Negative Priming and Response Relation

göttingen

Bernstein Center for Computational Neuroscience G¨ ottingen University of G¨ ottingen

Negative Priming and Response Relation: Behavioural and Electroencephalographic Correlates Matthias Ihrke November 3, 2007 Prepared under the conditions of the examination regulations for psychology at the Georg-August university of G¨ ottingen (3rd November 1998).

Date of Acceptance: July, 24th, 2007 Date of Delivery: November, 5th, 2007 First examiner: Second examiner: Mentor:

Prof. Dr. Marcus Hasselhorn PD Dr. Henning Gibbons Dr. J¨org Behrendt Hecke Schrobsdorff

Address of the author: Matthias Ihrke Schillerstrasse 23 16225 Eberswalde Tel.: 03334/24598 E-Mail: [email protected]

I hereby declare according to §24(6) of the examination regulations for psychology at the Georg-August university of G¨ottingen, that I composed the thesis on hand entitled Negative Priming and Response Relation: Behavioural and Electroencephalographic Correlates independently and without help. I did not use any other resources than documented. All parts of the thesis that contain material that was taken from other sources are marked accordingly and a reference to the original source is given.

Matthias Ihrke, G¨ottingen, January 26, 2008

Contents 1 Introduction

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2 Theory 2.1 Selective Attention . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 The Negative Priming Effect . . . . . . . . . . . . . . . . . . . . . . . 2.3 Negative Priming Theories . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 The Distractor Inhibition Theory . . . . . . . . . . . . . . . . 2.3.2 The Episodic Retrieval Theory . . . . . . . . . . . . . . . . . 2.3.3 The Response Retrieval Theory . . . . . . . . . . . . . . . . . 2.3.4 The Imago-Semantic Action Model . . . . . . . . . . . . . . . 2.3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Negative Priming and Electrophysiological Data . . . . . . . . . . . . 2.5 Conclusions and Open Questions . . . . . . . . . . . . . . . . . . . . 2.5.1 Response-Repetition Effect in Alternative Priming Conditions? 2.5.2 Response Repetition or Yes-No Effect? . . . . . . . . . . . . . 2.5.3 Selection or Response Effect? . . . . . . . . . . . . . . . . . . 2.5.4 Event-Related EEG Data . . . . . . . . . . . . . . . . . . . .

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3 Method 3.1 Terminology . . . . . . . . . . . . . . . . . . . . 3.2 Design . . . . . . . . . . . . . . . . . . . . . . . 3.3 Participants . . . . . . . . . . . . . . . . . . . . 3.4 Material . . . . . . . . . . . . . . . . . . . . . . 3.5 Procedure . . . . . . . . . . . . . . . . . . . . . 3.6 EEG-Acquisition . . . . . . . . . . . . . . . . . 3.7 Data Preparation . . . . . . . . . . . . . . . . . 3.7.1 Outlier Correction of Behavioural Data . 3.7.2 Preprocessing of the EEG-Data . . . . . 3.7.3 ERP Components and Nomenclature . . 3.7.4 Extraction of the Partial Reaction Times 3.8 Data Analysis . . . . . . . . . . . . . . . . . . .

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4 Results and Discussion 4.1 Behavioural Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Response-Repetition Effect in Alternative Priming Conditions? 4.1.2 Response Repetition or Yes-No Effect? . . . . . . . . . . . . . 4.1.3 Partial Reaction Times – Target or Response Selection? . . . . 4.2 Event-Related EEG-Data . . . . . . . . . . . . . . . . . . . . . . . .

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Contents 4.2.1 4.2.2

Testing of the Hypotheses: P300 and PSW . . . . . . . . . . 64 Correlates for Behavioural Effects . . . . . . . . . . . . . . . . 68

5 Summary and Discussion 73 5.1 Behavioural Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.2 ERP Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.3 General Discussion and Conclusion . . . . . . . . . . . . . . . . . . . 82 Acknowledgements

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List of Figures

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List of Tables

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Bibliography

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Nomenclature h·ij . . . . . . . . . . . . . Rrs . . . . . . . . . . . . . Rts . . . . . . . . . . . . . Rj . . . . . . . . . . . . . scj (t) . . . . . . . . . . . . ADACL . . . . . . . . ANOVA . . . . . . . . BP . . . . . . . . . . . . . DD . . . . . . . . . . . . . DDTT . . . . . . . . .

DT . . . . . . . . . . . . . DTTD . . . . . . . . .

EEG . . . . . . . . . . . EOG . . . . . . . . . . . EP . . . . . . . . . . . . . EPSP . . . . . . . . . . ERP . . . . . . . . . . . ERSP . . . . . . . . . . hEOG . . . . . . . . . . ICA . . . . . . . . . . . . IPSP . . . . . . . . . . . ISAM . . . . . . . . . . LOP . . . . . . . . . . . LRP . . . . . . . . . . . NP . . . . . . . . . . . . . NP2 . . . . . . . . . . . . OFC . . . . . . . . . . . PP . . . . . . . . . . . . . PSD . . . . . . . . . . . . PSW . . . . . . . . . . . rERP . . . . . . . . . . RSI . . . . . . . . . . . .

P Arithmetic Mean over index j: hsij ij = N1 N j=1 sij Partial reaction time for the response selection Partial reaction time for the target selection Reaction time in trial j Recorded ERP-signal from electrode c and trial j at time t Activation-Deactivation Adjective Checklist Analysis of Variance Bereitschaftspotential (Readiness Potential) Distractor (prime display) → Distractor (probe display) – Distractor Repetition Distractor (prime display) → Distractor (probe display) and Target (prime display) → Target (probe display) – Augmented Positive Priming (PP2) Distractor (prime display) → Target (probe display) = Negative Priming Distractor (prime display) → Target (probe display) and Target (prime display) → Distractor (probe display) – Augmented Negative Priming (NP2) Electroencephalogram Electrooculogram Evoked Potential Excitatory Postsynaptic Potential Event Related Potential Event-Related Spectral Perturbation horizontal Electrooculogram Independent Component Analysis Inhibitory Postsynaptic Potential Imago-Semantic Action Model Levels of Processing Lateralized Readiness Potential Negative Priming Augmented Negative Priming (NP2) Orbitofrontal Cortex Positive Priming Power Spectral Density Positive Slow Wave Response-locked Event-Related Potential Response to Stimulus Interval

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Contents sERP . . . . . . . . . . Stimulus-locked Event-Related Potential TD . . . . . . . . . . . . . Target (prime display) → Distractor (probe display) TT . . . . . . . . . . . . . Target (prime display) → Target (probe display) – Target Repetition = Positive Priming vEOG . . . . . . . . . . vertical Electrooculogram

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Abstract Negative priming (NP) describes a prolonged reaction time in settings where a previously ignored stimulus has to be attended to. One way to explain this finding is to assume that the response given in the first trial (prime) is associated with the current display (probe) which is retrieved in the following trial (Rothermund et al., 2005). The study reported here put this assumptions to a critical test, by (a) testing predicitions derived from this theory for other priming constellations, (b) analysing the timecourse of processing in each trial to investigate whether NP can in fact be caused by response-conflicts and (c) by recording and analysing event-related EEG-data. The complex pattern of results supports the conclusion that response-retrieval is not the only active mechanism and that, depending on the experimental paradigm, other processes can be assumed to be effective. The results are discussed in terms of an integrative model of negative priming.

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1 Introduction The study of human attentional processing is a controversial and challenging topic of research. Even though the statement by James (1890) Everyone knows what attention is. It is the taking possession by the mind, in clear and vivid form, of one out of what seems several simultaneously possible objects or trains of thought. Focalisation, concentration of consciousness are of its essence. It implies withdrawal from some things in order to deal effectively with others. seems to be intuitively straightforward from everyday experiences, the careful study of attentional processes reveals a large number of open questions. Nevertheless, the most important aspects of attentation that are still the main target of research were already formulated in this classical “definition”. Attention appears to be a conscious process that is accompagnied both by focalisation and withdrawal (or suppression) from irrelevant information. More generally, attention is thought to be a process that provides the answer to the question: How can a system of limited capacity as the brain process, analyze and react to the tremendous amount of information that is recorded by our sensory systems? This question is motivated by the fact, that the brain somehow manages to integrate the input of about 250 million sensory neurons. It is argued, that by selecting relevant from irrelevant information, by suppressing and ignoring situationspecific unimportant as well as concentrating on situation-specific important aspects of the sensory input the amount of information that needs to be processed can be reduced to an amount that is manageable by the cognitive system. This selective aspect of the attentional process is explored by cognitive researchers under the term selective attention (Pashler, 1998). Of crucial importance for the understanding of the attentional mechanisms is the understanding of what happens to the ignored parts of the perceptual input. Research in that area has focused mainly on the negative priming paradigm, which gives a good indication of the impact that ignored stimuli have on attentional processing. Even though negative priming is a promising tool to explore attentional

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1 Introduction processing also in some sub-populations that show a variation in this measure (e.g. older people, small children and schizophrenics), the factors determining negative priming are not yet satisfactorily understood. Thus, since the theoretical framework concerning the negative priming effect and the mechanisms underlying the selection process is still under discussion, it seems to be desirable to conduct more fundamental research in order to better understand this process. The present study tries to advance in that direction by putting state-of-the art theories of negative priming to a critical test. Given the situation that only very few studies have applied neurophysiological measures to the negative priming paradigm yet (Gibbons, 2006), a contribution to that line of research is also made by the simultaneous recording of EEG data in this experiment.

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2 Theory 2.1 Selective Attention As outlined above, selective attention is the process of extracting relevant information from the environment. In order to accomplish this, clearly some heuristics must be applied by the brain to decide which aspects of the information deserves further computationally expensive processing. It is directly conceivable, that these heuristics are of outstanding importance in an evolutionary sense and therefore underlie a strong evolutionary pressure. For example, an attentional system that focuses on the colour of trees rather than a fast moving tiger between them is not going to survive very long. The existence and nature of these heuristics can be explored phenomenologically by studying the behaviour of the attentional system in artificial, experimental settings. Interesting effects like change blindness, the failure to perceive (even striking) changes in a visual scene that are not behaviourally relevant (McConkie & Currie, 1996) or inattentional blindness, the apparent insensitivity of the cognitive system to unattended stimuli (Simons & Chabris, 1999) demonstrate impressively that the introspective impression of perceptual accuracy can be strongly violated under specific conditions. Following this line of argumentation, the question immediately arises, on which level of processing and thus on which characteristics of the input the selection is carried out. It is an introspective observation that we can not only concentrate on certain aspects of our surroundings, but that we can also shift the attentional focus according to characteristics that are of interest in a given situation. For example, we can scan a crowd of people for a face of a known person not by laboriously checking each and every face. Rather, the search process seems to be selectively guided by specific features from which we know that they must be present (e.g. the persons fair hair). Therefore there seems to be some sort of top-down process that guides the attentional focus by specifying physical properties of interest. On the other hand, it is also a familiar experience, that attention can be drawn towards some object that has some very striking characteristics, e.g. the flashing lights of a police car.

