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Oddball experiment design in E-prime

Oddball experiment design in E-prime

I want to set up an experiment in which there are 3 types of emotional stimuli (A, B, C). A and C would have a 1% recurrence probability, for rare and unexpected stimuli. Is there any sample of oddball paradigm for E-Prime?


Pstnet, the editor of Eprime, does not provide a sample for oddball paradigms. And sharing Eprime scripts is rare but you may find a script of interest here

http://www.nitrc.org/frs/download.php/283/AO_version110804.zip

This is part of an initiative described at http://www.birncommunity.org/resources/tools/fire-primer/multi-site-cognitive-paradigms-for-fmri-studies. File summaries are here http://www.nitrc.org/projects/fbirn (click on "see all files"). This auditory oddball file is far from being a sample though.


Not that it answers your specific question, but there is this Inquisit script for auditory oddball: http://millisecond.com/download/library/oddball

There is a 30-day trial version of Inquisit.


E-Prime

E-Prime® is a suite of applications to fulfill all of your computerized experiment needs. With more than 100,000 users in research institutions and laboratories in over 60 countries, E-Prime® provides a truly easy-to-use environment for computerized experiment design, data collection, and analysis. E-Prime® provides millisecond precision timing to ensure the accuracy of your data. E-Prime’s flexibility to create simple to complex experiments is ideal for both novice and advanced users.

The E-Prime® suite of applications includes:
•E-Studio – Drag and drop graphical interface for experiment design
•E-Basic – Underlying scripting language of E-Prime
•E-Run – Once the experiment is generated with a single click, E-Run affords you the millisecond precision of stimulus presentation, synchronizations, and data collection.
•E-Merge – Merges your single session data files for group analysis
•E-DataAid – Data management utility
•E-Recovery – Recovers data files


Clinical Assessment

Clinical assessment of the corticospinal tract should involve a comprehensive neurological examination. As the corticospinal tract helps influence motor neurons, the musculature of the body can be focused on, but should not be done in isolation. The presentation and history will direct toward the most relevant clinical examinations to be undertaken, and to the appropriate investigations. In terms of the motor aspect of the neurological examination, this would include assessment of muscle tone and power (as described in Chapter 8 ), observation of the muscle groups to identify fasciculations and tendon reflexes. The tendon reflexes to be examined are the jaw, biceps, triceps, knee, ankle and plantar reflexes.


Oddball experiment design in E-prime - Psychology

The oddball paradigm is an experimental design used within psychology research. Presentations of sequences of repetitive stimuli are infrequently interrupted by a deviant stimulus. The reaction of the participant to this "oddball" stimulus is recorded. The oddball method was first used in event-related potential (ERP) research by Nancy Squires, Kenneth Squires and Steven Hillyard at the UC San Diego.[1] In ERP research it has been found that an event-related potential across the parieto-central area of the skull that usually occurs around 300 ms after stimuli presentation called P300 is larger after the target stimulus. The P300 wave only occurs if the subject is actively engaged in the task of detecting the targets. Its amplitude varies with the improbability of the targets. Its latency varies with the difficulty of discriminating the target stimulus from the standard stimuli.[2] Detection of these targets reliably evokes transient activity in prefrontal cortical regions. Measuring hemodynamic brain activity in the prefrontal cortex using functional magnetic resonance imaging (fMRI) revealed that the dorsolateral prefrontal cortex is associated with dynamic changes in the mapping of stimuli to responses (e.g. response strategies), independently of any changes in behavior.[3] Since P300 has been shown to be an attention-dependent cognitive component in wakefulness, one might suppose that it would be absent during sleep a time in which information processing of external stimuli is commonly thought to be inhibited. Research to date indicates that P300 can be recorded during the transition to sleep and then reappears in REM sleep. Stimuli that are rare and intrusive are more likely to elicit the classic parietal P300 in REM sleep. There is, however, little or no positivity at frontal sites. This is consistent with brain imaging studies that show frontal deactivation is characteristic of REM sleep. These findings indicate that while sleepers may be able to detect stimulus deviance in stage 1 and REM, the frontal contribution to consciousness may be lost.[4] Studies of cognition often use an oddball paradigm to study effects of stimulus novelty and significance on information processing. However, an oddball tends to be perceptually more novel than the standard, repeated stimulus as well as more relevant to the ongoing task, making it difficult to disentangle effects due to perceptual novelty and stimulus significance. Evaluating different brain ERPs can decipher this effect. A frontro-central N2 component of ERP is primarily affected by perceptual novelty, whereas only the centro-parietal P3 component is modulated by both stimulus significance and novelty.[5] The classic auditory oddball paradigm can be modified to produce different neural responses and can therefore be used to investigate dysfunctions in sensory and cognitive processing in clinical samples.[6] A unique application of the oddball paradigm is being used heavily in Schizophrenia research to study the effects in neuronal generator patterns in continuous recognition memory, and the endophenotypes, which provide model on genetic relation of psychiatric diseases that represents phenotypes between manifest clinical syndrome and genetic underpinnings.[7]

