Detecting Valence From Unidentifed Images: A Link Between Familiarity And Positivity in Recognition Without Identifcation Part 2

The present study

The goal of the present research is to reconcile the conflicting ideas reviewed above regarding how emotional aspects of a stimulus may guide our judgments even for unidentified stimuli. In Experiments 1–4, we used positive and negative images (vs. threatening and nonthreatening images as used by Cleary et al., 2013) in this study. 

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This choice was partly practical: The image database from which we (and Cleary et al., 2013) obtained the stimuli does not include norming data related to threat, per se (Lang et al., 2005; Libkuman et al., 2007). More importantly, we thought that using positive and negative stimuli would allow greater generality in our results, as “threat” would seem to be a subset of “negative.” Given that our primary question concerns the link between positivity and familiarity, we thought that using positive and negative images would best address these more general goals. 

Moreover, unidentifiable negative stimuli are perceived as more familiar, then this would have implications for theories that propose that positive affect and familiarity are strongly linked.

We were also interested in whether the arousal dimension of emotion is a required factor in detecting emotion unidentifiable images. It is not yet known whether valence alone is sufficient in detecting emotion in images that are below the threshold identification and whether that information can be used to make familiarity judgments. We therefore used an image set equated on arousal in Experiment 4 to examine the possibility that high arousal leads to increased feelings of familiarity for unidentifable images. In Experiment 5 we used the same threatening and nonthreatening images used by Cleary et al. (2013).

Power analysis

The number of participants in this study was determined by a power analysis conducted using G*Power (Faul et al., 2007). We assumed a medieffectect size (d = .5), as found in Cleary et al. (2013, Experiment 3). This analysis revealed that an N = 54 would result in a power level of .95 using a .significancence criterion. Any deviations from this were due to counterbalancing and scheduling.

Experiment 1

An implicit assumption of past research outlined above is that participants have seen whether an image is positive or negative even if they cannot identify the content of the image. In other words, we are assuming that there is enough information coming through filtered to identify some of the taffectiveive qualities of the image but not enough for conscioidentificationion of the content. If this assumption is correct, then participants should be able to accurately judge whether an image is positive or negative even when it cannot be identified. The goal of Experiment 1 is therefore to test whether participants can accurately judge whether the image behind tfilterter is positive or negative.

Method

Participants for this experiment included 53 Binghamton University undergraduate students who were compensated with partial credit toward a course requirement.

Materials The stimuli were 83 images from the International Afective Picture System (IAPS; Lang et al., 2005) and fve images from the Open Afective Standardized Image Set (OASIS; Kurdi et al., 2017), both of which include normative valence ratings.

The five images were used to have an even number of images in each category. Images were either positive (M = 7.41, SD = 0.38) or negative (M = 2.51, SD = 0.82) and depicted either animate or inanimate things, with 22 images in each of the four categories (afivefve OASIS images used in the negativeinanimate category). Valence ratings were matched between respective animate and inanimate categories. 

As mentioned previously, the dimension of arousal was not controlled for in this experiment. The images were 350 × 350 pixels and filtered in Photoshop using a monochromatic Gaussian no-filter of 150% to hindidentificationion. This filter was used by Cleary et al. (2013) and was successful at hindering identification. A list of the exact images and their descriptions can be found on the Open Science Framework (https://osf.io/kwc9m/?view_only=00f285ab6cd46e7a16f 17808c80fc19).

Procedure All filtered red images were presented in a different random order for each participant. With the image present on a screen, participants were asked to try to identify the image content by typing a response. Regardless of whether or not they produced a correct identifcation, participants were then asked to rate whether the image seemed positive or negative by pressing P or N on a keyboard. This was a forced-choice binary response. Each trial was self-preceded, but participants were encouraged to move on after 5–10 seconds if they were unable to identify it. The exact directions were as follows:

“In this experiment, you will be rating a series of images. The images are filtered so that they are fuzzy and difficult to see. For each image, you will do 2 things:

(1) Identify the image.

Type your response using the keyboard. If you don’t know what the image is, please use the “Don’t know” button located at the bottom of the screen.

(2) Give a rating of whether you think the underlying image is positive (good) or negative (bad). Even if you can figure out what the image is, just give your best guess. This could be a “gut” feeling. Use the P and N keys on your keyboard.”

Results

The noifilterter was successful at hinderiidentificationion, with an over-identification rate of 15% (see Table 1 for a breakdown by image category). The trials of interest were those in which the image could not be unidentified. As such, trials with successful identification were excluded from the analysis.

