We recruited 40 undergraduates (35 females, five males; M
age = 22.3 years, SD = 4.1) who participated for course credit. The sample size was based on a power analysis (G*Power 3.1.7) in order to allow sufficient power (β = .80, α = .05, two-tailed) to detect medium-sized effects (d = 0.5). All participants provided written informed consent, reported normal or corrected-to-normal vision acuity, and passed the Ishihara test for normal color vision. The study was conducted in accordance with the Helsinki Declaration and the University Research Ethics Standards. The data reported here are a subset (with additional EEG data collection) of the data already presented in Spachtholz, Kuhbandner, and Pekrun, (2016). All data exclusions, manipulations, and measures in the study are reported.
We selected 200 images of real-world objects from published sets of stimuli (Brady et al., 2008, 2013). For each object we created four different images, resulting from the combination of two different states (e.g., open/closed) and two different colors (e.g., yellow/blue). The two state versions were already available from the stimulus sets. To create two color versions, we first selected a random hue value for the first version and then rotated this hue value (which can be represented as an angle on an isoluminant color circle) by 180° for the second version. We selected only objects whose colors were not intrinsically related to the objects (see Fig. 1 for examples).
Design and procedure
Participants were tested individually with E-Prime 2.0 (Psychology Software Tools, Inc., Pittsburgh, PA), using a procedure adapted from Brady et al. (2013). The experiment consisted of an incidental study phase and a surprise test phase. During the study phase, we directed participants’ attention to the centrally presented objects by asking them to decide whether to buy each of them. Each trial started with the presentation of an object for 200 ms, followed by a blank screen for 1,700 ms, during which participants made their buying decisions via button presses. The next trial started after a blank screen of 500-ms duration.
The study phase was divided into two blocks of 100 objects each. At the beginning of each block, either positive or negative affect was induced, by asking participants to recall a happy or sad autobiographical event for 3 min while listening to appropriate music (Jefferies, Smilek, Eich, & Enns, 2008). The order of the affect conditions and the assignment of objects to affect conditions were counterbalanced across participants. In the test phase, participants completed a forced choice recognition memory test. Each object was presented in all four possible feature combinations, and participants were asked to select the picture they had seen during the study phase (see Fig. 1). Memory for half of the objects of each affect condition was tested immediately after the study phase. The remaining half were tested in a delayed memory test one day after the study phase (the results from the delayed test were not analyzed because participants’ performance showed floor effects in both the negative condition, M
Pboth = .03, SD = .06, and the positive condition, M
Pboth = .08, SD = .10).Footnote 2
Participants initially completed 20 practice trials of the study task using objects different from those used later in the experiment. The success of the affect induction was retrospectively measured after each affect-induction block using the Affect Grid (Russell, Weiss, & Mendelsohn, 1989), which assesses experienced affect on the dimensions of valence (1 = extremely negative, 9 = extremely positive) and arousal (1 = low arousal, 9 = high arousal).
The degree of dependency between stored features can be calculated as the strength of association between memories for the individual features. For example, complete dependency would imply that if the first feature is remembered successfully, the second feature should also be remembered, and if the first feature is not remembered, the second feature should also not be remembered. This strength of association corresponds to the correlation between memory for the state feature and memory for the color feature, which, for binary variables (remembered vs. not remembered), can be calculated using the phi coefficient. Performing this calculation requires the probabilities of remembering both features (P
Both), a single feature—that is, either state (P
Single_State) or color (P
Single_Color)—and none of the features (P
None) of the objects. P
Single_Color, and P
None are directly related to the observed proportions of correctly reporting both features, only one of the features (either state or color), or none of the features. However, to estimate the respective probabilities, the effect of guessing must also be considered. Observers could report neither of the features correctly only when they remembered none of features and did not guess any feature by chance. If observers reported only one feature (either state or color) correctly, there would be two possibilities: Either they remembered only one feature and did not guess the other feature by chance, or they remembered neither of the features and guessed exactly one by chance. If observers reported both features correctly, there would be three possibilities: They remembered both features, they remembered only one feature (state or color) and guessed the other by chance, or they remembered none of the features and guessed both features by chance.
To estimate P
Single_Color, and P
None, we formulated a model representing these relations (see Table 1). The best-fitting parameters were determined for each participant and condition using maximum likelihood estimation (Myung, 2003), in which the parameters were restricted to a range of [0, 1].
EEG recording and analysis
Electrocortical activity was recorded from 30 active electrodes (Brain Products, Gilching, Germany), which were positioned according to the extended 10–20 system and originally referenced to an electrode at Cz. The signals were digitized with a sampling rate of 500 Hz (BrainAmp Amplifiers, Brain Products, Gilching, Germany), and the impedances of all electrodes were kept below 20 kΩ. Recording was done in a dimly lit, sound-attenuated, and electrically shielded chamber.
Offline, the continuous data of the study phase were segmented into epochs of − 600 to 1,800 ms, time-locked to stimulus onset, and epochs containing electrode or movement artifacts were removed. The data were then subjected to an infomax independent components analysis, and artifactual components were identified by visual inspection of the component topographies and power spectra. The main sources of artifacts were eye blinks, eye movements, and muscle activity. Components identified as artifactual were removed, and the remaining components were back-projected into EEG signal space. Epochs were again inspected and were rejected if they contained residual artifacts. On average, 93 trials (range 78–100 trials) per participant remained for the analysis (negative: M
Both = 24.4, M
Single = 17.8; positive: M
Both = 24.5, M
Single = 16.6). The relatively small number of trials per condition was compensated for by the large sample size of the study. The analysis was performed using Fieldtrip (Oostenveld, Fries, Maris, & Schoffelen, 2011) and custom MATLAB code. For the event-related potential (ERP) analysis, epochs were band-pass-filtered (0.05 to 40 Hz), resliced into epochs from − 150 to 750 ms relative to stimulus onset, and re-referenced to an average reference. A baseline correction was applied using the entire prestimulus interval.
For the statistical analysis, in a first step we identified subsequent-memory effects (i.e., time clusters in which activity was related to the number of stored object features) for each affect condition separately. To this end, we contrasted the activity at encoding for trials in which both features were later recalled correctly (Bothobs) versus trials in which a single feature was later recalled correctly (Singleobs). For this purpose, a two-stage randomization procedure was used for each sample point after stimulus onset (Blair & Karniski, 1993; Karniski, Blair, & Snider, 1994). At the first stage, paired t tests were performed for each electrode and the resulting t values were recorded. Then the sum of the squared t values, t
sum2, was calculated over all electrodes, as a measure of both the strength and the spatial extent of the differences between conditions. Then, to correct these results for multiple comparisons across electrodes, 10,000 permutation runs were performed in which conditions were randomly swapped within participants. In each run, paired t tests were performed for each electrode and t
sum2 was recorded. This created a distribution of values of t
sum2 that would be expected under the null hypothesis of no difference between conditions. From this reference distribution, the corrected p value (p
corr) for a given observed t
sum2 from the first stage of the analysis could be calculated as the proportion of permutation runs yielding an equal or higher value of t
sum2. To also account for multiple comparisons across time, time clusters with significant differences between the conditions were only considered when they extended over six or more consecutive sample points (i.e., 12 ms or longer; for a similar procedure, see Volberg & Greenlee, 2014).
In a second step, we compared the subsequent-memory effects between affect conditions. To this end, we averaged activity over the time windows of the subsequent-memory effects detected in the first step of the analysis. Then, first, we examined whether these effects were evident in each affect condition separately, and second, we determined whether the effects differed between affect conditions by using additional permutation tests.