Letter Targets (default) Two blocks are run, each with 40 trials (32 control, 8 target/oddball trials). The stimulus set is the letters A, B, C, D, E. The stimulus duration is 500 ms, with 1000 ms between stimulus offset and subsequent stimulus onset.
Image Targets This configuration uses the same conditions, timing, and trial counts as the default. However, the stimuli are a set of five color patches (black, red, green, blue, purple).

Baddeley, A.D., & Hitch, G.J.L. (1974). Working memory. In G.A. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (pp. 47-89). New York: Academic Press.

Cowan, N. (1995). Attention and memory: An integrated framework. New York: Oxford University Press.


Sample description

Twenty-nine recent EOP experiments (taken from 19 publications), published between 2000 and 2017, involve a memory assessment after presentation of a sequence of stimuli. Noteworthy, it is not memory for the deviant stimulus itself that is most often of highest interest for the researchers, but rather memory for the (mostly) emotionally neutral stimuli preceding and following the deviant stimulus in the encoding stage. We only included EOP experiments in our detailed analysis that employed memory tasks and that involved a comparison between emotional and non-emotional deviants, which led to the exclusion of seven studies (no non-emotional control condition: Richardson, Strange, & Dolan, 2004a Richardson, Strange, Duncan, & Dolan, 2003, 2006 Richardson, Strange, Thompson, Baxendale, Duncan, & Dolan, 2004b Schmidt, 2002 no report of behavioral/memory data: Strange & Dolan, 2001, 2007).

Twenty-three out of 29 of the reviewed EOP experiments included an encoding task. Of these, all experiments with pictorial stimuli required a classification task in the encoding stage that involved deep encoding (n = 6 e.g., “Is the presented picture natural or artificial?”). Also, the majority of EOP experiments with words employed classification tasks, with more shallow (n = 9 e.g., “Does the first letter of the presented word have an enclosed space?”) than deep encoding tasks (n = 5). Four experiments with words employed both types of tasks, while one experiment employed a shallow deviant detection task instead of a classification task during encoding. Four EOP experiments with pictures and two with words did not demand any specific task (related with a button-press) during the encoding stage other than to memorize each stimulus.

The identified experiments employed one or two of three types of memory tasks. When facing free-recall tasks, participants are prompted to recall certain events or stimuli without the presentation of any kind of cue that might facilitate recollection. By contrast, in cued recall tasks, in addition to the prompt, a cue is provided. This might be, for example, the first letter of a word, which helps to recollect the target item. Recognition tasks provide the strongest cues by presenting stimuli that might involve the original stimulus itself. The participants’ task now is to decide whether the given stimulus was presented before or not. Recognition tasks may involve the exact same stimuli, similar stimuli, and stimuli that had not been presented during encoding. Presenting these three classes of stimuli during the recognition stage allows for the calculation of recognition rates in different ways, either assessing specific recognition (i.e., did the participant recollect the item in detail?) or general recognition (i.e., did the participant recollect the general theme – also called gist information – of an item but not the exact same item?). The most common memory task in the 29 identified EOP experiments was the free-recall task (n = 16), followed by recognition tasks (n = 9) and cued recall tasks (n = 8 note that in two experiments both recall and recognition tasks were used, while one experiment involved a free and a cued recall task).

The memory tasks used in the identified EOP literature also differ with regard to the time at which memory was assessed and how the material was learned. Table 1 provides an overview of the number of immediate and delayed-memory tasks and the number of instructed and incidental tasks.

