Search

Zeitschrift für empirische Hochschulforschung : ZeHf 5 (2021) 2, S. 162-183, Realistische Erwartungen und Passung zwischen Interessen und Studieninhalten sind zentrale Ansatzpunkte bei der Steuerung von Studienwahlentscheidungen. In einem neu entwickelten fachspezifischen Erwartungs- und Interessenstest (E × I - Test) für Psychologie werden erstmals Erwartungsdiskrepanzen und Interessen kombiniert betrachtet und dementsprechend auch übertroffene oder enttäuschte Erwartungen erfasst und rückgemeldet. Die zu den Studieninhalten des neuen Verfahrens entwickelten Items konnten annähernd perfekt den Studienfachbereichen zugeordnet werden und deckten diese weitgehend vollständig und gleichmäßig ab. 2,033 Studieninteressierte bearbeiteten den E × I - Test im Rahmen eines Online-Self-Assessments und fühlten sich danach informierter als vorher. Insgesamt bewerteten die Studieninteressierten das neue Verfahren positiv und 94% würden es weiterempfehlen. Auf Basis des vorgestellten Verfahrens für das Bachelor-Psychologiestudium könnten weitere E × I - Tests für die Orientierung in andere Studienfächer oder Berufe entwickelt und validiert werden, für welche sowohl spezifische Interessen als auch enttäuschte Erwartungen eine Rolle spielen. (DIPF/Orig.), Both, realistic expectations and fit between interests and study content are crucial to guide study choice decisions. A newly developed subject-specific Expectation-Interest Test (E × I - Test) for psychology considers, for the first time, expectation discrepancies and interests in combination. Thus, exceeded or disappointed expectations are assessed and reported back. It was shown that the newly developed items which represent study contents can be assigned almost perfectly to the study subject areas and cover them to a large extent completely and evenly. 2,033 prospective students completed the E × I - Test as part of an online self-assessment and felt more informed afterwards than before. Overall, prospective students rated the new procedure positively and 94% would recommend it to others. Based on the presented procedures for the bachelor psychology studies, further E × I - Tests could be developed and validated for orientation to other fields of study or professions, for which both specific interests and disappointed expectations play a role. (DIPF/Orig.)

It contains codebooks, data files and relevant scripts of a meta-analytic review on emotion-control interactions.

This is the second of a series of experiments on time-based expectancy for finger responses to vibrotactile stimulation. In this experiment, we induce time-based expectancy by pairing two events and two preparatory intervals. In a binary randomized response choice setting, participants respond by keypresses to vibrotactile stimulation to their fingers. Stimulus response mapping is compatible (i.e. respond with the finger that was stimulated), and stimuli are preceded by either a short or a long preparatory interval which predicts with 90% probability which finger will be stimulated next. This pairing of vibrotactile stimulation (left-right finger) and interval duration (short-long) is counterbalanced across participants. For half of the participants the short interval will predict stimulation on the left finger, and the long interval will predict stimulation on the right finger with 90% validity. For the other half, this relation will be inverted. We decide between two hypotheses: 1) Participants respond about equally fast to frequent combinations of interval duration and stimulation side than infrequent ones. Such finding would mean that participants did not form time-based expectancies to vibrotactile stimuli. 2) Participants respond faster to frequent than infrequent combinations of interval duration and stimulation side. Such finding would mean that participants build time-based expectancies to vibrotactile stimuli, and would be consistent with the time-based expectancy literature (see, Thomaschke et al., 2011; Thomaschke et al., 2015) We decide between these hypotheses by a Bayesian analogue to a paired sample t-test (Morey & Rouder, 2011). We cumulatively test participants until we reach a JZS Bayes factor over 5 for either of the hypotheses, or until we have tested 120 participants without reaching a JZS Bayes factor over 5. We collect data with the E-Prime program uploaded to this repository. We will pre-process the data as follows: We do not include participants with an overall mean RT or an overall mean error rate lying more than 2.5 SDs away from the sample mean. We do not include the first trial of each block, the first block, error trials, and trials following an error. Since we will compare two conditions observations (frequent vs. infrequent) with different number of, we will follow special recommendations to calculate the median RT. We will take the regular RT median for the infrequent condition and we will compare it with a special “average median” from the frequent condition, separately for the short and long intervals durations (for a more detailed explanation, see Miller, 1988) By frequent we mean trials with combinations of interval duration and stimulation side with 90% validity and as infrequent count trials with combinations of interval duration and stimulation side with 10% validity. We conduct the Bayesian t-test over these conditions. All further analyses will be of exploratory nature: For example, effects on error rates, effects of interval duration, etc. For following exploratory analyses, the script will be extended.

Humans are able to intentionally forget previously learned information (as often measured using the directed forgetting, short DF, paradigm, for an overview see Pastötter et al., 2017). In the item-method of DF (see MacLeod, 1998 for a review), for instance, participants are instructed to memorize stimuli that are presented to them sequentially. Each sequentially presented stimulus is directly followed by a memory cue informing participants to either forget or remember the stimulus. People typically recall fewer to-be-forgotten (TBF) than to-be-remembered (TBR) stimuli (Bjork, 1970). Whereas various studies have already demonstrated that humans are able to intentionally forget previously learned information, only few studies have analyzed whether DF affects motor representations (Tempel & Frings, 2016) or incidentally learned information (e.g., Hockley et al., 2016; Jou, 2010). Recent studies provided first evidence that typical findings regarding retrieval-induced forgetting and effects of DF extend to motor memory (in particular, motor sequences, Tempel et al., 2016; Tempel & Frings, 2013, 2016) and stimulus-response (S-R) associations (Dreisbach & Bäuml, 2014). Stimulus-response (S-R) associations are formed when stimuli and responses repeatedly co-occur and thus bind together – a notion that is supported by (item-specific) repetition priming effects (see Logan, 1988, 1990). Here, participants’ responses are, for instance, generally faster for stimuli that require the same as opposed to a different, previously-executed response. The current study aims to gain a deeper understanding of the mechanisms underlying the DF effect on the retrieval of S-R associations. For this, we will investigate whether (not explicitly instructed) learned S-R associations can be intentionally forgotten based on forget/remember cues following stimuli. To address this question, we will combine the DF item-method paradigm and an item-specific priming paradigm assessing the encoding and retrieval of S-R associations (see below and also Hsu & Waszak, 2012; Moutsopoulou et al., 2015). Information on the task and study design Our study will consist of three major phases: (1) A learning, (2) a distraction, and (3) a test phase. 1. Learning Phase: During the learning phase, participants’ task is to classify images of objects as containing a mechanism or not. We will extend the DF paradigm to S-R mappings using a classification task (drawing on an item-specific priming paradigm; Hsu & Waszak, 2012; Moutsopoulou et al., 2015; Pfeuffer, Moutsopoulou et al., 2017). By categorizing the stimuli, participants encode S-R associations (stimulus-action, S-A, associations between stimuli and motor outputs according to the terminology provided by Moutsopoulou et al., 2015). Importantly, whether a left or right response is required for the classification of a specific stimulus will differ between stimuli and will be indicated by a task cue detailing the classification-response mapping prior to the stimulus’ presentation. For example, the task cue “M + N” will indicate that an object image has to be classified as “mechanic” by pressing a left key and as “non-mechanic” by pressing a right key. Conversely, the task cue “N + M” will indicate that a right key press is required for mechanic objects and a left key press for non-mechanic ones. Participants will respond to each stimulus four times (in four blocks, see detailed task description below) in the exact same way (i.e., same S-R mapping). Per block, participants will also be instructed to memorize some of the object images for a later memory test and forget others. Directly after the response to a stimulus, a memory cue will inform participants to either forget or remember the stimulus. The same memory cue will appear after each of the four presentations of a specific stimulus. 2. Distraction Phase: In a subsequent distractor task, participants will solve a visual working memory task (Corsi) for 1.5 minutes to spurge short-term memory. 3. Test Phase: In the test phase, participants will be presented once more with the stimuli from the learning phase (both TBF and TBR objects) and new object images. Importantly, for the old objects, half of the stimuli per memory condition, the S-R mapping will be the same as in the learning phase (item-specific response repetition between learning and test). For the other half of the stimuli, the S-R mapping will be the opposite (item-specific response switch between learning and test). Response switches are therefore defined as item-specific switches of S-R mappings between the learning and the test phase of a stimulus (e.g., the stimulus requires a right key press during all four presentations in the learning phase, but a left key press during the test phase). Response repetitions are in turn defined as item-specific repetitions of the required response between the learning and the test phase (e.g., the stimulus requires a right key press during both the learning and test phase). Typically, RTs and error rates for item-specific response repetitions between learning phase and test phase trials are lower as compared to item-specific response switches, indicating that S-R associations have been formed during the learning phase and were retrieved in the test phase. We refer to these effects as item-specific S-R effects. Trial structure and task instructions will be equivalent to the last appearance of stimuli during the learning phase except that no memory cues will be presented anymore. Participants will be instructed that they will only need to respond to stimuli fast and accurately and that there will be no memory cues for this phase. Participants will also be informed that there will be no later memory test. They are told that they will respond to both TBR and TBF stimuli and that they should simply respond as fast and accurately as possible regardless of the previous memory instruction. 4. Distraction Phase: Participants will again solve a visual working memory task (Corsi) for 1.5 minutes to spurge short-term memory. How we aim to measure a DF effect on S-R associations We will use the difference in RTs and error rates in the test phase between trials that require an item-specific response switch and an item-specific response repetition to determine a DF effect based on the remember/forget cues. In other words, we will measure the strength of item-specific S-R effects for TBF as compared to TBR stimuli. Applying the observations in typical DF paradigms to the context of S-R associations we expect the following observations: If DF influences the retrieval of S-R associations, S-R effects, the performance differences between item-specific response repetitions and response switches in the test phase (see Hypotheses section) should be smaller for TBF as compared to TBR stimuli. Additionally, we will assess a DF effect on S-R associations by looking at the decrease in RTs and error rates across the four presentations of a stimulus (repetition priming effect) in the learning phase and compare them between TBR and TBR stimuli.

