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From a theoretical stance, it is important to clarify, that the RSE can be conceived as an umbrella term, enveloping different mechanisms, depending on the specific experimental situation at hand (that is, task, modalities and stimuli involved). This notion was already brought forward by Miller and Reynolds (2003). Although applying different experimental tasks and setups to the question of signal integration is necessary to come to a conclusive and robust answer concerning the structure and integration mechanism, it entails the risk of invalidly and unconsciously pooling across inherently different phenomena and mechanisms. In order to thoroughly understand the mechanisms and architecture necessary for the

cognitive system to produce the data patterns for each experimental paradigm, a systematic and exhaustive inquiry is inevitable. A classification system for both the paradigms and explanatory models of the RSE is an important conceptual step (see Töllner et al., 2011). The diffusion model analysis (as adopted in the present study) will further help disentangle different accounts of coactivation models and thus shed light on the source(s) of the RSE. By applying this modeling approach to further experimental tasks and modality combinations, it can be determined whether the RSE has to be divided into subphenomena and according to what categories (e.g. locus of coactivation and contribution of loci). The present study provides a first step, examining simple detection tasks in the audiovisual domain.

On a critical note, it is theoretically possible that this program might fail because of an untestable presupposition. The formulation of the RMI bound and its application to empirical data rests heavily on the assumption of context independence. Context independence assumes that the processing rate of each channel is invariant, across the single and redundant signals conditions. It is crucial for the RMI test, as it justifies the comparison between data gathered in the single and redundant conditions. It is however untestable, at least with respect to mental chronometry. Similar to this barrier, is the equivalent in the motor regime. It assumes that the base times are equal across conditions (and thus independent of the decision stage). If any of these assumptions do not hold, the whole RMI test is

5.1.2 Methodological

The present study used an implementation of the Ratcliff diffusion model to generate and fit the data for race and coactivation models. Ratcliff diffusion models, as an instance of sequential sampling models, allow for a direct mapping of psychological variables to the model parameters and have shown to exhibit excellent fits to empirical data on behavioral and neuronal level. In contrast to descriptive (and atheoretic) models of reaction times, sequential sampling models give a full account of the observers performance, as it provides accuracy and reaction time distribution for both correct and incorrect responses.

It is conceivable, that these models will be refined and modified over time as further research will necessitate this. Certainly, the results of the simulations in the present study depend on the reaction time generating model. The use of alternative models of decision making (and thus, reaction times) can come to differential results. Due to the modular design and flexibility, the generality and transferability of results can be explicitly investigated. If for example, researchers want to test the RMI bound using other models for decision making or different distribution functions, only one module in the simulation framework needs to be adapted. Also the application of different violation criteria or alternative RMI tests is facilitated by the modular design of the framework. Alternative tests of the RMI have been proposed (see Colonius & Diederich, 2006; Maris & Maris, 2003), however the current study has focused on investigating and implementing only the most widespread version of RMI testing. It is imaginable that one of these alternative test will feature more favorable statistical

properties (in terms of type I errors and power rates) for specific boundary conditions

compared to the standard RMI test. This now is testable as the simulation framework allows for implementing different tests and test criteria.

With regards to the performance of the fitting procedure in chapter 3, further theoretical and analytical investigations are necessary. One question is whether, one can draw conclusions from the general fitting performance (in terms of failed fits) to the aptitude of a specific model for the data. The fitting results of Experiment 1 and 2 exhibit this

remarkable pattern, as the model with the best overall fit was at the same time the model with the highest rate of failed fits.

Another aspect of the fitting procedure that must be looked into to further strengthen the results of the locus analysis is a series of validation simulations. Data generated by known parameters have to be fit by the used procedure to see, whether it is able to recover the instantiated parameters. This would serve as a proof for the validity of the fitting results. One way to assess this parameter recovery – a parametric bootstrap method - has been presented by Wagenmakers, Ratcliff, Gomez and Iverson (2004).

Concerning the goodness-of-fit measure, a comparative analysis of in the fitting procedure could help optimize the computational effort and fitting precision for different cost functions. Also, procedures to deal with contaminant reaction times could provide a clearer distinction between models (see Ratcliff & Tuerlinckx, 2002).

A combination of methods would also prove beneficial for the fitting procedure (improving the rate of successful fits), as different measures (e.g. physiological or response related measures) might exclude or further constrain parameters of the models. Especially in Experiment 1, an estimate of the base time variance would be obtainable, using the dual response paradigm by Ulrich and Stapf (1984; see chapter 2.2.6). Adapting this experiment by introducing another contrast level for the auditory and visual stimulus would allow the application of equation 5 to estimate stfor the fitting.