Lack of standards for the size of figures in relation to letters

– leads to inconsistency across different vision tests and manufacturers.

  • It is an obvious task for ophthalmological organizations and research institutions to investigate widespread symbol optotypes and measure reliable compensation factors. By following a more calibrated and standardized approach, one can ensure that vision tests with symbols provide comparable and reliable results.
  • The lack of research-based standards for the weighting between symbol-based and letter-based optotypes leads to inconsistent and often over-favorable compensations.
  • Manufacturers choose to over-enlarge symbols to be on the safe side and ensure that test subjects can see them clearly.
  • But the symbols are often too large in relation to what is cognitively necessary. This can lead to the vision tests underestimating the degree of visual problems. Test subjects may achieve better results than their real vision level warrants.

Empirical studies and research form the starting point for standardized weighting factors

  • In order to determine whether symbols are over-enlarged, it is important to conduct empirical studies comparing performance on vision tests with symbols versus letters. The empirical studies form the background for the necessary exact calibration of the size of the figures in relation to children's ability to recognize them. It is essential to ensure fair and accurate test results.
  • Based on the empirical studies and research, standards should therefore be developed for the weighting factors with which the respective symbol-based optotype sets can and must be regulated in order to give comparable vision test results.
  • Keep the level of difficulty as consistent as possible in each optotype set.
  • One should avoid that figures in an optotype set vary too much in their design and complexity, as this makes it difficult to establish a common standard compensation factor.
  • Manufacturers should always carry out critical evaluations of their own optotypes via empirical studies and ensure that each individual symbol does not have a significantly different level of cognitive difficulty from the other optotypes in the set.

In general, symbols that are all equally familiar and simple should be used to reduce cognitive load and minimize the degree of magnification. Also preferably choose symbols which, in their design and complexity, resemble letters as much as possible. Letters gain their recognition through the fact that they possess extremities, i.e. stands out in a conspicuous way. Letters like K, H, V and T are good examples. If easily recognizable symbols are produced with these letter shapes in mind, the need to over-enlarge the symbols to compensate for cognitive complexity is reduced, as extremities of symbols bring them cognitively closer to letter-based optotypes.

Line thickness etc

  • If compensatory weightings are performed correctly to compensate for cognitive differences, it is no longer crucial whether the line thickness applied to symbols corresponds to the line thickness of the letters.
  • Make sure shapes have high contrast against the background, just like letters usually do. Lower contrast figures may require larger sizes to achieve the same visibility as letters.
  • Symbols with less complex shapes are more easily compared to letters. More complex symbols need to be magnified more than simple figures to achieve the same level of recognition.
  • Test shapes and letters in a controlled experimental setup to calibrate the sizes. This involves having subjects go through vision tests with both symbols and letters and adjusting the sizes until the results are comparable.
  • Use statistical analysis to determine the optimal sizes for shapes versus letters based on the test results.

ISOeyes has carried out extensive empirical studies and subsequently carried out calibrations of all our self-developed Optotypes - so that it is ensured that they give identical vision test results!

The differences in the perception of letters versus shapes

Scientific research in visual perception and cognitive psychology has investigated the differences in the perception of letters versus shapes. Science indicates that letters are generally cognitively easier for the eye and brain to identify compared to shapes due to higher levels of familiarity, specialized brain areas, and their designed distinction. This makes letters an effective tool in many visual tasks, although shapes also have their place, especially when working with non-literate individuals or specific visual tests:

Familiarity and Experience

  • Letters: People who are literate have a lot of experience in recognizing letters as they constantly interact with them in daily life. This higher degree of familiarity makes it cognitively easier for the eye and brain to identify letters quickly and accurately.
  • Figures: Figures can vary more in complexity and shape than letters. Although simple shapes (such as circles, squares, and hearts) may be easy to recognize, more complex or unfamiliar shapes may require more cognitive processing.

Cognitive Processes

  • Letters: The recognition of letters involves specialized areas of the brain, such as the visual word form area (VWFA), which is responsible for processing written words and letters. This specialization makes the recognition of letters very efficient.
  • Figures: The recognition of figures involves several different areas of the brain, depending on the complexity and meaning of the figure. This may make the process more cognitively demanding compared to letters.

