An intro to what visual perception experiments can teach us

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An intro to what visual perception experiments can teach us


Transcript: Babies can't talk, but they sure are tellingresearchers at the University of Minnesota a lot about how the human brain develops. "It's so fascinating to me, that you cantake an infant, that can't tell you anything, can't tell you what they'reseeing or what they're not seeing, and you're able to manipulate stimuliin such a way that you're able to figure out what they're seeing anyways." The stimuli student researcherSherryse Corrow is talking about? It's a wire frame that presentsthe illusion that it's a cube. "When you look at this cube, especiallywhen it's against a white background, you perceive that there'sactually a surface here." And that's enough to blow a baby's mind. "As it turns out, those babies are no worse thana lot of adults when it comes to the figuring out exactly what shape the box is, andthat tells researchers an awful lot about when our brain developsand how it develops." For example, this work at the U of M'sInstitute of Child Development is proving that you really should onlybelieve half of what you see. "Because there's a two-dimensional image on yourretina, your brain has to take that and form it into a three-dimensional image of the world. When there's... See more →

Key quotes:

  • "Started in the late 1980’s by computer scientists and others who were inspired by the possibilities for using interactive graphics to amplify cognition about data, visualization research is now a thriving community with a conference (IEEE VIS) that draws ~1200 people annually."
  • "Visualization researchers work on a number of problems that can help inform how anyone understands, designs, or evaluates visual representations of data: Tools that make it easier to create visualizations Have you heard of Tableau Software, Spotfire, or D3? A common research goal in the visualization community is to develop technology that makes it easier to create effective visualizations, so as to increase the amount of value that people can get from data even without advanced training in design or statistics."
  • "Visualization research develops tools to scaffold the exploratory analysis process and deepens our understanding of how visualizations support meaning-making about data."
  • "A focus on performance keeps visualization research relevant to the world, where people want to know which chart to use."
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Key quotes:

  • "In the illusion above, you will notice that each element in the band with alternating light blue and black boxes appears smaller in size in a specific direction."
  • "Unlike Deep Learning networks that actually do capture images in its entirety, biological brains will use affordances (i.e."
  • "shortcuts and heuristics) to construct patterns that it will use for perception."
  • "Biological brains appear to work differently, rather than ignore invariances we are hardwired to make use of patterns that convey semantics."
  • "To achieve the kind of visual perception we find in humans, we must train networks to learn some basic human image recognition skills such as occlusion, perspective, and shadows: To illustrate how very different a Deep Learning system’s visual cognition is from that of humans, a recent paper “Investigating Human Priors for Playing Video Games” investigates removing human affordances for playing a game: Arcade games were modified to re-render the game’s textures."
  • "DeepMind’s Pyschlab is a setup to explore the difference between Deep Learning and Human visual recognition."
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Key quotes:

  • "This is my favorite quote about balance from a book by Rudolf Arnheim, Art and Visual Perception: A Psychology of the Creative Eye – “It must be remembered that visually as well as physically, balance is the state of distribution in which all action has come to a standstill…In a balanced composition all such factors as shape, direction, and location are mutually determined in such a way that no change seems possible, and the whole assumes the character of “necessity” in all its parts."
  • "He discovered the Gestalt principle of unit forming, which explained how humans sort out and understand visual patterns — things that are similar will be perceptually grouped together."
  • "Using lines, boxes or shapes to separate information, only creates unnecessary visual noise and hinders the purpose of the design."
  • "This is actually a very ineffective way because it adds a lot of unnecessary elements to the design by adding the lines and the negative shapes the lines create."
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Key quotes:

  • "It is a groundswell of research focused on the discovery of the human potential for directly perceiving key aspects of what can genuinely be referred to as the “fabric of the universe.” What is astonishing about this news is that science is moving toward a day when human direct, sensory perception of the quantum may answer lingering questions about physics."
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Key quotes:

  • "Take this famous optical experiment as an example: Do the orange circles appear to be the same size to you? Now, without further ado, let’s take a look at some of the best optical illusions we’ve found on the web and what they reveal about the blind spots in our visual perception."
  • "Source: Wikimedia The Chubb illusion was first discovered by Charles Chubb and colleagues Sperling and Solomon in the late 1980s when they experimented with perceived contrast by placing low-contrast visual objects on various backgrounds."
  • "Although so far no theories have satisfactorily explained this visual error, the prevailing belief is that our brain attempts to interpret a 2D image with 3D properties and distorts the depth between lines."
  • "In this illusion, the perceptual error was caused by the fact that our brain couldn’t help but make a 3D interpretation of the 2D pictures, and perceive very different sizes because of perspective foreshortening: The closer the object is in distance, the larger it is on our retina."
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