The perceptual cues that reshape expert reasoning

Abstract

The earliest stages in our perception of the world have a subtle but powerful influence on later thought processes; they provide the contextual cues within which our thoughts are framed and they adapt to many different environments throughout our lives. Understanding the changes in these cues is crucial to understanding how our perceptual ability develops, but these changes are often difficult to quantify in sufficiently complex tasks where objective measures of development are available. Here we simulate perceptual learning using neural networks and demonstrate fundamental changes in these cues as a function of skill. These cues are cognitively grouped together to form perceptual templates that enable rapid 'whole scene' categorisation of complex stimuli. Such categories reduce the computational load on our capacity limited thought processes, they inform our higher cognitive processes and they suggest a framework of perceptual pre-processing that captures the central role of perception in expertise.

Figure 1. The original Ponzo illusion is shown in 1.a.

The corresponding Perceptual Template (the converging lines) is shown in 1.b along with the red bars about which a decision regarding their relative lengths needs to be made. In 1.b the detailed information has been removed but the information that informs our judgement of the length of the red bars is retained. Without the perceptual template the illusion vanishes as can be seen in 1.c.

Figure 2. The computational implementation of Self-Organizing Maps for extracting the structured information from Go games given a move k.

The resultant trained SoM output at the end contains neurons with continuous weights, it is a further step to threshold these internal neuronal weights in order to generate the unique templates.

Figure 3. The probability distributions for the sizes of the templates (the number of stones each template contains).

From top to bottom the threshold values are: 0.95, 0.90, 0.85 and 0.80. The mean and standard deviation of each distribution are plotted on interval lines above each plot.

Figure 4. The intersection between experts and non-experts.

Top: A schematic representation of the intersection between sets of templates. The set of amateur templates are held constant while the set of professional templates is allowed to increase. The size of the professional set is increasing in the positive direction of the x-axis in the bottom plot, i.e. the larger the professional set the larger and more complex the templates are that have been included. Bottom: The size of the intersecting set of amateur and professional templates as a function of the professionals' indexing. Three different thresholds shown, from bottom to top: 0.95, 0.90 and 0.85. Inset: An expansion near the origin.

Figure 5. A cognitive model of the implementation of perceptual templates.

Early processing encodes the environment such that it can be compared in parallel with a large number of neural representations of templates. A single template is found that is a ‘best fit given the current environment. This template signals where the eye's gaze should saccade to first as well as providing the context that informs later cognitive processing. Amateurs and professionals differ in their perceptual templates resulting in different interpretations of the context and different patterns of eye saccades.