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2 Theory These perceptually guided aspects of attention are labeled as bottom-up processes (Anderson, 2001) and are especially popular among advertising specialists. Historically, the spotlight-of-attention model was constructed to explain these facts for the visual system (Anderson, 2001). This model postulates a one-process mechanism that can highlight or activate portions of the perceived scene (therefore the metaphor spotlight) and thus extract the relevant features. Relevant in this theory is thus equal to activated while irrelevant is simply thought to be a not-activated state. Later theoretical accounts considered the notion of an active ignoring process, rather than a non-activation of the irrelevant stimuli. Some empirical evidence for the assumption of an active process for ignoring stimuli comes from the study of the inhibition of return paradigm (Milliken & Tipper, 1998). This finding describes the effect of a prolonged reaction time when a location within the visual field is required to be attended to. The effect emerges, whenever the attended location was already attended shortly before. Because this finding seems to be of a very general nature (it has been found in a variety of tasks), there seems to be a bias against attending to the same location twice. This can also be interpreted as a bias for novelty in the sense that the processing of novel information is favoured against old information. However, the explanatory power of this phenomenon is limited to spatially organized stimuli. A more general access to the processing of distracting stimuli is provided by the negative priming (NP) paradigm. Because of its general nature, the NP effect is often considered the most direct approach to assess the selective aspect of attentional processing, i.e. the active processing of the ignored, distracting stimuli (e.g. Houghton & Tipper, 1994).

2.2 The Negative Priming Effect The positive priming (PP) effect, i.e. a facilitatory impact of presented stimuli for later processing, manifested e.g. in shorter reaction times, is an unsurprising phenomenon that is empirically relatively well researched and understood. The notion that an internal representation of the primed object gets activated and remains so for some time, thus facilitating a subsequent response, is both intuitive and largely accepted. The negative priming effect (NP) however, which describes the reversed effect of an impeded reaction because of prior exposition, is subject to strong discussions among cognitive theorists. A conventional definition of the negative priming effect

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2.2 The Negative Priming Effect can be formulated as a prolonged reaction time in tasks where target stimuli have to be selected in the presence of one or more distractor stimuli. This behavioural cost in the response-latency occurs when the target-stimulus had to be ignored on a previous trial as compared to control trials where no stimulus is repeated. This effect is very interesting for the study of selective attention, because it contains information about the processing of the ignored parts of a display, rather than the attended parts. This is due to the fact, that the negative priming effect stems exclusively from the processing of irrelevant stimuli, i.e. the distractors. With respect to recent results however (Rothermund, Wentura & Houwer, 2005), it is maybe more appropriate to extend the conventional definition. Rothermund et al. found that a slowdown or an acceleration in reaction time is not only determined by the choice of the priming condition but also by response-repetition effects. In that broader view of NP, one would term each constellation in which an increase of the reaction time is observed as negative priming. Accordingly, every condition which produces a facilitatory effect on the probe response could be labeled as positive priming. The NP effect was discovered first in a Stroop-like colour-word ink-naming task (Dalrymple-Alford & Budayr, 1966), where delays in reaction times were discovered in trials where the colour-word in the prime display was identical to the colour of the ink in the probe trial. In this setting, the semantic meaning of the word served as the distractor, because it had to be actively suppressed in order to be able to correctly name the colour of the ink. These results were replicated in similar settings (Neill, 1977) but have also been found in a wide variety of other tasks and stimulus modalities (Mayr & Buchner, 2007). NP is therefore thought to be a quite robust if sensitive phenomenon (for reviews, see Fox, 1995; May et al., 1995). For example, negative priming has been elicited using different stimuli, such as line drawings (Tipper, 1985), letters (Neill, Valdes, Terry & Gorfein, 1992), words, auditory stimuli (Mayr & Buchner, 2006) and nonsense shapes. Experimental tasks which found negative priming effects comprise same-different matching, naming (Tipper & Cranston, 1985), spatial localization (Kabisch, 2003) and many others. Thus, negative priming seems to be a general behavioural phenomenon rather than an isolated, artificial effect. In spite of this robustness regarding the choice of experimental procedures, the NP-effect is repeatedly reported to be also a very sensitive phenomenon in the sense that it depends on a variety of other parameters. For instance, it has been shown that some characteristics of NP depend on the response-to-stimulus interval (RSI)

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2 Theory between prime and probe (Kabisch, 2003). It has even been shown, that a NP constellation can for very short RSI paradoxically even produce a facilitatory effect (Lowe, 1985). To complicate the matter, the previous RSI (that is, the interval between previous probe and current prime) interacts with this effect (Neill & Valdes, 1992). Another factor discussed in the literature is the presence or absence of a distractor in the probe display (Moore, 1994). Again, facilitatory effects in the absence of distractors in the probe displays can be found. Furthermore, the saliency of the distractors influences the amount of NP observed, where a more salient distractor typically increases the magnitude of the NP effect (Grison & Strayer, 2001), an effect that is known under the label of reactive inhibition in the literature. To draw a complete picture of NP, also various other experimental parameters have to be considered. One important distinction concerns the type of paradigm that is applied to study NP. The huge majority of studies on NP have implemented a location or an identity priming task. Even though the effects from both paradigms seem to be equivalent on a global level, compelling evidence for the distinctiveness of the underlying mechanisms for both paradigms has been provided (Fox, 1995). Also the type of priming studied appears to influence the observed effect. For instance, even though NP can clearly be found in semantic priming tasks (i.e. tasks in which the prime and the probe are only semantically related, not identical), the effect seems to be more fragile in this case. Also more incidental aspects of the experimental setting are known to influence negative priming. One such subtle parameters influencing NP, is the instruction whether the participants should focus more on speed or on accuracy when performing the experimental task. The general finding is that NP is reduced (or even reversed to a response facilitation) when the emphasis of the instruction is put on speed rather than accuracy (Neumann & Deschepper, 1992, exp. 2). The number of distractors presented in the prime display is another factor that apparently has a strong impact on the strength of the observed NP effect. Data from studies investigating this issue is quite contradictory. Some studies report a positive relation between the number of prime distractors and NP effect, some report a negative relation (Fox, 1995). The covert intention of the previous discussion was to point out the rich diversity of the phenomenon negative priming. Given the empirical heterogeneity and the sometimes even contradictory results of the discussed studies, it may not be a surprising fact that the theoretical accounts for this phenomenon diverge even on basic assumptions.

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2.3 Negative Priming Theories

2.3 Negative Priming Theories Because of the controversial nature of the NP effect, a variety of different theoretical accounts have been developed since its discovery. The most influential theories comprise the distractor inhibition account (Houghton & Tipper, 1994), the episodic retrieval account (Neill & Valdes, 1992) and the response retrieval theory Rothermund et al. (2005). In addition, a recent model by Kabisch (2003) will be discussed. The intriguing complexity of the NP phenomenon becomes apparent, when one considers that none of the proposed theoretical accounts can explain all effects obtained in empirical research. Each of the theories has its strong points as well as its shortcomings and it seems safe to conclude that the last word about the foundations of the NP effect is not yet spoken.

2.3.1 The Distractor Inhibition Theory The idea of distractor inhibition has initially been proposed by Neill (1977) and been expanded and elaborated by Tipper (1985). The basic idea of this theoretical account is that irrelevant stimuli representations are actively suppressed to support the selection of the relevant target stimuli. This inhibition is assumed to persist for some time. Because of the persisting timecourse of the inhibitory process for the distractor stimuli, a delay is caused when the inhibited representation is activated in the probe trial. This results directly in the negative priming effect. Following this line of reasoning, there are two complementary processes involved in the attentional selection process: one that activates and amplifies the representation of the target stimulus and another one that suppresses and inhibits all irrelevant information. The slowdown of the reaction in the probe trial can therefore be seen as a direct indicator for the state of activation of the internal representations in the prime display. Because the previously inhibited distractor becomes relevant in the current display, the activation of this object’s representation is delayed because of the persisting inhibition. The selection in this model is assumed to operate on a semantical or postcategorial level (Houghton & Tipper, 1994), thus taking into accounts findings that report NP in semantic priming tasks (e.g. Tipper & Driver, 1988). The theory as presented so far has several vulnerabilities. First, if the representation of an object is indeed inhibited, this inhibition should be strongest immediately after the selection. Although there is a general trend of NP to decay with increasing time between prime and probe (Neill & Valdes, 1992), no NP was observed in several studies when the RSI’s between prime and probe were very short (e.g. 20

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2 Theory ms Lowe, 1985). This suggests that if inhibitory processes play a role in producing the NP effect, they must develop after the selection took place, a conclusion that is not easily accounted for in terms of the inhibition model. A second critic concerns the incompatibility of the theory with empirical results which suggest that priming constellations normally producing NP effects can actually cause facilitatory priming when no probe distractor is present (and hence no selection is necessary in the probe) as reported by Moore (1994). An extension of the basic distractor inhibition account that can handle this finding concerns the notion of what is actually inhibited. While early accounts (Neill, 1977) suggested that the semantic representations of the distractors themselves are inhibited, Tipper & Cranston (1985) rather proposed an inhibition of the link between the (activated) representation of the distractor and the response system. More explicitly, Tipper & Cranston (1985) assumed a selection state of the response system in which the time-consuming resolving of the inhibition of the link between representation and response is responsible for the NP effect. In situations where no such selection is necessary, the response may still be facilitated because of the activation of the distractor representation. The inhibition model is also challenged by the empirical finding of long-term negative priming effects (Grison et al., 2005). In some studies, negative priming effects over periods as long as one month between prime and probe have been reported (DeSchepper & Treisman, 1996). Clearly some sort of memory must be involved to produce such long-lasting effects. Tipper (2001) tries to integrate these findings by emphasizing the fact that different mechanisms might underlie the behaviourally visible effect in different settings (dual-mechanism hypothesis). It is also stated that a retrieval of an episode (as postulated by the episodic retrieval theory) might retrieve also the inhibitory status of the previously ignored distractor. A strong point of the inhibition based models comes from the study of varied distractor saliency. It has been found that the negative priming effect increases with growing saliency of the distractor, an effect known as reactive inhibition (Grison & Strayer, 2001; Lavie & Fox, 2000; Tipper et al., 2002). This effect can be very well explained in terms of the inhibition model, since a stronger distractor would require more inhibition that would cause a stronger inhibitory rebound, thus leading to a prolonged reaction time. Also the explanation of effects of depth of processing (Craik & Lockhart, 1972; Craik, 2002) on the amount of negative priming observed can be easily explained by the inhibition model. Levels of Processing (LOP) describes the idea that there

