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. Author manuscript; available in PMC: 2023 Jul 1.
Published in final edited form as: Neuropsychol Dev Cogn B Aging Neuropsychol Cogn. 2021 Mar 29;29(4):720–732. doi: 10.1080/13825585.2021.1902937

Age-Differences in Interpreting the Valence of Ambiguous Facial Expressions: Evidence for Multiple Contributing Processes

Sarah J Barber 1, Andrej Schoeke 2, Mara Mather 3
PMCID: PMC8478973  NIHMSID: NIHMS1691845  PMID: 33780306

Abstract

Surprised facial expressions, which are ambiguous in valence, are interpreted more positively by older adults than by younger adults. To evaluate the processes that contribute to this age difference, we varied the spatial frequency of the surprised-face stimuli. Replicating prior results, we found that older adults rated the unfiltered surprised faces more positively than did younger adults. However, valence ratings were affected by spatial frequency. When the surprised faces were presented in a low-spatial-frequency band, it biased the participants to rate them negatively. Even though this occurred for both the younger and older adults, the older adults’ ratings of the low-spatial-frequency faces were still more positive than that of the younger adults. This suggests that there is an age-related reduction in the default negativity of interpretations. Evidence for additional processes comes from analyses of the high-spatial-frequency faces. Although older adults, as a whole, rated the high-spatial frequency faces to be more positive than the younger adults, this effect was eliminated for the subset of older adults with poor high-spatial-frequency perception abilities. We suggest that this is because these older adults needed to use top-down cognitive resources to compensate for the decrease in bottom-up input, and this cognitive load interfered with their ability to increase the positivity of their ratings for the difficult-to-perceive faces. Thus, older adults’ more positive interpretations of surprised faces can be attributed to age-related reduction in default negativity and also to older adults’ use of cognitively-effortful top-down regulatory processes that serve to increase positivity.

Keywords: emotional faces, low spatial frequency, aging, positivity effect

The ability to interpret and decode the emotional facial expressions of others is an important skill that contributes to successful social interactions (Adolphs, 2002). In examining this, surprised facial expressions are an especially interesting case because they can be associated with either positive (e.g., a surprise party) or negative emotions (e.g., receiving bad news), and this ambiguity provides an opportunity to probe for emotional biases. Previous studies with younger adults have shown that there are individual differences in how surprised faces are interpreted. Whereas some younger adults show a bias towards interpreting surprised faces as negative others are biased to interpret them as positive, and these individual differences are relatively stable even a year later (Neta et al., 2009).

Although there are individual differences in interpretation biases amongst younger adults, at the group level interpretation biases also differ between younger and older adults. For instance, amongst participants aged 19 to 71 increased age is associated with a reduced tendency to interpret surprised faces as negative (Neta & Tong, 2016; Experiment 1). Likewise, another study found that surprised faces tend to be interpreted less negatively by older adults than by younger adults (Shuster et al., 2017). This is consistent with other research showing that compared to younger adults, older adults also interpret ambiguous scenarios more positively (Mikels & Shuster, 2016), and have more positive first impressions of others (Barber et al., 2019; Zebrowitz et al., 2013). These findings are also broadly consistent with the phenomenon of the age-related positivity effect. This is an age-by-valence interaction seen in attention and memory in which older adults, compared to younger adults, have a relative preference to attend to and remember positive over negative information (for reviews, see Mather & Carstensen, 2005; Reed & Carstensen, 2012).

This type of age-by-valence interaction has been seen across a number of studies examining attention to or memory for emotional stimuli (Reed et al., 2014). For example, relative to younger adults, older adults preferentially direct their eye gaze towards positive rather than negative scenes (Knight et al., 2007), and selectively look towards positive faces and/or away from negative faces (Isaacowitz et al., 2006a, 200b; Knight et al., 2007; Mather & Carstensen, 2003). Older adults also preferentially recall and recognize positive rather than negative pictures (Charles et al., 2003; Mather & Knight, 2005), and recall more positive than negative autobiographical events (Kennedy et al., 2004; Levine & Bluck, 1997). These age-by-valence interactions indicative of the positivity effect are seen not only in terms of what is remembered accurately, but also in terms of the response biases people have when trying to determine if a stimulus was remembered or not (Kapucu et al., 2008).