Humans are able to intentionally forget previously learned information (as often measured using the directed forgetting, short DF, paradigm, for an overview see Pastötter, Tempel, & Bäuml, 2017): In the item-method of DF (see MacLeod, 1998 for a review), for instance, participants are instructed to memorize stimuli that are presented to them sequentially. Each sequentially presented stimulus is directly followed by a cue informing participants to either forget or remember the stimulus. People typically recall fewer to-be-forgotten (TBF) than to-be-remembered (TBR) stimuli (Bjork, 1970). Whereas various studies have already demonstrated that humans are able to intentionally forget previously learned information, only few studies have analyzed whether DF affects motor representations (Tempel & Frings, 2016) or incidentally learned information (e.g., Hockley, Ahmad, & Nicholson, 2016; Jou, 2010). Recent studies provided first evidence that typical findings regarding retrieval-induced forgetting and effects of DF extend to motor memory (in particular, motor sequences, Tempel, Aslan, & Frings, 2016; Tempel & Frings, 2013, 2016) and stimulus-response (S-R) associations (Dreisbach & Bäuml, 2014). Stimulus-response (S-R) associations are formed when stimuli and responses repeatedly co-occur and thus bind together – a notion that is supported by (item-specific) repetition priming effects (see Logan, 1988, 1990). Here, participants’ responses are, for instance, generally faster for stimuli that require the same as opposed to a different, previously-executed response. The current study aims to gain a deeper understanding of the mechanisms underlying the DF effect on the retrieval of S-R associations. For this, we will investigate whether (not explicitly instructed) learned S-R associations can be intentionally forgotten based on forget/remember cues following stimuli. To address this question, we will combine the DF item-method paradigm and an item-specific priming paradigm assessing the encoding and retrieval of S-R associations (see below and also Hsu & Waszak, 2012; Moutsopoulou, Yang, Desantis, & Waszak., 2015). ***Information on the task and study design*** Our study will consist of five major phases: (1) A learning, (2) a distraction, (3) a test, (4) another distraction, and (5) a recognition phase. 1. Learning Phase: During the learning phase, participants’ task is to classify images of objects as containing a mechanism or not. We will extend the DF paradigm to S-R mappings using a classification task (drawing on an item-specific priming paradigm; Hsu & Waszak, 2012; Moutsopoulou, Yang, Desantis, & Waszak, 2015; Pfeuffer, Moutsopoulou, Pfister, Waszak, & Kiesel, 2017). By categorizing the stimuli, participants encode S-R associations (stimulus-action, S-A, associations between stimuli and motor outputs according to the terminology provided by Moutsopoulou et al., 2015). Importantly, whether a left or right response is required for the classification of a specific stimulus will differ between stimuli and will be indicated by a task-cue detailing the classification-response mapping prior to the stimulus’ presentation. For example, the task cue “M + N” will indicate that an object image has to be classified as “mechanic” by pressing a left key and as “non-mechanic” by pressing a right key. Conversely, the task cue “N + M” will indicate that a right key press is required for mechanic objects and a left key press for non-mechanic ones. Participants will respond to each stimulus four times (in four blocks, see detailed task description below) in the exact same way (i.e., same S-R mapping). In the fourth block, participants will also be instructed to memorize some of the object images for a later memory test. In this block, directly after the response to a stimulus, a cue will inform participants to either forget or remember the stimulus. 2. Distraction Phase: In a subsequent distractor task, participants will solve a visual working memory task for 1.5 minutes to spurge short-term memory. 3. Test Phase: In the test phase, participants will be presented once with the stimuli from the learning phase (both TBF and TBR objects). Importantly, for half of the stimuli, the S-R mapping are the same as in the learning phase (item-specific response repetition between learning and test). For the other half of the stimuli, the S-R mapping will be the opposite (item-specific response switch between learning and test). Response switches are therefore defined as item-specific switches of the stimuli´ S-R mappings between the learning and the test phase (e.g., stimulus requires a right key press during the learning phase, but a left key press during the test phase). Response repetitions are in turn defined as item-specific repetitions of the required response between the learning and the test phase (stimulus requires a right key press during both the learning and test phase). Typically, RTs and error rates for item-specific response repetitions between learning phase and test phase trials are lower as compared to item-specific response switches, indicating that S-R associations have been formed during the learning phase and were retrieved in the test phase. We refer to these effects as item-specific S-R effects. Trial structure and task instructions will be equivalent to the last appearance of stimuli during the learning phase. Again, after responding to each stimulus, the same remember or forget cue as in the 4th block of the learning phase will be presented. Participants will be reminded to only remember the TBR stimuli. 4. Distraction Phase: Participants will again solve a visual working memory task for 1.5 minutes to spurge short-term memory. 5. Recognition Phase: In the recognition phase, participants will be presented with all the object images from the learning phase and an equal number of new object images. Participants will be instructed to identify whether an object has previously been presented (category: “old”) or not (category: “new”) – regardless of its associated memory instruction – by pressing either left or right response key. On a given trial, an old or new stimulus image is presented in the center of the screen, and participants are instructed to categorize the stimulus as quickly and accurately as possible as either “old” or “new” according to the category labels that are displayed in the right or left upper screen corner. ***How we aim to measure a DF effect on S-R associations*** We will use the difference in RTs and error rates between trials that require an item-specific response switch and an item-specific response repetition to determine a DF effect based on the remember/forget cues. In other words, we will measure the strength of item-specific S-R effects for TBF as compared to TBR stimuli. Applying the observations in typical DF paradigms to the context of S-R associations we expect the following observations: If DF influences the retrieval of S-R associations, S-R effects, the performance differences between item-specific response repetitions and response switches (see Hypotheses section) should be smaller for TBF as compared to TBR stimuli.

