Accessible Vs. extreme automatic attitudes
Creators: name: Shechter, Anat
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Social Psychology
subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology

description: The effects of automaticity vs. extremity in their influence on evaluative priming: study plan Purpose The purpose of the present research is to test whether the typical outcome measure of the evaluative priming task (EPT), the priming effect, confounds automaticity with extremity. Background In two previous studies we examined how manipulations of amount of practice and of valence extremity affected the priming effect in the EPT. o We examined this because we suspected that priming effects confound evaluation extremity with evaluation automaticity (i.e., the likelihood that evaluation will be activated). o We previously conducted two experiments, with results that were not easy to interpret.  In AutoExt1, we used attractive and unattractive faces as the target stimuli. Participants practiced rating half of the faces.  We found that practice and extremity each affected EP scores, but also affected explicit ratings. Practice increased priming in EP, but also the extremity of the self-reported liking. • Because we did not control for a mere exposure effect, mere exposure effect was an alternative account for the effect on self-reported evaluation.  In autoext2, we balanced the amount of exposure each target received so that each target was be presented equally, but the number of evaluations varied according to condition.  In addition, in autoext2 participants had to evaluate targets based on description of behaviors instead of attractiveness of faces (as was the case in autoext1) because we suspected that attractiveness judgments might be somewhat automatic to begin with.  In autoext2, we found a similar effect of practice on both EP (although it did not reach significance) and on self-reported ratings. Moreover, controlling the valence of the targets by assigning them behaviors resulted in quite extreme ratings, even for mildly positive and mildly negative targets. We did find instances of practice effects on EP for moderately positive or negative targets, but only a few and still, the ratings themselves were not mild. - In summary, the previous experiments did not successfully test our research question (does EP confound extremity and automaticity) because practice in evaluation that was supposed to manipulate automaticity also influenced extremity, because the stimuli in autoext1 might have been automatically evaluated prior to the experiment, and perhaps also because the induction of evaluation in autoext2 was a bit cumbersome (behavioral information). The present research - In the present research, we will try a different approach. We will try to manipulate the variability of the values attributed to various novel objects (non-words), as a possible means of manipulating automaticity of evaluating those objects (without affecting extremity). - We hypothesize that the more different values an object is associated with, the less automatic the evaluation of the object is. o For example, participants can learn that an object is worth, on average, 5 cents, by seeing that object in association with its value (i.e., 5) twice, or, by seeing the object with the values 3 and 7. Both objects in the above example are worth the same, but only one had been associated with its (average) value directly more often than the other one.  As a result, perhaps the first object would activate the value 5 quite quickly, whereas the other object will elicit various values that are, on average, 5. Therefore, 5 might not be activated as automatically if the object was associated with multiple values around 5. Procedure Largely following the procedure of Quandt et al., (2021), we will teach participants about the effect of 6 non-words on the money they accumulate in the task. 3 non-words will be associated with the same positive mean value (i.e., 5), but with different value distributions, and 3 others will be associated with the same negative mean value (i.e., -5), and with different distributions. The learning phase of each non-word will consist of 24 trials. On each trial a non-word will be presented with a card that carries the value of -1,-3,-5,-7, -9, 1,3,5,7, or 9. Importantly, the mean value will be 5 for the 3 positive non-words and -5 for the three negative non-words, but the distributions will vary; two non-word will be associated with zero variance (all 24 trials will draw a 5 card for the positive non-word and -5 card for the negative non-word), two will be associated with medium variance (12 trials of a 3 and 12 trials of a 7 for the positive non-word and -3 and -7 for the negative non-word) and two will have large variance (6 trials * [1,3,7,9] for the positive non-words and 6*[-1,-3,-7,-9] for the negative non-word). Participant will learn that the money that they would accumulate by the end of the task, would be a bonus payment in this study. To further ensure learning, after each learning block, participants