The dependent variable was the proportion of “positive” responses. A 2 (valence: positive vs. negative) × 2 (animacy: animate vs. inanimate) repeated-measures analysis of variance (ANOVA) revealed a main effect of valence, where positive unidentified images were rated more positively (M = .586, SD = .15) than negative images (M = .533, SD = .167), F(1, 52) = 15.33, p < .001, MSE = .01, �p 2 = .228. There was no significant difference in positivity ratings between animate (M = .547, SD = .16) and inanimate (M = .573, SD = .161) images, F(1, 52) = 2.899, p = .095, MSE = .012, �p 2 = .053. There was no interaction between valence and animacy, F(1, 52) = 1.416, p = .239, MSE = .013, �p 2 = .027 (see Fig. 1).

We also used signal detection analyses to disambiguate response bias from accuracy. The sensitivity measure d′ was computed for each participant, and the mean was significantly above zero (chance), (M = 0.41, SD = 0.24), t(52) = 12.45, p < .001, SE = .033, d = 1.71, showing evidence of accurate valence discrimination, consistent with the findings of the previous analysis.

Consistent with past research on perception without awareness, the results of Experiment 1 suggest that participants can discriminate between positive and negative images even when they cannot identify the content of the photo.

Experiment 2

Our second goal was to determine whether unidentified negative images would be rated as more familiar than unidentified positive images. This is similar to the experiment by Cleary et al. (2013), with the madifferencence being that we used positive and negative images rather than threatening and nonthreatening images per se.

Method

Participants Participants were 63 Binghamton University undergraduate students who were compensated with partial credit toward a course requirement.

Materials The materials were identical to Experiment 1.

Procedure

The procedure was identical to that of Experiment 1, except that instead of rating the images on whether they seemed positive or negative, participants rated how familiar the image seemed on a scale from 1 to 8. The exact directions for the familiarity rating were as follows:

“Rate the image on how familiar it seems to you. This could be a vague feeling that you may have seen the underlying image at some point before this experiment. Submit your response using the number keys on top of the keyboard.”

Results

The noise filter was again successful at hindering identification, with an overall identification rate of 13% (see Table 1 for a breakdown by image category). Trials of interest were those in which the image could not be identified. As such, trials with successful identification were not included in the main analyses. The dependent variable was the familiarity ratings given to the images. A 2 (valence: positive vs. negative) × 2 (animacy: animate vs. inanimate) repeated measures ANOVA revealed a main effect of valence, where positive unidentified images were rated as more familiar (M = 2.76, SD = 1.03) than negative images (M = 2.34, SD = 1.13), F(1, 62) = 95.24, p < .001, MSE = .12, �p 2 = .606. 

There was also a main effect of animacy, where animate images were rated as more familiar (M = 2.69, SD = 1.15) than inanimate images (M = 2.41, SD = 1.02), F(1, 62) =24.745, p < .001, MSE = .19, �p 2 = .285. There was not a significant interaction between valence and animacy, F(1, 62) < 1, p = .496, MSE = .11, �p 2 = .008, (see Fig. 2).

Although not the focus of this experiment, we note that the same pattern was found for the images that were identified. Positive images (M = 5.09, SD = 1.51) were rated as more familiar than negative images (M = 4.72, SD = 1.73), t(62) = 2.26, p = .03, d = 0.29, and animate images (M = 5.4, SD = 1.57) were rated as more familiar than inanimate images (M = 4.25, SD = 1.75), t(60) = 7.2, p < .001, d = 0.92.

In summary, contrary to past research, we found that among unidentified images, positive images were rated as more familiar than negative images. Although not a focus of this inquiry, we also found that unidentified images depicting animate subjects were rated as more familiar than images depicting inanimate subjects, replicating the results of Cleary et al. (2013).

Experiments 3A and 3B

In Experiment 2, unidentifed positive images were rated as more familiar than unidentifed negative images. These results are contrary to the results of Cleary et al. (2013, Experiment 3). Although we used the same image filter and similar stimuli as the previous study, we note that there was a discrepancy between our identification rates of 13% and that of Cleary et al. of 32%. It is possible that this discrepancy identification rate can account for the disparate results, therefore in Experiments 3A and, 3B, we reduced the intensity of filterers to augmeidentificationion rates. Otherwise, the experiments were the same as Experiment 2.

Method

Participants included a total of 58 (30 in 3A and 28 in 3B) Binghamton University undergraduate students who were compensated with partial credit toward a course requirement.

Materials The materials were the same as in Experiment 1, except for the filter intensity. A Gaussian monochromatic noise filter of 125% was used in Experiment 3A, and 110% in Experiment 3B.

Procedure The procedure was identical to that used in Experiment 2.

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