Humans are able to intentionally forget previously learned information (as often measured using the directed forgetting, short DF, paradigm, for an overview see Pastötter et al., 2017). In the item-method of DF (see MacLeod, 1998 for a review), for instance, participants are instructed to memorize stimuli that are presented to them sequentially. Each sequentially presented stimulus is directly followed by a memory cue informing participants to either forget or remember the stimulus. People typically recall fewer to-be-forgotten (TBF) than to-be-remembered (TBR) stimuli (Bjork, 1970). Whereas various studies have already demonstrated that humans are able to intentionally forget previously learned information, only few studies have analyzed whether DF affects motor representations (Tempel & Frings, 2016) or incidentally learned information (e.g., Hockley et al., 2016; Jou, 2010). Recent studies provided first evidence that typical findings regarding retrieval-induced forgetting and effects of DF extend to motor memory (in particular, motor sequences, Tempel et al., 2016; Tempel & Frings, 2013, 2016) and stimulus-response (S-R) associations (Dreisbach & Bäuml, 2014). Stimulus-response (S-R) associations are formed when stimuli and responses repeatedly co-occur and thus bind together – a notion that is supported by (item-specific) repetition priming effects (see Logan, 1988, 1990). Here, participants’ responses are, for instance, generally faster for stimuli that require the same as opposed to a different, previously-executed response. The current study aims to gain a deeper understanding of the mechanisms underlying the DF effect on the retrieval of S-R associations. For this, we will investigate whether (not explicitly instructed) learned S-R associations can be intentionally forgotten based on forget/remember cues following stimuli. To address this question, we will combine the DF item-method paradigm and an item-specific priming paradigm assessing the encoding and retrieval of S-R associations (see below and also Hsu & Waszak, 2012; Moutsopoulou et al., 2015). Information on the task and study design Our study will consist of three major phases: (1) A learning, (2) a distraction, and (3) a test phase. 1. Learning Phase: We will extend the DF paradigm to S-R mappings using a classification task (drawing on an item-specific priming paradigm; Hsu & Waszak, 2012; Moutsopoulou et al., 2015; Pfeuffer, Moutsopoulou et al., 2017). During the learning phase, participants’ task is to classify words (referring to concrete objects) as animate or inanimate (animacy task). By categorizing the stimuli, participants encode S-R associations (stimulus-action, S-A, associations between stimuli and motor outputs according to the terminology provided by Moutsopoulou et al., 2015). Importantly, whether a left or right response is required for an animate/inanimate classification of a specific stimulus will differ between stimuli and will be indicated by a task cue detailing the classification-response mapping prior to the stimulus’ presentation. For example, the task cue “A + I” will indicate that a word has to be classified as “animate” by pressing a left key and as “inanimate” by pressing a right key. Conversely, the task cue “I + A” will indicate that a right key press is required for words denoting animate stimuli and a left key press for words denoting inanimate stimuli. At the same time, participants will be instructed to memorize some of the words for a later memory test and forget others. Directly after the response to a stimulus, a memory cue will inform participants to either forget or remember the stimulus. 2. Distraction Phase: In a subsequent distractor task, participants will solve a visual working memory task (Corsi) for 1.5 minutes to spurge short-term memory. 3. Test Phase: In the test phase, participants will be presented once more with the stimuli from the learning phase (both TBF and TBR words) and new words. Importantly, for the old words, half of the stimuli per memory condition, the S-R mapping will be the same as in the learning phase (item-specific response repetition between learning and test). For the other half of the stimuli, the S-R mapping will be the opposite (item-specific response switch between learning and test). Response switches are therefore defined as item-specific switches of S-R mappings between the learning and the test phase of a stimulus (e.g., the stimulus requires a right key press in the learning phase, but a left key press in the test phase). Response repetitions are, in turn, defined as item-specific repetitions of the required response between the learning and the test phase (e.g., the stimulus requires a right key press in both the learning and test phase). Typically, RTs and error rates for item-specific response repetitions between learning phase and test trials are lower as compared to item-specific response switches, indicating that S-R associations have been formed during the learning phase and were retrieved in the test phase. We refer to this effect as item-specific S-R effect. Trial structure and task instructions in the test phase will be equivalent to the learning phase except that no memory cues will be presented anymore. Participants will be instructed that they will only need to respond to stimuli fast and accurately and that there will be no memory cues for this phase. Participants will also be informed that there will be no later memory test. They are told that they will respond to both TBR and TBF stimuli and that they should simply respond as fast and accurately as possible regardless of the previous memory instruction. How we aim to measure a DF effect on S-R associations We will use the difference in RTs and error rates in the test phase between trials that require an item-specific response switch and an item-specific response repetition (S-R effect) to determine a DF effect based on the remember/forget cues. In other words, we will measure the strength of item-specific S-R effects for TBF as compared to TBR stimuli. If there was a DF effect on the retrieval or encoding of S-R associations, the performance differences between item-specific response repetitions and response switches (S-R effect) in the test phase (see Hypotheses section) should be smaller for TBF as compared to TBR stimuli.

Cognitive-Affective Maps (Thagard, 2010), hereafter named CAMs, represent concepts and their affective connotation on a given topic of interest. Our goal is to take a further look on how elaborating a CAM of someone other can change one’s opinion. We will measure this potential opinion change by use of modified versions of the questionnaires Technology Acceptance Model 3 (Venkatesh & Bala, 2008), Ethics Scales Inventory (Reidenbach & Robin, 1988), Positive And Negative Affect Schedule (Watson, Clark & Tellegen, 1988) and Conflict Management Scale (Austin, Gregory & Martin, 2009) and by asking subjects to draw a CAM representing their opinion.