will complete a test block wherein participants choose between two non-words, and receive the value that is associated with that non-word (thus motivating participants to pay attention to the values that are associated with each non-word). On each block participants will be able to win a sum between -25 to 25 points, based on their decisions. Participants will learn that every 5 points in the game are worth 1 cent. Thus, participants will be able to win up to 15 cents (5 cents on each block *3) as a bonus in the task. Because of the possibility that they will lose 75 points (i.e., 15 cents), participants will be given an initial endowment of 15 cents. Participant will therefore be able to win a bonus of 0-30 cents in the game. After the learning phase, we will have participants complete an EPT with the non-words as prime stimuli. After the EPT we will ask participants to (1) provide their self-reported evaluation of each non-word, (2) estimate the average monetary value associated with each non-word Stimuli For each participant, the six non-words will be selected randomly from the following list: "BAUBED", "PEEMIN", "QUESTL", "EBELSA", "HANE", "THEET", “DRIBS", "HUBE", "ODWOUN", "OTAIR", "EMILINS", "SOCIEVE". Practice Six non-words were chosen as novel objects and were randomly (across participants) matched to one of the six value distributions. The learning phase will comprise of 3 learning blocks and 3 test blocks. On each learning block each non-word will be presented 8 times (with its exemplars presented equally). On test blocks participants will make 9 choices, between couples of non-words: 1. Between a positive low variance non-word /a positive medium variance non-word [low variance wins], 2. Between a positive low variance non-word / a positive medium variance non-word [medium variance wins], 3. Between a positive medium variance non-word/ a positive high variance non-word [medium variance wins], 4. Between a positive medium variance non-word/ a positive high variance non-word [high variance wins], 5. Between a positive high variance non-word/ a negative high variance non-word, 6. Between a negative high variance non-word/ a negative medium variance non-word [high variance wins], 7. Between a negative high variance non-word/ a negative medium variance non-word [medium variance wins], 8. Between a negative medium variance non-word/ a negative low variance non-word [medium variance wins], 9. Between a negative medium variance non-word/ a negative low variance non-word [low variance wins]. EPT Participants will complete an evaluative priming task with the 6 non-words as primes: Within each of the task’s four blocks, each non-word will appear five times before good targets and five times before bad targets, for a total of 60 trials on each block (total of 240 trials in the task). Overall, each non-word will appear in 40 trials, 20 before positive targets and 20 before negative targets. The trial sequence will be based on a recent Project Implicit study in our lab that showed good sensitivity to quite nuanced attitudes (e.g., Navon et al., 2021): · Fixation duration: 500, · Prime duration: 200, · Error FB duration: 1500, · No deadline (target appears until response). · ITI duration: 500 · Targets: o Good words: positive, pleasant, good, outstanding, beautiful, magnificent, marvelous, excellent, appealing, delightful, nice o Bad words: negative, bad, horrible, miserable, hideous, dreadful, painful, repulsive, awful, ugly Primes: · There will be a total of 12 trial conditions (each condition will repeat 5 times within each of 4 blocks) in the 2 (prime value: positive, negative), X 3 (variance level: low, medium, , high) x 2 (target category: good/bad). Self-reported evaluation Using three questions, participants will indicate how positive each of the (non-) words are, in their opinion, on a 1-7 scale. 1. How much do you like or dislike the word X? (1 - Extremely dislike, 4 – Neutral, 7- Extremely like) 2. How negative or positive do you feel that the word X is? (1- Extremely negative, 4- Neutral, 7- Extremely positive) 3. How aesthetically pleasant or unpleasant is the word X? (1- Extremely unpleasant, 4- Neutral, 7- Extremely pleasant) Value rating Participants will indicate how much money, on average, each non-word was associated with, from 1-9. Question text: On average, how many point did you lose or earn when this word appeared? Response options: -9 (lost 9 points), -8, -7… 7, 8, +9 (earned 9 points). Data processing - We will compute EPT scores as in previous research in our lab (e.g., Navon et al., 2021): To compute the automatic evaluation score of each of the six non-words, we will subtract Log RTs of trials in which a prime preceded a good word from trials in which that prime preceded a bad word, and divide by the standard deviation of these two conditions. That score will be computed for each non-word in each of the four blocks separately. For each non-word, the evaluation score of the whole task will be the average score of all four blocks. - We will compute EP scores variability estimate: We