In the analyzed experiments, the median number of stimuli per sequence is 14 (range: 5–480). The time between the onsets of two meaningful stimuli in a sequence is often called stimulus-onset asynchrony (SOA). Mean SOA in the reviewed experiments is about 3 s (M = 3,206 ms SD = 1,178 ms range: 1,000–5,000 ms), while stimulus presentation time is about 1 s on average (M = 1,294 ms SD = 572 ms range: 250–3,000 ms). Please see Figs. 1b and 1c for more detailed information on experimental procedures.

Critical for concluding whether emotional deviant stimuli led to amnesic or hypermnesic effects on neighboring stimuli is to determine the baseline to which memory performance for items surrounding the deviants is compared. One way to calculate a baseline for comparison is to randomly draw a number of neutral standards (usually two) from each trial and use the average memory performance of all drawn standard stimuli as a means to express memory performance for neutral standard information (we will refer to this as the “compare with standards method”). Thus, retrograde and anterograde memory effects are reported as deviations from standard recall/recognition (Miu, Heilman, Opre, & Miclea, 2005 Müller et al., 2009 Smith & Beversdorf, 2008 Strange et al., 2000, 2003 Strange & Dolan, 2004 Strange, Kroes, Roiser, Tan, & Dolan, 2008 West Saxvig, Johansen Lundervold, Gronli, Ursin, Bjorvatn, & Portas, 2008). Note that some authors who used this strategy acknowledged possible primacy effects by excluding the first lot of neutral standards of a sequence from analysis (e.g., Müller et al., 2009 Strange et al., 2003, 2004, 2008), while others did not (e.g., Smith & Beversdorf, 2008).

Another common method of comparison is to directly contrast trials that include emotionally neutral deviants (i.e., perceptual or semantic deviants) with emotionally positive/negative deviant trials (we will refer to this as the “contrastive method”). This way, memory performances for neutral deviants as well as for the neutral standards directly preceding and following the neutral deviants serve as a baseline (Clewett, Sakaki, Nielsen, Petzinger, & Mather, 2017 Hurlemann et al., 2005, 2007a, 2007b, Knight & Mather, 2009 Sakaki et al., 2014, Strange et al., 2010).

We illustrate retrograde (i.e., from a deviant to the preceding standard) and anterograde (i.e., from the deviant to the following standard) memory effects in experiments that employed immediate-recall tasks using the “compare with standards method” in Fig. 2 and the “contrastive method” in Fig. 3. In addition, these figures provide information about the magnitude of deviant recall. The figures will be discussed in detail in the summary section.

Deviations of averaged recall rates for all investigated stimulus positions as compared with the averaged recall rates for standard stimuli (in percent) from the respective EOP experiment (i.e., “compare with standards method”). Included are only EOP experiments that involve an immediate free-recall task. Data for picture-label pairs are derived from Hurlemann et al. (2005: Exp. 1 2007a: healthy controls 2007b: healthy controls), data for words are derived from Strange et al. (2003: Exp. 1, split in shallow and deep encoding, Exp. 2 and Exp. 3) and Strange et al. (2008: split for genotypes investigated). While all experiments using picture-label pairs employed deep encoding tasks (black quadratic figures), light gray round figures indicate that the word EOP used a shallow encoding task and the dark gray round figures stand for an experiment with words and a deep encoding task.

Please note that exact values were only available for Hurlemann et al. (2007a). Other values had to be estimated from diagrams. Data from Miu et al. (2005) are not included because no differentiation was made between positive and negative deviants. Positive values indicate hypermnesic effects (i.e. an enhanced recall rate for the respective position compared with the average neutral standard recall of the respective experiment), negative values indicate amnesic effects (i.e., a decreased recall rate)

Deviations of averaged recall rates for all investigated stimulus positions as compared with the averaged recall rates for the neutral deviant (in percent) from the respective EOP experiment (i.e., “contrastive method”). Included are only EOP experiments that involve an immediate free-recall task. Data for picture-label pairs are derived from Hurlemann et al. (2005: Exp. 1 and Exp. 2’s placebo group 2007a: healthy controls 2007b: healthy controls) and Knight and Mather (2009), data for words are derived from Strange et al. (2003: Exp. 1, split in shallow and deep encoding, Exp. 2 and Exp. 3) and Strange et al. (2008: split for genotypes investigated). While all experiments using picture-label pairs employed deep encoding tasks (black quadratic figures), light gray round figures indicate that the word EOP used a shallow encoding task and the dark gray round figures stand for an experiment with words and a deep encoding task. Please note that exact values were available for all EOP experiments with pictorial stimuli, but nor for experiments with verbal material. In these cases, values had to be estimated from diagrams. Data from Miu et al. (2005) are not included because no differentiation was made between positive and negative deviants.