Based on stimulus–response (S-R) associations, the presentation of a certain stimulus can automatically trigger associated responses (e.g., Henson, et al., 2014; Hommel, 1998; Logan, 1990). S-R associations are formed when stimuli and responses co-occur and thus bind together – a notion that is supported by (item-specific) repetition priming effects (see Logan, 1988, 1990). Here, participants’ responses are, for instance, generally faster for items that require the same as opposed to a different, previously not executed response. Such item-specific S-R priming effects are in turn composed of item-specific stimulus–action (S-A) priming effects and stimulus–classification (S-C) priming effects. That is, S-R associations can be divided into two components: S-A associations between stimuli and motor outputs and S-C associations between stimuli and their task-specific semantic classifications (see Hsu & Waszak, 2012; Moutsopoulou et al., 2015). These two components of S-R associations are assumed to be independent (e.g., Pfeuffer et al., 2019). S-A and S-C associations are investigated using item-specific priming paradigms (e.g., Hsu & Waszak, 2012; Moutsopoulou et al., 2015; Pfeuffer et al., 2017). Here, the classification task participants perform upon an item can either repeat or switch between its prime and probe instance (e.g., prime classification according to mechanisms; probe: classification according to size). Similarly, the required action (left vs. right key press) independently repeats or switches between prime and probe instance of a specific item. Differences in response times, RTs, and error rates between probe trials that require an item-specific action repetition versus switch and item-specific classification repetition versus switch reflect the impact of S-A and S-C associations retrieved during probe trials. That is, both influences on encoding during prime trials and influences on retrieval during probe trials may contribute to item-specific S-A and S-C priming effects. Surprisingly, no study has yet investigated whether and how the timing of the task cues (i.e., the temporal relation of stimulus and task instructions and/or corresponding processing) affects the encoding and/or retrieval of S-A and S-C associations. In a first study, we thus manipulated the timing of the task cue relative to stimulus presentation (i.e., task cue presented before, simultaneously with, or after the stimulus) during prime and probe trials. One central finding of this study was that prime task cue timing during S-A/S-C encoding affected S-A and S-C associations. Item-specific S-A and S-C priming effects in the probe trials were weakened when the task cue was presented after the stimulus as compared to when it was presented before or simultaneously with the stimulus. The present study aims to assess two contrary hypotheses regarding the mechanisms underlying this influence of prime task cue timing on item-specific S-A/S-C priming effects: Perception-Processing Overlap Hypothesis: The greater the overlap between the percept of the stimulus and the task-dependent processing of the stimulus (based on the task cue) is, the stronger is the corresponding item-specific S-A/S-C association. In the previous Experiment 1, in the prime after condition, there was no temporal overlap between the percept of the stimulus and the task-dependent processing of the stimulus. That is, the stimulus was not on-screen anymore when the task cue was presented and participants actually performed the classification task and determined the correct action. In contrast, in the prime before condition, participants were instructed about the task classification- and action-mappings information before to onset of the stimulus. Thus, task-relevant processing as well as processing of the stimulus and its perception occurred in parallel upon stimulus presentation. The same was the case for the prime condition where stimulus and task cue appeared at the same time (referred to as the prime with condition). Thus, the perception-processing overlap hypothesis can explain why item-specific S-A/S-C priming effects in the probe were reduced when task cues were presented after the stimulus in its prime trials as compared to before or with the stimulus. Pre-Classification Hypothesis: In the prime after condition, participants had to process the stimulus already before the task cue was presented. As participants did not yet know which classification would be needed, they might classify the stimulus according to both classification tasks initially, thereby associating the stimulus with both classifications and the corresponding actions (or, alternatively, they may have pre-classified the stimulus based on the prior trial’s classification task). When the task cue was subsequently presented, participants then strengthened the prepared S-A/S-C links. This resulted in observable, but still weaker item-specific S-A/S-C priming effects in the probe trial for the prime after condition. In contrast to the prime before and prime with condition in which the stimulus appeared after/with the task cue and a pre-classification was not adaptive. To distinguish between the two hypotheses, in a second experiment, we will contrast the prime with condition with several prime after conditions differing in terms of the temporal overlap of stimulus and task cue (i.e., perceptual-processing overlap). Please refer to the attached Figure in the Design Plan and Analysis Plan section for an illustration of the experimental manipulation. In all prime after conditions, we will first present the stimulus for at least a duration of 1000ms. Following this 1000ms stimulus presentation, we gradually manipulate the temporal overlap of stimulus and task cue (0, 100 ms, 300 ms, 500 ms, 1000ms). If the task cue was presented for less than 1000 ms, task cue presentation will continue after stimulus offset until the task cue was present for 1000 ms in each condition (unless participants respond beforehand). Nevertheless, participants will be instructed to respond as soon as stimulus and task cue have been presented in all conditions. The second attached Figure in the Design Plan and Analysis Plan section illustrates the predicted results that align with the tests we pre-register in the next section. The perception-processing overlap hypothesis predicts that S-C priming effects in the probe should increase with the temporal overlap of stimulus and task cue, that is the related processes. At a temporal overlap of 1000ms, the temporal overlap in the prime with and prime after condition are the same and the only difference is that the stimulus has already been presented for 1000 ms in the prime after condition. Thus, based on the perception-processing overlap hypothesis there should be no difference between the prime after and prime with condition anymore as the overlap of stimulus perception and task-related processing in the two conditions is identical. In contrast, the pre-classification hypothesis predicts that the S-C priming effects (note, that we will also investigate the S-A priming effects but focus on the S-C priming effects in the present preregistration as S-A priming effects are weaker than S-C priming effects) in the probe should not change as a function of the temporal overlap, because participants can already prepare for and associate both classifications with the stimulus during the first 1000ms of stimulus presentation in the prime after condition. Thus, the pre-classification hypothesis predicts that S-C priming effects in the probe will be reduced in the prime after as compared to prime with condition regardless of the temporal overlap of stimulus and task cue. If both assumed processes play a role, S-C priming effects should gradually increase in the prime after condition with increasing temporal overlap (perception-processing overlap hypothesis). At a temporal overlap of 1000ms, however, S-C priming effects should be weaker in the prime after condition than in the prime with condition. That is, even though perception-processing overlap is the same in the prime with and prime after overlap 1000 ms condition, participants pre-activated and associated both classifications with the stimulus during the initial 1000 ms of stimulus presentation in the prime after condition. This leads to reduced S-C priming effects as compared to the prime with condition.

The ability to inhibit now unwanted but already planned actions is essential to flexibly adapt to fast changing environments (e.g., Logan & Cowan, 1984; Verbruggen et al., 2019). Stop signal experiments (e.g., Verbruggen et al., 2019; Verbruggen & Logan, 2008 a,b, 2009) allow researchers to assess individuals’ ability to inhibit responses to specific stimuli (e.g., participants are instructed to withhold from responding when they hear a certain tone). In the present study we aim to assess the influence of inhibition on associative learning, specifically, the encoding and/or retrieval of stimulus-response associations (see e.g., Henson et al., 2014, for an overview over stimulus-response associations. We will investigate how inhibiting a response to a stimulus affects item-specific associations between the stimulus and response components. In particular, we will look at how inhibition affects stimulus-action (S A) associations – associations between a stimulus and a specific motor output (e.g., apple – right) - and stimulus-classification (S-C) associations – associations between a stimulus and its task-specific classification (e.g., apple – small) - (see e.g., Moutsopoulou et al., 2015). S-A and S-C associations are formed when stimuli and actions/classifications (repeatedly) co-occur and thus bind together – a notion that is supported by item-specific repetition priming effects (see e.g., Logan, 1988, 1990. S-A and S-C associations are investigated using item-specific priming paradigms (e.g., Hsu & Waszak, 2012; Moutsopoulou et al., 2015; Pfeuffer et al., 2017). Item-specific repetition priming effects can be tested by having participants respond to a stimulus twice (prime and probe) with a lag of several trials. S-A and S-C associations are encoded in the prime and retrieved in the stimulus’ corresponding probe. That is, participants are faster and commit fewer errors when the same action/classification as in the prime is required in a stimulus’ probe (item-specific S-A/S-C repetition priming effect). We expect that intentionally stopping/inhibiting responses in the prime/probe will affect the encoding and/or retrieval of S-A and S-C associations. This should become evident in effects on item-specific repetition priming effects in probe trials. In the present study, participants classify images of everyday objects according to mechanisms or size. The classification task that participants perform upon an item can either repeat or switch between its prime and probe instance (e.g., prime: classification according to mechanisms; probe: classification according to size; item-specific S-C mapping). Similarly, the required action (left vs. right key press) independently repeats or switches between the prime and probe instance of a specific item. Differences in response times, RTs, and error rates between probe trials that require an item-specific action/classification repetition versus switch reflect the impact of S-A/S-C associations retrieved during probe trials. In a portion of prime/probe trials, we will present an auditory stop signal indicating to participants that they should withhold from responding. We assess the impact of successful/unsuccessful stopping on item-specific repetition priming effects.