Visual Disambiguation

  • Letters: Most letters are designed to be visually distinct even when closely packed together, aiding in quick recognition.
  • Shapes: Shapes can vary more in shape and size, which can make them more difficult to distinguish, especially if they are complex or unfamiliar.

Contrast and Shape

  • Letters: Often have strong contrasts and well-defined shapes that make them easy to see and recognize, even at low resolution or from a distance.
  • Figures: Depending on their design, figures can vary more in contrast and shape, which can affect visibility and recognition.

Empirical Studies

  • Studies on Word Recognition: Many studies have shown that humans can recognize words and letters extremely quickly (in a few milliseconds), suggesting a high degree of automation in this process.
  • Studies on Figure Recognition: Recognition of figures, especially unfamiliar or complex figures, can take longer and require more cognitive processing.

Practical Consequences

  • Sight tests: Sight tests that use letters can take advantage of people's familiarity with these symbols and thus provide more consistent results. Tests with figures can be useful for testing vision in people who cannot read (such as young children), but can vary more depending on the design of the figures.
  • Designing Visual Materials: When designing visual materials for quick recognition (such as signs, warnings, etc.), it may be more effective to use letters or very simple shapes to ensure quick and accurate perception.

Comparing two drawings of an apple:

  1. Drawing 1:
    • Square of 5×5 cm
    • Line thickness of 0,8 cm
  2. Drawing 2:
    • Square of 10×10 cm
    • Line thickness of 0,4 cm

Conclusions about visual perception and recognition:

  1. Size and Scale:
    • A- Larger images provide more visual information and detail, making them easier to recognize.
    • B- Larger drawings project larger images onto the retina, facilitating visual acuity and object recognition.
  2. Line thickness:
    • Thicker lines can increase contrast and make contours more distinct, but the size of the image plays a greater role in overall recognition.
  3. Improved recognition at different distances:
    • Larger drawings are easier to see and recognize from longer distances, even if the line is thinner.
  4. Contrast and visibility:
    • Even if the line is thinner, the contrast between the line and the background may still be sufficient to make the shape easily recognizable in larger drawings.
  5. Physiology of vision:
    • Human visual acuity is better adapted to see objects of medium to large size.

Literature references:

  • relating to. item 1A: Goldstein, EB (2013). Sensation and Perception (9th ed.). Cengage Learning. Chapter 5, pages 100-135.
  • relating to. item 1B: Pelli, DG, Robson, JG, & Wilkins, AJ (1988). The Design of a New Letter Chart for Measuring Contrast Sensitivity. Clinical Vision Sciences, 2(3), 187-199.
  • relating to. point 2: Wang, K., & Cottrell, GW (2012). The Strengths and Weaknesses of the Stroke Width Transform for Text Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(6), 1173-1186.
  • relating to. item 3: Hecht, S., Shlaer, S., & Pirenne, MH (1942). Energy, Quanta, and Vision. The Journal of General Physiology, 25(6), 819-840.
  • relating to. item 4: Kandel, ER, Schwartz, JH, & Jessell, TM (2000). Principles of Neural Science (4th ed.). McGraw-Hill. Chapter 27, pages 492-525.
  • relating to. item 5: Duin, RPW, & Pavešić, N. (2001). Visual Pattern Recognition in Machine Vision. Pattern Recognition, 34(11), 2213-2226.

Bibliography

  1. Goldstein, EB (2013). Sensation and Perception (9th ed.). Cengage Learning. Chapter 5, pages 100-135.
  2. Pelli, DG, Robson, JG, & Wilkins, AJ (1988). The Design of a New Letter Chart for Measuring Contrast Sensitivity. Clinical Vision Sciences, 2(3), 187-199.
  3. Wang, K., & Cottrell, GW (2012). The Strengths and Weaknesses of the Stroke Width Transform for Text Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(6), 1173-1186.
  4. Hecht, S., Shlaer, S., & Pirenne, MH (1942). Energy, Quanta, and Vision. The Journal of General Physiology, 25(6), 819-840.
  5. Kandel, ER, Schwartz, JH, & Jessell, TM (2000). Principles of Neural Science (4th ed.). McGraw-Hill. Chapter 27, pages 492-525.
  6. Duin, RPW, & Pavešić, N. (2001). Visual Pattern Recognition in Machine Vision. Pattern Recognition, 34(11), 2213-2226.