Detecting Valence From Unidentifed Images: A Link Between Familiarity And Positivity in Recognition Without Identifcation Part 3

Results

The overall identification rates for Experiments 3A and 3B were 20.3% and 27.9%, respectively (see Table 1 for a breakdown by image category). For both flters, there was a main efect of valence, where positive images (3A: M = 2.8, SD = 1.14; 3B: M = 3.01, SD = .95) were rated as more familiar than negative images (3A: M = 2.42, SD = 1.12 , 3B: M = 2.56, SD = .8), 3A: F(1, 29) = 23.54, p < .001, MSE = .21, �p 2 = .45; 3B: F(1, 27) = 50.8, p < .001, MSE = .13, �p 2 = .65. There was also a main efect of animacy, where animate images (3A: M = 2.7, SD = 1.11; 3B: M = 2.92, SD = .9) were rated as more familiar than inanimate images (3A: M = 2.48, SD = 1.12, 3B: M = 2.62, SD = .85), 3A: F(1, 29) = 15.96, p < .001, MSE = .13, �p 2 = .36; 3B: F(1, 27) = 21.45, p < .001, MSE= .14, 𝜂 �p 2 = .44. There was no signifcant interaction in Experiment 3A, F(1, 29) = 2.32, p = .14, MSE = .14, �p 2 = .02, but the interaction was signifcant in Experiment 3B, F(1, 27) = 8.56, p = .007, MSE = .16, �p 2 = .24, such that higher familiarity ratings were given for animate items only in the positive category, t(27) = 4.69, p < .001, d = 0.89. There was no effect of animacy in the negative category, t(27) = 1.26 p = .22, d = 0.24. In sum, despite the higher identification rates in Experiments 3A and 3B, the patterns of results were similar to those of Experiment 2 (see Fig. 2).

Non-animated images are closely related to memory. According to scientific research, visual information is easier to remember and recognize than other sensory inputs. Therefore, using non-animated images can help us remember and understand information better.

Compared to animated images, non-animated images are static and do not contain a lot of change and movement. This allows us to better focus and focus on the details shown in the image. Therefore, it is easier for us to remember information with non-animated images than with animated images.

Additionally, non-animated images promote our creativity and imagination. By observing and analyzing different elements in images, we can try to apply these elements to our daily lives, thus promoting our creativity and imagination.

All in all, the relationship between non-animated images and memory is closely linked. Both individuals and businesses can achieve better results by using non-animated images to enhance memory and comprehension. It can be seen that we need to improve memory, and Cistanche deserticola can significantly improve memory, because Cistanche deserticola can also regulate the balance of neurotransmitters, such as increasing the levels of acetylcholine and growth factors. These substances are essential for memory and learning. In addition, Meat can also improve blood flow and promote oxygen delivery, which can ensure that the brain receives sufficient nutrients and energy, thereby improving brain vitality and endurance.

Click know supplements to improve memory

The familiarity ratings of identified images follow a similar pattern. Positive images (3A: M = 5.54, SD = 1.36; 3B: M = 5.65, SD = 1.32) were rated as more familiar than negative images (3A: SD = 5.17, SD = 1.58; 3B: M = 5.33, SD = 1.25) in both Experiment 3A, albeit just shy of significance, t(29) = 1.94, p = .06, d = 0.35, and 3B, t(27) = 2.08, p =048, d = 0.39. Animate images (3A: M = 5.67, SD = 1.34; 3B: M = 5.63, SD = 1.23) were rated as more familiar than inanimate images (3A: SD = 5, SD = 1.6; 3B: M = 5.46, SD = 1.4) in Experiment 3A, t(29) = 3.9, p < .001, d = 0.71, but not in Experiment 3B, t(27) = 1.12, p = .27, d = 0.21.

The results of Experiments 2, 3A, and 3B are counter to the idea that unidentified threatening/negative images will appear to be more familiar than unidentified nonthreatening/ positive images. Instead, we found that among unidentified images, positive images were rated as more familiar. We consistently found this effect when decreasing the intensity of the image filters to more closely match the identification rates of previous findings. Making the images more identifiable by lowering the filter did not change the pattern of results, suggesting accurate valence identification among unidentified images as robust funding.

Experiment 4

The specific qualities of the images that were used in Experiments 1–3 may explain why our results differed from prior research. Importantly, there are two dimensions of emotion, valence (whether something is positive or negative) and arousal (the intensity of the emotion), and each dimension appears to affect memory with different underlying mechanisms (Kensinger, 2004). The image sets used in Experiments 1–3, as well as those used by Cleary et al. (2013, Experiment 3), was not equated to arousal, and therefore, the results could have been an effect of arousal and not valence.

