Aging has been associated with differential effects on the three attentional networks. There is evidence that healthy aging is associated with decreased alerting efficiency (Jennings et al., 2007; Isaacowitz et al., 2008; Gamboz et al., 2010). Age-related changes in the noradrenergic system, which make it difficult for older adults to maintain a vigilant state, have been attributed to agerelated declines in alerting functioning (Jennings et al., 2007). On the other hand, no reliable age differences in orienting or executive attention networks were reported (e.g., Festa-Martino et al., 2004; Jennings et al., 2007; Gamboz et al., 2010), although a decline in neural functioning of these networks was reported (Lorenzo-Lopez et al., 2002; West and Moore, 2005). One study by Mahoney et al. (2010),which included a wide range of older adults, found that increasing age is associated with decreased executive attention efficiency. The findings on age differences in executive attention may support the recent argument by Verhaeghen (2011) that age-related deficits in executive control are overstated, as executive control related to resistance to interference (often measured by Flanker and Stroop tasks) shows a lack of age-related declines.

It has been well-demonstrated that individuals with mood disorders such as anxiety and depression show attentional bias toward negative information as these individual have difficulty disengaging attention from such stimuli (e.g., Koster et al., 2005; Mogg et al., 2008). Similarly, PA has been associated with attentional bias toward reward-related stimuli (Tamir and Robinson, 2007). In addition to attentional biases to emotional information modulated by PA or NA, affect also influences multiple aspects of attention that involve non-emotional information.

According to the Broaden-and-Build Theory (Fredrickson, 1998, 2001, 2004; Isen, 1999), PA expands or broadens attentional scope, leading to more creative and flexible thinking (Ashby et al., 1999). The down side of this broadening effect is that PA has been linked to impaired executive attention, as PA increases inhibition costs (Biss et al., 2009). PA has been associated with a larger flanker interference (Rowe et al., 2007) or a larger Stroop interference (Phillips et al., 2002a), impaired planning (Phillips et al., 2002b) and greater priming for distraction (Biss et al., 2009; Biss and Hasher, 2011). There is also evidence that PA enhances the rapid covert orienting of attention (Compton et al., 2004), but this PA modulation was found in individuals with low PA. Compton et al. (2004) interpreted their findings as showing "a corresponding lack of flexibility in attentional processing associated with low PA" (p. 741), which may increase more attentional bias toward a cued location. In contrast to the link between PA and attention, NA has been linked to the narrowing scope of attention (Fredrickson and Branigan, 2005), focusing more on details of a stimulus or the environment (Forgas, 1999). For example, Vermeulen (2010) has shown that NA increased inhibitory responses to task-irrelevant stimuli, while PA decreased the inhibition of distractors.

Relatively few studies have examined the link between affective states and different functions of attention, using the ANT (e.g.,Moriya and Tanno, 2009; Pacheco-Unguetti et al., 2010; Jiang et al., 2011; Lyche et al., 2011). Across these studies, NA was associated with enhanced alerting (Pacheco-Unguetti et al., 2010; Jiang et al., 2011; Lyche et al., 2011; cf., Moriya and Tanno, 2009). This pattern is consistent with the idea suggesting that NA modulates the activation of noradrengeric systems, which maintain vigilant attention (Sullivan et al., 1999; Jiang et al., 2011). The studies yielded mixed results regarding the relationship between NA and orienting. Pacheco-Unguetti et al. (2010) showed that NA (particularly an anxious state) was related to enhanced alerting and orienting, while Moriya and Tanno (2009) found NA (state and trait anxiety, and depression) associated with decreased orienting efficiency (Moriya and Tanno, 2009). On the other hand, some studies found no association between NA and orienting (Finucane et al., 2010; Jiang et al., 2011; Lyche et al., 2011). These mixed findings may be attributable to method variance in the diverse ways affect is assessed; in these studies, individual differences in affect is assessed by self-reported questionnaires, or by experimentally induced mood procedures.

Investigations into the link between PA and attentional networks are limited (Finucane et al., 2010; Jiang et al., 2011). The few existing studies reported no association between PA and any of the three networks, although one might expect that PA may be associated with orienting (Compton et al., 2004) and executive attention (e.g., Phillips et al., 2002a; Rowe et al., 2007; Biss and Hasher, 2011). Nevertheless, the association between PA and attentional networks has not been significantly explored yet; therefore, it seems to be important to further examine the effect of PA on the functions of attentional networks.

Findings that state affect influences attention raises questions about the attentional functions of individuals who tend to experience certain affective states more frequently than others. A large body of literature indicates that older adults report experiencing more PA and less NA than their younger counterparts (e.g., Mroczek and Kolarz, 1998; Charles et al., 2001; Carstensen et al., 2011). According to socioemotional selectivity theory (SST; Carstensen and Turk-Charles, 1994; Carstensen et al., 1999), such positive emotional experiences are attributable to limited future time perspective that leads older adults (and others facing similar time horizons) to prioritize goals related to emotion regulation over other goals. Such motivational shifts, occurring with age, have been attributed what has been labeled the *positivity effect* when processing emotional information displayed by older adults. The positivity effect is a pattern of preferential processing of positive information over negative information (e.g., Carstensen and Mikels, 2005). A number of studies using eye-tracking to measure gaze patterns occurring during the processing of emotional stimuli, have demonstrated that older adults, compared to their younger counterparts, showed looking preferences toward positive and away from negative stimuli (when the positive or negative stimulus was paired with neutral stimuli; Isaacowitz et al., 2006; Isaacowitz and Noh, 2011,for a review). Indeed, older adults' positive gaze patterns have been directly linked to their attempt to regulate emotion.  In the same task, younger adults showed more mood-congruent gaze patterns, such that their gaze preferences reflected their mood states (i.e., a more positive gaze pattern when in good moods, a more negative gaze pattern when in bad moods; Isaacowitz et al., 2008). Parallel to these behavioral findings, at the neural level, amygdalar engagement was equivalent among both younger and older adults (the amygdalar is the key structure responsible for processing of emotional information; Forgas, 2008); however, older adults have shown enhanced recruitment of dorsolaterral prefrontal regions (DLPFC) during encoding of negative stimuli (St Jacques et al., 2010). DLPFC is known as the key structure responsible for cognitive control of emotion (for reviews see Vuilleumier and Huang, 2009; Dolcos et al., 2011). Therefore, greater frequency of activation of the DLPFC may explain the increased motivation found among older adults to regulate emotion generated by negative stimuli (St Jacques et al., 2010; Dolcos et al., 2011).

Despite the growing knowledge linking the DLPFC, amygdalar activation, and emotion regulation, it remains unclear whether there are age differences in multiple aspects of attention as function of PA and NA when processing non-emotional information. A study by Phillips et al. (2002b) examined whether there were age differences in the effects of induced positive and negative mood on executive attention (i.e., planning). They found that older adults in both positive and negative mood states showed greater impairment in a planning task than younger adults. The authors interpreted this results as evidence that experiencing emotionally salient events before tasks may have more adverse effects on executive attention in older adults. There is also indirect evidence that older adults with good alerting and executive attention efficiency were more likely to rely on attentional strategies (i.e., attending to positive information) in order to regulate their emotions (Isaacowitz et al., 2009; Noh et al., 2011). It therefore seems reasonable to expect that there may be age differences in how affect influences the fundamental processes by which attention operates.

The current study aimed at investigating age differences in the relationship of state PA and NA to attentional networks using the ANT. While state PA and NA and trait PA and NA are related (Watson and Clark, 1984), focusing on state PA and NA allowed us to determine how potentially modifiable state affect can impact the functioning of the attentional networks. Moreover, given agerelated changes in affective states (e.g., Mroczek and Kolarz, 1998), examining the link between state affect, and attentional networks across age groups may increase variability of state PA and NA, and thus could help resolve past mixed findings on this link. We hypothesized that PA and NA may exert differential effects on attentional networks and the pattern would vary by age groups (Phillips et al., 2002b). For alerting, we predicted that NA would be associated with enhanced alerting efficiency (e.g., Pacheco-Unguetti et al., 2010; Jiang et al., 2011). For orienting, PA would be associated with diminished orienting efficiency (Compton et al., 2004). NA may also be associated with orienting efficiency; however, previous findings on this subject are somewhat conflicting. In light of these mixed results, the current researchers feel it is important to further examine the link between NA and orienting in the current study. For executive attention, PA would be associated with decreased executive attention efficiency (Phillips et al., 2002a; Rowe et al., 2007). Although studies using the ANT found a lack of evidence for linking PA and executive attention, investigations were limited; therefore, it seems to be reasonable to expect such an association given previously reported findings (e.g., Phillips et al., 2002a; Rowe et al., 2007). With regard to age differences, we made three predictions. First, despite age-related declines in alerting efficiency (Jennings et al., 2007),we anticipated that older adults in a higher NA state would be more alert than those with in a lower NA state, as older adults with higher NA are motivated to regulate their emotion, which increases vigilance. Second,in light of findings that link older adults' selective attention toward positive information to their mood states (Isaacowitz et al., 2008, 2009; Noh et al., 2011), we hypothesized that older adults' orienting efficiency would be more likely to be influenced by their affective states than younger adults. In particular, NA would generate more efficient orienting, even for non-emotional processing. Although the ANT measures covert orienting while previous eyetracking studies (showing age-related positive gaze preferences as a function of mood states) measured *overt* orienting attention (Isaacowitz et al., 2008, 2009; Noh et al., 2011), it has been suggested that both types of attentional orienting share the same neruroanotomical substrates (Fernandez-Duque and Posner, 2001). Third, older adults in both PA and NA states would show diminished executive attention as demonstrated by Phillips et al. (2002b).

