scene complexity

Given that many factors, including divided attention and sustained attention, can affect change detection; therefore, the purpose of this research is to predict change blindness based on the components of scene complexity, stimulus arousal, symbolic memory, and sustained attention.

Methods and Materials:

 The research method was relational-exploratory. The statistical population of this research was the students of Payam Noor and Azad Islamic University, Zarand branch, and 200 of them were selected by available sampling method. . In order to collect data, real images with positive, negative and neutral emotional load were used in the form of software prepared to measure change blindness, stimulus emotional load and stimulus complexity. Also, Sperling's test was used to measure symbolic memory and continuous performance test was used to measure sustained attention. Structural equation modeling and confirmatory factor analysis through path analysis and structural equation modeling (SEM) were applied. All analyzes were performed using AMOS and SPSS version 21, and correlation and regression coefficients were used.

According to the findings of the research, the provided indicators showed the perfect fit of the model. In the final model, the ratio of the chi-square to the degree of freedom or the relative chi-square (2.10), the goodness of fit index (GFI) equals 0.97, the adjusted goodness-of-fit index (AGFI) equals 0.97, the incremental goodness-of-fit index (IFI) is equal to 0.96, Tucker-Lewis Index (TLI) is equal to 0.92, Normalized Fit Index (NFI) is equal to 0.94, and Root Mean Square Error (RMSEA) is equal to 0.06.

The findings showed that the components of scene complexity, stimulus emotional load, symbolic memory and sustained attention have a direct and negative effect on change blindness. Also, the findings indicate that the initial fit indices in Nayed patterns affecting change blindness confirm the appropriateness of the pattern.