## 3 Results  
### Analysis of Success  
Observing the parameter estimate outputs, we can see that the parameters that should be integrated in the model can be unequivocally detected. Based on the results it can be stated that the following parameters are significant (\(\alpha<0.05\)): subject \(\times\) success \(\times\) image, success \(\times\) image, subject, success and image (see Table 3). Therefore, we excluded the following parameters from the model: subject \(\times\) success and subject \(\times\) image. The loglinear model was built up based on these relevant parameters. Further on the parameter of success was excluded, because the HILOGLINEAR module of SPSS estimates the output of the categorical parameters, but it cannot handle covariant variables. Finally, when the model with the covariant parameters was tested, the effects of success and image were also excluded and only the effect of their interaction remained in the model. Based on the results of the model subject x success and subject x image interactions had no significant effect and were left out from the final model. (The success\(\times\)ordinal number parameter was included, because as the ordinal number increases, the task is getting harder to complete, hence the rate of success decreases (the coefficient is \(-0.106\); see Table 4). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Effect** & **Parameter** & **Estimate** & **SE** & **Z Score** & **\(p\)-Value** & \begin{tabular}{c} **Lower** \\ **CI (95\%)** \\ \end{tabular} & \begin{tabular}{c} **Upper** \\ **CI (95\%)** \\ \end{tabular} \\ \hline \begin{tabular}{c} Subject \(\times\) \\ success \(\times\) \\ image \\ \end{tabular} & \(1\) & \(0.213\) & \(0.067\) & \(3.187\) & \(0.001\) & \(0.082\) & \(0.344\) \\ \begin{tabular}{c} Subject \(\times\) \\ success \\ \end{tabular} & \(1\) & \(-0.073\) & \(0.067\) & \(-1.094\) & \(0.274\) & \(-0.204\) & \(0.058\) \\ \begin{tabular}{c} Subject \(\times\) \\ image \\ \end{tabular} & \(1\) & \(0.097\) & \(0.067\) & \(1.458\) & \(0.145\) & \(-0.034\) & \(0.228\) \\ \begin{tabular}{c} Success \(\times\) \\ image \\ \end{tabular} & \(1\) & \(-0.281\) & \(0.067\) & \(-4.204\) & \(<0.001\) & \(-0.412\) & \(-0.150\) \\ \begin{tabular}{c} Subject \(1\) \\ \end{tabular} & \(1\) & \(1.052\) & \(0.067\) & \(15.761\) & \(<0.001\) & \(0.921\) & \(1.183\) \\ \
\begin{tabular}{c} Success \(1\) \\ \end{tabular} & \(1\) & \(-0.483\) & \(0.067\) & \(-7.233\) & \(<0.001\) & \(-0.614\) & \(-0.352\) \\
\begin{tabular}{c} Image \\ \end{tabular} & \(1\) & \(-0.466\) & \(0.067\) & \(-6.975\) & \(<0.001\) & \(-0.597\) & \(-0.335\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter Estimates and their confidence intervals (CI). Success was not significant, so it was excluded from the final model, which only included the following parameters: success \(\times\) image, success \(\times\) ordinal number and subject \(\times\) success \(\times\) image. The loglinear model fits the data as well as the full saturated hierarchical model (LR \(\chi^{2}\) = 0.384; \(p\) = 0.535). In the loglinear model, each three parameter interactions were significant at the 10% level (see Table 5). The coefficient of success\(\times\)image is \(-\)0.102. Because of the loglinear model, this value was multiplied by four, and we took the exponential of this value with base \(e\) (odds ratio = 0.665). We prepared the analysis of the cross-tabulation of the success \(\times\) image parameter for the validation of that (see Table 6). Based on the results of all subjects, there were 285 successes and 77 failures in the case of achromatic images. Therefore, the odds of success are 285/77 = 3.701. Based on the results of all subjects, there were 498 successes and 218 failures in the case of colored images. Therefore, the odds of success are 498/218 = 2.284. By dividing the odds in the