## **MATERIALS AND METHODS**

## **PARTICIPANTS**

Seventy-six younger adults (44 female; aged 18–25 years) and 69 older adults (53 female; aged 60–85 years) participated in the current study. Younger participants were recruited from an introductory psychology course and flyers posted on campus. Older adults were recruited from a lifelong learning program. Participants received either course credit or monetary stipend. The data for the current study come from a larger study that looked age differences in gaze preferences (Isaacowitz et al., 2008). An additional one younger and two older adults were tested, but their data were removed for high error rates (mean accuracy rate >80%). **Table 1** shows the participants' demographic characteristics, visual acuity, and cognitive functioning.

## **MEASURES**

## **Affect**

The Positive and Negative Affect Schedule (PANAS; Watson et al., 1988) was used to assess the participant's current emotional experiences (or states). The PANAS is a 20-item, self-rated measure comprised of two independent dimensions of PA and NA. It consists of 20 adjectives reflecting of positive (10 items, e.g.,"interested,""alert,""excited," and "enthusiastic") and negative (10 items, e.g., "guilty," "upset," "distressed," and "irritable") affect. Respondents are asked to rate the extent to which they have felt this way in the indicated time frame (*today* for the current study) on fivepoint Likert-type scales. The scale points are: 1 – "very slightly or not at all," 2 – "a little," 3 – "moderately," 4 – "quite a bit," and

**Table 1 | Means and standard deviations of participants' demographic information and results from perceptual and cognitive tests.**

The tests used were as follows: self-reported current health, ranging from 1 (poor) to 5 (excellent); Rosenbaum pocket vision screener for near vision (Rosenbaum, 1984); Pelli-Robson contrast sensitivity chart (Pelli et al., 1988); mini-mental state examination (MMSE; Folstein et al., 1975); digit symbol substitution, a subtest of the Wechsler adult intelligence scale – revised (Wechsler, 1981); Shipley vocabulary test (Zachary, 1986); positive and negative affect schedule (PANAS; Watson et al., 1988); attention network task (ANT; Fan et al., 2002). All participants had a MMSE score of 27 or higher. RT, response time. \*Significant age differences at p < 0.05.

5 – "extremely." The PANAS is the most widely used scale to measure current affective states (Crawford and Henry, 2004). Watson et al. (1988) reported alpha coefficients for various time-frames ranging from 0.86 to 0.90 for the PA scale and 0.84 to 0.87 for the NA scale. Test-retest reliability for an 8-week period ranged from 0.47 to 0.68 for PA and from 0.39 to 0.71 for NA. Crawford and Henry (2004), in a study involving a large sample drawn from the general adult population (18–91 years), obtained similar reliability scores of both scales to those found in Watson et al. (1988) and demonstrated the construct validity of the PANAS scales. The reliabilities for the PANAS found in the current study were high in both our younger and older samples (younger adults: αPA = 0.92 and αNA = 0.82; older adults: αPA = 0.83 and αNA = 0.93).

## **Attention**

**Figure 1** illustrates the sequence of the ANT. In the ANT, a fixation cross of variable duration (400–1600 ms) was presented at the beginning of each trial. This was followed by one of four cue conditions: no cue, center cue, double cue, and spatial cue. The cue (i.e., an image of an asterisk) was presented for 100 ms. On no cue trials, no cue appeared, but a fixation cross was presented; on center cue trials the cue occurred at the location of the fixation cross; on double cue trials the cues appeared above and below the fixation cross; and on spatial cue trials the cue appeared in the same location as the upcoming target, thus it always predicted the target location. After the cue disappeared, another fixation period of 400 ms was provided and a target-flanker display appeared above or below the fixation cross until the participant gave a response, but for no longer than 1700 ms. The target stimulus (i.e., central arrow pointing left or right) was surrounded by flankers on each side and there were three flanker types: congruent (arrows pointing the same direction as the target, central arrow),incongruent (arrows pointing the opposite direction of the target arrow), and neutral (dashes). After the response was given, the last fixation period was presented (3500 ms minus the first fixation period minus target RT). The total duration for each trial was 4000 ms. The JAVA version of the ANT (downloaded from Fan J's website) was used in the current study.

## **PROCEDURE**

Participants were tested individually. After providing informed consent, participants completed a demographic questionnaire and measures of visual acuity, cognition, and affect, followed by the ANT. In the ANT, participants were instructed to press a key indicating the direction of a centrally presented target arrow (pointing either left or right) that appeared above or below a fixation cross, shown in the center of the screen. The ANT began with 24 practice trials (with feedback following errors), followed by three experimental blocks (96 trials/block without feedback) separated by a short break. Participants were instructed to maintain their fixation at the fixation cross all the time, to identify the direction of the target arrow, and that quick and accurate responses would be important.

## **RESULTS**

## **PRELIMINARY ANALYSES**

**Table 1** depicts mean group differences in the PANAS and the ANT assessed using one-way analysis of variances (ANOVAs) between younger and older groups. For age differences in the PANAS, the older group scored significantly higher on the PA scale compared to the younger group, *F*(1, 143) = 16.25, *p* < 0.001, but scored lower on the NA scale, *F*(1, 143) = 6.81, *p* < 0.05. Thus, consistent with numerous previous findings (e.g.,Mroczek and Kolarz,1998), older adults, on average, reported experiencing more positive states and less negative states than their younger counterparts.

Regarding the ANT performance, the JAVA-ANT provides precalculated scores for each network. The efficiency of each attentional network is calculated using subtractions to determine the influence of alerting cues, orienting cues, and flankers on RTs. The alerting effect is calculated as *RT*no cue − *RT*double cue. The orienting effect is calculated as *RT*center cue − *RT*double cue. The conflict effect (i.e., executive attention) is defined as *RT*incongruent − *RT*congruent. Higher scores on the alerting and orienting effects are indicative of faster cue-related performance [i.e., faster RTs due to warning (alerting) and spatial (orienting) cues]. Higher scores on the conflict effect are indicative of slower RTs due to incongruent flankers (i.e., the costs associated with conflict resolution).

One sample *t*-tests were used to examine whether calculated network scores were different from zero and the results indicated that all the network scores were significantly different from zero (all *p*s < 0.001) for both younger and older adults. Thus, each network score for both age groups provides a usable index of efficiency of each network. Next, age differences in the ANT performance were tested and the results revealed a significantly reduced alerting effect in the older compared to the younger group, *F*(1, 143) = 11.83, *p* < 0.01 (see **Table 1**), and equivalent orienting and conflict effects in both groups, *F* < 1, and *F*(1, 143) = 1.22, *p* = 0.27, respectively. In order to control for the possibility that age-related declines in speed of processing (Salthouse, 1991) influenced the age difference in the alerting effect, the alerting effect as a function age was re-tested while controlling for age differences in speed. The scores on the Digit Symbol Substitution test (Wechsler,

**Table 2 | Correlations between the study's main variables in the two age groups.**

**PA NA Alerting Orienting Conflict**

Correlations below the diagonal are from the younger age group. Correlations above the diagonal are from the older age group. †p < 0.07, \*p < 0.05.

1981), which indeed revealed age-related slowing in processing speed (see **Table 1**), were used as a covariate. The age differences in the alerting effect remained significant after adjusting for age differences in speed. As shown in **Table 1**, there were no age differences in the mean ANT accuracy, indicating accuracy was equally high for both younger and older adults, *F*(1, 143) = 1.23, *p* = 0.27.

Next, **Table 2** presents the correlations between the study's main variables (attention network scores, PA, and NA) in the two age groups. Within the ANT, there were no significant correlations among alerting, orienting, and executive attention for both age groups (e.g., Fan et al., 2002). In the younger group, PA was inversely associated with alerting efficiency, whereas NA was positively associated with orienting efficiency. In the older group, neither PA nor NA was associated with alerting efficiency; however, PA was positively related with orienting efficiency, whereas NA tended to be inversely associated with orienting efficiency. Neither PA nor NA was associated with conflict resolution efficiency (i.e., executive attention) for both age groups.

#### **THE MODERATING ROLE OF AGE**

To test whether the effects of PA and NA on attentional networks were moderated by age, we performed moderated regressions

analyses. With each attentional network as a dependent variable, two hierarchical multiple regression analyses were conducted separately for PA and NA. Thus, a total of six regression models were conducted. In the first step of the regression analyses, we included gender (0 = male) and speed of processing as control variables, because age groups had a higher percentage of women, and older adults, on average, were slower in their processing speed than younger adults. In the second step, we tested the main effects of age (0 = younger adults) and PA or NA for significance. Finally, we entered the interaction terms between age group and PA or NA in the last step of the analyses. All continuous predictor variables, except for gender and age, were standardized prior to performance of analyses.