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2.3 Negative Priming Theories are qualitatively different stages on which stimuli can be processed. As an example, shallow processing of a drawing would involve the identification and encoding of physical features (e.g. colour) while deep or conceptual processing would in addition imply the encoding of semantic information. In a study by Yee et al. (2000), the depth of processing was varied independently both for target selection (e.g. selection of an animal-word by colour or by size of the animal) and response selection (e.g. respond with letter case of the target word or the dangerousness of the animal). Interestingly, the amount of NP observed in their experiments depended only on the depth of processing on the target selection stage (with processing on a conceptual level producing more NP) but not on the response selection level. This finding can be naturally interpreted in terms of the inhibition model. An assumption of the model is that the amount of inhibition produced depends on the initial activation of the distractor. This activation is of course higher, when the distractor must be processed on a conceptual level than when it can be rejected by considering physical characteristics only, resulting in the enhanced NP effect for deeper processed stimuli. Another strength of the model is that a computational implementation of the theory has been given by its authors (Houghton & Tipper, 1994). This allows for a quantitative prediction of the effects that are expected given the validity of the model. It has also been tried to link data from neurophysiological studies to the mechanisms postulated in the theory (Houghton & Tipper, 1996). Tipper (2001) argued that prefrontal areas of the cortex should be selectively activated during NP trials. These areas are known to be involved in inhibitory processing. In a recent fMRI study, Wright, Keuthen, Savage, Martis, Williams, Wedig, McMullin & Rauch (2006) indeed found a selective activation of prefrontal areas in the NP condition. In contrast, the positive priming condition was accompanied by prefrontal and more distributed deactivations. These findings yield support for the inhibition theory, since the distinctiveness of the underlying processes between positive and negative priming assumed there, is apparently reflected in this discriminative pattern of results.

2.3.2 The Episodic Retrieval Theory The basic assumption underlying the episodic retrieval account initially proposed by Neill and colleagues (e.g. Neill & Valdes, 1992) is that the NP effect is a memory phenomenon rather than due to persisting inhibition of an abstract representation of the distractor. The theory builds on Logan’s (1988) instance theory of automatization, which states two possible processes underlying the performance in specific

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2 Theory trials. Either the task is explicitly processed which is assumed to be of a slow, effortful, algorithmic nature or useful information stored in memory can be applied to solve the task more efficiently. The decision which kind of processing is used is not a conscious one but depends on bottom-up and top-down parameters. E.g. with increasing experience with the task (i.e. increasing number of trials in NP experiments), the subjects become more likely to use the memory-retrieval approach because the probability of retrieving the information depends on the amount of similar episodes in memory. Only when the retrieved information proves to be wrong, the explicit-processing strategy is used. Transferring that idea to the NP setting, the similarity between prime and probe triggers a fast retrieval of the prime episode. During processing of the prime however, a do-not-respond-tag was assigned to the object representation of the distractor as part of the selection task. This distractor-related information is retrieved as an integral part of the prime episode during probe processing. The retrieved information thus conflicts with the task of responding to the target object in the probe trial, resulting in the observed delay in the reaction times caused by the resolving of this conflict. According to Neill (1997), the main determinants of the strength of the episodic retrieval are (a) the recency of the memory trace and (b) the strength of the memory representation of the trial. It is assumed that more recent memory traces are more likely to be retrieved than older ones. This assumption in the framework of episodic retrieval is necessary, because it allows its application to experimental settings which feature many repetitions of highly similar episodes. Without this assumption, all similar episodes would be recalled with equal probability, resulting in response conflicts in all trial types. The assumption that recency is a determining factor receives empirical support from studies that could show a negative correlation between RSI and NP-effect (Neill & Valdes, 1992). A related argument is that temporal discriminability of related episodes can influence the probability of retrieval. Neill et al. (1992) report an influence of the interval preceding prime onset on the negative priming effect. This effect challenges the inhibition-based accounts, but is easily accounted for in terms of episodic retrieval. When the pre-prime RSI is larger than the actual probe-RSI, the prime-probe pair is more easily separable from the train of stimuli. Hence the retrieval of the prime episode in the probe task is enhanced. A complementary argument applies for the reversed setting. The finding of facilitated response at very short RSI discussed above (Lowe, 1985) however, is difficult to explain also in terms

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2.3 Negative Priming Theories of the episodic retrieval framework. The strength of a memory trace can be influenced by several factors. For example the depth of processing discussed above may influence the probability with which an episode is retrieved, where the probability increases with deeper levels of processing. From the perspective of episodic retrieval approaches, the memory trace produced by the prime episode would become more elaborated with deeper processing of the stimuli, thus increasing its probability of retrieval. As discussed above, the data by Yee et al. (2000) is consistent with this interpretation. A peculiarly strong empirical indication for the involvement of memory processes in negative priming are studies that show that the effect increases quantitatively when the contextual similarity between prime and probe situation is increased (Stolz & Neely, 2001). In their study, Stolz & Neely (2001) found an increased NP effect both when the prime/probe episodes were more similar in terms of visual characteristics (dim vs. bright stimuli) and when the required response was identical (contributing to the increasing similarity of the episodes). A weaker point of the theory however is the explanation of semantic negative priming effects. It is difficult to conceive that the semantical similarity of the displays should trigger the retrieval of the do-not-respond information tagged on the original distractor item. The inhibition-based theories provide a more natural framework for interpreting these effects since spreading inhibition through semantical networks sensu Quillian (1966).

2.3.3 The Response Retrieval Theory Recently, a new variant of the episodic retrieval theory, the response retrieval account of negative priming, has been proposed by Rothermund et al. (2005). The main assumption of this account is that the NP effect is due to the retrieval of incidental stimulus-response associations rather than to the retrieval of the complete episode. In contrast to the classical episodic retrieval theories which assume an association between distractor and do-not-respond information, the response actually carried out by the subject in the prime trial is associated with the memory trace of the prime display, according to this theory. This association is proposed to be incidental in the sense that the response is associated with all aspects of the prime display. The NP effect is thus produced simply by a conflict between the currently required and the automatically retrieved response from the preceding trial. Similarly to the classical episodic retrieval account, the retrieval of the prime response only takes place in settings which encourage the retrieval of this episode (see section 2.3.2 for details).

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2 Theory A special merit of this theoretical consideration is the disentangling of prime condition and required response. As pointed out by Rothermund et al. (2005), in most classical NP experiments, the repetition or non-repetition of a required response is confounded with the priming condition. In positive priming conditions, where the target is repeated, the response is also repeated in many settings, e.g. in object-naming tasks, while on the other side in negative priming conditions the response is always changed. Rothermund et al. (2005) therefore implemented a variant of the letter-matching task initially developed by Neill et al. (1990). In this task, 5-letter strings are presented to the subjects and they have to judge whether the second and the third letter are identical (yes or no answer). The remaining three letters are always the same and function as distractors in this setting. The advantage of this paradigm is that the repetition or non-repetition of the response can be varied independently from the priming condition: it is both possible to require a yes or a no answer in NP trials because the answer does not depend on the identity of the target, but on its relation to the second target. This paradigm encounters another difficulty however, especially in the view of episodic retrieval theories and the determinants of the probability of retrieval of the prime episode. The similarity of the prime and probe display in the described paradigm is of course also dependent on whether both targets match or not. In fact the highest similarity is achieved in a yes-repetition-priming case, because in the no-case the left or the right target might be repeated, respectively. This could again bias the reaction times in favour of the yes answers. However, two other experiments described in Rothermund et al. (2005) implement task-switching paradigms, where the described problem does not occur. Because the response-retrieval account is essentially an extension or specification of the episodic-retrieval theory of negative priming, it encounters the same difficulties as discussed for these accounts.

2.3.4 The Imago-Semantic Action Model Finally, a last theoretical account of NP that did not yet receive much attention, the imago-semantic action model (ISAM; Kabisch, 2003) will be presented. One reason for discussing this model is that it provides intrinsic properties that highlight interesting aspects of the selection process. Secondly, especially the findings concerning the RSI dependency of the NP effect can be explained by that model in a particularly elegant way. Thirdly, the model provides a basis for quantitative predictions not only for negative priming but also for a large number of other priming

20

2.3 Negative Priming Theories constellations, as it has been computationally implemented by Schrobsdorff, Ihrke, Kabisch, Behrendt, Hasselhorn & Herrmann (2007). The model can also be seen as a first step towards a general computational model that tries to integrate, evaluate and analyze current theoretical assumptions about the nature of the negative priming phenomenon (Schrobsdorff, Ihrke, Behrendt, Hasselhorn & Herrmann, 2007, in preparation). The ISAM differs from other models, by postulating only one central, effective mechanism for the emergence of both positive and negative priming effects. This mechanism comprises the adaptation of a threshold to the global mean activity level that is acting in the same way for different stimuli. The original model presented in Kabisch (2003) was intended to provide a framework for the explanation of decisionmaking in general. However, the application of this more general model to the NP case is relatively straightforward (Schrobsdorff et al., 2007). In the model, the process of low-level perception and pattern recognition is abstracted, i.e. the presented objects are assumed to undergo a pre-attentive processing and perception stage, resulting in an abstract cognitive representation of the objects. On this abstract level, it is possible to control perceptual accuracy and speed by means of an acuteness variable which is based on the situational context (i.e. in cases that require fast action, the acuteness variable will speed up the decision-making). Initially the stimuli are assumed to be processed automatically according to a relevance rating based on low-level features such as motion or color. The stimuli are sorted hierarchically by their (automatically assigned) relevancy. Attention is modeled as a truncation of the perceived stimulus set by a threshold which is assumed to be controlled by activity in the anterior cingulate cortex (Winterer, Adams, Jones & Knutson, 2002). In concrete modeling terms, the threshold increases with growing overall activity, i.e. if more than one option for action exists, the threshold further increases, thus limiting the number of remaining options above threshold. Therefore the stimulus relevancy does not need to be fixed beforehand. After the initial, largely automatically controlled rating of relevancy, the individual has the possibility to consciously assign and modify the relevancy values to the respective objects. According to the dual-code hypothesis (Krause, B., Gibbons & Kriese, 1997), this happens in a semantic space where stimuli are processed jointly with the implied actions. The relevance of certain stimuli can be changed in a posterior rating of relevance in this semantic space, which takes into account all the available information and affects the adaptation process of the threshold by a feedback mechanism (the semantic feedback