Positive values indicate hypermnesic effects (i.e., an enhanced recall rate for the respective position compared with the same position in the neutral deviant condition of the respective experiment), negative values indicate amnesic effects (i.e., a decreased recall rate)


Abstract

We report the results of oddball experiments in which an irrelevant stimulus (standard, deviant) was presented before a target stimulus and the modality of these stimuli was manipulated orthogonally (visual/auditory). Experiment 1 showed that auditory deviants yielded distraction irrespective of the target’s modality while visual deviants did not impact on performance. When participants were forced to attend the distractors in order to detect a rare target (“target-distractor”), auditory deviants yielded distraction irrespective of the target’s modality and visual deviants yielded a small distraction effect when targets were auditory (Experiments 2 & 3). Visual deviants only produced distraction for visual targets when deviant stimuli were not visually distinct from the other distractors (Experiment 4). Our results indicate that while auditory deviants yield distraction irrespective of the targets’ modality, visual deviants only do so when attended and under selective conditions, at least when irrelevant and target stimuli are temporally and perceptually decoupled.

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Discussion

The aim of the current study was to investigate the impact of emotion on visual attention. In particular the experiment was designed to measure whether positive emotions broaden visual attention, and whether negative emotions lead to attentional narrowing. This was explored using a change detection flicker task which allowed for the manipulation of ‘location’ whereby changes (centrally located or in the periphery) were made to images and participants had to detect these changes as quickly as possible. In accordance with the broaden-and-build-theory (Fredrickson, 1998, 2001), it was predicted that change blindness would reduce for changes in the periphery when participants were induced into positive mood but would increase when negative mood was induced (compared to a neutral condition).

The change detection flicker paradigm is a well-utilized method for studying attention, and differences between detection of central and peripheral changes have been evidenced in a number of studies. For instance, Rensink et al. (1997) found that changes to areas of central interest were detected faster than changes to peripheral areas. The current findings support this past research. Participants were significantly more accurate and significantly quicker to locate central changes than peripheral changes. This main effect is very important in endorsing the design of the flicker experiment. To establish any impact of emotion on the allocation of visual attention to central and peripheral locations it was vital to first show that visual attention and search followed the expected pattern, with resources located to the center of a scene before the periphery. We can therefore have confidence in the experimental paradigm.

Despite showing that attention is allocated to information at the center of a scene before being allocated to the periphery, there was no evidence that this varied according to mood. In contrast to past research (e.g., Fredrickson and Branigan, 2005 Wadlinger and Isaacowitz, 2006 Rowe et al., 2007) and contradicting the-broaden-and-build theory (Fredrickson, 1998, 2001), participants did not show a wider spread of attention when induced with positive mood. The current study therefore gives no support to the suggestion that positive emotions broaden attention (and that negative emotions lead to attentional narrowing). This finding was unexpected given that previous evidence has demonstrated an effect of emotion on attention (Fredrickson and Branigan, 2005 Wadlinger and Isaacowitz, 2006 Rowe et al., 2007). It should be noted that despite the non-significant interaction between mood and location in the change detection task, there was increased accuracy in detecting central changes compared to peripheral changes in the positive condition. However, even this non-significant trend is inconsistent with the predictions of the broaden-and-build theory as it provides no evidence for a broadening of spatial attention under positive mood conditions. At most we would argue that this effect may be partially responsible for the significant difference between detection accuracy of central and peripheral changes. In addition, in many change blindness studies (including the current experiment) the most important measure of performance is reaction time (rather than accuracy) and analysis of this dependent variable shows no comparable trend.

One possible explanation for the non-significant effect of emotion in the current study could be that participants were not successfully induced into the experimental mood conditions. For instance the PANAS self-report data were collected immediately after mood induction, and whilst analysis showed that emotion was successfully induced, it is not known whether this induced affect persisted throughout the change detection task. Given that the IAPS has been successfully used previously to induce emotion in research studies (Jiamsanguanwong and Umemuro, 2014 Lee et al., 2014 Limonero et al., 2015), we would argue that emotion was successfully induced but that this had no influence upon attention, however, in future it would be prudent to collect self-report data after each change detection block.