Although the age-related positivity effect has primarily been documented and studied in the domains of attention and memory, some studies have seen age-by-valence interactions in valence ratings of emotional faces, especially when the faces show mixed or ambiguous emotions. For instance, in one study, younger, middle-aged, and older participants rated over a thousand facial poses of fear, sadness, anger, disgust, happiness, and neutrality on a continuous scale for each of the six emotion categories. Most facial poses were rated as multi-faceted, depicting both the emotion that the poser intended to show as well as additional expressions. Moreover, this varied by age and valence. When the facial pose was intended to depict disgust, anger, fear, or sadness, there was an age-related decrease in participants’ tendency to attribute these negative affective states to the faces. Accompanying this, there was also an age-related increase in participants’ tendency to attribute positive and neutral affect to these faces (Riediger et al., 2011). Likewise, in another study, older adults rated angry, anxious and neutral faces as more positive than younger adults did, with no age difference seen for happy faces (Czerwon et al., 2011).

Thus, across several studies, there is evidence that older adults assess facial expressions as more positive and/or as less negative than do younger adults (but see Werheid et al, 2010 for a failure to observe this). Furthermore, this effect is particularly pronounced when there is ambiguity in the facial expressions (Kellough & Knight, 2012). For example, whereas Shuster, Mikels, and Camras (2017) found no age difference in the valence ratings of happy or angry expressions, they found that older adults rated ambiguously-valenced surprised faces as more positive than did younger adults. Even though this age difference is consistent with robust age-related positivity effects seen in other domains, there is still debate about the mechanisms that underlie positivity effects (for a recent review, see Barber & Kim, in press). On the one hand, some research suggests that older adults’ positivity reflects cognitively effortful top-down regulatory processes (e.g., Mather & Knight, 2005) that serve to override initial negativity (e.g., Knight et al., 2007). On the other hand, positivity effects are not always correlated with cognitive control capabilities (e.g., Barber et al., 2020) and occur even in early perceptual processes (e.g., Johnson & Whiting, 2013; Mienaltowski et al., 2011) when cognitive control processes have been assumed not to be likely to have an impact. However, a recent study manipulating cognitive load during an emotion-induced blindness paradigm indicates that cognitive resources can influence positivity effects even in early attention (Kennedy et al., 2019).

Given these interesting findings in early attention, further investigation of the low-level perceptual influences over the age differences in the interpretation of ambiguously-valenced surprised faces may be informative. To do so, we varied the spatial frequency content of the images used in the current study. As described below, this allowed us to examine for potential age differences in default negativity, and also to test whether older adults’ positivity preferences occur even for stimuli that are more cognitively effortful to perceive.

Spatial frequency

Spatial frequency of visual stimuli describes the periodic distributions of light and dark in an image, and is a fundamental aspect of visual perception (Campbell & Robson, 1968; De Valois & De Valois, 1980). The low-spatial-frequency information transmits coarse visual information whereas the high-spatial frequency information transmits fine-grained details (see Table 1). When viewing a clear visual representation both the low and high spatial frequency information is available to the observer. However, in many real-world situations the visual information available is degraded. For example, when an object is viewed at a distance or via peripheral vision it will lack fine-grained high spatial frequency details.

Table 1.

Mean proportions of the angry and happy faces correctly labeled by younger and older adults as a function of spatial frequency (broadband, high spatial frequency, or low spatial frequency).

Angry Faces Correctly Labelled Negative
Broadband High Frequency Low Frequency
Younger adults .95 (.10) .90 (.17) .93 (.09)
Older adults .93 (.10) .56 (.31) .84 (.16)
Happy Faces Correctly Labelled Positive
Broadband High Frequency Low Frequency
Younger adults .96 (.06) .92 (.15) .94 (.09)
Older adults .93 (.09) .76 (.30) .92 (.08)
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Note: For the angry and happy trials, there were clear correct answers (i.e., angry = negative, happy = positive), allowing us to also assess accuracy. Results showed that older adults were less accurate at identifying the valence of the facial expressions, particularly when the face was high in spatial frequency or displaying an angry expression.