Humans are able to intentionally forget previously learned information (as often measured using the directed forgetting, short DF, paradigm, for an overview see Pastötter et al., 2017). In the item-method of DF (see MacLeod, 1998 for a review), for instance, participants are instructed to memorize stimuli that are presented to them sequentially. Each sequentially presented stimulus is directly followed by a memory cue informing participants to either forget or remember the stimulus. People typically recall fewer to-be-forgotten (TBF) than to-be-remembered (TBR) stimuli (Bjork, 1970). Whereas various studies have already demonstrated that humans are able to intentionally forget previously learned information, only few studies have analyzed whether DF affects motor representations (Tempel & Frings, 2016) or incidentally learned information (e.g., Hockley et al., 2016; Jou, 2010). Recent studies provided first evidence that typical findings regarding retrieval-induced forgetting and effects of DF extend to motor memory (in particular, motor sequences, Tempel et al., 2016; Tempel & Frings, 2013, 2016) and stimulus-response (S-R) associations (Dreisbach & Bäuml, 2014). Stimulus-response (S-R) associations are formed when stimuli and responses repeatedly co-occur and thus bind together – a notion that is supported by (item-specific) repetition priming effects (see Logan, 1988, 1990). Here, participants’ responses are, for instance, generally faster for stimuli that require the same, previously-executed as opposed to a different response. In a previous study we found that in the presence of the instruction to remember/forget the stimulus, typical item-specific S-R effects (see below) were absent. The current study aims to test whether the instruction to forget/remember (hence, an additional memory task) weakens the encoding/retrieval of S-R associations as compared to a condition where no such additional memory task is present. Information on the task and study design Participants will be divided into two group: One group (no-memory group) will work on an item-specific priming task (e.g., Hsu & Waszak, 2012; Moutsopoulou et al., 2015; Pfeuffer, Moutsopoulou et al., 2017). The other group (memory group) will do the same but in addition receive the instruction to remember some of the stimuli (and forget others). Our study will consist of three major phases: (1) A learning, (2) a distraction, and (3) a test phase. 1. Learning Phase: During the learning phase, participants’ task is to classify images of objects as mechanic or non-mechanic. By categorizing the stimuli, participants encode S-R associations (stimulus-action, S-A, associations between stimuli and motor outputs according to the terminology provided by Moutsopoulou et al., 2015). Importantly, whether a left or right response is required for a mechanic/non-mechanic classification of a specific stimulus will differ between stimuli and will be indicated by a task cue detailing the classification-response mapping prior to the stimulus’ presentation. For example, the task cue “M + N” will indicate that an object image has to be classified as “mechanic” by pressing a left key and as “non-mechanic” by pressing a right key. Conversely, the task cue “N + M” will indicate that a right key press is required for mechanic objects and a left key press for non-mechanic ones. At the same time, half of the participants will be instructed to memorize some of the images for a later memory test and forget others. Directly after the response to a stimulus, a memory cue will inform participants to either forget or remember the stimulus (memory-group only). For the other half of the participants, the same cues will be presented after participants’ responses, but they will be told that these stimuli signal the end of a trial and will not receive a memory instruction that is associated with the cues (no-memory group). Thereby, visual input will be similar between the two groups. 2. Distraction Phase: In a subsequent distractor task, participants will solve a visual working memory task (Corsi) for 1.5 minutes to spurge short-term memory. 3. Test Phase: In the test phase, participants will be presented once more with the stimuli from the learning phase (both to-be-forgotten and to-be-remembered images for the memory-group) and new images. Importantly, for the old images, half of the stimuli per memory condition, the S-R mapping will be the same as in the learning phase (item-specific response repetition between learning and test). For the other half of the stimuli, the S-R mapping will be the opposite (item-specific response switch between learning and test). Response switches are therefore defined as item-specific switches of S-R mappings between the learning and the test phase of a stimulus (e.g., the stimulus requires a right key press in the learning phase, but a left key press in the test phase). Response repetitions are, in turn, defined as item-specific repetitions of the required response between the learning and the test phase (e.g., the stimulus requires a right key press in both the learning and test phase). Typically, RTs and error rates for item-specific response repetitions between learning phase and test trials are lower as compared to item-specific response switches, indicating that S-R associations have been formed during the learning phase and were retrieved in the test phase. We refer to this effect as the item-specific S-R effect. Trial structure and task instructions in the test phase will be equivalent to the learning phase except that no memory cues will be presented anymore. Participants will be instructed that they will only need to respond to stimuli fast and accurately and that there will be no memory cues for this phase. Participants in the memory-group will also be informed that there will be no later memory test. They are told that they will respond to both TBR and TBF stimuli and that they should simply respond as fast and accurately as possible regardless of the previous memory instruction. How we aim to measure an effect of memory-instruction on S-R associations We will determine whether the memory instruction affects the difference in RTs and error rates in the test phase between trials that require an item-specific response switch and an item-specific response repetition (S-R effect). In other words, we will measure the strength of item-specific S-R effects for the memory group as compared to no-memory group. If the memory instruction negatively influences the retrieval and/or encoding of S-R associations, performance differences between item-specific response repetitions and response switches (S-R effect) in the test phase (see Hypotheses section) should be smaller for the memory group as compared to no-memory group. We anticipate that the intention to remember/forget the stimulus hinders associative learning of S-R associations: When participants aim to remember the stimulus itself but not the S-R associations, they may rehearse the respective stimulus thereby re-activating its representation. We assume that upon responding to a stimulus, a S-R association is formed (e.g., via Hebbian learning; for an overview see Abrahamse et al. 2016; Verguts, 2017). If the stimulus is now activated again (e.g., being rehearsed to conform with the memory instruction) but no response is given, the distributed associative memory (Rumelhart, et al., 1986) may send signals through the existing connection weights and predict a response (the network’s output based on the previously learned S-R association). The predicted response would then be compared to the actual responses which in this case is absent. Hence, an error is produced that may, in turn, result in weight changes between the stimuli and the associated responses (the weight matrix). As a result, intending to memorize the stimulus without also reactivating the associated response, may indeed result in the loss of the previously learned S-R association (in particular, when this association is weak to begin with).

Novel technologies are concurrently developed. Yet, the social acceptance of these is often examined rather late during the developmental process (or even ignored). The technological acceptance is measured in this study by questionnaires as well as by the method “Cognitive-Affective Mapping” (CAM). Our goal is to find out how CAMs compare to the established method of questionnaires.

The project investigates how time as a source of information supports multitasking. The experiments proposed here will systematically investigate the mechanism underlying the effect from time-based predictability in one task on performance in a concurrent task. Hypotheses are derived from converging theorizing about predictability by multiple projects in the priority program SPP 1772. A first set of experiments will investigate how performance in both tasks is related to each other, and which interval durations primarily drive the effect. A second set of experiments is devoted to the domain specificity of the effects. Does time-based predictability in one task support timing related second tasks in particular? A last set of experiments will be deal with the cognitive representations of time processed in the adaptation effect. Does it involve relative representations, such as adaptation to task prediction? Or does it employ absolute representation, as typical for some related movement timing tasks?

The mechanism underlying congruence sequence effects (CSE) has been an issue since Mayr, Awh, and Laurey (2003) illustrated that the CSE observed in a simple two alternative force choice (2-AFC) flanker task was significant only when targets repeated. While some researchers or theories argue that CSEs are rooted in feature integration (Hommel, 2004), there is evidence showing that the CSE without feature integration is also accessible in some conditions (Kim & Cho, 2014; Schmidt & Weissman, 2014; Weissman, Jiang, & Egner, 2014). Among previous studies that investigated affective effects on CSEs, few controlled for the contribution of feature integration to CSEs. Given the observation that affect influences feature integration (for a review, see e.g., Mather & Sutherland, 2011), we attempt to test affective influences on CSEs with and without feature integration in the current experiment.

