Ed R. Hope, Keval Patel, James Feist, Oliver R. Runswick, Jamie S. North

Examining the importance of local and global patterns for familiarity detection in soccer action sequences

  • Artificial Intelligence
  • Sensory Systems
  • Experimental and Cognitive Psychology
  • Ophthalmology
Save to CiteDrive Show PDF

Pattern recognition is a defining characteristic of expertise across multiple domains. Given the dynamic interactions at local and global levels, team sports can provide a vehicle for investigating skilled pattern recognition. The aims of this study were to investigate whether global patterns could be recognised on the basis of localised relational information and if relations between certain display features were more important than others for successful pattern recognition. Elite ( n = 20), skilled ( n = 34) and less-skilled ( n = 37) soccer players completed three recognition paradigms of stimuli presented in point-light format across three counterbalanced conditions: ‘whole-part’; ‘part-whole’; and ‘whole-whole’. ‘Whole’ clips represented a 11 vs. 11 soccer match and ‘part’ clips presented the same passages of play with only two central attacking players or two peripheral players shown. Elite players recognised significantly more accurately than the skilled and less-skilled groups. Participants were significantly more accurate in the ‘whole-whole’ condition compared to others, and recognised stimuli featuring the two central attacking players significantly more accurately than those featuring peripheral players. Findings provide evidence that elite players can encode localised relations and then extrapolate this information to recognise more global macro patterns.

Need a simple solution for managing your BibTeX entries? Explore CiteDrive!

  • Web-based, modern reference management
  • Collaborate and share with fellow researchers
  • Integration with Overleaf
  • Comprehensive BibTeX/BibLaTeX support
  • Save articles and websites directly from your browser
  • Search for new articles from a database of tens of millions of references
Try out CiteDrive

More from our Archive