The results of the main effects and interaction effects are reported in **Table 3**. Across all regression models, gender failed to account for the efficiency of attentional networks, and speed only accounted for alerting efficiency in that slower speed of processing predicted reduced alerting efficiency. For the alerting model with PA, age did not significantly predict alerting efficiency; however, higher levels of PA were associated with lower alerting efficiency. Despite this association, the age by PA interaction was not significant. For the alerting model with NA, the main effect of age was significant as older adults exhibited lower alerting efficiency than younger adults. Neither the main effect of NA nor the age by NA interaction was significant. Thus, the alerting models indicated that age did not moderate the effects of PA and NA on alerting efficiency.

For the orienting model with PA, neither age nor PA predicted orienting efficiency; however, there was a significant age by PA interaction. In addition, for the orienting model with NA, none of the main effects were significant; though, the age by NA interaction effect was significant. To illustrate the obtained interaction effects, we plotted in **Figure 2** the association between PA and NA (1 SD above and below the sample mean) and orienting efficiency separately for younger and older adults employing commonly used regression techniques (Aiken and West, 1991). The obtained pattern of results in **Figure 2A** indicated that the largest orienting effect was found among older adults with higher levels of PA. A calculation of the simple slopes further supported this interpretation: experiencing higher levels of PA significantly associated with greater orienting efficiency among older adults (β = 13.29, *p* < 0.05), but not among younger adults (β = 1.02, *p* = 0.73). Conversely, the obtained pattern of results in **Figure 2B** showed that larger orienting effects were found among older adults with lower levels of NA and younger adults with higher levels of NA. A calculation of the simple slopes, however, indicated that the slope for older adults was significant (β = −7.25, *p* < 0.05), but not for younger adults (β = 5.39, *p* = 0.12). Thus, experiencing lower levels of NA was significantly associated with greater orienting efficiency among older adults. Finally, for the conflict models, neither PA nor NA predicted conflict resolution. Moreover, age did not exert a significant interaction with PA or NA on conflict resolution.

## **THE MEDIATING ROLE OF STATE AFFECT**

To determine whether age differences in PA and NA accounted for age differences in alerting, we performed a mediation analysis, using age and alerting as independent and dependent variables

**Table 3 | Hierarchical regression analyses predicting attentional networks from age group, PA, NA, and the age group by PA/NA interactions.**

Gender was coded as 0 (male) and 1 (female). Age coded as 0 (younger adults) and 1 (older adults). PA, positive affect; NA, negative affect. †p < 0.07, \*p < 0.05, and \*\*p < 0.01.

and affect variables as mediators. While recent work has suggested important problems with meditational analyses using crosssectional aging data when the mediator is supposed to represent a developmental process (see Lindenberger et al., 2011), we considered PA and NA as potential *states* that could mediate the relationships of interest, not as developmental outcomes themselves. Thus, meditational analyses may still be appropriate in this context.

The analysis of mediation effects used a multiple mediation model with both PA and NA entered simultaneously using Preacher and Hayes' (2008) method for testing mediation. By testing for mediation effects through both PA and NA, we were able to compare the size of each effect as well as its significance as a mediation effect. Following Preacher and Hayes' (2008) method, we used a bootstrapping procedure to compute SEs and 95% confidence intervals around the indirect effect (i.e., the effect of age on alerting through PA and NA). This method uses 5,000 bootstrapped samples to estimate the bias-corrected and accelerated confidence intervals. Indirect effects were considered as significant when the confidence interval did not include zero. The SPSS macros that Preacher and Hayes provided were utilized for this procedure. In this analysis, age (0 = younger adults) was the independent variable, alerting was the dependent variable, and PA and NA were mediators. Preacher and Hayes (2008) recommended use of bootstrap SEs and confidence intervals over the Sobel test because the latter involves the assumption of the normality of the estimates of the indirect effect, which is normal only in large samples. However, for convenience, we also report the traditional mediation significance test by the Sobel test. In addition, unstandardized regression/path coefficients were reported in the current model because standardized coefficients for a dichotomous variable (i.e., *age*) had no meaningful interpretation (Preacher and Hayes, 2008). Significance tests of the mediation effect can be found in **Table 4**.

**Table 4 | Magnitude and confidence intervals of the multiple mediation test of the relationship BETWEEN age and alerting through PA and NA.**

Boldface type highlights a significant effect as determined by the 95% biascorrected and accelerated confidence interval based on 5,000 bootstrap samples.

The total mediation effect of PA and NA was significant; however, this effect was driven by PA, as the mediation effect of NA did not attain significance. Although the result of the Sobel test was only marginally significant (see **Figure 3**), the fact that zero fell outside the confidence interval (−12.89 to−1.30; see**Table 4**) indicated a significant mediation effect (Preacher and Hayes, 2008). As can be seen in **Figure 3**, older adults were more likely to report a higher state of PA (*b* = 0.80, *p* < 0.01), which had a negative prediction effect for differences in alerting efficiency (*b* = −6.61, *p* < 0.05). At the same time, age was a significant predictor of alerting without the mediators in the model (*b* = −18.45, *p* < 0.05), but the direct effect became non-significant in the presence of the mediators, suggesting that PA largely mediated the age differences in alerting.

# **DISCUSSION**

The current study investigated age differences in the links between affect and attentional functioning. From preliminarily analyses on mean differences, we found that older adults in the current study reported experiencing more PA and less NA than did younger adults. Thus, the finding is consistent with previous studies of agerelated decreases in experience of NA (e.g., Mroczek and Kolarz, 1998; Carstensen et al., 2011), which suggest that as people get older, they report more positive and less negative affective states. With regard to age differences in the efficiency of attentional networks, older adults were found to show decreased alerting efficiency compared with younger adults (even when adjusting for speed of processing), but there was no evidence of age differences in the efficiency of orienting and executive attention. These results are consistent with previous findings (Festa-Martino et al., 2004; Fernandez-Duque and Black, 2006; Jennings et al., 2007; Gamboz et al., 2010). Noradrenergic transmission has been linked to alerting (Fernandez-Duque and Posner, 2001; Fan et al., 2009), as well as associated with NA (Sullivan et al., 1999), suggesting that agerelated declines in the noradrenergic activation system (Jennings et al., 2007) might explain the main effect of age on both alerting and NA.

Results from the correlation analysis showed no association among the three network scores for both younger and older age groups, indicating the functional independence of alerting, orienting, and executive attention (e.g., Fan et al., 2002; Moriya and Tanno, 2009). Younger and older adults showed somewhat different patterns of correlations between affect and attentional networks, which were further examined by the moderation and mediation analyses.

# **AGE DIFFERENCES IN THE ASSOCIATION BETWEEN AFFECT AND ALERTING**

Higher PA was associated with diminished alerting, but age group did not moderate the relationship between PA and alerting. This finding is inconsistent with our hypothesis and divergent from previous work linking NA and alerting (e.g., Pacheco-Unguetti et al., 2010; Jiang et al., 2011; Lyche et al., 2011). It is perplexing why PA, not NA, was related with alerting functioning in the current study. If PA broadens attention and thinking (Fredrickson, 1998, 2001), which is hypothesized to induce loosened control of attention (Biss and Hasher, 2011), PA could also modulate the alerting network in similar ways. It has been shown that the processes involved in alerting and executive attention share a common brain region in the frontal-parietal lobe, though the two processes are dissociable at the behavioral level (Fan et al., 2007). The executive attention system was associated with dopaminergic transmission (Fan et al., 2009), which was associated with PA (Ashby et al., 2002). Together, the findings provide indirect evidence that PA may also be related to alerting. However, given no evidence for the link between PA and executive attention in the current study, it seems to be unlikely that high PA is also linked to impaired alerting. The reduced alerting effect can also be interpreted as the facilitated ability to prepare and sustain alertness in the *absence* of a warning cue. Therefore, it may be more plausible, however speculative, that diminished alerting associated with PA indicates higher vigilance and, therefore, an increased ability to maintain alertness in the *absence* of a warning cue.

The results from the mediation analysis further indicate that the majority of the age-related variance in the alerting effect was associated with PA. PA fully mediated age differences in alerting. The present finding may, in part, align with the earlier finding of Phillips et al. (2002b) that older adults in both positive and negative mood states showed worse performance on a task of executive attention than younger adults. It is interesting that we observed this pattern in alerting performance. The present finding, in conjunction with Phillips et al.'s (2002b), may suggest that increased PA is more likely to influence attentional functioning for older adults than for younger adults.