More than two decades ago cognitive neuroscientists started to use brain imaging techniques to investigate the neurocognitive bases of task switching. But has this research contributed to our understanding of the cognitive processes underlying cognitive flexibility? In the present chapter, we will give an overview of brain imaging research on task switching using fMRI. Furthermore, we will address the question to what extent this research has contributed to our understanding of the cognitive processes involved in task switching. We will argue that brain imaging has provided a unique contribution to the cognitive literature in at least three different ways. First, brain imaging findings helped to decompose component processes related to task switching. Second, brain imaging helped to support the idea that common processes underly task switching paradigms in different domains. Finally, it helped to show that some manipulations that were believed to tap into the same processes, in fact reflected different cognitive operations.

supplementary material for the scoping review to develop the “Ethics Scale of Technology Assessment”

In the article, we propose an innovative combination of newly developed methods consisting of two data collections (Cognitive-Affective Mapping and large-scale survey with different questionnaires/scales) and three types of data analyses (analyses of Cognitive-Affective Maps, analyses of survey data using factor analysis and cluster analytical procedures). This is intended to assess the acceptability and potential ethical concerns related to SAI.

The notion of symbiosis has been increasingly mentioned in research on physically coupled human-machine systems. Yet, a uniform specification on which aspects constitute human-machine symbiosis is missing. By combining the expertise of different disciplines, we elaborate on a multivariate perspective of symbiosis as the highest form of physically coupled human-machine systems. Four dimensions are considered: Task, interaction, performance, and experience. First, human and machine work together to accomplish a common task conceptualized on both a decision and an action level (task dimension). Second, each partner possesses an internal representation of own as well as the other partner's intentions and influence on the environment. This alignment, which is the core of the interaction, constitutes the symbiotic understanding between both partners, being the basis of a joint, highly coordinated and effective action (interaction dimension). Third, the symbiotic interaction leads to synergetic effects regarding the intention recognition and complementary strengths of the partners, resulting in a higher overall performance (performance dimension). Fourth, symbiotic systems specifically change the user's experiences, like flow, acceptance, sense of agency, and embodiment (experience dimension). This multivariate perspective is flexible and generic and is also applicable in diverse human-machine scenarios, helping to bridge barriers between different disciplines., 28 pages, 7 figures

We tested a novel method for studying human experience (thoughts and affect). We utilized Cognitive-Affective Maps (CAMs)–an approach to visually represent thoughts and their affective connotations as networks of concepts that individuals associate with a given event. Using an innovative software tool, we recruited a comparative sample of (n = 93) Canadians and (n = 100) Germans to draw a CAM of their experience (events, thoughts, feelings) with the Covid-19 pandemic. We treated these CAM networks as a series of directed graphs and examined the extent to which their structural properties (latent and emotional) are predictive for the perceived coronavirus threat (PCT). Across multiple models, we found consistent and significant relationships between these network variables and the PCT in both the Canadian and German sample. Our results provide unique insights into individuals' thinking and perceptions of the viral outbreak. Our results also demonstrate that a network analysis of CAMs' properties is a promising method to study the relationship between human thought and affective connotation. We suggest that CAMs can bridge several gaps between qualitative and quantitative methods. Unlike when using quantitative tools (e.g., questionnaires), participants' answers are not restricted by response items as participants are free to incorporate any thoughts and feelings on the given topic. Furthermore, as compared to traditional qualitative measures, such as structured interviews, the CAM technique may better enable researchers to objectively assess and integrate the substance of a shared experience for large samples of participants.

In interference tasks, the magnitude of the congruency effect is reduced in trials that follow an incongruent trial. This congruence sequence effect (CSE) reflects cognitive control processes, yet accounts disagree when and how control is exerted. Here, we address these questions in the context of the prime-target task. In this task, control can either modulate early prime or late target information. Furthermore, control can utilize information specific to the stimulus (perceptual features) or relational information between stimuli (temporal order). Two experiments (N = 41 | N = 62) were conducted using a prime-target task with arrows (prime) and letters (target). We presented either the prime before the target or the target before the prime. For both trial-type transitions, the CSE was assessed. Regarding the first question, when is control exerted, results showed a larger CSE for prime→target relative to target→prime trials. This suggests that control in the prime-target task modulates prime activity. Regarding the second question, how is control exerted, a combined analysis of both experiments showed a larger CSE for repetition of the same prime and target order across two trials (e.g., previous trial: prime→target; current trial: prime→target) compared to changes (e.g., previous trial: prime→target; current trial: target→prime), suggesting that control in the prime-target task can employ temporal selection.

When our actions yield predictable consequences in the environment, our eyes often already saccade towards the locations we expect these consequences to appear at. Such spontaneous anticipatory saccades occur based on bi-directional associations between action and effect formed by prior experience. That is, our eye movements are guided by expectations derived from prior learning history. Anticipatory saccades presumably reflect a proactive effect monitoring process that prepares a later comparison of expected and actual effect. Here, we examined whether anticipatory saccades emerged under forced choice conditions when only actions but not target stimuli were predictive of future effects and whether action mode (forced choice vs. free choice, i.e., stimulus-based vs. stimulus-independent choice) affected anticipatory saccades. Participants produced predictable visual effects on the left/right side via forced choice and free choice left/right key presses. Action and visual effect were spatially compatible in one half of the experiment and spatially incompatible in the other half. Irrespective of whether effects were predicted by target stimuli in addition to participants' actions, in both action modes, we observed anticipatory saccades towards the location of future effects. Importantly, neither the frequency, nor latency or amplitude of these anticipatory saccades significantly differed between forced choice and free choice action modes. Overall, our findings suggest that proactive effect monitoring of future action consequences as reflected in anticipatory saccades does not differ between forced choice and free choice action modes.

Supplemental material, QJE-STD-20-081.R2-Supplementary_Materials for Balancing cognitive and environmental constraints when deciding to switch tasks: Exploring self-reported task-selection strategies in self-organised multitasking by Victor Mittelstädt, Insa Schaffernak, Jeff Miller and Andrea Kiesel in Quarterly Journal of Experimental Psychology

Dual-tasking often requires prioritizing one task over the other. For example, in the psychological refractory period (PRP) paradigm, participants are instructed to initially respond to Task 1 (T1) and only then to Task 2 (T2). Furthermore, in the prioritized processing paradigm (PP), participants are instructed to perform T2 only if T1 was a no-go trial—requiring even more prioritization. The present study investigated the limits of task prioritization. Two experiments compared performance in the PRP paradigm and the PP paradigm. To manipulate task prioritization, tasks were rewarded differently (e.g., high reward for T1, low reward for T2, and vice versa). We hypothesized (a) that performance will improve for the highly rewarded task (Experiments 1 and 2) and (b) that there are stronger reward effects for T1 in the PRP than in the PP paradigm (Experiment 2). Results showed an influence of reward on task prioritization: For T1, high reward (compared to low reward) caused a speed-up of responses that did not differ between the two paradigms. However, for T2, reward influenced response speed selectively in the PP paradigm, but not in the PRP paradigm. Based on paradigm-specific response demands, we propose that the coordination of two motor responses plays a crucial role in prioritizing tasks and might limit the flexibility of the allocation of preparatory capacity.

