VL6 Perceptual Expertise

together define 2D space

—> without both no 2D possible

smallest detail you can resolve

faintest difference in luminance you can detect

• spatial frequency

• low —> higher contrast sensitivity

• mid —> best contrast sensitivity

• high —> bad contrast sensitivity

• distance of objects from center of gaze

• acuity decreasesas you move away from fovea

  • lower density of cones

• peripheral crowding

  • less spatial resolution —> harder perception of individual objects

1. proximity elements close to each other: belong together

2. similarity —> elements similar grouped together

   1. shape

   2. size

   3. color

   4. orientation

3. closure —> contour completion

4. continuity —> smooth flowing lines perceived instead of discontinuous or jagged

5. symmetry —> group or figure

6. common fate —> move in same direction

• search error

• recognition error

• decision error

useful field of view

• area of visual space within which information can be perceived and processed without eye or head movements

• target never fell into UFOV

targets that fell into UFOV for <1 second

• if target attracted foveal vision for >1 second

• then: decision to deem irrelevant

• volumetric: hundreds or thousands of images

  • all roi cannot be foveated exhaustively (rely on peripheral vision -> low accuracy)

  • in CT —> only between 27 and 69% of lung tissue foveated

• but: dynamic (motion-based) information is often used

  —> observer performance in 2D search might not be generalizable to volumetric tasks

• Scanners

  • start at top

  • left to right

  • move down

  • left to right

• Drillers

  • eye in one quadrant and move up and down

• Drillers outperform scanners in detection

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• scenes can be understood very quickly (“gist”)

• but details & objects are forgotten

—> could there be a gist of breast cancer

—> radiologists have a “feeling” -> subconscious

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• flash mammogram for 250 msec —> would they call patient back?

• outperformed chance

• UNEXPLOITED IMAGING BIOMARKER

• for naturalistic image processing

• can be adapted to radiology

• selective pathway

• non-selective pathway

• often requires attention

• binds features and recognizes objects

• capacity-limited —> represented as bottleneck

• controlled by guidance mechanisms from non-selective pathway

  • prioritizes likely target items

  • classic guidance prefers items with basic target features e.g. color

• guides selective pathway via important features (color, orientation etc.)

  • semantic elements

  • episodic elements

• no capacity limitation

• extracts global statistics from entire scene

• enables certain amount of semantic processing

• no precise object recognition but scene categorization

experts:

• know where not to look

• faster at fixation on abnormalities

• total image search time decreases with increasing level of expertise

• fewer total fixations than novices

—> more effective

—> perceptual aspects of image interpretation develop earlier than correct interpretation of abnormalities or integration into diagnosis

—> perceptual skills grow from start of exposure

—> radiology-specific factual knowledge contributes little to this initial development

• machines still make errors (muffin/chihuahua challenge)

  —> radiologists are encouraged to work with AI as collective intelligence

  —> radiology is perceptual task-dominated field

  —> radiology needs greater understanding of perceptual expertise to improve accuracy, reduce error

• radiology as specialty will become obsolete due to exponential improvement oft deep learning medical image analysis software —> not true up until now

• 
  

• no standardized training datasets

• poor interoperability standards

• regional variances in image acquisition/processing

• delayed adoption of technological advances in healthcare industry

• radiologists could capitalize on clinical judgments about data not detection

• AI could train radiologists —> provide them with images they tend to make most mistakes on

• 
  

• is fundamental to most activities

• computer system often make major errors in comparison to humans

• example of perceptual expertise: radiologist