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Jonas Dall, Katsumi Watanabe, Thomas Sørensen; Expertise prompts initial faster processing followed by increased short-term memory. Journal of Vision 2017;17(10):875. doi: https://doi.org/10.1167/17.10.875.
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Attention is a process of prioritising cognitive recourses to task-relevant stimulus. A number of studies have demonstrated various processing limitations in attention; for example, visual short-term memory (VSTM) only retains a limited number of objects (Sperling, 1960). An increasing number of studies exploit formal models like Theory of Visual Attention (Bundesen, 1990) to investigate the modulation of attentional parameters in more specific detail (e.g. in synaesthesia; see Ásgeirsson, Nordfang & Sørensen, 2015). Previous studies (e.g. Chen, Eng & Jiang, 2006) have argued that capacity limitations are surprisingly robust and resilient to training and expertise. This notion has been challenged by recent studies on the noticeable influence of expertise on VSTM (Sørensen & Kyllingsbæk, 2012; Dall et all, 2016). The current study extends the results reported by Dall et al. (2016) on the influence of expertise on VSTM, into attention by analysing the processing speed and the threshold of visual attention as well as VSTM. The experiment was comprised by three conditions: two control conditions (line drawings and Latin letters) and a critical condition (Japanese hiragana). Three groups with varying degrees of expertise in Japanese reading participated: Danish students (novice), Danish students studying Japanese (trained), and Japanese students (expert). The results showed that while expertise enhanced VSTM only for the expert group, replicating our previous study (Dall et al., 2016), it did not affect the threshold for visual attention. Furthermore, the processing speed was significantly slower for the novice group, leaving the other two (trained and expert) groups about the same. The present results suggest that, while the processing speed is affected by expertise, the effects reach an asymptote long before changes in VSTM.
Meeting abstract presented at VSS 2017
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