When performing multiple tasks in succession, self-organization of task order might be superior compared to external-controlled task schedules, because self-organization allows optimizing processing modes and thus reduces switch costs, and it increases commitment to task goals. However, self-organization is an additional executive control process that is not required if task order is externally specified and as such it is considered as time-consuming and effortful. To compare self-organized and externally controlled task scheduling, we suggest assessing global subjective and objectives measures of effort in addition to local performance measures. In our new experimental approach, we combined characteristics of dual tasking settings and task switching settings and compared local and global measures of effort in a condition with free choice of task sequence and a condition with cued task sequence. In a multi-tasking environment, participants chose the task order while the task requirement of the not-yet-performed task remained the same. This task preview allowed participants to work on the previously non-chosen items in parallel and resulted in faster responses and fewer errors in task switch trials than in task repetition trials. The free-choice group profited more from this task preview than the cued group when considering local performance measures. Nevertheless, the free-choice group invested more effort than the cued group when considering global measures. Thus, self-organization in task scheduling seems to be effortful even in conditions in which it is beneficiary for task processing. In a second experiment, we reduced the possibility of task preview for the not-yet-performed tasks in order to hinder efficient self-organization. Here neither local nor global measures revealed substantial differences between the free-choice and a cued task sequence condition. Based on the results of both experiments, we suggest that global assessment of effort in addition to local performance measures might be a useful tool for multitasking research.

Alerting signals often serve to reduce temporal uncertainty by predicting the time of stimulus onset. The resulting response time benefits have often been explained by facilitated translation of stimulus codes into response codes on the basis of established stimulus-response (S-R) links. In paradigms of masked S-R priming alerting signals also modulate response activation processes triggered by subliminally presented prime stimuli. In the present study we tested whether facilitation of visuo-motor translation processes due to alerting signals critically depends on established S-R links. Alerting signals resulted in significantly enhanced masked priming effects for masked prime stimuli that included and that did not include established S-R links (i.e., target vs. novel primes). Yet, the alerting-priming interaction was more pronounced for target than for novel primes. These results suggest that effects of alerting signals on masked priming are especially evident when S-R links between prime and target exist. At the same time, an alerting-priming interaction also for novel primes suggests that alerting signals also facilitate stimulus-response translation processes when masked prime stimuli provide action-trigger conditions in terms of programmed S-R links.

In dieser Arbeit werden handlungsdeterminierende Prozesse beim Aufgabenwechsel untersucht. Die ersten durchgeführten Experimente zeigten, dass es notwendig ist, Wechselkosten in verschiedene Teile zu zerlegen, so dass die Dekomposition von Wechselkosten einen großen Teil des Empirieteils ausmacht. Im ersten Kapitel der Arbeit werden verschiedene Theorien zur Handlungsdetermination vorgestellt. Die Theorien werden danach unterschieden, ob sie eher annehmen, dass Umweltreize das menschliche Handeln determinieren oder aber davon ausgehen, dass Handeln überwiegend durch intentionale Prozesse determiniert wird. Zunächst werden der Behaviorismus und einige Ansätze der kognitiven Psychologie erläutert, die den Einfluss von Reizen auf die Handlungsdetermination in den Vordergrund stellen. Das ideomotorische Prinzip dagegen betont die Determination menschlichen Handelns durch das Anstreben von Zielen. Schließlich werden Hybridmodelle (z.B. ABC-Theorie; Hoffmann, 1993) besprochen, die den gemeinsamen Einfluss von Intentionen und Außenreizen auf menschliches Handeln berücksichtigen. Im zweiten und dritten Kapitel wird die Entwicklung des Aufgabenwechselparadigmas und die vermuteten Prozesse und Faktoren beim Aufgabenwechsel erläutert. Ursprünglich wurde das Aufgabenwechselparadigma verwendet, um die Anpassung an flexible Handlungsanforderungen und die dabei notwendigen exekutiven Steuerungsmechanismen zu untersuchen. Doch in vielen Untersuchungen zu Aufgabenwechseln wurde dieses Ziel aus den Augen verloren und die Untersuchung der Vorgänge beim Wechseln wurde zum Selbstzweck. Intentionen und das Anstreben von Zielen wurden innerhalb des Aufgabenwechselparadigmas bisher nicht thematisiert. Im vierten Kapitel wird deshalb versucht Aufgabenwechsel aus der Perspektive der ABC-Theorie (Hoffmann, 1993) zu betrachten, einer Theorie, die sowohl den Einfluss von Intentionen als auch von Außenreizen auf menschliches Handeln berücksichtigt. Aus der ABC-Theorie wurden folgende Vorhersagen für den Aufgabenwechselkontext abgeleitet: 1. Eine parallele Aktivierung verschiedener Handlungsbereitschaften ist prinzipiell möglich. Wechsel zwischen Aufgaben sind nur nötig, wenn sich die Handlungsbereitschaften der Aufgaben widersprechen. 2. Ein Wechsel der Intention und der entsprechenden Handlungsbereitschaften erfolgt sobald die auszuführende Aufgabe feststeht und kann abgeschlossen werden, bevor der Reiz erscheint. Kosten für den exekutiven Prozess des Intentionswechsels können deshalb nur bei kurzer Vorbereitungszeit erfasst werden. Wechselkosten, die nach langer Vorbereitungszeit für die Aufgabe verbleiben (residuale Wechselkosten) spiegeln nicht den Aufwand exekutiver Prozesse wider. 3. Wechsel zwischen Intentionen verursachen mehr exekutiven Aufwand, wenn die Zielzustände der Handlungsbereitschaften überlappen, da dann erst eine Entkopplung des Zielzustandes mit einem Startzustand notwendig ist, bevor der Zielzustand an einen anderen Startzustand gekoppelt werden kann. In den ersten drei Experimenten konnten die Vorhersagen der ABC-Theorie für den Aufgabenwechselkontext nicht bestätigt werden. Beim Vergleich von Durchgängen, in denen die Aufgabe wechselt, mit Durchgängen, in denen die Aufgabe wiederholt wird, zeigten sich auch bei langer Vorbereitungszeit deutliche Wechselkosten, d.h. höhere Reaktionszeiten in den Wechseltrials. Weiterhin ergab sich kein Unterschied der Wechselkosten für Wechsel zwischen Aufgaben mit verschiedenen oder gleichen Zielzuständen (operationalisiert als verschiedene vs. gleiche auszuführende Aktionen). Aus diesen Ergebnissen und vielen Hinweisen in der Aufgabenwechselliteratur ergaben sich Zweifel an der Aussagekraft des herkömmlich berechneten Wechselkostenmaßes als Differenz der Reaktionszeiten bei Aufgabenwechseln minus Aufgabenwiederholungen. Deshalb wird die Entwicklung einer neue Methode zur Dekomposition von Wechselkosten vorgeschlagen, die es ermöglicht, die Reaktionszeitdifferenz zwischen Aufgabenwechseln und Aufgabenwiederholungen in Anteile zu unterteilen, die a) spezifisch die Anforderung beim Wechseln (Intentionswechselkosten) widerspiegeln oder b) durch die Erleichterung eine Reaktion auf denselben Reiz hin zu wiederholen (Repetition Priming) entstehen, oder c) durch die Erschwernis entstehen, auf einen Reiz reagieren zu müssen, auf den zuvor nicht reagiert werden durfte (Negatives Priming). In den Experimenten 4 bis 7 wird diese Methode experimentell validiert. Werden Wechselkosten in die verschiedenen Anteile zerlegt, stimmen die empirischen Befunde mit den Vorhersagen der ABC-Theorie überein: Ein Wechsel der Intention und der entsprechenden Handlungsbereitschaften kann bei genügend langer Vorbereitungszeit abgeschlossen werden, bevor der Stimulus erscheint. Residuale Wechselkosten (Wechselkosten, die bei langer Vorbereitungszeit verbleiben) sind nicht auf exekutive Kontrollprozesse zurückzuführen, sondern durch Repetition Priming und Negatives Priming verursacht. Weiterhin sind Wechsel einfacher, wenn die zu antizipierenden Zielzustände (operationalisiert als die auszuführenden Aktionen) der Aufgaben verschieden sind. In der Abschlussdiskussion werden der theoretische und der methodische Schwerpunkt der Arbeit noch einmal überblicksartig zusammengestellt und es wird ein Ausblick gegeben, wie untersucht werden könnte, ob die ABC-Theorie besser geeignet ist zur Erklärung der Phänomene beim Aufgabenwechsel als bisherige Theorien und Modelle., The thesis deals with the investigation of action control processes while switching between tasks. The first experiments showed the necessity to decompose switch costs in different dues. Thus, the decomposition of switch costs constitutes the main part of the empirical work. The first chapter of the thesis sketches different theories of action control. The theories differ according to whether they emphasise the role of external stimuli or intentional processes for human actions. Initially, the behaviourism and some accounts within cognitive psychology are illustrated. They mainly consider the impact of external stimuli on action control. In contrast, the ideomotor principle claims that actions are determined by intentions to reach specific goals. Finally, hybrid models (e.g. ABC-theory; Hoffmann, 1993) are illustrated that consider the mutual impact of intentions and stimuli on human actions. The development of the task switching paradigm and the assumed processes and variables while switching between tasks are discussed in the second and third chapter. Originally, the task switching paradigm was introduced to investigate how humans adapt on flexible action requirements and which executive control processes are involved. But in many task switching studies this goal eclipsed and the investigation of the processes while switching became important. Within the task switching paradigm intentions and the aim to reach specific goals haven’t been debated. Therefore, task switching is surveyed from the point of view of the ABC-theory (Hoffmann, 1993) as the ABC-theory considers the impact of intentions as well as external stimuli on human acting. The following predictions for the task switching context are deducted from the ABC-theory: 1. In principle, parallel activation of different action dispositions is possible. Switching between tasks is only necessary when the action dispositions of different tasks conflict. 2. Switching between intentions and action dispositions starts as soon as participants are informed about the identity of the task and can be completed before the stimulus occurs. Switch costs that remain after long preparation time for the task (residual switch costs) do not reflect the effort of executive processes. 3. Switching between intentions causes more effort of executive processes when the action dispositions aim for the same goal states as in this case it is first necessary to decouple the goal state from one start condition before this goal state can get coupled to another start condition. The first three experiments were not able to confirm the predictions of the ABC-theory. When comparing trials in which the task switches with trials in which the task repeats there are significant switch costs, e.g. longer reaction times for switch trials, even when participants have sufficient time to prepare for the task. Furthermore, switch costs do not differ for switching between tasks with different vs. equal goal states (operationalised as different vs. equal required actions). These results and many cues from the task switching literature led to doubts about the informational value of the measurement of switch costs computed as difference of reaction times of task switches minus task repetitions. Hence a new method is proposed that decomposes switch costs into a) the specific requirements due to the necessity to switch (intentional switch costs) b) the benefit to repeat a response afforded by the same stimulus (repetition priming) and c) the difficulty to respond to a stimulus to which no response was allowed shortly before (negative priming). Experiments 4 to 7 validate this new method experimentally. If switch costs are decomposed into the different parts, the empirical results fit to the predictions of the ABC-theory: Switching between tasks (i.e. between intentions and appropriate action dispositions) can be fully completed when the preparation time is sufficient before the stimulus is presented. Residual switch costs (switch costs that remain for long preparation time intervals) are not due to executive control processes but due to repetition priming and negative priming. Furthermore, switching between tasks is easier if the anticipated goals / the to-be-performed actions for the tasks differ. The final discussion summarizes the theoretical and the empirical focus of the thesis. A prospect is exemplified how to investigate whether the ABC-theory is more appropriate to account for the phenomena while switching between tasks as current theories and models.