These two types of information are thought to be transmitted to the amygdala via distinct neural networks. Low-spatial-frequency information, which contains the “gist” of the visual scene, is transmitted quickly via a direct colliculo-pulvinar projection to the amygdala (Morris et al.,, 1999). In contrast, high-spatial-frequency information is transmitted more slowly through the visual cortex (Bar et al., 2006; Hughes et al., 1996; Vuilleumier et al., 2003; but for criticisms see Pessoa & Adolphs, 2010). Perhaps because low-spatial-frequency information also activates the amygdala more strongly (Vuilleumier et al., 2003), when younger adults are shown ambiguously-valenced images that are filtered to include only the low-spatial-frequency details, they show a default negativity bias in their interpretations. For example, in two studies, Neta and Whalen (2010) demonstrated that younger adults were more likely to interpret ambiguous-valence surprised faces as negative when the images were filtered to include only low-spatial-frequency details, as opposed to when the images consisted of either high-spatial-frequency details, or were intact and not filtered.

In the current study, we tested whether or not a similar default negativity bias also occurs for older adults. Prior research has shown that unlike high-spatial-frequency sensitivity, which tends to decline with age, low-spatial-frequency sensitivity shows little impact of aging in well-lit situations (for a review see Owsley, 2011). Given these minimal changes with age, we predicted that when ambiguously-valenced face stimuli are presented in a low-spatial-frequency, it would result in a default negativity bias for older adults, as it had for the younger adults in Neta and Whalen’s (2010) study. That is, presentation of ambiguously-valenced faces within a low spatial frequency band should serve to bias both younger and older adults to interpret the facial expressions negatively. Furthermore, if this default negativity of interpretations is age-invariant, then younger and older adults should interpret the low spatial frequency information similarly. This would also suggest that age differences in the interpretation of broadband ambiguously-valenced surprised faces are unlikely to be due to age differences in default negativity.

Although we expected older adults to interpret the broadband surprised faces more positively than younger adults, we also tested whether this age-related positivity bias would be eliminated for high-spatial-frequency information. As previously noted, high-spatial-frequency sensitivity declines with age (see Owsley, 2011). Because of this perceptual degradation, when viewing images that are filtered to include only high-spatial-frequency details, older (but not younger) adults may need to use top-down cognitive resources to compensate for the decrease in bottom-up input (see Boutet & Meinhardt-Injac, 2019; Monge & Madden, 2016). This is important as the initial negativity hypothesis posits that interpretations of ambiguously-valenced surprised faces are initially negative, and that positive valence biases rely at least in part upon participants’ ability to override this bias via cognitive reappraisal emotion regulation processes (Petro et al., 2018). Given that cognitive reappraisal relies upon selective attention and working memory (McRae et al., 2012), this suggests that positive interpretation biases should be eliminated under cognitive load. In this case, older adults’ positive interpretation biases should be attenuated for high-spatial-frequency information, and this should be particularly true for older adults who have experienced greater degradation in their high-spatial-frequency perception abilities.

Current Study

To examine whether there are age differences in how spatial frequency influences the interpretation of emotional faces, we showed younger and older participants angry, happy and surprised faces one at a time, each displayed briefly in either broadband, high spatial frequency, or low spatial frequency and asked them to indicate whether each face was positive or negative, or whether they did not see the face. Our outcome of interest was the interpretation of the emotionally-ambiguous surprised faces. For broadband surprised faces, we expected to replicate previous findings of an age-related positivity bias, with older adults rating these faces more positively than younger adults (Neta & Tong, 2016; Shuster et al., 2017). As a novel contribution, we examined this effect’s underlying mechanisms by testing whether or not it would be maintained across low and high spatial frequencies.