Although we predicted that older adults with higher NA would be more alert that those with lower NA, as they might be more motivated to regulate emotion, we found no supporting evidence for this link. Thus, SST does not seem to be a fitting explanation for this pattern. There may be several tentative explanations for the association between PA and alerting in older adults. One possibility is that, as we speculated above, if the effect of PA on alerting reflects better ability to prepare and sustain alertness in the absence of warning cues, the present result may suggest that high PA could enhance alertness in the absence of warning cues for older adults. Another possibility is that, as we speculated above, if the effect of PA on alerting reflects greater vigilance in the *absence* of warning cues, the present result may suggest that high PA could enhance alertness when warning cues are *not* present. An underlying mechanism for this greater alertness may be related to dopaminergic and norepinephrinergic pathways from the amygdala (LeDoux, 2007 for review of afferent amygdala input) which could be affected by levels of PA. We should caution that this interpretation is speculative and PA actually may be related to worse alerting. In addition, more work is needed in order to determine how the relationship between age and PA influences the neural functioning that is implicated in alerting.

The lack of NA effect on alerting can be explained, in part, by the fact that the NA state measured in the current study may not be strong enough to generate findings similar to those measured in previous studies. For example, previous studies either selected individuals with extreme NA states, such as depression (Lyche et al., 2011), or experimentally manipulated negative mood (Pacheco-Unguetti et al., 2010; Jiang et al., 2011). The current study, on the other hand, assessed naturally occurring individual differences in NA in healthy participants. The arousal levels involved in these varying NA states may differ (Jefferies et al., 2008), though the possibility of this phenomenon does require further investigation. In the current study, the average level of NA reported by the participants was quite low (as indicated in **Table 1**). This might have resulted in the relatively lower level of arousal of NA found as compared to previous studies. Future studies should address how different ranges of NA states, as well as levels of arousal, may be related to the function of alerting, and how these factors interact with one another to influence the function of alerting, as well as age differences in the NA-alerting link.

# **AGE DIFFERENCES IN THE ASSOCIATION BETWEEN AFFECT AND ORIENTING**

Age group did moderate the relationship between PA and orienting as well as NA and orienting. That is, older adults experiencing higher PA and lower NA exhibited better orienting followed by spatial cues than those experiencing lower PA and higher NA. On the other hand, orienting efficiency among younger adults was not significantly influenced by the levels of PA and NA, although the results of the correlations indicate that NA was positively associated with orienting for younger adults (Pacheco-Unguetti et al., 2010). Thus, supporting our hypothesis, affect states were more likely to influence orienting for older adults. Though our hypothesis was supported, we particularly expected that higher NA would produce more efficient orienting in older adults as previous studies demonstrated pronounced gaze preferences toward positive stimuli in a negative mood state among older adults (e.g., Isaacowitz et al., 2008); however, the pattern in the data showed that older adults experiencing more positive and less negative affective states oriented attention to the cued location much more quickly. This finding conflicts with Compton et al.'s (2004) reports that individuals with low PA showed greater spatial cueing effects than individuals with high PA. As Compton et al. (2004) argued, if greater spatial cueing effects associated with low PA are due to decreased cognitive flexibility associated with low PA, the present finding with older adults may also reflect decreased cognitive flexibility associated with high PA and low NA among older adults. However, as high PA has been closely linked to increased cognitive flexibility (Ashby et al., 1999), this may not be the case. It is noteworthy that there are some methodological differences between Compton et al. (2004) and the current study. Compton et al. (2004) only included younger participants and their orienting task included both valid and invalid cue trials, while the ANT task used in the current study only included valid cue trials. Therefore, the present finding indicates that older adults with high PA and low NA benefited more from valid cues in orienting attention, but cannot address the impact of invalid cues. Future studies should further test whether older adults with high PA or low NA also have difficulty disengaging attention from invalidly cued locations. This may help elucidate whether or not the orienting effect associated with high PA and low NA in older adults reflects decreased cognitive flexibility.

The orienting network is governed by two neural mechanisms: the dorsal frontospatial network (DAN) and ventral frontoparietal network (VAN; Corbetta and Shulman, 2002). DAN is involved in top-down control of directing attention to the location-directing cue and making eye-movements to sensory stimuli, while VAN is involved in disengagement of attention when invalid cue is presented so attention has to be shifted away from its current focus toward the opposite visual field (Corbetta and Shulman, 2002). Because invalid cue trials were not included in the ANT in the current study, the present orienting effect may be related to DAN. However, it is proposed that DAN and VAN interact in a way that DAN provides a signal to VAN in order to maintain a certain attentional balance between the two systems when focusing attention (Corbetta et al., 2008). The present results, therefore, may indicate this orienting system remains balanced in younger adults and is somehow disrupted in older adults by high PA and low NA states. Further, both networks rely on cholinergic transmission (Fan et al., 2009). A deficit in cholinergic transmission in Alzheimer's disease produces enhanced benefit from valid cues (Parasuraman et al., 1992). Therefore, the present orienting effects could also reflect cholinergic transmission associated with high PA and low NA, which may affect activity in DAN during engagement of attention to valid cues. This possible link needs more investigation.

Alternatively, our finding may indicate that older adults with high levels of PA states may seek out more PA in an effort to perpetuate an "upward spiral" of positive emotion (Fredrickson and Joiner, 2002). These individuals may find more positive information in the world around them, and may maintain greater happiness because of what they find, which could lead them to rely on environmental cues to orient their attention. This possibility might be supported by a recent study by Waldinger et al. (2011). They showed that older adults high in life satisfaction displayed increased connectivity of within an amygdala-mediated network in response to positive information, as compared to negative information. In line with Waldinger et al. (2011), our findings may suggest that older adults reporting high levels of PA states may be more sensitive to environmental cues to orient attention. However, it remains important to test whether individual differences in affective states in older adults are related to orienting attention, and more specifically to emotionally valenced information.

# **AGE DIFFERENCES IN THE ASSOCIATION BETWEEN AFFECT AND EXECUTIVE ATTENTION**

There is no evidence from the present results that PA had any effect on executive attention. Thus, the present finding is not in line with the Broaden-and-Build Theory of positive emotions (Fredrickson, 1998). Martin and Kerns (2011) suggest that PA is related to some aspects of executive attention, such as working memory and planning, but it may have little impact on prepotent response inhibition, which is related to flanker interference. However, Rowe et al. (2007)found supporting evidencefor the Broaden-and-Build Theory using the flanker task. The discrepancy between the result of this study and that of Rowe et al. (2007) may lie in a difference in the flanker task, as Rowe et al. (2007) manipulated the distance between the flankers and target, while the current study had no such manipulation. Consistent with the present finding, previous studies using the ANT also reported a lack of association between PA and flanker interference (Jiang et al., 2011; Lyche et al., 2011; Martin and Kerns, 2011; cf., Rowe et al., 2007). Thus, future research should, therefore, not only consider different executive functions, but also take into account variations of the flanker task.

Inconsistent with our hypothesis and Phillips et al.'s (2002b) results, we found no evidence for diminished executive attention among older adults in either PA or NA states. The discrepancy between our finding and those of Phillips et al. (2002b) may lie in the task used to assess executive attention and the way affect was measured. Such methodological differences may cause different results. Therefore,future research should be conducted to examine whether the influence of affect on executive attention is dependent upon how executive attention and affect (or mood) are assessed.

## **LIMITATIONS AND CONCLUSION**

The current study has several important limitations that are important to note. Most importantly, the data in the current study come from self-reported measures of affect and from non-clinical participants. Thus, the data may reflect restricted ranges of affect that may not be sensitive to detect possible affect-attention relationships. In addition, because the context of emotion regulation was not directly manipulated in the current study, how age-related changes in affective experiences and shifts in emotional goals interact to influence attentional functioning remains in question. This limitation could be attributed to the fact that we found no evidence for our hypotheses regarding the attentional performance of older adults with high NA. Future research will need to make explicit the connections between age differences in affect-attention links as a function of emotional goals. Further, this study is cross-sectional and does not include middle-aged participants, which precludes a direct examination of age-related changes in affect and attentional networks and how these changes influence one another. Finally, although the current study provides initial behavioral evidence regarding age differences in the effects of PA and NA on attentional networks, future brain-imaging studies will shed further light on the interactive processing of affect and attentional networks at a neural level.

These limitations notwithstanding, the results presented here show that attention is indeed a multifaceted construct and each

# **REFERENCES**

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facet can be affected differently by PA and NA. Additionally, individual difference factors such as age should continue to be taken into account when researchers study the affect-attention relationship. Extending the previous studies showing age differences in attentional orienting to emotional information, the present results indicate that age differences in alerting and orienting can be modulated by state PA or/and NA when the task is non-emotional. Experiencing high positive and low negative affective states may be adaptive in later years of life. The present results that such positive affective profile is more likely to influence attention for older adults than younger adults suggest that this relationship may occur in order to compensate for age-related changes in internal resources. This relationship needs further investigation.

## **ACKNOWLEDGMENTS**

This research was supported by National Institutes of Health Grant R01 AG026323 to Derek M. Isaacowitz.