As previously mentioned, there is evidence that arousal does indeed influence participants’ ratings for unidentifiable stimuli (e.g., Goldinger & Hansen, 2005; Morris et al., 2008). However, it is not yet known whether valence can be detected through a noise mask for unidentified images independent of arousal and whether this information alone can be used to make decisions about familiarity. It is thus important for the present study to analyze the effect of valence while holding arousal constant, as it may shed light on the underlying mechanisms of valence recognition without image identification. The goal of Experiment 4 was therefore to explore this possibility by using a new image set in which arousal was equated across all conditions.

Method

Participants for this experiment included 66 Binghamton University undergraduate students who were compensated with partial credit toward a course requirement. Due to restrictions related to COVID-19, this experiment was conducted online using Pavlovia.

Materials The stimuli were 80 images from the International Affective Picture System (IAPS; Lang et al., 2005, with 20 images in each category. As before, valence ratings were matched between animate and inanimate categories for both positive (M = 7.31, SD = 0.37) and negative (M = 2.76, SD = 0.32) categories. The same filter as in Experiment 1 was used. Importantly, the image sets were equated on arousal, such that there were no significant differences in arousal between any of the four categories (M = 4.81–4.9, SD = 0.18–0.45), F(3, 76) < 1, p = .74, MSE = .08, �p 2 = .02. A list of the exact images and their descriptions can be found on the Open Science Framework.

Procedure The procedure was identical to that of Experiments 2–3.

Results

The overall identification rates for Experiment 4 were 16.5% (see Table 1 for a breakdown by image category). There was again a main efect of valence, F(1, 65) = 34.68, p < .001, MSE = .12, �p 2 = .35, where positive images (M = 2.05, SD = .95) were rated as more familiar than negative images (M = 1.8, SD = .76). There was a main efect of animacy, F(1, 65) = 27.2, p < .001, MSE = .12, �p 2 = .30, where animate images (M = 2.02, SD = .92) were rated as more familiar than inanimate images (M = 1.82, SD = .8). There was no interaction, F(1, 65) < 1, p = .79, MSE = .04, �p 2 = .001 (see Fig. 3).

The familiarity ratings of identified images again followed the same pattern. Positive images (M = 3.32, SD = 1.28) were rated as more familiar than negative images (M = 3.04, SD = 1.35), t(55) = 3.1, p = .003, d = 0.42. However, there was no diference between animate (M = 3.61, SD = 1.24) and inanimate items (M = 3.46, SD = 1.18), t(38) = .92, p = .36, d = 0.15.

To summarize, arousal was equated in Experiment 4. However, the results showed the same pattern as was found in Experiments 2 and 3, showing, once again, that positive valence is perceived as a sense of familiarity even when the image cannot be identified, and these effects cannot be explained by differences in arousal.

Experiment 5

The results of Experiments 2–4 show that positive images are more likely to be classified as familiar, independent of arousal, which seems at odds with the results of Cleary et al. (2013). One possible reason for the discrepancy is that somewhat different images were used in each study. There are more than 1,000 images in the IAPS database, so the types of images we chose may be responsible for the differing results. A potentially more substantial issue is that Cleary et al. (2013) categorized their images as nonthreatening versus threatening, while the present experiments categorized them as positive versus negative. 

This could potentially be a contributing factor to the conflicting results, as non-threatening could be synonymous with neutral as opposed to positive. The present experiments did not include a neutral image category, and it is possible that neutrally valenced images could lead to processing that is different from that of images on the extreme ends of the valence scale. As mentioned in the introduction, the normative ratings provided with the IAPS database do not include a “threat” category. 

However, in an attempt to better understand the degree to which our positive and negative images line up with threat versus nonthreat, we conducted an additional study asking a new sample of participants from the same pool as participated in Experiments 1–4 to provide ratings for the images used in the present experiments as either positive/negative (N = 24) or nonthreatening/threatening (N = 24). In an item-wise comparison, we found a very strong positive correlation between valence ratings and threat ratings, r(79) = .93, p < .001, suggesting that the constructs are highly overlapping, with 86% shared variance. In sum, our results suggest that positive/negative image categories should largely correspond with nonthreatening/threatening image categories.

Nevertheless, in Experiment 5, we used the exact images used by Cleary et al. (Experiment 3) to investigate whether our image sets were fundamentally different and would produce different results. The most notable difference between the two stimuli sets was in the animate category. Both of our animate sets included pictures involving humans as well as animals, while the set used by Cleary et al. was composed exclusively of animals. It is therefore reasonable to consider that our results may be tapping into a somewhat different phenomenon. Other than the stimuli, Experiment 5 was identical to Experiment 2.

