After the sketch of a comparison of episodic retrieval and temporal discrimination theory we will now show how a relatively small change in the form of a weak modulation of information transfer will enhance priming. We expose the General Model to a word-picture comparison task, as it was introduced in section 7.1 despite the long distance between objects and comparison word, such that a parallel evaluation of shapes and words is possible. In this paradigm both shape and word layers project into the layer of semantic representations. Whenever both converge, much stronger input is delivered to the corresponding semantic concept and a yes response is triggered.
We again start off with a straight episodic retrieval setting. The present simulation was run with the following values of the relevant parameters: Ξer=1,Ξrr=1,Ξib=1,Ξgt=0,Ξfsb=0, Ξsab=0,Ξtd=0,α=0.001, F=1, trecognition=50, tafterimage=30, tmotor=80,ρf =0.01,δf = 0.003, ˆb=0.05, #b=7,ρb=0.01,δb=0.005,τsθ=0.0015, νsθ=0.5,ρa=0.004,δa=0.002, τaθ=0.002,νaθ=0.5, ˆe=0.002,δe=0.003.
Results are given in table A.23 in appendix A.6. No strong artifacts are visible, but the desired facilitation in TT trials does not exist.
Parameter changes in the second simulation introducing a slight modulation of information transmission, shown in figure 8.13, are given by: Ξfsb=0.3, Ξsab=0.3τblock =0.5. Whenever the model encounters a similarity signal strong enough to classify the percept as old, bottom up information flow is hampered by a relative shutdown of the connection between feature layers and semantic layer and in parallel the retrieval of the former response is facilitated. On the contrary a classification as new eases bottom up information flow and hampers retrieval.
Reaction time results are given in table A.24 in appendix A.6. Even if the overall reaction time is rather fast, the pattern of priming effects shows realistic values, as compared to the vanishing facilitation in TT trials the simulation with pure episodic retrieval linearly based on the similarity signal shows.
8.7 Discussion
The simulated reaction times in the tables in section A.6 show that the behavior of the General Model is far from being robust against even small parameter changes. This may be a hint that the complexity level is chosen about right in order to account for the multitude of different findings in connection with negative priming. But we have to face the question whether the model is built such that it can basically fit any data pattern with just the right parameter settings. Due to the
8.8 Summary high dimensionality of the parameter space and the sensitivity of the General Model, this question can not be answered conclusively by the means of parameter scanning techniques. In fact, an important next step for the General Model is the parameter reduction by determining as many values as possible by comparisons with trusted experimental results, e.g. for the availability of after-images, decay times of feature bindings, etc. The detailedness of the General Model is also easily capable of showing partial reaction times. Therefore a good way to limit the range of the parameter space would be to have a series of time-marker experiments specially designed to reveal processing stages that are also measurable in the General Model. Up to now the General Model can only be a basis for discussion on the concrete nature of negative priming theories and paradigms.
In order to really decide which of the theories explains what part of the negative priming ef-fect, a thorough dialogue with the psychologists who invented the theories is necessary. As the discussion can best be triggered by a prominent introduction of the General Model to the priming community we are in a vicious cycle. No acknowledged simulation results are possible without an exchange between theoreticians and modelers, but also no discussion in the community is possible without recognized results. The first attempt to break the cycle by advertising the General Model at several conferences did not succeed yet. The second attempt, trying to reproduce a large portion of empirical data, is still work in progress.
8.8 Summary
We presented the proof of concept for our neurophysiological model of perception based action selection. Based on the cognitive demands of a negative priming paradigm we reviewed exper-imental findings as well as theoretical concepts that characterize the mechanisms suspected to contribute to trial processing. We then implemented several model layers for the different stages, each devoted to a specialized purpose and with certain characteristics. But all layers are working with the same realistic rate dynamics we introduced with the implementation of the ISAM.
The result is a comprehensive model able to recognize perceptual objects by feature decompo-sition and a binding mechanism that keeps track of the object entities. These objects are translated into a semantic representation where the attentional mechanism selects the most important item and propagates the information to an action selection layer. This chooses the appropriate responses and triggers its execution. In parallel a memory component observes the repetition of perceptual stimuli and triggers the retrieval of previously encountered stimuli together with the results of trial processing in order to facilitate responding.
The General Model gives a unified framework to quantify each of the theories for negative prim-ing. The identification of setscrews for the different accounts makes it convenient to compare the different predictions in a certain setting. But the application to clarify the explanations of nega-tive priming is still confronted with several hurdles to take. The high dimensionality of parameter space makes it impossible to exclude possibilities of behavior. And the implementation of the different theories still should undergo a debate with the priming community in order to best match the concepts of the theories, which are a matter of common sense.
8 The General Model for Negative Priming
Figure 8.10: Activation traces in the different layers of the General Model in the voicekey paradigm described in section 2.2. The model is in classical episodic retrieval mode, i.e. Ξer=1,Ξrr=1,Ξib=1,Ξgt=0,Ξfsb=0,Ξsab=0 andΞtd=0. That means we have a target boost and retrieval of the entire episode, but no forced decay of activation, no old/new classification. Retrieval is visible by the relatively small rise of formerly active variables. Resulting reaction times are summarized in table A.21.
8.9 Simulation Plots
Figure 8.11: Activation traces in the different layers of the General Model in the voicekey paradigm described in section 2.2. The model is in temporal discrimination mode, i.e.Ξer=1,Ξrr=1,Ξib=1,Ξgt=0,Ξfsb=1,Ξsab=1 andΞtd=1. That means we have a target boost and retrieval of the entire episode, but only if an episode is clas-sified as old, what happens if the retrieval variable (orange) leaves the uncertainty region (yellow). Resulting reaction times are summarized in table A.22.