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Cadaveria, F. (2002). Covert orienting of visuospatial attention in the early stages of aging. *Neuroreport* 13, 1459–1462.

negative mood on planning. *Emotion* 2, 263–272.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 28 April 2012; accepted: 07 August 2012; published online: 31 August 2012.*

*Citation: Noh SR, Larcom MJ, Liu X and Isaacowitz DM (2012) The role of affect in attentional functioning for younger and older adults. Front. Psychology 3:311. doi: 10.3389/fpsyg.2012.00311*

*This article was submitted to Frontiers in Emotion Science, a specialty of Frontiers in Psychology.*

*Copyright © 2012 Noh, Larcom, Liu and Isaacowitz. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

# Electrophysiological evidence for adult age-related sparing and decrements in emotion perception and attention

#### *Joshua W. Pollock1, Nadia Khoja2, Kevin P. Kaut1, Mei-Ching Lien2 and Philip A. Allen1 \**

*<sup>1</sup> Department of Psychology, University of Akron, Akron, OH, USA*

*<sup>2</sup> Department of Psychology, Oregon State University, Corvallis, OR, USA*

#### *Edited by:*

*Mara Mather, University of Southern California, USA*

#### *Reviewed by:*

*Pierfilippo De Sanctis, Albert Einstein College of Medicine, USA Ying-hui Chou, Duke University Medical Center, USA*

#### *\*Correspondence:*

*Philip A. Allen, Department of Psychology, University of Akron, Akron, OH 44325-4301, USA. e-mail<EMAIL_ADDRESS>
The present study examined adult age differences in processing emotional faces using a psychological refractory period paradigm. We used both behavioral and event-related potential (P1 component) measures. Task 1 was tone discrimination (fuzzy vs. pure tones) and Task 2 was emotional facial discrimination ("happy" vs. "angry" faces). The stimulus onset asynchrony (SOA) between the two tasks was 100, 300, and 900 ms. Earlier research observed larger age deficits in emotional facial discrimination for negative (angry) than for positive (happy) faces (Baena et al., 2010). Thus, we predicted that older adults would show decreased attentional efficiency in carrying out dual-task processing on the P1 (a component linked to amygdalar modulation of visual perception; Rotshtein et al., 2010). Both younger and older groups showed significantly higher P1 amplitudes at 100- and 300-ms SOAs than at the 900-ms SOA, and this suggests that both age groups could process Task 2 faces without central attention. Also, younger adults showed significantly higher P1 activations for angry than for happy faces, but older adults showed no difference. These results are consistent with the idea that younger adults exhibited amygdalar modulation of visual perception, but that older adults did not.

**Keywords: aging, emotion perception, automaticity, ERPs, psychological refractory period**

# **ELECTROPHYSIOLOGICAL EVIDENCE FOR ADULT AGE-RELATED SPARING AND DECREMENTS IN EMOTION PERCEPTION AND ATTENTION**

Some studies with younger adults have shown that early emotion perception of angry faces does not require attentional resources, suggesting that some emotion perception is automatic (e.g., Shaw et al., 2011). However, there is evidence suggesting that for some older adults, emotional processing of negative stimuli (Leigland et al., 2004; Baena et al., 2010) and positive stimuli (Allen et al., 2011) is compromised (relative to younger adults). We hypothesize that this is the result of age-related changes in the ventral affective system (see Dolcos et al., 2011). The ventral affective system is hypothesized to be a reflexive system involving early emotional evaluation in threat perception (including the visual cortex, fusiform gyrus, amygdala, and ventromedial prefrontal cortex [VMPFC]), whereas the dorsal attentional stream is thought to involve later "cognitive" executive functions (the frontoparietal attentional system) (Dolcos et al., 2011; see also, Corbetta et al., 2008). Evidence consistent with a ventral affective system deficit is that older adults show deficits in: discriminating emotional faces—especially negatively valenced emotional faces (Baena et al., 2010), emotional decision making (Denburg et al., 2005), and emotionally linked episodic memory (Allen et al., 2005, 2011). These are known symptoms of individuals with VMPFC damage (Bechara et al., 2000; Denburg et al., 2005) and/or amygdalar deficits (Leigland et al., 2004).

The goal of the present study is therefore to examine potential age differences in emotional processing. Similar to Shaw et al. (2011), we used Psychological Refractory Period (PRP) paradigm and measured the event-related potential (ERP) elicited by emotion stimuli. However, while Shaw et al. examined spatial attention automaticity using the N2pc ERP component, we examined attentional automaticity across age (younger and older adults) using the P1 ERP component. The P1 component (measured at the O1 and O2 electrode sites) is a visual perceptual response known to be modulated by the amygdalar function with emotional faces (Rotshtein et al., 2010). The P1 component is a particularly sensitive measure of emotional processing because Rotshtein et al. isolated the P1 ERP effect on epilepsy patients. They compared healthy controls, individuals with medial temporal lobe epilepsy (MTLE) surgery that spared the amygdala (MTLE-control), and individuals with MTLE surgery that resulted in amygdalar damage (MTLE-amygdala). The MTLEamygdala patients (with damage to the amygdala) showed no appreciable P1 effects to emotional faces, but the MTLE-control and healthy participants did show large P1 effects to emotional stimuli (e.g., fearful vs. neutral faces). It is important that the larger P1 effect for fearful faces than for neutral faces in the two control groups was not the result of general perceptual activation because inverted faces showed no increased positivity for the fearful faces for the P1 component. Also, Holmes et al. (2008, 2009b) used the P1 component to study emotional facial discrimination in individuals with low and high trait anxiety. Consequently, there is evidence that the P1 ERP is a measure of perceptual processing and is generated by extrastriate visual cortex and fusiform gyrus (Di Russo et al., 2002; Amaral, 2003; Phelps and LeDoux, 2005). Critical to the present study is the finding that the amygdala appears to modulate perceptual processing when a visual stimulus has an emotional valence (Holmes et al., 2008, 2009a,b; Rotshtein et al., 2010). An additional question to be addressed in the present study is whether increased adult age modulates exogenous attention involving emotional stimuli [what Dolcos et al. (2011) termed the ventral affective system] in a reflexive manner. Our present working hypothesis is that if older adults exhibit a deficit in ventral affective processing, then they should show a reduced P1 emotional valance effect relative to younger adults. We also predict that this age difference should be particularly salient when central attentional resources are engaged by another non-emotional task (in the present study, Task 1).

## **PSYCHOLOGICAL REFRACTORY PERIOD PARADIGM**

We aimed to determine if emotional faces (Task 2) can still be processed even when central attentional resources are engaged by the processing of another task (Task 1) and how the processing is modulated by age. We used the PRP paradigm (Telford, 1931; Welford, 1952; Pashler, 1984), which is a widely used method for the examination of dual-task processing. In this paradigm participants are required to perform two tasks (Task 1 and Task 2) for which the stimuli are separated by a variable time interval, which is known as the stimulus onset asynchrony (SOA). The common finding is that Task 2 performance tends to decline as SOA decreases—and this phenomenon is known as the PRP effect (Telford, 1931; Pashler, 1984).

Pashler (1984) proposed the central bottleneck model to account for the PRP effect (see also Welford, 1952). The model postulates that central processing stages, such as response selection, for Task 1 and Task 2 do not operate in parallel and are instead processed serially. However, peripheral processing stages, such as perceptual encoding, can occur in parallel with all the other stages. The variable of SOA is theorized to measure the duration of the central attentional bottleneck (see **Figure 1**). This is because the basic assumption of this model is that Task 1 response selection must be completed before Task 2 response selection can begin. At long SOAs, there is enough time to complete Task 1 response selection before Task 2 response selection begins, so there is no bottleneck. However, at short SOAs, Task 2 is presented before Task 1 response selection is complete, and this results in a delay before Task 2 response selection can begin. On the other hand, Task 1 performance typically is unaffected by SOA because response selection for this task is completed before that of Task 2, so there is no delay (Pashler, 1984; Lien and Proctor, 2002; Ruthruff et al., 2009).

The locus-of-slack logic is a common method used to determine which operations are subject to this central bottleneck (Schweickert, 1978). According to this logic, if the manipulated

Task 2 variable affects the stages prior to the bottleneck, then the effects of the Task 2 variable should be much smaller at short SOAs than at long SOAs, reflecting in an underadditive interaction between its effect and SOA (i.e., the slack effect). However, if the manipulated Task 2 variable affects the stages during or after the bottleneck, then the effects of the Task 2 variable will be additive with the SOA effect. A number of processes are indicated to be subject to the bottleneck, including response selection (Lien et al., 2002), word identification (Lien et al., 2008), memory encoding (Jolicoeur, 1998), mental rotation (Ruthruff et al., 1995), memory retrieval (Carrier and Pashler, 1995), and difficult perceptual judgments such as box-width judgment (Johnston and McCann, 2006). Due to the relatively wide variety of processes which are influenced by the bottleneck, the overall general resource attributed to all of them is commonly referred to as central attention (Johnston et al., 1995). However, some highly skilled tasks like word frequency effects in visual word recognition have been shown to exhibit slack effects (Allen et al., 2002; Lien et al., 2006).

