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Initial performance predicts improvements in computerized cognitive training: Evidence from a selective attention

Pan Zhang1, Di Wu2, Yunfeng Shang3

  • 1Department of Psychology, Hebei Normal University, Shijiazhuang, China.

Psych Journal
|July 5, 2021
PubMed
Summary
This summary is machine-generated.

Computerized cognitive training (CCT) shows that initial performance predicts learning. Poorer initial skills correlate with greater improvement, faster learning, and higher final performance, supporting a compensation model for cognitive training.

Keywords:
computerized cognitive traininginitial performancelearning curveselective attention

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Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Human-Computer Interaction

Background:

  • Computerized cognitive training (CCT) enhances cognitive skills like attention and memory.
  • The relationship between initial performance and learning trajectories in CCT remains unclear.

Purpose of the Study:

  • To mathematically model the learning curve in CCT using an exponential function.
  • To investigate the correlations between initial performance and learning parameters (improvement, time constant, asymptotic level).

Main Methods:

  • Selective attention training was administered to 29 college students over ~10 days.
  • Presentation time was measured using adaptive algorithms; learning curves were fitted with exponential functions.
  • Pearson correlation analyses examined relationships between initial performance and learning parameters.

Main Results:

  • Initial performance positively correlated with amount of improvement and asymptotic performance level.
  • Initial performance negatively correlated with the time constant.
  • Poorer initial performance predicted greater improvement, shorter time constants, and higher asymptotic levels.

Conclusions:

  • Results support the compensation account, suggesting individuals with lower initial cognitive skills may show more pronounced learning gains.
  • Findings aid in understanding CCT processes and developing customized training protocols for enhanced efficiency.