21

2 Theory loop). These backward acting processes control the way perceptual input is classified, thus possibly achieving a reordering of the original hierarchy. The activation corresponding to a target is typically amplified (due to its nature of being a ”target”) such that even if the primary features imply the contrary, the target-related activation eventually becomes significantly stronger than that of any distractor, thus triggering the correct action. In this general framework, NP is generated by a forced decay of the representation of the former distractor object, which mirrors an internal reorganization process. Because the same representation of the object must be activated albeit in a different function, the previous activation must first decay. It is this decay of the reorganized neural compounds that effectively leads to a lower global level of activation, thus resulting in a slower adaptation of the threshold which finally yields a slower reaction. (a) reactive inhibition effect

(b) RSI depency of priming effects

900 850

0

NP CO

750

priming effect [ms]

absolute reaction time [ms]

NP2 800

PP PP2

700 650 600

−50 NP2 NP CO PP PP2

−100

−150

550 500 450

1

1.05

1.1

1.15

relative distractor saliency

1.2

1.25

500

1000

1500 RSI [ms]

2000

Figure 2.1: Quantitative predictions of the ISAM for (a) distractor saliency and (b) the RSI. The model makes reasonable predictions about (a) the NP effect growing with the distractor saliency (NP and control curve diverge) and about (b) the paradoxical RSI effects discussed above. Because the ISA-model has been implemented computationally, deriving predictions about unusual settings or different experimental paradigms can simply be carried out by plugging these parameters in the program and observing the results. As can be seen in figure 2.1, the model makes predictions regarding the distractor saliency and the behaviour of the NP effect with different RSI that are supported by the empirical results (see section 2.2 for details). This illustrates the usefulness of concrete mathematical modelling of a theory. To build a computational implementation, a much more precise formulation of the theoretical assumptions and the supposed acting mechanisms is required compared to an intuitive formulation of the theory. This is of even greater importance, as

22

2.4 Negative Priming and Electrophysiological Data some of the explanatory models for negative priming presented above, rely on a rather vague and abstract notion of how and on what level attentional processes operate. This abstract formulation reduces the falsifiability of the theory and may even open the way for arbitrary deductions and thus different predictions from the same theory.

2.3.5 Conclusion It should have become clear from the previous discussion that there are certain experimental findings favouring each of the reviewed models and conflicting with the others. This is surely a rather discontenting state of research as none of the perspectives currently under discussion can fully account for all of the empirical results. In their dual-mechanism account of negative priming, May et al. (1995) propose that inhibition as well as memory retrieval can be the source of negative priming, but that the experimental context specifies which of the two mechanisms is expected to operate. Another attempt to integrate the conflicting views comes from Tipper (2001). He assumes that a complete explanation of as complex a phenomenon as negative priming must include forward-acting (encoding) and backward-acting (retrieval) processes at the same time. These integrative concepts seem to provide the better framework to explain the various facets of the negative priming effect, because they raise the level of complexity and the number of possibly contributing parameters considered in the theory. However, a more explicit formulation than the one given by the mentioned accounts would be desirable in many cases. For example, the determination whether encoding or retrieval processes play the main role (or to what extend they contribute to the effect) is widely left to the interpretation of the individual researcher and thus liable to arbitrariness. From this point of view, a comprehensive model that allows for exact, ideally quantitative predictions for a wide variety of paradigms, tasks and other experimental parameters would be desirable.

2.4 Negative Priming and Electrophysiological Data Electrophysiological data, and especially the study of event-related potentials (ERP), provides a promising framework for investigating and evaluating the validity of the discussed theoretical accounts. In the analysis of event-related potentials, the continuous stream of data resulting from an electroencephalographic measurement is cut into segments based on time markers that usually indicate trial onset. After

23

2 Theory pointwise averaging of the resulting segments, this procedure results in event-related potentials, short segments of electrophysiological data based on the average of several similar fragments of the original data. The definition of similar in this context is left to the researcher, usually data from the same experimental condition is used for the averaging procedure. The rationale behind this approach is that a reliable component that is locked to the event is present in the data. This component is then thought to be evoked by the event, hence the ERPs are alternatively termed evoked potentials (EP). This stimulus-locked analysis of the electrophysiological data is extremely useful for attention research, as it reflects the timecourse of the neurological processes underlying the perceptual and cognitive processing following stimulus onset. Surprisingly, even though the study of ERP-correlates has been extensively used in priming studies in general, ERP studies on the negative priming effect are relatively sparse (Gibbons, 2006). Only in some very recent studies, direct ERP correlates of NP were reported (Buchner & Naumann, 2006). A potential candidate for a NP-sensitive ERP component is the P300, a positive wave in the stimulus-locked ERP that has its peak around 300 ms. The P300 occurs reliably, when an improbable target stimulus is detected in a train of standard stimuli (Picton et al., 1995) and is generally considered to reflect the completion of stimulus evaluation (Gibbons, 2006). In a study implementing an identity negative priming task, Behrendt, Gibbons, Schrobsdorff, Ihrke, Herrmann & Hasselhorn (2007) found a reduced P300 amplitude in both positive and negative priming conditions compared to the control trials. This amplitude reduction was restricted to the left-hemispheric electrodes. This finding was in accordance with the results from two successive studies by Gibbons et al. (Gibbons, 2006; Gibbons, Rammsayer & Stahl, 2006) and also with the results from Stahl & Gibbons (2007). Behrendt et al. (2007) argued that the reduction in the P300 amplitude reflects a reduced effort required for stimulus evaluation in all priming conditions. This interpretation would challenge classical inhibition-based accounts of NP, since these predict an increase in the required effort for NP because of the persisting inhibition. Another finding from Behrendt et al.’s (2007) study was a prime-condition sensitive modulation of the amplitude of the positive slow wave (PSW). Investigating negative priming, positive priming and control trials, they found that the PSW amplitudes differed significantly between the conditions and that it was largest for NP and smallest for PP. Since the PSW amplitude is generally associated with coordination complexity (Gerloff, 2005), these findings challenge both the inhibition

24

2.4 Negative Priming and Electrophysiological Data and the episodic retrieval accounts. Arguing from an inhibition perspective, no amplitude differences in this late component are expected, since the impact of the persisting inhibition should manifest itself earlier during stimulus selection. Also the episodic retrieval theory cannot clearly account for the differences in amplitude between NP and control because the mismatch-detection postulated by this theory should manifest itself earlier. Another potentially revealing measure for the evaluation of the cognitive processes underlying the selection process, the lateralized readiness potential (LRP) has been introduced to negative priming research by Gibbons et al. (2006). The LRP can be computed in experimental settings that elicit the Bereitschaftspotential (BP), which manifests itself as a negative potential deflection in the electrodes attached above the motor cortex (especially electrode position C3 and C4, respectively). The LRP is then computed as the difference wave between contra- and ipsilateral electrodes relative to the correct response hand:

LRPC3,C4 (t) =





X 1  1 X C4 1 C4  (sj (t) − sC3 (sC3 j (t)) + j (t) − sj (t)) 2 #Ileft j∈I #Iright j∈I left

right

(2.1) where Ileft , Iright ⊆ {1, . . . , N } are the indices of the the trials where the response had to be given with the left or the right hand, respectively, scj (t) denotes the recorded signal at electrode c and time t in trial j and #I is the cardinality of I (the number of elements for discrete sets). The BP is generally reported in settings where the participants have to execute voluntary movements with the hands (e.g. pressing a button) shortly before the reaction takes place. Since the negative BP amplitude is reported to be larger on the contralateral recording sites, the shape of the LRP allows to derive conclusions about the preparation of voluntary movements. More specifically, a negative deflection in the LRP indicates a preparation of the correct hand where the largest amplitude of the LRP indicates the moment of the actual response, and a positive deflection monitors false preactivations or inhibition of the correct response. This characteristic of the LRP is of great use in the study of negative priming. One feature to look for in this context is the latency of the onset of the correct response in the LRP. This latency indicates the time taken to complete stimulus selection and evaluation, because the response hand is chosen at that point. A priming-sensitive modulation of this latency could therefore illuminate the question, whether the increase in reaction time is caused before or during the motor processing stage, a

25

2 Theory problem that is important for the validity of some of the theoretical accounts. The inhibition theory for instance would predict that the time before the onset of the motor command should be prolonged, because the postulated inhibition applies to the selection process, not to the response. The same expectation can be formulated in the other theories, except the response-retrieval account which postulates a conflict between responses and thus possibly a prolonged time after LRP onset. It is not clear however, whether a response conflict could really be captured by LRP-latency modulations, since it could also take place on a premotor stage of the processing. Another interesting feature of the LRP are potential wrong preactivations, i.e. positive deflections from zero. A waveform where the correct response is preceded by positive peaks in the LRP would thus favor the interpretation of a conflict in response initiation as it is postulated in the response-retrieval account (Rothermund et al., 2005). The finding of such early positive components would thus be supporting the response-retrieval theory. In an identification priming study, Gibbons (2006) found such positive deflections in the LRP exclusively in the negative priming condition, albeit in a very early time-window (50 − 150 ms). The authors consequently interpret their findings in support of the response retrieval theory. However, their results actually show a more complex pattern, as the LRP in the DTTD (augmented negative priming) condition did not show such preactivations even though a distractor to target shift was realized in that condition. In contrast Gibbons (2006) found an earlier onset of the LRP negativity only in the DTTD trials, a finding that may be attributed to the specific experimental setting used in this study. Also the results of another LRP study Gibbons & Stahl (2007a) lends support to the response retrieval theory. The results of that study indicated that the LRP onset was earlier in NP trials that required the same response as in the probe, while the LRP onset was delayed for all response-shift conditions. This pattern of results is only compatible with the response retrieval account of NP. Drawing a preliminary conclusion, the evidence from the LRP-based research is thus mostly in support of the response-retrieval theory.