Another (perhaps more direct) method of measuring parallel processing in the PRP paradigm was to use the ERP measure, which was applied by Shaw et al. (2011). Shaw et al. used a dual-task paradigm in which Task 1 involved two-choice tone discrimination (pure vs. fuzzy tones). For Task 2, one happy face and one angry face were presented adjacent to each other. This study had some similarities to the study of Tomasik et al. (2009) that used a behavioral version of this PRP paradigm and failed to observe slack effects for difficulty effects in emotional facial discrimination. That is, Tomasik et al. found additivity between emotional facial discrimination difficulty effects and SOA, suggesting that emotion perception was not automatic. However, the Shaw et al. study did not directly test for emotional discrimination and difficulty effects. Instead, their participants were asked to decide the gender (Experiment 1) or spatial location (Experiment 2) of a given facial emotion (emotion type was a between-subjects variable and facial emotion was easy to determine in Shaw et al.). The rationale for the Shaw et al. study was that behavioral measures of performance such as reaction time (RT) and accuracy might not be sensitive to early processing, but that electrophysiological-based ERP measures might be more sensitive to this early type of processing.

Shaw et al. (2011) used SOAs between Task 1 and Task 2 of 50, 200, and 1000 ms. They reasoned that if the shift of spatial attention to the targeted facial emotion (as indexed by the N2pc effect in ERPs) could occur without central attention resources, then N2pc effects linked to this face should not appreciably differ across SOA. That is, N2pc effects should be approximately constant across SOA. Alternatively, if this shift in spatial attention demands central attentional resources, then the N2pc effect should be delayed or attenuated at short SOAs (in an analogous manner to latencies from Task 2 being prolonged by 200–400 ms in the PRP effect). In contrast to this prediction, Shaw et al. observed statistically equivalent N2pc amplitudes at all three SOAs, with the effect elicited by angry faces being more pronounced than the effect elicited by happy faces, suggesting that emotion perception can be processed automatically (i.e., without central attention) for younger adults. Even

though there was not an Emotion Type × SOA interaction in Shaw et al.'s study (suggesting a similar pattern for N2pc effects across SOA for both angry and happy faces), the N2pc amplitudes across all three SOAs were greater for angry faces than for happy faces—suggesting the attentional bias toward angry faces (also see Holmes et al., 2009a; but see Brosch et al., 2011). Different from Shaw et al. (2011), we examined directly emotion perception by asking participants to determine whether a single human face was "happy" or "angry". Also, we used the P1 component (Rotshtein et al., 2010) instead of the N2pc (Luck and Hillyard, 1994) component used by Shaw et al. to assess emotional perception during dual-task processing because the P1 component is thought to measure amygdalar modulation of visual perceptual responses to human faces that differ in emotional valance (Holmes et al., 2008, 2009a,b; Rotshtein et al., 2010) (in our case, the emotional valence effect). However, the same logic holds for the P1 component as the N2pc component except that we were interested in whether participants can begin Task 2 response selection of a single emotional face before completing Task 1 (pure vs. fuzzy tone) response selection. Based upon Shaw et al. (who observed a much stronger N2pc effect for angry faces than for happy faces—see their Figures 2 and 3) we predict an attentional bias for angry faces, at least for younger adults.

# **EMOTIONAL PROCESSING IN THE BRAIN**

Adaptive behavior tends to rely on fast recognition of cues from the environment to establish threat or safety, and one such cue is facial expression (Fitzgerald et al., 2006). Research has suggested that humans are particularly efficient at processing human emotional expressions (Vuilleumier, 2000, 2002; Frischen et al., 2008). Vuilleumier et al. (2001) and Anderson et al. (2003) both observed amygdalar responses to facial expressions that seemed to be independent of attentional modulation using fMRI methods. Also, as noted above, Shaw et al. (2011) found very early N2pc activation using ERP methods for emotional faces that almost certainly was a reflexive effect, and this suggests that these early emotional effects were modulated by the amygdala. Overall, then, these results provide strong evidence that the processing of facial emotional processing can have early effects on attention. However, in the following section we will develop a more thorough model that can account for apparent early reflexive, affective processes, and later cognitive processing involved in selective attention.

As noted earlier, there is accumulating evidence for an early, reflexive ventral affective system that is modulated by affective valences and a later, controlled-process dorsal attentional stream that is cognitive in nature (Corbetta et al., 2008; Dolcos et al., 2011). The ventral affective system includes the amygdala and VMPFC and is likely a "survival" system (Allen et al., 2008). This system is likely what is referred to as exogenous attention for emotional stimuli. This system monitors incoming sensory information for potential threat and can "disengage" existing cognitive attention toward an incoming perceptual threat if such a threat is encountered (and the same process could occur if incoming information with a positive emotional valence suggested available safety). For example, if an individual steps out onto a crosswalk (directed by cognitive attention) and then automatically jumps back onto the sidewalk because exogenous attention has detected a rapidly approaching car that has run a red light, this would be an example of the ventral affective system (exogenous attention) "grabbing" attention away from endogenous cognitive attention. The dorsal attentional stream involves "endogenous" attention and is thought to be mediated by the dorsolateral prefrontal cortex, the anterior cingulate cortex, and the frontoparietal attentional pathway (Corbetta et al., 2008; Dolcos et al., 2011).

The PRP paradigm allowed us to "peek into the black box" of attentional dynamics between these two systems. The most parsimonious interpretation of Shaw et al. (2011) results in which short SOAs resulted in the same amplitude N2pc effect as longer SOAs is that the ventral affective system was able to process Task 2 negative emotions in parallel with Task 1 response selection. In the present study, we predict that the same pattern of results in P1 effects will occur for younger adults, but that older adults will show attenuated P1 emotional valence effects due to a deficit in the ventral affective system. The rationale for this hypothesis will be further developed in the next section.

## **AN EMOTION PERCEPTION DEFICIT MODEL OF AGING**

There have been many aging studies that examined behavioral and neuropsychological assessment tasks associated with ventral affective system function (Lamar and Resnick, 2004; Allen et al., 2005, 2011; Denburg et al., 2005; Baena et al., 2010). Also, Lamar et al. (2004) found fMRI evidence of an orbitofrontal cortex deficit for older adults (using a delayed match and nonmatch to sample paradigm), Fjell et al. (2009) found a drop in longitudinal MRI volume in healthy aging for the amygdala, and especially critical to the present study, St. Jacques et al. (2010) found impaired functional connectivity with the amygdala and visual cortical areas in older adults (relative to younger adults). Thus, past research has suggested multiple possibilities as to why older adults have less efficient emotional processing than younger adults. Some research has suggested that older adults may exhibit neural degeneration of the amygdala relative to younger adults (Leigland et al., 2004; Fjell et al., 2009) and that this results in different areas of the brain, such as the VMPFC, compensating for this loss. However, studies have also reported age-related decline in tasks associated with VMPFC function (Allen et al., 2005, 2011; Denburg et al., 2005; Baena et al., 2010; although see MacPherson et al., 2002). Indeed, Timpe et al. (2011) found that older adults with emotional decision-making deficits showed a reduction in white-matter intactness in the frontal cortex (relative to older adults without emotional decision-making deficits) as measured by diffusion tensor imaging. Thus, there are age-related deficits for either amygdalar processing or VMPFC processing, or both based on results from imaging and behavioral studies (e.g., Lamar et al., 2004; Allen et al., 2005, 2011; Denburg et al., 2005; Fjell et al., 2009; Baena et al., 2010; St. Jacques et al., 2010; Timpe et al., 2011). We hypothesize that these imaging and behavioral results are consistent with an *emotion perception deficit model* of aging. Specifically, it is proposed that older adults exhibit either structural of functional deficits that make it more difficult for the amygdala to modulate visual perception.

# **SOCIOEMOTIONAL SELECTIVITY THEORY**

Carstensen et al. (1999) have proposed that older adults become more sensitive to positively valenced emotional stimuli because of social contexts and motivation. This model is termed Socioemotional Selectivity Theory (SST). These researchers postulated that this positive bias is due to the fact that seniors have less remaining life expectancy, so they tend to identify negative experiences as having less useful information, and, therefore, attribute more emphasis on positive affect (known as the "late positivity effect"). Some researchers have suggested that older adults become more efficient at behavioral regulation of physiological responses to emotional stimuli than do younger adults (Carstensen and Mikels, 2005; Mather and Carstensen, 2005). For example, Mather et al. (2004) reported that older adults had reduced fMRI amygdalar activation for negative pictures relative to younger adults (but not so for positive pictures). Also, LeClerc and Kensinger (2008) found greater fMRI activation in the VMPFC for positive emotional stimuli for older adults but for negative emotional stimuli for younger adults. The reason that SST is pertinent to the present study is because it predicts that older adults' positivity bias is the result of a later-life positivity bias accomplished through emotional regulation—rather than due to a neural deficit occurring earlier in the ventral affective system. The present study provides a mechanism to test the emotion perception deficit hypothesis of Allen et al. (2005) and the SST of Carstensen et al. (1999). This is because the two models make different predictions with regard to how P1 ERP amplitude will vary across younger and older adults using a PRP paradigm. The emotion perception deficit model predicts, in at least some older adults, that the ventral affective system declines and that these changes impair emotion perception and emotional decision making. Thus, this model predicts age differences in P1 amplitude for emotional facial discrimination. Specifically, younger adults should show larger P1 effects for angry faces than for happy faces (based on Holmes et al., 2008), but that older adults should show no emotional valence effects. In other words, it is predicted that younger adults will show emotional modulation of the P1 perceptual component, but that older adults will show just a perceptual response not modulated by emotional valence. Alternatively, the SST predicts that older adults experience a developmental change resulting in better emotional regulation so that negative emotions are inhibited allowing positive emotions to be more pronounced. Consequently, the SST predicts an attenuated effect for older adults for negative emotional faces but a stronger emotional response by older adults to positive emotional faces (e.g., Mather et al., 2004; LeClerc and Kensinger, 2008). Also, older adults should show higher P1 amplitudes for happy faces than for angry faces, whereas younger adults should show the opposite effect (LeClerc and Kensinger, 2008).