Chapter 1 starts with providing the theoretical background against which the experimental work in this thesis can be viewed. It provides the main approaches, theories and views on consciousness and the main challenges in the field. Specifically, it does so in relation to first-order and second- order neuronal processing, which will be explained later on. Furthermore, Chapter 1 discusses the conscious brain in its larger context of an embodied mind and the environment in which the agent lives. Lastly, the final section reviews the possibility of consciousness being a social construct. Chapter 2 continues with examining what happens when information-processing is limited to first-order processing, which is the case when information remains subliminal. Subliminal information does get processed up to a certain level, since brain activity in response to the stimulus can be measured. Yet, it is not processed up to the level that renders the stimulus conscious. The study presented in Chapter 2 aims to answer whether perceptual information presented below the conscious threshold can still affect behaviour? The outcome of this and similar studies would tell us more about the possible functions of consciousness. If subliminal stimuli are not able to influence behaviour, it would suggest that consciousness is necessary in order to guide or regulate human behaviour. Chapter 3 discusses how (changes in) perceptual content influences the subjective experience of time, a concept that is highly related to consciousness. Consciousness inevitably needs a reference or content to be conscious of. Similarly, time needs external physical events to occur to have any meaning, since time is generally only defined in terms of changes of state, mass or energy. Atomic clocks measure time by detecting changes in energy levels of electrons in atoms and are the most accurate timekeepers we have with an error rate of only 1 second per 30 million years. Therefore, no matter how small the event is, without any such event like a change in physical state of the electron the concept of time would be meaningless. Thus, the concept of time would be completely irrelevant in a universe without mass or matter. In such a universe the passing of a single nanosecond would be exactly the same as a billion years. This dependence on external events is what makes time perception such an interesting topic to study in the field of consciousness. The critical question here is how subjective experience of time relates to conscious (changes in) perceptual content.Chapter 4 further explores the relationship between perceptual content and consciousness. The study described in this chapter examines the transition of first-order information to second-order processing. Does a gradual increase in first-order perceptual evidence result in similarly gradual judgments of subjective experience? This chapter discusses levels of representation, perceptual evidence and their effect on subjective judgments. The key question here is whether increasing perceptual evidence while maintaining a fixed level of representation will result in higher levels of subjective measures as well or whether such measures only increase with higher levels of representation. In short, can you be more or less conscious in a graded manner or is consciousness an all-or-none type of phenomenon? This answer will have important consequences for distinguishing between the main theories on consciousness since their predictions about graded consciousness differ and therefore could be strongly challenged by the answer to this question. Chapter 5 tests the idea of consciousness being an acquired ability rather than an innate property of the brain by examining the possibility of training or improving second-order processing, which is one of the key assumptions of the Radical Plasticity Theory. The study described in this chapter explores plasticity of consciousness by performing a perceptual learning study of multiple sessions over several days. The effects of this training paradigm on both first- and second order processing will be discussed in this chapter. Chapter 6 looks deeper into such second-order subjective judgments and what kinds of first- order information is used to make such judgments. It has been suggested that such measures of conscious experience not only incorporate sensory information but also includes information from non-sensory brain areas such as the motor cortex. In light of the sensorimotor accounts of consciousness the influence of motor cortex, and thus action, on the subjective experience of visual stimuli would be an important result and would support such accounts wherein perception and action are tightly intertwined. Finally, chapter 7 summarizes the main findings and discusses the results within the larger framework or first- and second order processing. It also addresses the consequences or implications of these findings for some of the most promising theories on consciousness, and Radical Plasticity thesis in particular., Doctorat en Sciences psychologiques et de l'éducation, info:eu-repo/semantics/nonPublished