2.5 Conclusions and Open Questions In the previous discussion, it was highlighted that there are a number of open questions surrounding the phenomenon or maybe even the class of phenomena summarized under the label of negative priming. The study reported here tries to shed light

26

2.5 Conclusions and Open Questions on some of the problems concerned with the interaction of NP and response relation, i.e. whether the response is repeated or not. As pointed out earlier, Rothermund et al. (2005) constructed a theoretical framework in which NP is exclusively determined by response repetition effects because of an incidental association that is build up between response and the objects presented in the prime episode. Following this line of reasoning, the underlying assumptions of this theory will be put to a critical test. In previous work, Menge (2007) already challenged the response-retrieval theory in a perceptual (experiment 1) and a conceptual matching (experiment 2) task. Surprisingly, the interaction between priming and response relation predicted by the response-retrieval theory was only observable in a conceptual matching task. This is in stark contrast to the findings from experiment 4 of Rothermund et al.’s (2005) study, who found this effect in a task that required only perceptual matching between two letters. Menge (2007) argued that the failure to find the postulated effects in his study was due to the fact that activation of a semantical representation of the target object might be crucial for the occurrence of the predicted interaction. This semantical representation can be thought to be activated much faster for letter stimuli than for the rather abstract pictograms of objects used by Menge (2007) since the recognition of letters is a highly automated process (Anderson, 2001). Continuing the work of Menge (2007), the study described here further elaborates on the question whether response-retrieval is the key component in producing NP by applying a conceptual matching task similar to the one described by (Menge, 2007, exp. 2). At first, predictions for priming conditions that were not realized by Rothermund et al. (2005) will be derived from the response-retrieval theory and tested. In a next step, it will be investigated whether the response-repetition effect observed by Rothermund et al. (2005) could not be due to an effect stemming from the confirmation vs. negating dimension hidden in the experimental setting. Furthermore, the assumption that NP is a response-related phenomenon will be tested by analysing the timecourse of the NP effect. Finally, event-related electrophysiological data will be analysed to obtain further information about the location and timecourse of the processes during task execution. Therefore, the current study will implement a paradigm similar to that used in (Menge, 2007, exp. 2), i.e. a conceptual matching task, where a dichotomous (yes or no) response is given depending on whether target object and comparison word match or not.

27

2 Theory

2.5.1 Response-Repetition Effect in Alternative Priming Conditions? In most of the experiments from their pivotal study, Rothermund et al. (2005) implemented only NP and control conditions. In experiment 4, the authors found support for predictions for a distractor repetition condition (DD) which, viewed from the response-retrieval account, is somewhat similar to a positive priming condition. However, the response retrieval theory can be used to derive predictions for a lot of other priming conditions in a very straightforward manner. Because the incidental association of the response performed in the prime trial and any aspect of the prime display is thought to be responsible for the priming effects in interaction with the response relation, it can be argued that whenever a response is repeated in the probe trial and any feature from the prime trial is repeated, a facilitatory effect (i.e. positive priming) on probe response should occur. Accordingly, whenever the response required in the probe is not equal to the prime response, a negative priming effect should occur whenever the prime and the probe share common features (see table 2.1). Table 2.1: Predictions derived from Rothermund et al.’s (2005) response-retrieval theory. A negative priming effect is predicted for all conditions which imply the repetition of any aspect of the prime trial when the response is repeated, a positive priming effect when the required response is different.

TT (PP) DT (NP) DD TD DTTD DDTT a

Priming Effectsa same response different response − + − + − + − + −− ++ −− ++

A ’+’ indicates a NP effect (i.e. a longer reaction time than in control) a ’−’ indicates a PP effect. ’++’ and ’−−’ indicate a stronger effect.

Menge (2007) derived and tested predictions for two priming constellations in addition to NP, positive priming and augmented negative priming that includes both a distractor-to-target and a target-to-distractor shift from prime to probe (DTTD).

28

2.5 Conclusions and Open Questions It could be predicted from the response retrieval theory that the effects should be larger when more features are shared between prime and probe display. In the DTTD case, the number of shared features is of course higher than in the standard DT (negative priming) condition, where only one object is repeated. Also, the magnitude of the observed effects in distractor and target repetition trials should be stronger compared to the negative priming condition, because in these cases the identity as well as the colour of the objects is repeated, thereby achieving a greater similarity between prime and probe. Challenging these assumptions, Menge (2007) did not find that pattern of results. Rather the DTTD condition seemed to diverge from the expectations, showing a smaller effect than the DT condition. The question remains, whether the predictions made by the response retrieval theory hold true for other priming conditions as well. Of special interest seems to be the TD case, where a target in the prime trial is repeated as the distractor in the probe trial. The predictions derived from the response-retrieval theory are distinct to those from the other theoretical accounts. The inhibition account of NP would predict a slowdown in reaction time, as remaining activation of the prime target has to be reduced before the probe distractor can actually be inhibited. In contrast, episodic retrieval based accounts would not expect any modulation in reaction time, since no do-not-respond information is present in the memory traces. The computational implementation of the ISAM allows for a quantitative prediction of the results. Unfortunately, the implementation given in Schrobsdorff et al. (2007) does not comprise the possibility to vary response relation, therefore only some of the conceivable conditions can be predicted (see table 2.2). Regarding the TD condition, the ISAM predicts a negative priming effect for the different response condition. Following the response-retrieval theory however, the same pattern as expected for the other priming conditions is predicted: a positive priming effect in the case where the response is repeated and a negative priming effect when the response is different in the probe trial. Since Menge (2007) did not directly investigate the distractor repetition condition (DD) implemented in the original study by Rothermund et al. (2005), it is also of interest whether this condition produces the results as predicted by the response retrieval theory in the conceptual matching task. To answer these questions, the study reported here will implement distractor repetition (DD) and target-todistractor (TD) conditions in addition to the classical negative (DT) and positive priming (TT) constellations.

29

2 Theory

Table 2.2: Predictions derived from Schrobsdorff et al.’s (2007) computational implementation of the ISAM.

TT (PP) DT (NP) DD TD a

Priming Effectsa same response different response −30.98 n.a. n.a. 24.80 n.a. −49.44 n.a. 17.74

Priming effects are computed as the difference of the reaction time in the control condition and the prime condition, respectively.

2.5.2 Response Repetition or Yes-No Effect? It is a well-known fact that responses that require a confirmative “Yes”-answer can be given faster than negative “No”-answers. Singer (1984) explained this fact in terms of an additive processing model, which assumes that additional cognitive effort is required to generate a negative answer compared to confirmative answers. A recent fMRI study (Alia-Klein, Goldstein, Tomasi, Zhang, Fagin-Jones, Telang, Wang, Fowler & Volkow, 2007) found neural evidence for the distinctiveness of “Yes” and “No” responses. The authors found that the lateral orbitofrontal cortex (OFC) shows differential patterns of activity, depending on whether yes or no-answers had to be given. While yes evoked a positive signal, the no-answers induced a negative signal in the right-hemispheric OFC. Taking these findings into account, one could hypothesize that the findings attributed to the response-repetition setting discussed above are really due to a confirm vs. negate effect. However, this argument does not hold without an additional assumption, since the yes and no responses are equally distributed over the sameresponse vs. different response trials (same response comprises “Yes-Yes” and “NoNo”, different response includes “Yes-No” and “No-Yes” trials). In order to achieve a selective impact on the response repetition effect, the presumed Yes-No effect would have to be particularly strong in one of the conditions. From a theoretical point of view, the “Yes-No” condition could be a candidate for this effect. The data from experiment 3 of Singer (1984) suggests that participants enter into a confirm/negate decision internally defaulting to yes. In a setting were the response switches from yes to no, the response-delay due to the no response could be particularly strong

30

2.5 Conclusions and Open Questions compared to the no-no condition, because of the preceding activation of the default answer yes. same response

different response

}| { z Yes-Yes No-No

}| { z No-Yes Yes-No

Yes-Yes | {z No-Yes}

No-No | {z Yes-No}

yes response

no response

Figure 2.2: Clustering of the response-relation variable. To investigate whether this assumption has an empirical basis, it is suggested to cluster the variable response relation not only according to whether the response was repeated or not, but also according to which response was required in the probe (see figure 2.2). An interaction effect between probe response and priming condition similar to the one predicted by response-retrieval theory for responserepetition would indicate that the yes-no dimension has an impact on the interaction effect reported by Rothermund et al. (2005).

2.5.3 Selection or Response Effect? Most of the discussed theories implicitly or explicitly assume that the prolonged reaction time observed in NP trials stems from a conflict of stimulus representations, either by direct interactions of the activated or inhibited representations or by interactions of the retrieved memory trace and the current representation. The response retrieval theory however assumes that the delay in reaction time is due to a conflict between prime and probe response. This fundamental assumption of the theory can be tested in an experimental setting that records both, the reaction time until the selection is finished and the reaction time until the response is given. Such an experimental setup must ensure that no information about the response that has to be given by the participant later is available after finishing the selection task. One possible solution for that would be the presentation of the word with which the target has to be compared, delayed by a fixed time lag that ensures that the comparison object is presented after the target has been selected. However, this simple manipulation would not allow for a direct comparison between target selection and response selection time, but would only permit to investigate whether NP is present in the response-selection part of the trial. It would be desirable to simultaneously acquire information about the time taken

31

2 Theory eyemovement motor command 111111 000000 00 11 0000000000 1111111111 0000000000 1111111111 0000000000 1111111111 00 11 000000 111111 0000000000 0000000000 1111111111 00response selection1111111111 11 000000 111111

1111111111111111 0000000000000000 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111 0000000000000000 1111111111111111

target selection

trial onset

glance onset

response

time

Figure 2.3: Hypothetical timecourse of processing during one trial. First the target and distractor objects are identified (selected), followed by an eyemovement to focus the comparison word. Then the response is selected and executed. until the target-selection process finished and until the response selection finished without interfering with the natural course of processing of the participant. In order to achieve such a result, a natural marker of the completion of the target selection has to be measured. In the study described here, the movement of the eyes from the target/distractor compound in the direction of the comparison word is used. The overlapping alignment of target and distractor, which has already been described in Tipper (1985), allows for a foveal processing of target and distractor at the same time. To elicit an eyemovement that is strong and reliable enough to be measured validly, the distance between target/distractor compound and the comparison word is varied. The underlying assumption of serial and additive processing in a trial is visualized in figure 2.3. If this assumption is correct, then it can be argued that the reaction time in each trial is composed of several parts: the time taken by the target selection Rts , the response selection part Rrs and a constant time for the eyemovement and the motor command ζ Rj = Rjts + Rjrs + ζ.