# **THE PRESENT STUDY**

The P1 ERP component is the peak associated with an early visual perceptual response that can be modulated by emotional valence. Our basic model is illustrated in **Figure 2**. This model (based upon Allen et al., 2005, 2011; Holmes et al., 2008, 2009a,b; Baena et al., 2010; Rotshtein et al., 2010; Dolcos et al., 2011) includes a "posterior portion" as well as an "anterior portion." The posterior

portion of the model includes the primary (V1) and secondary (V2) visual cortices, the fusiform gyrus (areas known to be closely associated with face perception), and the amygdala (the brain location most often associated with emotional arousal/activation, see **Figure 2A**). Conceptually, the anterior portion of the model includes the amygdala (the emotional "accelerator"), VMPFC (socioemotional control), and the dorsolateral prefrontal cortex (cognitive control). The present study is designed to examine the posterior portion of the model while largely attenuating (because of the 70–170 ms recording window of the P1) what is believed to be top-down feedback associated with the ventromedial and dorsolateral prefrontal cortex areas (i.e., the frontal portion) from biasing a more direct examination of amygdalar modulation of early visual perception (by using just an early ERP component).

The present ERP study with its emphasis on temporal precision can be used to replicate and extend the findings of fMRI study reported St. Jacques et al. (2010). Specifically, the present P1 ERP paradigm (refer to **Figure 2B**) allows a more temporally precise measure (the hemodynamic response in fMRI takes 2000 ms per stimulus to occur; Buckner, 1998) of their fMRI-based observation of an age deficit in functional connectivity between the amygdala and the bi-lateral visual cortex (our recording sites of O1 and O2 are located bi-laterally over the visual cortices). Our method also allows a distinction to be made between a perceptual deficit and what is posited to be an amygdalar modulation deficit. Evidence consistent with a perceptual deficit would be a main effect for age, and evidence consistent with an amygdalar modulation of visual perception would be an interaction involving age and emotion type (e.g., the logic used by Holmes et al., 2008, 2009a,b and Rotshtein et al., 2010). Note that we are assuming that early amygdalar activation is greater for negatively valanced emotional faces than positively valenced emotional faces, and this seems to be consistent with Holmes et al. (2008, 2009a,b)—who emphasized the importance of negatively valenced faces.

# **PREDICTIONS**

There are two categories of predictions in the present study, conceptually embellished in **Figure 2**. First, if emotion perception can occur automatically (without central attention), then either RT or accuracy for Task 2 (facial emotion discrimination task) should show progressively smaller task difficulty effects as SOA decreases (an SOA × Difficulty interaction, refer to **Figures 1** and **2C**), or the P1 effect should remain constant across SOA (or the shortest SOA should show at least as high of P1 amplitude as the longest) (Shaw et al., 2011). If increased adult age moderates attentional effects, then the aforementioned effects should interact with age.

The second category of predictions concerns whether SST (e.g., Carstensen et al., 1999) or the emotion perception deficit model (Allen et al., 2005; Denburg et al., 2005) better fit the present behavioral and/or electrophysiological (P1) results. As noted above, SST predicts better performance for older adults on happy faces than on angry faces, but the reverse for younger adults (Mather et al., 2004; LeClerc and Kensinger, 2008). On the other hand, the emotion perception deficit model predicts that older adults are especially likely to show a performance deficit for angry faces (relative to younger adults), but similar performance to younger adults on happy faces (Baena et al., 2010). That is, younger adults should show a larger P1 effect for angry faces than for happy faces, but older adults should show similar P1 effects for both angry and happy faces.

# **METHOD**

## **PARTICIPANTS**

There were 14 younger adults (10 female) and 14 older adults (7 female) who participated in this study. Data from one older adult were excluded due to low accuracy (*<*80%). Thus, a total of 14 younger and 13 older adults were included in the final data analyses. Younger adults were undergraduates at Oregon State University who participated in exchange for extra course credit. Their mean age was 19 years (range: 18–23 years). Older adults were individuals who resided in nearby communities. They were paid \$20 for their participation. Their mean age was 70 years (range: 61–85 years). All participants reported having normal or corrected-to-normal visual acuity. None reported having any cognitive, neurophysiological dysfunction.

## **APPARATUS AND STIMULI**

Stimuli were presented on an IBM-compatible microcomputer connected to a 19-in. ViewSonic monitor and were viewed from a distance of about 55 cm. The Task-1 stimulus was a pure tone or white noise (22 kHz, 8 bits, and 100 ms duration) and was presented via speakers on both sides of the computer monitor. The Task-2 stimuli contained one picture in the center of the screen, which subtended a visual angel of 6.23◦ (width) × 8.79◦ (height). There were 40 pictures with different actors (four categories: 10 male/angry, 10 male/happy, 10 female/angry, and 10 female/happy) taken from Tottenham et al. (2009). Within each category, the emotion expression was easy to determine for half of the faces (the easy condition) and was difficult to determine for the other half of the faces (the difficult condition; see Tomasik et al., 2009, for details). Each face was presented 30 times (excluding practice trials) per participant. For both tasks, manual responses were collected using a response box containing five buttons labeled 1–5 from left to right.

# **DESIGN AND PROCEDURE**

Each trial started with the presentation of the fixation display for 800 ms. The Task-1 auditory stimulus then sounded for 100 ms. After one of three SOAs (100, 300, or 900 ms) randomized within blocks, the Task-2 picture appeared in the center until the participant responded.

 For Task 2, participants were instructed to respond to the emotion expression of the face.  They were asked to respond to Task 1 and Task 2 quickly and accurately. Also, they were asked to respond to Task 1 before Task 2. Immediately after a response was recorded, the next trial began with the 800-ms fixation display.

Participants performed one practice block of 24 trials, followed by 15 experimental blocks of 80 trials each (a total of 1200 experimental trials). After each block, participants received a summary of their mean RT and accuracy for that block and were encouraged to take a break.

## **EEG RECORDING AND ANALYSES**

The EEG activity was recorded using Q-cap AgCl electrodes from F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, O1, Oz, O2, T7, T8, P7, P8, PO7, and PO8. These sites and the right mastoid were recorded in relation to a reference electrode at the left mastoid. The ERP waveforms were then re-referenced offline to the average of the left and right mastoids (see Luck, 2005). The horizontal electrooculogram (HEOG) was recorded bipolarly from electrodes at the outer canthi of both eyes, and the vertical electrooculogram (VEOG) was recorded from electrodes above and below the midpoint of the left eye. Electrode impedance was kept below 5 k*-*. EEG, HEOG, and VEOG were amplified using Synamps2 (Neuroscan) with a gain of 2,000 and a bandpass of 0.1–50 Hz. The amplified signals were digitized at 500 Hz.

Trials with possible ocular artifacts were identified in two steps (see also Lien et al., 2008). First, trials with ocular artifacts were rejected automatically using a threshold of ± 75μV for a 1400 ms epoch beginning 200 ms before Task-2 stimulus onset to 1200 ms after Task-2 stimulus onset. Next, each of these candidate artifact trials was inspected manually. Rejection of trials with ocular artifacts in the EEG data led to the elimination of 5% of trials, but no more than 19% for any individual participant.

To quantify the overall magnitude of the P1 effect, we focused on the time window 70–170 ms after Task-2 stimulus onset. Specifically, the P1 effect was measured as the mean amplitude during this time window for electrode sites O1 and O2, relative to the mean amplitude during a 200-ms baseline period prior to Task-2 stimulus onset.

# **RESULTS**

In addition to trials with ocular artifacts, we excluded trials from the final analyses of behavioral data (RT and proportion of errors; PE) and EEG data if RT for Task 1 (RT1) or Task 2 (RT2) was less than 100 ms or greater than 3000 ms (0.7% of trials for younger adults and 0.5% of trials for older adults). Trials were also excluded from RT and EEG analyses if either response was incorrect. Analysis of variance (ANOVA) was used for all statistical analyses (see below for details), with an alpha level of 0.05 to ascertain statistical significance. The *p*-values were adjusted using the Greenhouse-Geisser epsilon correction for nonsphericity, where appropriate.