Method

Participants for this experiment included 51 Binghamton University undergraduate students who were compensated with partial credit toward a course requirement.

Materials The stimuli used were the exact filtered images used by Cleary et al. (2013, Experiment 3) with permission. While our method of filtering our own image sets used in Experiments 1–4 followed the same process as reported in Cleary et al., we used the authors’ prefiltered images in an attempt to replicate their experiment as closely as possible. This image set included 80 images, with 20 in each of the following categories: living-threat, living-nonthreat, nonliving-threat, and nonliving-nonthreat.

Results

The image filter was less successful at hindering identification than earlier experiments, yielding an overall identification rate of 48% (see Table 1 for a breakdown by image category).

Among unidentifed images, a 2 (animacy: inanimate vs. animate) × 2 (threat: threatening vs. non-threatening) ANOVA revealed a main effect of animacy, where animate images (M = 2.11, SD = .66) were rated as more familiar than inanimate images (M = 1.97, SD = .69), F(1, 50) = 10.6, p = .02, MSE = 0.17, �p 2 = .18. There was also a main effect of threat: non-threatening images (M = 2.19, SD = .73) were rated as more familiar than threatening images (M = 1.93, SD = .66), F(1, 50) = 19.97, p < .001, MSE = 0.19, �p 2 = .29. There was also an interaction, F(1, 50) = 9.75, p = .03, MSE = 0.26, �p 2 = .16, between animacy and threat. Follow-up t-tests revealed that familiarity ratings were higher for non-threatening images in the animate category, t(50) = 5, p < .001, SE = .09, d = 0.70, but not in the inanimate category, t(50) < 1, p = .55, SE = .10, d = 0.09 (see Fig. 4).

Among identified images, there was a main effect of threat, where non-threatening images were rated as more familiar (M = 4.90, SD = 1.03) than threatening images (M = 4.09, SD = 1.13), F(1, 50) = 101.01, p < .001, MSE =0.33, �p 2 = .67, no main effect of animacy, F(1, 50) = 1.11, p = .30, MSE = 0.27, �p 2 = .02, and no interaction, F(1, 50) < 1, p = .86, MSE = 0.21, �p 2 = .01.

The basic effect we found in Experiments 2–4 is replicated here, with the nonthreatening images rated as more familiar. There was, however, an interaction in which this effect was only seen for images of animate things, replicating the interaction seen in Experiment 3B. This may be due to the increased identification rates seen in Experiment 4, suggesting further that the effect of animacy on positive images relies on less intense image filters.

In sum, our results did not differ materially when we used the images used by Cleary et al. (2013). Although there were differences in the images and the identification rates, the funding that the more positive/non-threatening images are rated as more familiar compared with the negative/threatening images appears robust.

General Discussion

Our study yielded two key findings. First, we found that participants could discriminate between positive and negative images, even when they could not be identified (Experiment 1), consistent with the emotion-perception without awareness literature. Past studies on perception without awareness tended to use either emotional faces or valenced words, but studies using complex scenes such as the IAPS are scant (Kimura et al., 2004). The present research suggests that this effect does indeed extend to complex scenes. 

We also note that in typical perception without awareness experiments, the ability to consciously identify the stimulus is manipulated either by stimulus duration (e.g., Murphy & Zajonc, 1993; Pessoa et al., 2005) or stimulus location relative to an attended stimulus (e.g., Mack & Rock, 1998; Vuilleumier et al., 2002; Vuilleumier et al., 2001). These methods are chosen due to the automatic nature of emotion perception which occurs even in the absence of controlled cognitive input (i.e., conscious identification). 

The present study used degraded images with no real restrictions on exposure time or locus of attention. This method may have led participants to process the stimuli analytically, which may have had confounding effects on the automatic nature of valence perception and the feeling of familiarity. Indeed, Whittlesea and Price (2001) posit that the use of an analytic approach, such as scrutinizing test stimuli for recognizable features, regardless of whether it leads to recognition, prevents the overall experience of fluency and thus familiarity. In this vein, the use of obscured images of complex scenes in the present study may be inherently more vulnerable to analytic processing in general when compared with stimuli that may be processed more holistically, such as faces or words. Future research should investigate whether this has a meaningful effect on RWI.

Second, we found that when emotional images are unidentifiable under a visual noise filter, positive images were rated as more familiar than negative images. This effect appears to be robust and was present across three different filter intensities and three different image sets. Importantly, these findings were independent of arousal, providing novel evidence that valence alone can be utilized to make judgments about the familiarity of an image even if the image cannot be identified. 