(2.2)

The mean of the part of the reaction time until the eyemovement occurs, gives an estimation for Rts , while the time taken for response selection cannot be separated from the remaining part of the time. Since ζ can be assumed to be constant, or at least not varied systematically with priming or response condition, an effect in the latter part of the reaction time can still be interpreted as stemming from the response selection mechanism. Arguing for the response-retrieval theory, one would thus expect that the priming effect manifests itself in the response-selection part Rrs of the reaction time, while inhibition based accounts would expect the effect to

32

2.5 Conclusions and Open Questions be present in the target-selection time Rts .

2.5.4 Event-Related EEG Data As discussed in section 2.4, the research on the electrophysiological correlates of NP is very sparse. The sheer lack of such studies is reason enough to study the electroencephalogram in a NP setting. It is possible however, to derive some predictions from the literature regarding the amplitudes and latencies of specific ERP components. As already discussed in section 2.4, a potential candidate for a NP sensitive component is the P300. Since the current study is relatively similar to the design employed in Behrendt et al.’s (2007) study, similar results are to be expected. There are some modifications to the design used in the current study that could affect the occurrence of the expected P300 effect however. It could be argued for example that the eyemovement induced in one of the experimental conditions could trigger a second ’P300’ later in the ERP, because another stimulus has to be evaluated. It is thus unclear whether the expected reduction in P300 amplitude would occur for the first or the second part of the display (each possibility again carrying its own theoretical implication). Also the modulation in the late PSW could be hard to observe due to the expectation of generally longer reaction times (Behrendt et al. (2007) implemented an object-naming task which is behaviourally easier than the conceptual matching task applied here). Regarding the location of NP sensitive components on the scalp, a na¨ıve assumption could expect the prefrontal areas to yield the best results since these have been shown to be sensitive to NP in fMRI studies (Wright et al., 2006). It is of course difficult to transfer results from other imaging techniques to the EEG because of the very diverse nature of these measurement techniques.

33

34

3 Method The study reported here was conducted in the context of project C4 Aging effects in selective attention at the Bernstein Center for Computational Neuroscience, G¨ottingen. The overall goal of this project is the computational modelling of attentional processes and, more specifically, of aging effects in these processes. To that end, psychological experiments are carried out that contribute to the understanding of the processes that are to be modelled. The study reported here was conducted (a) to find electrophysiological correlates of the selection process, (b) to investigate theoretical implications of the response retrieval theory (Rothermund et al., 2005), (c) to examine the time-course of the selection processes and (d) to assess age differences in all these measures. In the framework of this thesis, only parts (a)–(c) will be analysed and discussed.

3.1 Terminology Because the stated hypotheses are derived from a response-retrieval point of view, the classical notation of NP and PP trials is not an appropriate choice, as classical NP or PP conditions can yield negative or positive priming effects. Instead, a nomenclature based on the actual sequence of object presentation will be used. The first letter in the sequence contains information about which part of the prime display is repeated in the probe display. A “D” represents the distractor, a “T” the target, respectively. The second letter indicates the role that this object is playing in the probe display. For example, the string DT refers to the condition in which the prime distractor (first D) is repeated in the probe trial as target (second T), hence it is a negative priming trial (see table 3.1 for a detailed listing). Furthermore the response relation is varied in the experiment. Given a dichotomous (yes/no) response modality, four different possibilities of prime-probe response distribution are conceivable: a yes answer in the prime and no answer in the probe, vice versa or a no or a yes answer in both the prime and the probe trial. The four possibilities are written in shorthand yn, ny, nn and yy, respectively. In the

35

3 Method

Table 3.1: Terminology for the prime conditions used in this study. The two letters specify which part of the prime is repeated in the probe. Prime Probe label

Distractor Target ↓ ↓ Target Target k k DT (NP) TT (PP)

Distractor Target ↓ ↓ Distractor Distractor k k DD TD

framework of Rothermund et al.’s (2005) response retrieval theory, especially response repetition vs. response shifts are of interest. Therefore, the conditions same response (yy and nn) and different response (yn and ny) are abbreviated as sr and dr. In the current experiment, two experimental conditions are realized, which differ in the distance between the target/distractor compound and the comparison word. Because the larger distance is applied in order to trigger an eyemovement, this condition is labeled glance. The condition implementing a short distance is referred to as no-glance, because all the objects are located in the retinal area. In condition glance, the overall reaction time in trial j, Rj is split into two successive parts. In accordance with equation (2.2), the first part is labeled as Rts (target selection) and the second as Rrs (response selection).

3.2 Design In order to produce strong priming effects, a conceptual object comparison task was used, where the response was given via a forced choice equal vs. not equal decision which was recorded via keystrokes (see section 3.4 for details). Thus, in each trial the subjects had to compare an object and a word that were presented simultaneously and to decide whether they were semantically identical or not. The displays comprised two pictograms of objects and a word representing a third object semantically: a green target object, a red distractor object and a grey comparison word (see figure 3.4). The trials were presented continuously, where trial n functioned as prime display for trial n + 1. The main part of the experiment consisted of 924 trials of the kind described above (see also section 3.5). During this stage of the experiment, the priming condition in each trial and the repetition of the required response served as independent

36

3.2 Design

Prime

CO

Probe

Bus

Ball

DT (NP)

Bus

Ball

TT (PP)

Bus

Ball

DD

Bus

Ball

TD

Ball

Bus

Figure 3.1: Priming Conditions realized in the experiment. variables that were varied within subjects. 5 priming conditions where realised in the experiment: a positive priming condition (TT), a negative priming condition (DT), a distractor repetition condition (DD), a target-to-distractor condition (TD) and a control condition (see figure 3.1). The sequence of required responses from trial n to trial n + 1 served as a second independent variable, which could take 4 different values depending on the required responses in prime and probe display. The possible values were thus yy, nn, yn and ny depending whether “yes” or “no” was the correct response in the prime and the probe trial respectively. Thus, a 5 × 4

37

3 Method design priming condition × response relation for the within-subject variables was realized (see table 3.2). Table 3.2: 5x4 within-subject design (priming condition × response relation)

response relation

        

z

DT-yy DT-nn DT-ny DT-yn

priming condition }|

TD-yy TD-nn TD-ny TD-yn

DD-yy TT-yy DD-nn TT-nn DD-ny TT-ny DD-yn TT-yn

{

CO-yy CO-nn CO-ny CO-yn

In addition to the within-subject variables, the distance between the target/distractor compound and the comparison word was varied between subjects. In the glance condition, the distance was chosen such that subjects had to move their eyes in order to focus the comparison word, i.e. subjects were not able to fixate the target/distractor objects and the word at the same time. In condition no-glance, the word was presented directly below the target/distractor objects, such that no eyemovements were necessary to process both information (see figure 3.2).

(a) condition glance

(b) condition no-glance

Bus

Bus

retinal focus display border Figure 3.2: Condition glance (a) and no-glance (b). In (a), the comparison word is printed outside the retinal focus while in (b) both target/distractor compound and comparison word can be focused simultaneously. Altogether, a 2 (glance vs. no-glance) × 5 (TT, DT, DD, TD, CO) × 4 (nn, yy, ny, yn) design with one between-subject (glance) and two within-subject variables (priming condition and response relation) was realized.

38

3.3 Participants Other factors that were known from previous research to influence the priming effects were balanced or randomized in the design. The response-to-stimulus interval (RSI) was randomized between 500 and 1500 ms, because this approach had yielded good results in terms of strong NP effects in previous experiments. The hand which corresponded to a yes- or a no-answer was distributed over the participants, such that an equal number had to give yes-answers with the right and no-answers with the left hand and vice versa. The number of trials were equal for each priming condition and for each of the four response relations. The different stimulus objects used in the study were equally distributed over the priming and response conditions as well. Care was taken to form a stimulus sequence that avoided predictable patterns, while still achieving the optimal distribution of the conditions over the trials.

3.3 Participants 32 (14 male, 18 female) participants, recruited mainly from the university of G¨ottingen took part in the study. Their age ranged from 20 years to 35 years (M = 24.88 years, SD = 3.21 years). Participants were rewarded either with a small amount of money (10 ¤), or by course credits for students of the psychological institute. All participants had normal or corrected-to-normal vision and were right-handed. All subjects were na¨ıve to the purpose of the experiment and had not taken part in a previous study employing similar stimulus material.

3.4 Material There were six different objects that served as stimuli in the experiment. These objects were represented by hand-drawn pictograms that could either appear in green or in red color on the screen (see figure 3.3). These objects were “bus”, “ball”, “tree”, “book”, “bed” and “bench”. The language of the experiment was german. In german, the words for these objects begin with a plosive “b” and consist of a single syllable (“Bus”, “Ball”, “Baum”, “Buch”, “Bett”, “Bank”). This consistency was due to the fact that these objects were already used in several experiments before which employed an object-naming task (Behrendt et al., 2007). While designing the pictograms, care was taken that the area covered by that object was approximately equal over different items. The items were drawn in a way to approximate a constant “visual complexity” (rated by the experimenters).

39

3 Method

Figure 3.3: Items used as target and distractor stimuli in this study. All words corresponding to the pictograms used appear very frequently in everydaylanguage and should thus be equally available to the participants. In the experiment, the target and the distractor item appeared superimposed such that large portions of the drawings overlapped. Nonetheless, both objects were clearly discernible at all times. The distractor appeared always in pure red (RGB= {255, 0, 0}), the target in pure green (RGB= {0, 255, 0}). Below this overlapping target/distractor compound, a word corresponding to one of the six items was presented. The distance and the actual word chosen depended on the experimental condition (see section 3.2). Trial n

Trial n + 1

Ball

Buch

time

RSI 500 ms (500 – 1000 ms)

Response−Keys

no

yes

no

yes

Figure 3.4: A sample trial. Trial n functions as prime for trial n + 1, while it is the probe for trial n − 1 which, in turn, primes trial n. The reaction, whether the target object corresponded to the written word or not, was given by pressing one of two buttons (equal or unequal), which were fixed on both sides of the experimental chair such that the participants could comfortably place the two hands.

3.5 Procedure The study was conducted in the facilities of the department for medical psychology, Waldweg 37, G¨ottingen, Germany. The main part of the experiment took place in the experimental chamber for EEG recordings which was isolated against acoustical and electrical interference. Two experimenters were present during all stages of the data acquisition.