Methods

Participants

Participants consisted of 72 college students (aged 18–34) and 68 older adults (aged 60–87). These participants were recruited from undergraduate and community participant pools. Five older adult participants were removed from analyses: Two had excessive missing responses (40% of trials or more), two failed to follow instructions, and one was unable to make key presses unassisted. Within our final sample, the younger adults (63% female) were on average 21.2 years old (SD = 2.9) and the older adults (46% female) were on average 70.8 years old (SD = 6.6). Older adults had more years of education than younger adults, who were all still students at the time of the study (Younger: M = 14.3, SD = 1.4; Older: M = 17.0, SD = 3.1), t(133) = 6.89, p < .001. Data was collected in two separate waves, but including this as a covariate did not change any of the reported patterns of results.

Materials

Stimuli were 27 faces from Neta and Whalen’s (2010) Experiment 2 set. These were each shown in three spatial frequency conditions (low pass filtered, high pass filtered and unfiltered broad band). Although our hypotheses focus on how participant age and spatial frequency affect interpretation of ambiguously-valenced surprised facial expressions, as in Neta and Walen’s (2010) Experiment 2, we also included faces displaying angry and happy expressions as well, as this would help ensure that participants used both positive and negative categorizations throughout the task rather than getting stuck in one category, which may have occurred if only surprised faces were shown.

Design

The experiment was a 2×3×3 mixed design with age as a between-subject variable and facial emotion and spatial frequency as within-subject variables. The experimental task (see Procedure) was divided into 2 blocks. Each block consisted of 81 trials. Of these, there were 27 trials for each facial expression: 9 in broadband, 9 in low spatial frequency, and 9 in high spatial frequency. Within each block, each of the 27 target faces (see Materials) was depicted only three times: Once with a happy expression, once with an angry expression, and once with a surprised expression.1 Within each block, presentation order of the trials was randomized, but the same random order was used for all participants.

Procedure

After providing informed consent, participants completed a face-rating task in a well-lit room. During the face-rating task, the participant was seated 70 cm away from the computer monitor with a chin rest to standardize viewing distance.2 After doing three example trials, participants performed the main task. Stimulus presentation and response collection were programmed using the Psychophysics Toolbox extensions for Matlab (Brainard, 1997; Kleiner et al., 2007; Pelli, 1997). Each trial started with a fixation cross in the center of the screen, displayed for 300 ms followed by a face picture displayed for 200 ms with a visual angle of 10.12 degree (stimulus size on screen 12.5 cm at a viewing distance of 70 cm). Of note, this presentation time was modelled after Neta and Whalen (2010). After that, the picture disappeared and the participant had 2500 ms to indicate if the facial emotion shown was positive or negative, or if they did not see the face. If the participant did not make a choice in the within the time limit, it was recorded as a null response. A 30-s fixation cross was shown in between the two blocks to avoid fatigue. All participants also completed a demographics form.

Results

A significance level of α = .05 was used for all analyses. Our primary aim was to examine the effects of spatial frequency on the positivity of younger and older adults’ ratings of faces with ambiguous emotional valence. The critical trials to test this question were the surprised faces, for which there was no pre-determined correct response. Because of this, analyses reported here are limited to the surprised faces, but descriptive statistics of the ratings given to the happy and angry faces as a function of spatial frequency and age are provided in Table 1.

‘Did not see’ Responses

We included a ‘did not see it’ option because older adults in a pilot study had difficulty detecting the high-frequency faces (see also Owsley, 2011) and we wanted to avoid making people guess when they could not detect the faces. Consistent with our pilot work, in 2 (age group: younger vs. older) X 3 (spatial frequency: broadband, high, and low) ANOVA on the proportion of surprised faces labeled ‘did not see it’, there was a main effect of spatial frequency, F(2, 272) = 69.83, p < .001, ηp2 = .34, and also of age, F(1, 136) = 4.15, p = .044, ηp2 = .03. These main effects were qualified by an interaction between age and spatial frequency, F(2, 272) = 42.75, p < .001, ηp2 = .24. This was because older adults had more difficulty than younger adults in perceiving the high-frequency faces. On average, older adults used the ‘did not see it’ option for 2% of the surprised broadband faces, 3% of the surprised low-frequency faces, and 38% of the surprised high-frequency faces. In contrast, younger adults’ use of the ‘did not see’ option did not vary based upon spatial frequency.