An alternative explanation for the current findings is that the experimental paradigm measures visual processing in a different way to some of the former studies. By manipulating the location of the changes the present study was able to investigate how visual attention is allocated across a real-world scene. Under standard viewing conditions an observer will allocate resources to the center of a display before attending to the periphery (e.g., Brockmole and Henderson, 2006 Tatler, 2007) therefore central changes will be detected faster than peripheral changes (as was found in the current experiment). If positive emotions expand the available attentional resources then the scope of attention should broaden (Fredrickson, 1998, 2001), allowing for faster (and more accurate) detection of peripheral changes compared to neutral or negative emotions (a prediction not supported by the current findings). In earlier studies this central/peripheral distinction was not possible. For instance, in some studies supporting the broaden-and-build theory participants are asked to make a judgment about one feature of a single stimulus or a small set of highly similar stimuli (the global–local task, e.g., Derryberry and Tucker, 1994 Basso et al., 1996, and the flanker task e.g., Rowe et al., 2007). We argue that these tasks measure visual processing style (i.e., global or local) rather than the spatial allocation of attention and are therefore unable to test the claim that positive emotions expand attentional resources. Taking the past findings into consideration, the current results would indicate that positive moods do not enhance attentional resources they merely bias the observer toward a particular method of processing information. This bias cannot be tested using the current methodology, yet incorporating global and local changes into a change detection paradigm may be one way to explore this further.

One study that comes close to measuring how visual attention is allocated through space and how emotion influences this was conducted by Wadlinger and Isaacowitz (2006). Similar to the current experiment they used natural scenes and measured search (via eye-tracking) to peripheral information. Their findings are, however, limited given that they did not analyze eye-movements to the center of the display, and they also utilized a display containing three separate images. This again does not allow for a true measure of how attention moves within a scene and lacks ecological validity. The study did support the broaden-and-build theory by showing that participants made more fixations on peripheral information under positive mood conditions, yet this only occurred when the information was mood-congruent. Supporting the findings of Wadlinger and Isaacowitz (2006), other studies have shown that the influence of emotion on cognition can be dependent upon the characteristics of the specific task stimuli used. For example, Grol et al. (2014) argue that positive emotion broadens attention but only when the stimuli are self-related. These findings suggest caution when interpreting the present results. It is possible that participants were induced into mood states as a result of viewing emotionally valenced stimuli (validated by the PANAS scores), only for the change detection task stimuli (which consisted of neutral images) to return mood to neutral.

The variation in findings in this field thus outline the importance of the experimental task used to investigate any impact of emotion on attention. The paradigms used range from very simple tasks with a relatively low level of difficulty (e.g., the flanker task) to more demanding tasks incorporating real-world stimuli. It is also possible that emotion has differing influences on overt attention (e.g., measured by Wadlinger and Isaacowitz (2006) and the current study) and covert attention (e.g., measured by Bradley et al., 2000 Rowe et al., 2007). This makes comparison across different experiments very difficult. It also suggests that any impact of emotion may be influenced by the characteristics of a task, for example the stimuli used and the demand of the task. This argument could be made about the paradigm used in the current study and it may be possible that the change detection flicker task was too difficult to elicit any influence of emotion. Whilst participants in the current experiment took significantly less time to detect changes (a mean of 11.3 s) compared to some change detection tasks (up to 20 s Shapiro, 2000), the difficulty of the task may have masked any potential influence of emotion on change blindness. Completion of a change detection task requires cognitive control to allow for focused attention toward relevant information and inhibition of irrelevant information. Regulating emotion also involves cognitive control processes whereby an individual may try to inhibit an inappropriate or unwanted response. It is therefore highly likely that task difficulty interacts with any impact of emotion, a suggestion supported by the study of Jasinska et al. (2012). In their study using the multi-source interference task (Bush and Shin, 2006) participants were presented with three numbers and had to identify an oddball number with a corresponding button press. The spatial position of the oddball could be congruent or incongruent to the correct response and on some trials threatening or rewarding distracters were also presented. Responses were slower with both threatening and rewarding distracters (compared to no distractor) for incongruent trials, but not for congruent trials. These data demonstrate that task difficulty can mitigate the influence of emotion on attention.