40

3.5 Procedure After the arrival of the participants, they were asked to accomplish a test to determine whether they had a red-green deficiency. Subjects with a thusly diagnosed red-green deficiency were not admitted to the experiment. The participants were then requested to fill out the Activation-Deactivation Adjective Checklist (ADACL) (Thayer, 1986), a vocabulary test (Schmidt & Metzler, 1992) and the number-symbol test from the N¨ urnberger Altersinventar (Oswald & Fleischman, 1982). The experimenter noted down age, gender, number years of education and potential medication. These measures where acquired only to match this sample of young participants with a sample of older participants that was to be acquired in a later stage of this study. The results from these measures are not further considered here, as only the younger participants are considered in the analyses. After being instructed about some basic facts about electroencephalographic studies, the participants were seated in the experimental chamber and the EEG cap was attached to their head. This preparatory session that lasted about half an hour, served to improve the accuracy of the EEG recording (see below for details). The preparation was closed by bringing the participant in a comfortable position in a standardized distance to the stimulation monitor. The distance was chosen such that the target/distractor commpound as well as the comparison word fell into the retinal area in conditon no-glance. This was achieved by providing a fixed chin-rest where the participants were asked to position their head on. The height of the chin-rest was slightly adaptable to improve the comfort for the participants. The experimental session started with a eyemovement calibration phase that required the participants to follow a moving object (a little bee) with their eyes without any other bodily movements. The subjects were instructed that large movements of the head after that phase would result in a disturbed measurement and should thus be suppressed whenever possible. At the beginning of the actual priming experiment, the participants were briefed by on-screen instructions to identify the green object while ignoring the red one and to compare it to the displayed word. The subjects could set the pace of the instruction displays themselves by pressing an arbitrary button. In experimental condition glance, the subjects were additionally instructed not make multiple eyemovements, but to first identify the target object beyond doubt, then to move the eyes and identify the word and finally, to press the correct button. The experiment started and ended with a baseline phase consisting of 36 trials each, where only green objects were presented. After completing 30 introductory training trials, the actual experiment started. Overall, 924 experimental trials were completed, where

41

3 Method small pauses were administered every 84 trials. There was one longer pause after 504 trials. Each of the experimental trials was preceded by the presentation of a fixation cross for 500 ms. The actual display containing the target, the distractor and a word used for comparison disappeared instantly as soon as the subject responded to it by pressing one of the buttons. After blanking out the display, the screen remained completely empty for a variable interval between 500 and 1000 ms which was randomized over all trials. When the participants had finished the priming experiment, they were released from the EEG cap and provided with the comfort of a hot shower. To complete the experimental session, subjects were asked to comment on the experiment. This question served the purpose to determine potential flaws in the experimental procedure and to reveal possible strategies used by the subjects. Participants were then thanked, rewarded and escorted from the lab.

3.6 EEG-Acquisition The EEG was acquired by placing 60 electrodes on the scalp following the standardized extended 10-20 system (Jasper, 1958; see fig. 3.5) using a 64-channel Synamps amplifier (Neuroscan Inc.). In addition, the left and right vertical and horizontal EOG electrodes were attached 1 cm below the left and right eye, and at a distance of 1-cm from the outer left and right canthi, respectively. The electrodes were made of highly conductive material (Ag/ACl) and were sintered ring electrodes. Sampling rate during recording was set to 5000 Hz, and band-pass during EEG acquisition was set to 0.1-70 Hz. In addition, an on-line notch filter was employed to suppress the 50-Hz band. FCz served as active reference electrode. To ensure a valid measurement, the resistance of the skin-electrode gap should not exceed 5 kΩ, therefore a highly conducting paste was used to prepare the measurement by filling the gap between electrode and skin until the resistance fell below that border. In order to keep the influence of external electrical fields as low as possible, the experimental chamber where the EEG was recorded was shielded against external electrical and acoustical influences. The voltage fluctuations thus recorded from the scalp were amplified and transmitted to a computer that recorded the incoming data (see figure 3.6). The subject received stimulation via a second computer on a standard 17” monitor that also recorded the subjects responses. The stimulation computer was also connected to

42

3.7 Data Preparation

Figure 3.5: The extended Ten-Twenty system of electrode placement described in Jasper (1958). Electrodes not used in this study are blacked out. the recording computer, transmitting time markers through that connection. These time markers allow for a segmentation of the data according to events that occurred during the stimulation. Markers were given for the onset of the successive stimulation displays and for the subjects responses.

3.7 Data Preparation 3.7.1 Outlier Correction of Behavioural Data To increase the statistical validity of the data, some procedures to reject outliers from the behavioural data were employed. In a first step, all reaction times that were produced in trials where a behavioural error occured (i.e. the participant pressed the wrong key), were removed from the analysis. Because of the continuous presentation of the prime and probe displays, the trial directly following an errortrial were excluded from the analysis, too. Another criterion for exclusion was the absolute and relative implausibility of the reaction times. Reactions that were faster than 250 ms or slower than 2000 ms were removed because it was argued that they must contain other processes than the ones under investigation due to their unusual short or long duration. Reaction times, where the difference to the mean of the

43

3 Method experimental chamber (shielded against acoustic and electrical disturbances)

Headbox

. . . . . .

Amplifier

Human subject electrodes

Computer used for data acquisition ”markers”

Computer used to present experimental stimuli

Figure 3.6: The assembly of the measuring station as used in this study. The signals recorded by the electrodes on the scalp of the participants are transported, amplified and combined with time-markers in the dataacquisition device. experimental condition exceeded two times the standard deviation were excluded, too. Explicitly, trials j for which |Rjc − hRjc ij | > 2 · σ c , where excluded, where c is the experimental condition split up by priming condition and response relation, hRi is the mean of R and σ c is the standard deviation of the reaction times in the current condition. Because most inferential statistics assume a normal distribution of the data, Kolmogorov-Smirnov tests were conducted for the reaction times within the experimental conditions. If the test showed that the normal distribution could not be assumed, single values were removed based on their probability given the normal distribution model until the Kolmogorov-Smirnov test yielded insignificant results. Overall, not more than 10% of the trials were exluded from the analysis for each participant.

3.7.2 Preprocessing of the EEG-Data A major problem of unspecific recording techniques such as the EEG that record the average activity of large clusters of neurons (as e.g. compared to the direct mea-

44

3.7 Data Preparation surement of the activity of single neurons), is that there is a very strong background activity component present in the data. In the recorded datasets, this is reflected as an apparently very strong noise component. This noise in the recorded potential fluctuations is only partly due to external oscillations such as these in the conductivity of the gap between electrodes and skin or the impact of external electric fields that can safely be qualified as noise. Another contributing factor is obviously the activity of deeper or uninteresting cell compounds, which is inevitably present due to the unspecific nature of the potential fluctuations recorded by the electrodes. In the actual data recorded by the EEG, the noise is significantly stronger than the ERP (Flexer, 2000). Whereas the electrical background activity is in the range of 1 − 200µV, the evoked potentials have an amplitude of only 1 − 30µV (see figure 3.7.2). measured signal (single trial) average over 132 trials

200

8 150

Fp1

6

100 4 50 2

µV

0

120

µV 0

100 −2

−50 80

−4

−100

−150 −500

60

100 −6

40 0

500 20

µV

1000

1500

2000 −8 −500

60

0

0

500

ms

1000

1500

2000

40

−20

20

−40

µV

−60 −80 −500

80

0

−20 0

−40 500 −60

1000

1500

2000

ms

−80 −100 −500

0

500

ms

1000

1500

2000

. . .

Figure 3.7: Noise and signal in EEG-data. Note that the approximate amplitude of the “signal” (the average ERP of 132 trials) is in the order of magnitude of ≈ 10µV, while single-trial recordings show amplitudes of ≈ 100µV. Thus, before the segments that form the basis of the average were chosen, a cascade of data cleaning and rejecting procedures was carried out. This is due to the fact, that the recording in the experimental setting is often subject to major disturbances (such as external noise) and that, as a consequence, there are parts of the data that are contaminated with much stronger noise than usual and tolerable. In order to get rid of these segments, (semi-)automatic data-rejection procedures were used. Furthermore, the eyeblinks produced by the subject during the recording cause major disturbances in the data, especially in the fronto-polar regions. The disturbances thus introduced, have to be removed either by traditional regressionbased techniques (Gratton, Coles & Donchin, 1983) or by more recently developed approaches founding on independent component analyses (Joyce, Gorodnitsky &

45

3 Method Kutas, 2004; Jung, Makeig, Humphries, Lee, McKeown, Iragui & Sejnowski, 2000; Delorme, Sejnowski & Makeig, 2007). In the study described here, the data was first downsampled to 500 Hz for convenience. In a next step, bandpass filtering with a low-cutoff of 0.5 Hz and a high-cutoff of 20 Hz was applied to the continuous data in order to introduce as few border artifacts as possible. The data was then split into segments of 2500 ms, reflecting the time window [−500, 2000] relative to the markers for the stimulus onset. For the following analysis, all trials that were tagged as invalid in the outlier analysis described in section 3.7.1 were excluded. For the group glance, the data was fed into the independent component analysis (ICA) as described in Delorme & Makeig (2004). This was due to the fact that strong eyemovement components were present in the data in all interesting trials, because this eyemovement was explicitly induced by the experimental instructions. The independent component analysis is a statistical linear decomposition of the data into distinct components with fixed scalp maps. Because the components specified by the axis directions are maximally independent, it is argued that each of the components contains distinct information. In the case of EEG data the components are interpreted as sources from which parts of the data come. It was therefore expected that the reliable and well-localized component elicited by the eyemovements could be validly extracted and rejected by the ICA, resulting in “cleaner” data. To determine the components that include the information from the ocular muscular movements, the procedure described by Delorme & Makeig (2004) was used. Following this procedure a component is judged to be due to eyemovements if (a) the power spectral density (PSD) spectrum of the EEG data is smoothly decreasing, (b) the scalp map of the component shows strong activation in the fronto-polar areas, but not in other areas and (c) eye-movements can be seen in the component wave of individual trials at the approximately predicted time (half-way between stimulus onset and response). The ICA-based approach was preferred over traditional regression-based approaches as e.g. developed by Gratton et al. (1983), because these appear in some cases to extract actual brain processes from the data (Croft et al., 2005). This consideration is especially valid, when the regression techniques are applied globally to all data segments to compute the regression coefficients. In this case, important information that is distributed over experimental conditions might be regressed out of the data. In the analysis described here, it was necessary to first apply the ocular correction method before artifact rejection, because of the fixed threshold applied there. For

46

3.7 Data Preparation the experimental condition no-glance, no ICA was computed. Next, a baseline correction in the interval [−1000] relative to display onset was carried out, followed by a crude artifact rejection that rejected all data that crossed 1000 µV in absolute values. Then, a new reference was computed relative to the mean of the TP9 and TP10 electrodes and again the baseline-correction applied in the interval [−1000]. Then, a finer artifact rejection with cutoff ±100 µV was employed. Finally the data was sorted according to experimental condition (see table 3.2) and an ocular correction developed by Gratton et al. (1983) was computed for the no-glance group per cell of the experimental design. Following these cleaning procedures, a pointwise average across the remaining trials was computed. Generally, this can either be done in the time- or the frequencydomain. Averaging in the time-domain results in the curve generally known as the ERP, averaging of the frequency power spectrum results in in a two-dimensional image (time vs. frequency, PSD displayed as colour) that is called the event-related spectral perturbation (ERSP) (Makeig et al., 2004).