Perceived Valence of Surprised Faces

Of the surprised faces that were seen, we next analyzed whether they tended to be rated as positive or negative, and whether this varied between younger and older adults. In testing this, we first limited analyses to the 67 younger adults and 48 older adults who could reliably see at least 1/3 of the surprised high-frequency faces (i.e., used the ‘did not see it’ option for 12 or fewer of the 18 trials). As noted in the Procedure above, there were three response options available during this task (i.e., the face could be labelled as positive, negative, or as not seen). This cut-off point therefore represents below chance use of the ‘did not see it’ response option. We then conducted a 2 (age group: younger vs. older) X 3 (spatial frequency: broadband, high, and low) ANOVA on the likelihood of labeling a surprised face as positive versus negative given that the face was seen. This valence bias was operationalized as follows: (# surprised faces labeled positive – # surprised faces labeled negative) / (# of surprised faces labeled positive + # surprised faces labeled negative). Positive values indicate a bias to label the surprised faces as positive whereas negative values represent a bias to label the faces as negative.

Results of this analysis showed significant main effects of spatial frequency, F(2, 226) = 20.31, p < .001, ηp2 = .15, and of age, F(1, 113) = 9.36, p = .003, ηp2 = .08. However, of critical relevance, there was no significant interaction between spatial frequency and age, F(2, 226) = 0.18, p = .837, ηp2 < .01. As can be seen in Figure 1, valence bias varied based upon spatial frequency. As in Neta and Whalen (2010), people were especially likely to rate the surprised faces as being negative when they were presented in a low-spatial-frequency band (younger adults = −0.66; older adults = −0.42) as compared to being presented in broadband (younger adults = −.45; older adults = −.22) or in high-spatial-frequency band (younger adults = −0.52; older adults = −0.25). This pattern was present for both younger and older adults. Paired-samples t-tests showed that low-spatial-frequency surprised faces were judged more negatively than broadband surprised faces both by younger adults, t(66) = 5.44, p < .001, as well as by older adults, t(47) = 4.69, p < .001. Likewise, low-spatial-frequency surprised faces were also judged more negatively than high-spatial-frequency surprised faces both by younger adults, t(66) = 4.29, p < .001, and also by older adults, t(47) = 2.44, p = .018. Finally, although both younger and older adults gave numerically more negative judgments to the high-spatial-frequency surprised faces as compared to the broadband surprised faces, this difference only approached statistical significance for the younger adults [younger adults: t(66) = 1.89, p = .063; older adults: t(47) = 0.42, p = .680].

Figure 1: Valence bias in the interpretation of the surprised faces by younger and older adults as a function of spatial frequency (broadband, high spatial frequency, or low spatial frequency).

Figure 1:

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Note: Valence bias was operationalized as follows: (# surprised faces labeled positive – # surprised faces labeled negative) / (# of surprised faces labeled positive + # surprised faces labeled negative). Positive values indicate a bias to label the surprised faces as positive whereas negative values represent a bias to label the faces as negative.

In addition to the effects of spatial frequency, as can be seen in Figure 1, regardless of spatial frequency, older adults classified fewer of the surprised faces as negative (and thus more of the surprised faces as positive) than did younger adults. Thus, as in prior studies (Neta & Tong, 2016; Shuster et al., 2017), when categorizing an ambiguous face, older adults favored positive judgments more than younger adults did. Independent samples t-tests confirmed that this age difference was present for each spatial frequency band. Compared to younger adults, on average, older adults were more likely to favor positive judgments for the surprised faces presented in broadband, t(113) = −2.51, p = .013, in the low-spatial-frequency band, t(113) = −2.79, p = .006, and also in the high-spatial-frequency band, t(113) = −2.99, p = .003.