Although this finding is contrary to a previously reported experiment using similar methods (Cleary et al., 2013), our results are consistent with a multitude of studies that have shown a strong link between familiarity and positive effect (Monin, 2003; Reber et al., 1998; Westerman et al., 2015; Whittlesea, 1993; Winkielman et al., 2003). The present result could be viewed as the flip side of the mere exposure effect (Zajonc, 1968). In the mere exposure effect, familiarity with a stimulus leads to a sense of positivity. Here, a sense of positivity is accompanied by a sense of familiarity even for stimuli that are unidentifiable.

Our results are at odds with the findings of Cleary et al. (2013), and important methodological differences may have accounted for this. In particular, a corrigendum of the original article was published recently that reveals a key difference between our methods and those of Cleary et al. Although the original article described the ratings as familiarity ratings, it appears that the instructions given to participants confated familiarity and threat. As the autParticipantsparticipants in Experiment 3 were instructed to judge the familiarity/likely threateningness of the image, with the emphasis in the trial-by-trial prompts placed on attempting to detect the threat.

Accordingly, these ratings are better described as threat likelihood ratings (or simply as ratings throughout the text), than as familiarity ratings” (Cleary et al., 2022, p. 1124). As described in our methods sections, participants in the present study were not told anything regarding threat, although they were made aware that some of the underlying images may be disturbing. If participants were instructed to conflate familiarity and threat, then it is more understandable why they would judge the negative images as more familiar. We suspect that these methodological differences go a long way toward explaining the differences in results between the two studies.

On the whole, our results are consistent with the view that familiarity and positive effects are assessed through largely automatic processes and suggest that they are so closely linked that conscious identification of the stimuli is not necessary for this link to manifest. A vague sense of familiarity may serve to guide us toward safety or favorable outcomes, in a sense that something “feels right” versus something “feels off.” The present experiments provide further evidence for the strong link between familiarity and effect, and the largely automatic nature of its categorization. Even when the content of an image is heavily obscured and cannot be identified, we can extract valence information, which appears to inform our impressions of the familiarity of a stimulus.

In Experiment 4, we found that this pattern of results held independently of arousal. The distinction between the effects of valence versus arousal is vital in understanding the underlying mechanisms of judgments made on unidentifiable emotional stimuli, and by teasing the two apart, we found that regardless of arousal, participants can detect valence in unidentifiable images. This result implies that participants can use information about valence to make other types of judgments, such as threat, independent of arousal and conscious identification. However, this does not mean that arousal does not also affect participants’ judgments. Future studies should investigate the effect of arousal independent of valence because participants may also be using arousal cues to make judgments on unidentifiable images, and valence and arousal may interact in important and meaningful ways.

Author note Both authors contributed to the conception and design of the experiments and the writing of the manuscript. S.D. performed the programming of experiments and data analysis.

We would like to express our gratitude to Anne Cleary, Ph.D., for giving us access to the original stimuli for use in Experiment 5.

References

1.Arndt, J., Lee, K., & Flora, D. B. (2008). Recognition without identification for words, pseudowords, and nonwords. Journal of Memory and Language, 59, 346–360.

2.Balcetis, E., & Dunning, D. (2006). See what you want to see: Motivational influences on visual perception. Journal of Personality and Social Psychology, 91(4), 612–625.

3.Bolte, A., & Goschke, T. (2008). Intuition in the context of object perception: Intuitive gestalt judgments rest on the unconscious activation of semantic representations. Cognition, 108(3), 608–616.

4. Bradley, M. M., Greenwald, M. K., Petry, M. C., & Lang, P. J. (1992). Remembering pictures: Pleasure and arousal in memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18(2), 379–390.

5.Calvillo, D. P., & Hawkins, W. C. (2016). Animate objects are detected more frequently than inanimate objects in inattentional blindness tasks independently of threat. The Journal of General Psychology, 143(2), 101–115.

6.Claparède, E. (1911). Recognition et moïté. Archives de Psychologie, 11, 79–90.

7. Claypool, H. M., Hall, C. E., Mackie, D. M., & Garcia-Marques, T. (2008). Positive mood, attribution, and the illusion of familiarity. Journal of Experimental Social Psychology, 44(3), 721–728.

8. Cleary, A. M., & Greene, R. L. (2000). Recognition without identification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(4), 1063–1069.

9. Cleary, A. M., & Greene, R. L. (2001). Memory for unidentified items: Evidence for the use of letter information in familiarity processes. Memory & Cognition, 29, 540–545.

10. Cleary, A. M., & Greene, R. L. (2004). True and false memory in the absence of perceptual identification. Memory, 12, 231–236.

For more information:1950477648nn@gmail.com