Discussion

3.4.1 Modeling Priming

The ISAM impressively reproduces experimental data without contradictions. Additionally it shows reasonable behavior when confronted with the touchstone of RSI-dependency. The im-plementation of the ISAM presented here is robust enough to provide predictions about stimulus constellations that have barely been considered in priming experiments thus far. This circumstance serves as a good opportunity to test the ISAM experimentally. Among the many theoretical ap-proaches to priming, we select three of the most popular models and compare their implications to empirical results.

The distractor-inhibition model (Tipper and Cranston, 1985; Houghton and Tipper, 1998) is one of the most influential theoretical accounts. Following this line of reasoning, the negative priming effect is supposed to be a cognitive index for the inhibitory component of selective attention. It is assumed that irrelevant stimuli representations are actively suppressed to support selection of the goal-relevant target stimulus, and that this inhibition persists for some time. When the former distractor becomes the behavioral relevant target in the subsequent display, responding is ham-pered because of the persistence of the inhibition imposed on it during the prime trial. However, shortcomings of this model emerge when explaining certain experimental results: Because nega-tive priming is described as an after-effect of distractor inhibition, a facilitatory effect of ignored

3.4 Discussion repetition trials without a distractor in the probe trial (e.g. Tipper and Cranston, 1985) cannot be reasonably accounted for in terms of this model.

A second important account, the episodic retrieval model, has originally been proposed by Neill and Valdes (1992). They argue that negative priming is the result of conflicting information caused by a retrieval of the prime episode when exposed to the probe stimulus triggered by similarities of the two situations. The probe target thus causes retrieval of the prime episode due to repetition of the prime distractor. The information from the retrieved episode (i.e. “ignore the stimulus”) conflicts with the need to respond to this same stimulus in the current probe episode, resulting in a time-consuming process. This causes an increased reaction time, characteristic of negative priming. A particular advantage, in comparison to the other models, is its explanatory power regarding the influence of temporal discriminability of the prime episode relative to the preceeding episodes. This is more easily accounted for by episodic retrieval than by inhibition-based models.

The same is true for experimental evidence which shows that the effect increases quantitatively when the contextual similarity between prime and probe situation is increased (e.g. Stolz and Neely, 2001). However, the model falls short of explaining semantic negative priming effects, where the response to a probe target is only semantically related to the prime distractor (such as dog to cat), which are more easily accounted for by the inhibition model (Tipper and Cranston, 1985). Both the inhibition model and the episodic retrieval model explain general reaction time increases in negative priming conditions in a straight-forward manner.

In contrast to the two previously described models, the temporal discrimination model (e.g. Mil-liken et al., 1998) does not assume selection processes during the prime task as the basis of negative priming. Instead, it assumes that negative priming is caused at the moment of response formation during the probe processing. Two response modes are postulated in the following context: the response can either be computed or directly retrieved from memory. Which of the two response modes guides behavior in the probe task depends on a categorization of the probe target as either

“old” or “new”. If the probe target is categorized as new, a response is generated on the basis of perceptual analysis. Otherwise, if the same task situation has already been encountered, the response can rely on a direct retrieval of the former response. For a probe target in an ignored repetition trial, an ambiguity in the categorization process is assumed, resulting in a longer reac-tion time. But there is also empirical evidence which contradicts the predicreac-tions derived from this model: In a repeated distractor condition, the model predicts a slowdown in responding, whereas a speed-up is repeatedly demonstrated in psychological experiments (e.g. Frings, 2005).

These considerations show that modeling of the negative priming effect is still in its develop-mental stages. Therefore we do not hesitate to implement the ISAM as a new contribution to the ongoing discussion of negative priming explanations. At first glance the ISAM looks similar to the inhibition based model (Tipper, 1985) as negative priming is the result of a negative interference.

However considering details the two models differ in crucial aspects. In the ISAM, priming effects are an interplay of remaining activation from the prime display and activation driven by the probe input.

Generally, none of the effects are present in the ISAM until the probe trial starts. For the inhi-bition based model, this is not the case, as the activation of the distractor is inhibited in the prime trial and negative priming is just an after-effect. The negative interference is not due to a control mechanism ruling the prime decision as postulated in the inhibition based model, but rather an in-trinsic interference within the neuronal circuits accounting for the prime distractor representation.

The negative priming effect is thus obtained by the added ”effort” of the distractor representation, which must switch states from ”do not respond to” to ”respond to”. This interference is more like the postulated conflict between memory traces and the current activation pattern responsible for negative priming in the episodic retrieval model. Another marking-off feature of the ISAM is that

3 Imago Semantic Action Model

representations cannot be pushed below baseline, as the absence of a representation is modeled by zero activation. The interference only affects time constants of decay or rise towards input equa-tions (3.7) and (3.8). Similarities to Tipper’s inhibition model can be found in the assumption of an external control mechanism responsible for the decision. But rather than inhibiting target acti-vation in the probe trial, the control mechanism postulated in the ISAM boosts the target actiacti-vation due to its semantic classification relative to the task.