Although the previous analysis suggested that overall, older adults are more likely than younger adults to classify the high-spatial-frequency surprised faces as positive, we next tested whether the magnitude of this effect is moderated by the extent to which older adult participants experienced high-spatial-frequency perceptual degradation. In doing so, the following analyses used all of the participants, except for the five older adults who used the ‘did not see it’ option for all of the high-spatial-frequency surprised face trials. We then conducted a single factor (age group: younger vs. older) ANCOVA on the valence bias scores for the high-frequency surprised faces, and included the proportion of high-spatial-frequency surprised faces that the participants’ labelled ‘did not see it’ as a covariate. Results showed a main effect of age, F (1, 129) = 9.60, p = .002, ηp2 = .07, which interacted with the proportion of high-spatial-frequency surprised faces that the participant was unable to perceive, F (1, 129) = 8.88, p = .003, ηp2 = .06.

To better understand this interaction, we next split our participants into two groups based upon their relative high-frequency perception abilities. Older adults were considered relatively good high frequency perceivers if they used the ‘did not see it’ option for less than 10% of the high-frequency surprised face trials (n = 29), whereas younger adults were considered relatively good high frequency perceivers if they used the ‘did not see it’ option for less than 5% of the high-spatial frequency surprised face trials (n = 40). Using these cut-offs, there were 29 older adults and 40 younger adults classified as good high-frequency perceivers. There were 32 older adults and 32 younger adults classified as poor high-frequency perceivers.

Follow-up analyses showed that amongst the good high-frequency perceivers, older adults’ ratings of the high-spatial-frequency surprised faces were significantly less negative (M = −0.23) as compared to the younger adults’ ratings (M = −0.53), t(67) = 2.41, p = .019. In contrast, amongst the poor high-spatial-frequency perceivers, ratings of the high-frequency surprised faces did not significantly differ between the older adults (M = −0.34) and younger adults (M = −0.40), t (62) = 0.44, p = .662. This was despite the fact that amongst the poor high-spatial-frequency perceivers, the older adults had a significantly less negative valence bias when classifying the broadband and low-spatial-frequency surprised faces than did the younger adults [t(62) = 2.66, p = .010 and t(62) = 2.63, p = .011, respectively].

Discussion

Previous research indicates that broadband surprised faces tend to be interpreted less negatively by older adults than by younger adults (Neta & Tong, 2016; Shuster et al., 2017), and we replicated this in the current study. Novel to this study, we also tested whether this age-related valence bias difference could be attributed to either age-related reduction in the default negativity of interpretations, or to older adults’ use of cognitively-effortful top-down regulatory processes to overcome initial negativity. To distinguish between these, we varied the spatial frequency of our stimuli.

In younger adults, low-spatial-frequency information within emotional stimuli often leads to a default negative interpretation, which may allow for rapid threat-detection (Neta & Whalen, 2010). Our results suggest that older adults also shift to more negative categorizations when interpreting low-spatial-frequency surprised faces compared with broadband faces, and this shift towards negativity was similar in magnitude as the shift shown by younger adults. Despite this, we also found that the low-spatial-frequency surprised faces were interpreted more positivity by older adults than by younger adults. This suggests that default negativity is reduced for older adults compared to that of younger adults.

This adds to other findings revealing that the age-related positivity effect is apparent even in early attention (Gong et al., 2019; Johnson & Whiting, 2013; Mienaltowski et al., 2011; Thomas & Hasher, 2006). For example, in a recent study by Kennedy, Huang, and Mather (2019), younger and older adults were asked to complete an emotion-induced blindness paradigm. Emotion-induced blindness refers to the phenomenon in which participants are less able to detect a rotated scene in a rapidly presented series of pictures (100 ms per picture) when the rotated scene appears shortly after an emotionally arousing picture (Most et al., 2005; Wang et al., 2012). In a series of experiments, there was an age-by-valence interaction in terms of whether positive or negative distractors disrupted detection of rotated scene more, such that older adults showed relatively less disruption in their detection of rotated pictures from negative than from positive distractors than did younger adults. In addition, this positivity effect disappeared in a cognitive load condition (Kennedy et al., 2019).