will compute the variability of the priming effect. To do that we will create 20 priming effects for each stimulus, for each participant, by subtracting the RT on the nth occasion where a prime precedes a congruent target (e.g., a positive prime preceding a positive target) from the nth time and when the prime preceded an incongruent target (e.g., a positive prime that preceded a negative target). Based on these 20 priming effects we will compute a priming effect variability estimate. - We will compute RT variability estimates: We will compute variability score for each prime stimulus, for each participant, based on the SD of the RT on trials where each prime appeared. Analysis plan We will exclude: - Participants with more than 40% error-rate in the EP task. - EP: Trials with error responses. 1) We will enter the 6 EP evaluation scores to a 2 (value: gained/lost) * 2 (variability: low, medium, high) ANOVA. a) A main effect of value would be a sanity check (we expect a large effect). b) We will test whether the effect of value would be moderated by the variability condition, such that priming would be stronger in low relative to medium and high variance conditions. 2) We will enter the 6 EP score variability estimate to a one-way ANOVA to compare variability estimates between prime variability conditions (low, medium, high) a) We will examine whether the variability changes in accordance with the prime variability; ep scores are more variable, the wider the prime value distribution is. 3) We will enter the 6 RT variability estimate to a one-way ANOVA to compare variability estimates between prime variability conditions (low, medium, high) a) We will examine whether the variability changes in accordance with the prime variability; RTs are more variable, the wider the prime value distribution is. 4) We will repeat the same ANOVA with block as another factor, using the per-block evaluation scores, to explore whether the moderation might occur early but not later in the EPT. Perhaps the effect dissipates quickly after participants see the prime a few times in the EPT. This is not very likely because the EPT does not require evaluating the prime (and thus reducing the effect of evaluation accessibility/automaticity), but let’s explore it, just to make sure we’re not missing something like this. a) We will examine not only the Block X Variability X value interaction, but also just the Variability X Value interaction within each block, to search for any hint of such an effect. 5) we will examine the value estimation, as a manipulation check. a) Using a Bayesian t-test, we will compare the two targets of each value condition (gained/lost). We want our manipulation to induce the same value in each pair of targets. A larger BF01 (indicating the superiority of the equality hypothesis) would suggest more successful manipulation. b) If we do find an effect of the variability manipulation on the estimated values, we will explore the correlation between the value and the EPT scores, to see whether the same effect might have influenced both. 6) We will examine the self-reported evaluation of the non-words, hoping to find no difference within the non-words that have the same value. We will use the same t-test as in the previous analysis. a) If we find more extreme evaluation of the low variance non-words than the medium or high variance non-words, that would be against the purpose of our present manipulation. However, such a result would converge with our previous experiments to suggest that automatizing the evaluation of an object influences the extremity of the deliberate evaluation of that object. 7) We will explore the response latency in the self-reported evaluation and the self-reported value measures, with two 2 (value: gained/lost) * 2 (variability: low, medium, high) ANOVA (one for each DV). a) We will test whether low variability leads to faster self-reported judgment, due to the same logic behind the hypothesis we are testing for the EPT. This is only an exploratory measure because there would be very few measurements for each condition for each participant, and response latency for self-report are quite noisy. 8) We will explore the effect of specific word on EPT evaluation score and self-reported evaluation, and the effect of the manipulation on each word. We will explore two questions: a) Are some of the non-words much more negative or positive than others? Ideally, within each role (e.g., gain, medium), there will be no more than one standard deviation between the mean of the least liked and the mean of the most liked nonword. b) Do some words show stronger effects than others? Perhaps for the next experiment, we will select a subset of non-words that would show the strongest effects. c) Power analysis An over-estimation of power for a 2x2 interaction in within-participants ANOVA can be computed as a dependent-samples t-test. 210 participants will achieve 95% power to detect a d=0.25 effect. Therefore, we will recruit 230 participants, allowing a dropout of up to 20 participants without missing the planned power.