Since the ISAM produces the priming effects at a very early stage of the probe trial, it displays no obvious similarities to the mechanisms presupposed in the temporal discrimination theory (Mil-liken et al., 1998). In terms of the functional effectiveness, it can rather be placed between the two models first discussed, the inhibition and the episodic retrieval theory. The ISAM thus incorpo-rates advantages from both approaches and integincorpo-rates assumptions from both models into a more complete theory comprising all (positive and negative) priming effects.

3.4.2 Phenomenological and Neural Models

Modeling provides an attractive approach to attentional mechanisms and thus in particular to prim-ing phenomena (Houghton and Tipper, 1994). First, mathematical modelprim-ing of a theory requires a precise formulation of the theoretical assumptions and mechanisms. This is of even greater im-portance as some of the explanatory models for negative priming presented above do not specify how and on what level attentional processes operate. Second, an explicit computational model can help to derive more specific predictions than possible from more abstract models, since not only qualitative but also quantitative predictions can be generated. Also, conducting computational experiments can produce new and even unexpected results that, in turn, can lead to experimental confirmation and extension of the model.

The ISAM presented here is implemented with effective equations. A refinement of neural net-works seems desirable but cannot provide deeper insight into the mechanisms generating negative priming, as the effect appears at a high level of brain function where computation is strongly dis-tributed. Therefore, no plausible neural model can be given at this stage of brain research. The reduction to an effective theory, in contrast, allows for direct observations of the functioning of the model itself without exhaustive investigations of the simulated data.

The difficulties of the model with short RSI point out the specificity of a certain approach that can never describe the whole behavior of the brain which, for example, in the context of changing the RSI uses several different strategies, such as short term memory and long term memory when changing the RSI from 500 to 1500 ms. With the present implementation, the ISAM moves up in line with the other explanations of priming effects like the inhibition or episodic retrieval approach. It shows possible simplifications of existing models, having in mind a minimal model that describes the generation of priming effects in the human brain.

3.4.3 The Implementation of the Model

The numerical implementation revealed an insufficiency of the ISAM concerning RSIs that involve short-term memory effects. For very short RSI its consequent implementation shows a reversal of priming effects. The reason for this strange behavior is an empty space of possible actions. At stimulus onset, the threshold level is still greater than any activation of the variables representing object recognition. Therefore, a decision is made when the first variable reaches the threshold. An increase of this limited range of RSI also requires the consideration of low-level neurophysiolog-ical details, which will be incorporated in an improved version of the model. Furthermore, our implementation is sensitive to the number of stimuli in one display. The factor ¹₂ in equation 3.10

3.5 Summary was chosen for the situation of one target and one distractor. If multiple distractors were present, the model may not come to a decision. In extending the ISAM to more paradigms the factor should be adjustable according to the stimulu configuration in a trial display.

All this together with the restriction of the ISAM to identity based voicekey paradigms built the basis for constructing the General Model for negative priming more or less from scratch. Chapter 8 will be devoted to the development of a computational model that is capable of dealing with nu-merous stimuli and response schemes while being complex enough to incorporate all mechanisms that are suspect to produce priming effects.

3.5 Summary

The imago semantic action model by Kabisch (2003), which has been described in section 2.4.7, is the attempt to explain negative and positive priming by one single mechanism, the presence of an adaptive threshold which decides on the semantic representations of stimuli which one of them to attend. The original description is sufficiently precise to make a straightforward implementation as a dynamic system possible.

We first derived a very simple dynamics describing the transient of the spike rate response of a neuronal compound to the onset and offset of external input. The resulting dynamics is used to model object representations as activation of a single variable even though this representation is most likely held by a distributed network of interconnected neurons. On the basis of these representation dynamics we implemented the ISAM with minimal additional assumptions and found that the reproduction of frequently encountered priming effects is easily achieved. We thus prove quantitatively that priming effects, both positive and negative, can be explained in terms of the ISAM. Further phenomena that are well described by the ISAM include the speedup of responses to single-object presentations, the preservation of priming effects in these cases, and the reproduction of the dependency of priming effects on varied distractor saliency.

3 Imago Semantic Action Model

4 EEG Correlates of Negative Priming

The second mainstay in the determination of the time course of negative priming is electrophysio-logically recording the processes happening during a negative priming trial. The electroencephalo-gram (EEG) is well suited for negative priming experiments due to its high temporal resolution.

Especially the consideration of event-related potentials (ERPs) is well suited to reveal the differ-entially time-consuming components of information processing that are responsible the negative priming effect.

In search of a cooperation, we were offered to use a fully equipped state-of-the-art 64 channel EEG-laboratory in the department for medical psychology Göttingen. We seized the opportunity to run a series of priming experiments while recording EEG activity. The results of the adaptation of our voicekey paradigm, presented in section 2.2, to the EEG environment provide the replication of the results of a recent study on EEG correlates of negative priming (Gibbons, 2006).

We will describe the derivation of EEG signals and traditional way of data analysis in tion 4.1. In section 4.2, we will give a detailed review of correlates of negative priming. In sec-tions 4.3 to 4.5 we will describe our study on electrophysiological correlates of negative priming, present the results in sections 4.6 and 4.7. The results are integrated into previous ERP correlates and evaluated in sections 4.8 and 4.9. Our publication (Behrendt et al., 2009) is the basis for the current chapter.