A further influence that may have contributed to the impact of task difficulty was if participants were trying to regulate their emotions during the change detection task. Emotional distraction occurring within a cognitive task depends on interactions between cognitive systems that allow an individual to stay focused on the task, and those systems that are responsible for the processing of emotional information (Dolcos et al., 2011). Here it is proposed that the two systems compete for processing resources where emotional distractors result in bottom–up processing of task irrelevant information and adversely influence task performance. This deficit in task performance can be mitigated by utilizing top𠄽own cognitive control processes. Detrimental influences of emotional distraction on task performance have been seen in studies using clinical and healthy populations (Jasinska et al., 2012 Krause-Utz et al., 2012). This is a possible avenue for further study as the current experiment did not take into account any influence of emotional regulation. However, if participants were using cognitive resources to manage their emotions and inhibit the emotional distraction following viewing of the IAPS stimuli we would expect better performance in the neutral condition compared to the both the positive and negative condition and this was not found. As a consequence it is unlikely that emotional regulation can explain the lack of any influence of emotion on attention in this study and instead other factors may play a role.

Cognitive theories attempt to explain behavior in terms of average group level performance however, these models often fit less accurately when they are applied to individuals (Parasuraman and Giambra, 1991). It is therefore possible that individual differences can mitigate any effect of emotion on cognition. For example, state and trait negative affect have been shown to have separate and combined influences on attentional processing (Crocker et al., 2012). The complexity of the relationship between emotion and visual attention due to individual differences is demonstrated in a study conducted by Grol and De Raedt (2014). Facial stimuli of varying emotions (happy, sad, and neutral) were used to investigate the influence of stimuli valence (under positive and neutral mood) on attentional breadth and participants were given the task of locating a small target appearing at varying distances from the face. The researchers also took a measure of depressive symptoms using the Beck Depression Inventory (BDI-II Beck et al., 1996). Mood and stimuli valence had no influence on attentional breadth and only an effect of distance was observed whereby accuracy was higher when the target was presented closer to the facial stimuli. However, participants with high BDI-II scores showed greater attentional narrowing in the task under positive emotion. Further, among individuals with high BDI-II scores, increases in positive mood were related to more pronounced attentional narrowing for positive stimuli. For participants with low BDI-II scores, increases in positive mood were related to attentional broadening for positive stimuli. It therefore appears that individual differences can mitigate the influence of emotion on visual attention. The current study did not take account of individual mood or depressive symptoms and this needs to be investigated further. Research investigating the role of individual differences on emotion and cognition is important as it will help to understand functioning in healthy populations as well as those factors that may increase susceptibility to a range of affective disorders.


Psychology Software Tools E-Prime

Psychology Software Tools E-Prime is a suite of applications used to design psychology experiments. The software offers computerized experiment design, data collection, and analysis. It is available in standard and professional editions.

  • E-Studio - Drag and drop interface for designing experiments.
  • E-Basic - Underlying scripting language of E-Prime and similar to Visual Basic.
  • E-Run - Used to run the E-Basic script and offers millisecond precision of stimulus presentation, synchronizations, and data collection.
  • E-Merge - Combines all of your single session data for group analysis.
  • E-DataAid - Utility used to manage your data, which includes editing, filtering, analyzing, and exporting data.
  • E-Recovery - Enables recovery of data in case of lost or corrupted files or early terminated experiments.

The applications support a variety of file formats, which includes the E-Run 2.0 Script file, E-DataAid 2.0, and E-Merge 2.0 Data proprietary formats. E-Prime also supports several image, audio, and video formats, such as JPEG, PNG, MPEG, AVI, WMV, MP3, and WMA.

The E-Prime suite is designed to be easy enough for novice users but also features advanced functionality that is utilized by more than 5,000 research institutions and laboratories around the world. You can design computerized experiments, execute them, and then collect and analyze data from the experiments. Psychology Software Tools E-Prime is a quality tool that will assist you in implementing randomized and fixed psychology experiments.