3.7.3 ERP Components and Nomenclature The interpretation of the ERPs involves the identification of the peaks and throughs (labeled as components in this terminology) as well as their classification and interpretation based on previous research. The evoked potentials are classified as endogenous or exogenous, the first referring to potential fluctuations that are emitted by the brain itself (i.e. without the necessity of an external stimulation), the latter to externally provoked potential changes. One nomenclature for the labeling of the components is based on the latency and the polarity of the peaks in the data. For instance, the P300 is a positive peak (hence the P) occurring at approximately 300 ms post-stimulus. In another even more descriptive system, the components in an ERP are numbered serially and provided with an ’N’ for negative or a ‘P’ for positive. In this terminology, the N1 is the first negative peak in the ERP under investigation.

3.7.4 Extraction of the Partial Reaction Times In order to find the moment in time in which the experimental subjects in condition glance moved their eyes in order to complete the trial, 4 EOG electrodes (left and right hEOG and vEOG respectively) were attached to the ocular muscles. Electroocular data is often used to record eye-movements (Joyce, Gorodnitsky, King &

47

3 Method Kutas, 2002) by measuring the potential fluctuations due to the movement of the eye which functions as a dipole. The angle of the eye-movement is approximately proportional to the resulting change in the recorded potential which allows for a valid interpretation of the data if the recording system has been calibrated before the measurement. In the setting described here, it was not necessary to extract the complete timecourse of the movements of the eyes. Instead it was sufficient to obtain a valid measure of the latency of the first big saccade downwards during each experimental trial. To that end, it was sufficient to consider only the left and the right vEOG electrodes, as these typically show the largest response to vertical eyemovements. The two hEOG electrodes were therefore not considered for the extraction of this saccade. Extracting the latency of this saccade was done using a computationally simple procedure. In a first step, the data from the EOG channels was low-pass filtered (cutoff at 20 Hz) in order to get rid of fast oscillations that would disturb the searched potential fluctuations that are on a slower timescale. The data was then segmented based on the markers for the experimental trials. Let scj (t) t ∈ {1, . . . , n}, j ∈ {1, . . . , N }, c ∈ {rvEOG, lvEOG} be these N segments of length n sampling points each obtained from the two vEOG channels. A function W depending on a window size w ∈ N given in sampling point units can then be constructed that relates the sampling points to the largest difference between two data points in the time window w by Wj,w (t) = sgn(τmax − τmin )|scj (τmax ) − scj (τmin )| (3.1) where τmax = arg max St,w

and τmin = arg min St,w

t

t

and  St,w := scj (i)|i ∈ {t, . . . , t + w} . Finding the latency Rjc of the largest vertical eyemovement detected in electrode c is then equivalent to solving Rjc = arg max(Wj,w (t)),

(3.2)

t

which gives the wanted latency in sampling point units. In order to increase the validity of this measure, this procedure was carried out for the two vEOG electrodes

48

3.7 Data Preparation

(a) right vEOG

Rechts unten

100

50

µV

0

−50

−100

−150 −500

0

500

ms

1000

1500

2000

1000

1500

2000

(b) left vEOG Links unten 100

50

0

µV −50

−100

−150 −500

0

500

ms

Figure 3.8: Extracting the largest vertical eyemovement. The plot shows the potential fluctuations in the (a) right and (b) left vEOG electrodes (blue line) and function Ww (t) from equation (3.1) (red line) in a sample trial. The maximum from eq. (3.2) is marked by a cross. separately and then combined using simple averaging: Rj =

RjlvEOG + RjrvEOG . 2

In the case that the two estimates of the eyemovement differed significantly (more than a fixed criterion C) |RjlvEOG − RjrvEOG | > C, the trial was marked as invalid concerning the partial reaction times, because the analysis was not considered to be of sufficient quality. In the analysis described here, a sampling rate of 100 Hz was chosen, resulting in n = 200 sampling points in the 2 seconds post-stimulus. The analysis time-window was chosen as w = 20 ms ≡ 2 sampling points and the criterion for invalid trials was fixed as C = 100 ms ≡ 10 sampling points.

49

3 Method

3.8 Data Analysis Behavioural Data The general approach taken in this study to analyze the behavioural data was to do a resolution-level wise analysis starting with the most general analysis and, in case of significant results, proceed to a finer-grained dissection. For the reaction times and the error rates, a global analysis of variance (ANOVA) was applied to the complete dataset (all between- and within-subject variations). In case of significant main effects or interactions within the factors, single ANOVAs for the factors were computed. Thus, the factors were broken down using separate ANOVAs until individual differences could be contrasted with single t-tests. If more than one t-test were applied to the same dataset, the bonferroni-correction was applied to the p-values. Generally, a result was treated as significant, when the p-value fell below .05. The codes of significance used are as follows: ∗ for p < .05, ∗∗ for p < .01 and ∗ ∗ ∗ for p < .001. Furthermore, a + indicates a result that approaches significance p < .10. ERP Data In a first step, visual inspection of the event-related EEG-data was used as an approximation for the location of possible effects. Once an approximate time-window had been found by this approach, an analysis of variance was carried out on the mean amplitude value, treating factor electrode and experimental condition as repeated measures. In the analysis, the main effect of the electrode that was always present in this ANOVA is considered trivial and is not reported in the text to increase readability. In case of significant interactions, pairwise plannedcontrasts were computed to find out which electrodes or conditions differed significantly. These contrasts were again guided by visual inspection. In case of several simultaneously applied contrasts, the bonferroni-correction was used to avoid a bias in the familywise error rate. Because the difference of the response-latency in condition glance and no-glance was considerable, the ERP data from these conditions could not be averaged as this would have disturbed the general shape of the ERPs. The behavioural results also suggested that different processing was applied by the subjects in the two conditions, further accentuating the need to distinguish between the groups in the analysis of the EEG-data. A last point for splitting the groups in the analysis comes from the examination of the actual shape of the curves in the grand-grand averages of both conditions (see figure 4.4). Apparently, the processing of the trials in the two experimental conditions differed considerably (for details see section 4.2).

50

4 Results and Discussion 4.1 Behavioural Data 4.1.1 Response-Repetition Effect in Alternative Priming Conditions? To test the assumption that the between-subjects modulation of the distance of comparison word to target/distractor compound did not influence the reaction times in a priming condition sensitive way, a 2 × 5 × 2 analysis of variance (ANOVA) with the between-subject factor distance between target/distractor and comparison word (glance vs. no-glance) and within-subject factors priming condition and response repetition was computed. A main effect for distance condition, F (1, 30) = 5.53, p < .03 , for priming condition, F (4, 120) = 7.57, p < .001 and for response repetition F (1, 30) = 22.31, p < .001 appeared. All interactions proved to be significant (distance × priming, F (4, 120) = 3.09, p < .02 ; distance × response, F (1, 30) = 7.72, p < .01 ; distance × priming × response, F (4, 120) = 3.11, p < 0.02 ). These findings were not supported by the equivalent analysis with the error rates as the dependent variable. In this ANOVA, only the interaction priming × response-repetition was significant, F (4, 120) = 7.21, p < .001 . Neither the distance × priming, F (4, 120) = 0.66, p = .62 , the distance × response, F (1, 30) = 0.23, p = .63 nor the distance × priming × response interaction, F (4, 120) = 1.85, p = .12 was significant. The significant impact of the between-subject condition distance that acts in a condition-sensitive way shown in the analysis of the reaction times violates the assumption that the same processing takes place in both conditions and that therefore the results from the glance-condition can be transferred to the no-glance condition. The findings from the error-rates are more coherent with the expectations, but are not overvalued because they were in general very low and thus lack discriminability in the general analyses. It is therefore indicated to analyse the data separately for both conditions.

51

4 Results and Discussion Condition no-glance For a summary of the results for condition no-glance, see table 4.1 and figure 4.1. The following analysis is executed in parallel for the reaction times and the error rates, respectively. Table 4.1: Reaction times and error rates for experimental condition no-glance. Mean Reaction

control DT (NP) TT (PP) TD DD DT (NP) TT (PP) TD DD a b

same response different response mean rt (ms)a error % mean rt (ms) error % 848.5 (302.2) 4.1 832.5 (261.2) 3.7 841.5 (286.4) 3.8 853.0 (286.6) 3.3 826.0 (268.6) 2.2 843.8 (275.3) 5.2 854.1 (286.0) 5.7 825.1 (272.3) 3.3 832.7 (283.4) 5.2 824.9 (260.8) 3.3 Priming Effectsb 7.1 0.3 −20.5 0.3 22.5 1.9 −11.2 −1.5 −5.6 −1.6 7.5 0.3 15.8 −1.1 7.6 0.4

The standard-deviation of the means is given in parentheses. Priming effects are computed as the difference of the reaction time in the control and the prime condition, respectively.

Reaction Times. A 5 × 2 ANOVA for the distance condition no-glance revealed a main effect for the priming condition F (4, 60) = 2.64, p < .05 and a significant interaction priming × response repetition, F (4, 60) = 3.15, p < .03 . To specify the exact location of these effects, 4 separate 2 × 2 (priming × response repetition) ANOVAs were carried out for each of the priming conditions against the control condition. DT For the DT condition, the 2 × 2 (control, DT vs. same, different response) ANOVA found only a main effect for response repetition, F (1, 15) = 4.45, p < .05 . The expected interaction was not significant, F (1, 15) = 2.91, p = .11 . Responding was thus significantly delayed when a different response had to be given. TT The 2 × 2 (control, TT vs. same, different response) ANOVA revealed a significant interaction prime × response repetition, F (1, 15) = 6.73, p < .02 .

52

4.1 Behavioural Data There were no main effects. Pairwise t-tests pointed out that the reaction was faster whenever the response was repeated, t(15) = 2.12, p < .05 but not when the response was shifted, t(15) = −1.29, p = .22 . TD In the TD condition, only a main effect for response repetition was significant, F (1, 15) = 6.35, p < .03 . Separate t-tests showed that the responses were significantly faster for response switches in the TD case, t(15) = −2.854, p < .005 but not significantly so in the control case, t(15) = −1.64, p = .10 . DD In the 2 × 2 (control, DD vs. same, different response) ANOVA, only a main effect for priming condition proved to be significant, F (1, 15) = 7.03, p < .02 . The expected interaction was not significant, F (1, 15) = 0.66, p = .43 . DD trials were thus significantly faster than control trials, regardless of the response repetition.

Table 4.2: Summary of significant results for condition no-glance. = indicates no significant difference between the priming conditions. response relation reaction timesa same = control different = same >∗ control different = same = control different = same >∗ control different >∗ a

error rates = DT = >∗ TT ∗ a

error rates = DT = = TT = = TD