Studying real-world perceptual expertise

Abstract

Significant insights into visual cognition have come from studying real-world perceptual expertise. Many have previously reviewed empirical findings and theoretical developments from this work. Here we instead provide a brief perspective on approaches, considerations, and challenges to studying real-world perceptual expertise. We discuss factors like choosing to use real-world versus artificial object domains of expertise, selecting a target domain of real-world perceptual expertise, recruiting experts, evaluating their level of expertise, and experimentally testing experts in the lab and online. Throughout our perspective, we highlight expert birding (also called birdwatching) as an example, as it has been used as a target domain for over two decades in the perceptual expertise literature.

Keywords: birding; categorization; expertise; learning; perceptual expertise; recognition.

FIGURE 1

Mean correct categorization response times for a novice domain (dogs) and an expert domain (birds) measured online. Following Tanaka and Taylor (1991), bird experts were tested in a speeded category verification task where they categorized images at the superordinate (animal), basic (bird or dog), or subordinate (specific species or breed) level. In their novice domain (dogs), a classic basic-level advantage was observed, whereby categorization at the basic level was significantly faster than the superordinate (t₂₂ = 2.67, p = 0.014) and subordinate level (t₂₂ = 6.75, p< 0.001). In their expert domain (birds), subordinate categorization was as fast as basic-level categorization (t₂₂ = 0.81, p = 0.429). This replication was conducted using an online Wordpress + Flash custom website with only 23 participants from a single short 10 min experimental session. Error bars represent 95% confidence intervals on the level × domain interaction.