Experimental Materials

To obtain valence, arousal, familiarity, and meaningfulness measurements of the stimulus words, pretests were conducted with individuals who did not participate in the main experiments. First, 413 two-character trait words were selected from a pool of Chinese personality-trait adjectives (Wang & Cui, 2005) and randomly divided into two groups (the first group contained 210 words, while the second contained 203 words). Second, 66 college students were recruited to rate the words (33 raters for the first-group words, 33 raters for the second-group words) in terms of valence using a 7-point Likert scale (1 = extremely bad to 7 = extremely good) 66 college students rated the words (33 raters for the first-group words, 33 raters for the second group) in terms of arousal (1 = extremely unexciting to 7 = extremely exciting) 64 college students rated the words (32 raters for the first-group words, 32 raters for the second group) in terms familiarity (1 = extremely unfamiliar to 7 = extremely familiar) and 60 college students rated the words (30 raters for the first-group words, 30 raters for the second group) in terms of meaningfulness (1 = extremely meaningless to 7 = extremely meaningful). Each student only rated one dimension of the words. Finally, 40 positive and 40 negative trait words were selected as the experimental materials for the first experiment and 80 positive and 80 negative trait words were selected as the experimental materials for the second experiment. Trait words of both experiments are provided in ESM 5.


GENERAL DISCUSSION

Our findings support recent findings in the fear appeal literature which suggest that people react defensively to threatening health information. In addition to the findings of earlier studies that used pictures (Kessels et al., 2010 , 2011 ), this study found neuroscientific evidence that threatening health commercials cause more attentional avoidance among those for whom the health threat is self-relevant. In two experiments, smokers showed an increased P300 amplitude in response to an auditory target while watching high-threat as opposed to low-threat commercials about the negative health consequences of smoking. This threat-induced moderation of the P300 was not found in smokers who watched non-smoking related commercials (Experiment 1) and was not found in non-smokers (Experiment 2). Further support for our defensive avoidance hypothesis for whom the threat information was self-relevant was found in the reaction time data in Experiment 1. Smokers responded faster to the auditory target while watching high-threat as opposed low-threat anti-smoking commercials.

The P300 findings for the smoking participants are in line with the view that people are motivated to reduce feelings of cognitive dissonance (Festinger, 1957 Kunda, 1990 ). According to the cognitive dissonance theory (Festinger, 1957 ) and Kunda's ( 1990 ) argument for motivated reasoning, people experiencing dissonance because of their self-image are threatened (e.g., smokers exposed to threatening health commercials about smoking) are motivated to reduce it by changing one of the implicated cognitive or behavioural elements, for example through avoidant and biased processing of presented information (e.g., Kessels et al., 2010 Liberman & Chaiken, 1992 ). While previous studies used self-report measures or implicit measures of reading time and response time (Brown & Locker, 2009 Klein & Harris, 2009 ), this study provided support for motivated reasoning through the use of attention measures during message processing. The P300 findings thus indicate that avoidance responses can arise during the early process of attention allocation at the interface between sensory and memory processing.

In the first session of Experiment 2 we also found support for an attention advantage of high-threat information over low-threat information when the health information was supposed to be less self-relevant. From an evolutionary perspective, an attention preference mechanism for imminent threat was expected, but only for those for whom the information was not self-relevant (Kessels et al., 2010 ).

A limitation of this study is that the indirect nature of the experimental paradigms do not exclude the possibility that the enhanced P300 during high-threat Vs. low-threat anti-smoking commercials was the result of better task performance due to increased levels of attentional capacity because of higher levels of arousal in the high-threat conditions (Proctor & Van Zandt, 1994 ). Also, our selections of health commercials in the two studies do not allow for a comparison between high-threat commercials and neutral commercials on processes of attention allocation. Some of the commercials in the low-threat conditions in both experiments included humouristic scenes. Humour has been associated with increased attention and recall. Therefore, to the extent that the low-threat commercials were indeed evaluated as humouristic, the effects on the P300 could have been further enhanced by using humouristic rather than neutral commercials in the low-threat conditions (Schmidt & Williams, 2001 ).

Another possible limitation is that use of the auditory oddball might have interacted and be affected by the auditory component of the commercials. In future research we might use the technique of event-related desynchronization (ERD) to measure approach and withdrawal every second while watching commercials (Pfurtscheller & Aranibar, 1977 ).

Our results complement those reported by Kessels et al. ( 2010 ) and provide further neuroscientific support to findings in the fear appeal literature that suggest that people react defensively to threatening health information, especially if this information is able to question self-relevant health behaviours such as smoking among daily smokers. In addition, the findings strongly suggest that threatening commercials are not an effective tool in motivating people to attend to health messages, but instead decrease chances of successful persuasion.


Watch the video: E-Prime 3 Webinar: Building an Auditory Oddball Experiment featuring Chronos (January 2022).