The fact that positivity effects disappear under cognitive load conditions (Joubert, Davidson, & Chainay, 2018; Kennedy et al., 2019; Knight et al., 2007; Mather & Knight, 2005), also led to our hypothesis that older adults’ positive interpretation biases should be attenuated for high spatial frequency information. This was predicted because high spatial frequency sensitivity declines with age (see Owsley, 2011), and as a result older adults likely need to use top-down cognitive resources to compensate for the decrease in bottom-up input (see Monge & Madden, 2016). This should in turn reduce the cognitive resources available for older adults to engage in emotion regulation processes to overcome negativity biases, which are assumed to be the default interpretation of ambiguous surprised faces (Petro et al., 2018). Our results supported this. Among the participants who perceived more than 90% of the high-spatial-frequency surprised faces as being present, the older adults were more positive in their ratings than were the younger adults. In contrast, among the participants who perceived less than 90% of the high-spatial-frequency surprised faces, there was no significant age difference in the positivity of the ratings.

There are also limitations of the current research that should be noted. First, because the faces were presented rapidly and we did not account for visual acuity, the older adults’ accuracy in detecting the faces may have been reduced, especially for the high spatial frequency faces. But even if this were the case, we still were able to see significant effects of spatial frequency on face interpretation that looked quite similar across age groups. In addition, we found the expected reduction in negative biases among older adults when rating the ambiguous surprised faces despite the fast display, consistent with other recent findings of an age-related positivity effect in early attention (Gong et al., 2019; Johnson & Whiting, 2013; Kennedy et al., 2019; Mienaltowski et al., 2011; Thomas & Hasher, 2006).

In summary, these studies varied the spatial frequency of the ambiguous-face stimuli in order to evaluate the processes that lead to age-differences in their interpretation. Our results showed that even though older adults rate surprised faces more negatively when they are presented in low spatial frequency as compared to broadband, their ratings are still more positive than that of younger adults. Thus, low-spatial-frequency information tends to bias interpretations negatively for both younger and older adults (presumably via rapid processing in the amygdala; Neta & Whalen, 2010). However, the fact that an age difference in valence biases remains for the low-spatial-frequency faces suggests a reduction in the default negativity of older adult’s interpretations. In addition, our results also suggest that this is not the only process that contributes to age differences in valence biases. Although older adults, as a whole, rated the high-spatial frequency faces to be more positive than the younger adults, this effect was eliminated for the subset of older adults who found the high-spatial-frequency faces difficult to perceive. Importantly, this was despite the fact that this same subset of older adults gave comparatively more positive ratings than the younger adults to both the low-spatial-frequency faces and also to the broadband faces. Thus, for individuals who have experienced high-spatial-frequency perceptual degradation, positivity effects only emerged for the stimuli that are perceptually easier to perceive. We propose that this is because these older adults needed to use top-down cognitive resources to compensate for the decrease in bottom-up input, and this cognitive load interfered with their ability to increase the positivity of their ratings. Thus, when considered together, our results suggest that the reduced negativity of older adults’ valence biases can be attributed to both reduction in the default negativity of interpretations and also to cognitively-effortful top-down regulatory processes that serve to increase positivity.

Acknowledgements

Our thanks to Kriti Cadambi, Megan Chesher, Dimitrius Ellisen, Briana Kennedy, Ringo Huang, Melissa Ortiz, Jordan Seliger, and Rico Velasco for assistance with data collection and coding.

Footnotes

Declaration of Interest Statement

The authors report no conflict of interest.

1.

Within block 2, two of the HSF surprised faces and two of the LSF surprised faces were previously seen in broadband format during block 1. Excluding these trials does not affect any of the reported results.

2.

Three older adult participants were unable to use the chin rest due to physical disabilities or discomfort. Excluding these participants does not change any of the reported patterns of results.

Contributor Information

Sarah J. Barber, Georgia State University

Andrej Schoeke, University of Southern California.

Mara Mather, University of Southern California.

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