3), 138–146. https://doi.org/10.1016/j.tics.2010.01.001

Zago, M., Bosco, G., Maffei, V., Iosa, M., Ivanenko, Y. P., & Lacquaniti, F. (2004). Internal models of target motion: Expected dynamics overrides measured kinematics in timing manual interceptions. Journal of Neurophysiology, 91 (4), 1620–1634. https://doi.org/10.1152/jn.00862.2003

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Acknowledgements

We sincerely thank all the participants who contributed to our research.

This study was funded by Air Force Medical University (AKJWS221J001-02, BKJ19J021, KJ2022A000415).

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Department of Basic Psychology, Department of Military Medical Psychology, Air Force Medical University, Xi’an, 710032, China

Tianqi Yang, Yaning Guo, Hui Wang & Xufeng Liu

Teaching Experiment Center, Department of Military Medical Psychology, Air Force Medical University, Xi’an, 710032, China

Xianyang Wang & Shengjun Wu

Department of Aviation Psychology, Department of Military Medical Psychology, Air Force Medical University, Xi’an, 710032, China

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Xufeng Liu, Yaning Guo and Tianqi Yang designed the study; Tianqi Yang and Xianyang Wang performed the experiment; Tianqi Yang, Shengjun Wu and Xiuchao Wang analyzed the data; Tianqi Yang and Hui Wang wrote the manuscript.

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Correspondence to Xufeng Liu .

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Yang, T., Guo, Y., Wang, X. et al. The influence of representational gravity on spatial orientation: an eye movement study. Curr Psychol 43 , 14485–14493 (2024). https://doi.org/10.1007/s12144-023-05470-8

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DOI : https://doi.org/10.1007/s12144-023-05470-8

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The visual representations of motion and of gravity are functionally independent: Evidence of a differential effect of smooth pursuit eye movements

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• PMID: 27106480
• DOI: 10.1007/s00221-016-4654-0

The memory for the final position of a moving object which suddenly disappears has been found to be displaced forward, in the direction of motion, and downwards, in the direction of gravity. These phenomena were coined, respectively, Representational Momentum and Representational Gravity. Although both these and similar effects have been systematically linked with the functioning of internal representations of physical variables (e.g. momentum and gravity), serious doubts have been raised for a cognitively based interpretation, favouring instead a major role of oculomotor and perceptual factors which, more often than not, were left uncontrolled and even ignored. The present work aims to determine the degree to which Representational Momentum and Representational Gravity are epiphenomenal to smooth pursuit eye movements. Observers were required to indicate the offset locations of targets moving along systematically varied directions after a variable imposed retention interval. Each participant completed the task twice, varying the eye movements' instructions: gaze was either constrained or left free to track the targets. A Fourier decomposition analysis of the localization responses was used to disentangle both phenomena. The results show unambiguously that constraining eye movements significantly eliminates the harmonic components which index Representational Momentum, but have no effect on Representational Gravity or its time course. The found outcomes offer promising prospects for the study of the visual representation of gravity and its neurological substrates.

Keywords: Internal models; Representational Gravity; Representational Momentum; Smooth pursuit eye movements; Spatial perception.

Publication types

• Research Support, Non-U.S. Gov't
• Gravitation*
• Memory / physiology*
• Motion Perception / physiology*
• Photic Stimulation / methods
• Pursuit, Smooth / physiology*
• Space Perception / physiology*
• Young Adult