## **BEHAVIORAL DATA ANALYSES**

Data were analyzed as a function of age group (younger vs. older adults), Task 2 difficulty (easy: an extreme version of an emotional face vs. difficult: a morphed face that was in between a given emotional expression and neutral), Task 2 emotion (angry vs. happy), and SOA (100, 300, or 900 ms). Age group was a betweensubject variable, whereas others were within-subject variables. **Tables 1** and **2** show mean RT and PE for Task 1 and Task 2, respectively.

For Task 1, RT1 decreased as SOA increased, *F(*2*,* <sup>50</sup>*)* = 8*.*46, *p <* 0*.*001, η<sup>2</sup> *<sup>p</sup>* = 0*.*25 (see **Figure 3**). This decrease was more pronounced for older adults than younger adults, *F(*2*,* <sup>50</sup>*)* = 6*.*48, *p <* 0*.*01, η<sup>2</sup> *<sup>p</sup>* = 0*.*21. Mean RT1 was 9 ms slower when Task 2 was an angry face (668 ms) than when it was a happy face (659 ms), *<sup>F</sup>(*1*,* <sup>25</sup>*)* <sup>=</sup> <sup>4</sup>*.*47, *<sup>p</sup> <sup>&</sup>lt;* <sup>0</sup>*.*05, <sup>η</sup><sup>2</sup> *<sup>p</sup>* = 0*.*15. The threeway interaction between age, Task 2 emotion, and SOA was significant, *<sup>F</sup>(*2*,* <sup>50</sup>*)* <sup>=</sup> <sup>3</sup>*.*80, *<sup>p</sup> <sup>&</sup>lt;* <sup>0</sup>*.*05, <sup>η</sup><sup>2</sup> *<sup>p</sup>* = 0*.*13. Older adults exhibited longer RT1 at short SOAs when Task 2 was an angry face than a happy face (difference in RT1 = 25, 18, and −10 ms at the 100, 300, and 900 ms SOAs, respectively), whereas no consistent pattern was observed for younger adults (difference in RT1 = 3, −8, and 11 ms at the 100, 300, and 900 ms SOAs, respectively).

Task 1 PE (PE1) decreased as SOA increased, *F(*2*,* <sup>50</sup>*)* = 24*.*10, *p <* 0*.*0001, η<sup>2</sup> *<sup>p</sup>* = 0*.*49. In contrast to RT, this decrease was more pronounced for younger adults than older adults, *F(*2*,* <sup>50</sup>*)* = 9*.*04, *p <* 0*.*001, η<sup>2</sup> *<sup>p</sup>* = 0*.*27. No other effects were significant.

For Task 2, the overall RT2 was longer for older adults (RT2 = 1019 ms) than younger adults (RT2 = 814 ms), *F(*1*,* <sup>25</sup>*)* = 15*.*08, *p <* 0*.*001, η<sup>2</sup> *<sup>p</sup>* = 0*.*38. A large PRP effect of 439 ms on RT2 was observed, *<sup>F</sup>(*2*,* <sup>50</sup>*)* <sup>=</sup> <sup>451</sup>*.*73, *<sup>p</sup> <sup>&</sup>lt;* <sup>0</sup>*.*0001, <sup>η</sup><sup>2</sup> *<sup>p</sup>* = 0*.*95. The PRP effect was larger for older adults (503 ms) than younger adults (379 ms), *<sup>F</sup>(*2*,* <sup>50</sup>*)* <sup>=</sup> <sup>9</sup>*.*73, *<sup>p</sup> <sup>&</sup>lt;* <sup>0</sup>*.*001, <sup>η</sup><sup>2</sup> *<sup>p</sup>* = 0*.*28. RT2 was 47 ms longer in the difficult condition (940 ms) than in the easy condition (893 ms), *<sup>F</sup>(*1*,* <sup>25</sup>*)* <sup>=</sup> <sup>100</sup>*.*46, *<sup>p</sup> <sup>&</sup>lt;* <sup>0</sup>*.*0001, <sup>η</sup><sup>2</sup> *<sup>p</sup>* = 0*.*80 (see **Figure 3**). The difficulty effect was larger for older adults

**Table 1 | Mean response time (RT in ms) and proportion of errors (PE) for Task 1 as a function of age group (younger vs. older), Task-2 difficulty (easy vs. difficult), Task 2 emotion (angry vs. happy), and stimulus onset asynchrony (100, 300, and 900 ms).**

*Note: The standard error of the mean is shown in parentheses.*

**Table 2 | Mean response time (RT in ms) and proportion of errors (PE) for Task 2 as a function of age group (younger vs. older), Task-2 difficulty (easy vs. difficult), Task 2 emotion (angry vs. happy), and stimulus onset asynchrony (100, 300, and 900 ms).**

*Note: The standard error of the mean is shown in parentheses.*

**emotion (angry vs. happy), and SOA (100, 300, or 900 ms).** SOA, stimulus onset asynchrony.

(64 ms) than younger adults (30 ms), *F(*1*,* <sup>25</sup>*)* = 13*.*13, *p <* 0*.*01, η2 *<sup>p</sup>* = 0*.*34. Older adults had longer RT2 for angry faces than happy faces (1029 ms vs. 1009 ms), whereas younger adults had longer RT2 for happy faces than angry faces (836 ms vs. 792 ms), *<sup>F</sup>(*1*,* <sup>25</sup>*)* <sup>=</sup> <sup>13</sup>*.*00, *<sup>p</sup> <sup>&</sup>lt;* <sup>0</sup>*.*01, <sup>η</sup><sup>2</sup> *<sup>p</sup>* = 0*.*34.

As in Tomasik et al. (2009) study, the interaction of Task 2 difficulty and SOA on RT2 was not significant, *F(*2*,* <sup>50</sup>*)* = 2*.*22, *<sup>p</sup>* <sup>=</sup> <sup>0</sup>*.*1195, <sup>η</sup><sup>2</sup> *<sup>p</sup>* = 0*.*08; the difficulty effect was 42, 41, and 59 ms at 100, 300, and 900 ms SOAs, respectively. However, there was a trend toward underadditivity because the difficulty effect was reduced by 17 ms. The additivity between Task 2 difficulty and SOA was similar for younger and older adults, *F <* 1*.*0. For younger adults, the difficulty effect was 30, 17, and 44 ms at the 100, 300, and 900 ms SOAs, respectively. For older adults, the effect was 54, 65, and 74 ms at the 100, 300, and 900 ms SOAs, respectively.

Task 2 PE (PE2) was 0.032 higher for the difficult condition than for the easy condition, *F(*1*,* <sup>25</sup>*)* = 28*.*10, *p <* 0*.*0001, η2 *<sup>p</sup>* = 0*.*53. As in RT2, older adults had higher PE2 for angry faces (0.065) than happy faces (0.048), whereas younger adults showed an opposite pattern (0.116 for happy faces and 0.067 for angry faces), *<sup>F</sup>(*1*,* <sup>25</sup>*)* <sup>=</sup> <sup>6</sup>*.*35, *<sup>p</sup> <sup>&</sup>lt;* <sup>0</sup>*.*05, <sup>η</sup><sup>2</sup> *<sup>p</sup>* = 0*.*20. The threeway interaction between age group, Task 2 difficulty, and Task 2 emotion was significant, *<sup>F</sup>(*1*,* <sup>25</sup>*)* <sup>=</sup> <sup>6</sup>*.*07, *<sup>p</sup> <sup>&</sup>lt;* <sup>0</sup>*.*05, <sup>η</sup><sup>2</sup> *<sup>p</sup>* = 0*.*10. For older adults, higher PE2 for angry faces than happy faces was evident in the Task 2 difficult condition (0.101 vs. 0.050, respectively) but not in the easy condition (0.028 vs. 0.045). For younger adults, higher PE2 for happy faces than angry faces was evident in both the easy condition (0.100 vs. 0.058) and the difficult condition (0.131 vs. 0.075). No other effects were significant.

## **ERP ANALYSES**

The P1 data analyses focused on the time window of 70–170 ms after Task-2 stimulus onset (Rotshtein et al., 2010, used 100–150 ms, but we slightly extended this to 70–170 ms, see **Figure 2B**). The P1 data were analyzed as a function of age group (younger vs. older adults), Task 2 difficulty (easy vs. difficult), Task 2 emotion (angry vs. happy), hemifield (left [O1 electrode] vs. right [O2 electrode]), and SOA (100, 300, or 900 ms). **Figure 4** shows these P1 effects averaged across the electrodes O1 and O2. For each participant, there were a total of 1200 experimental trials. With the variables of SOA (3 levels), Task 2 Difficult (2 levels), and Task 2 Emotion (2 levels), there were a total of 100 observed trials for each SOA before trials that fell outside our RT cutoff or showed ocular artifacts were rejected.

The overall P1 effect was larger at the 300 ms SOA (5.357μV) than at the 100 ms SOA (3.240 μV) or the 900 ms SOA (0.812 <sup>μ</sup>V), *<sup>F</sup>(*2*,* <sup>50</sup>*)* <sup>=</sup> <sup>54</sup>*.*15, *<sup>p</sup> <sup>&</sup>lt;* <sup>0</sup>*.*0001, <sup>η</sup><sup>2</sup> *<sup>p</sup>* = 0*.*68. This interpretation of the main effect was confirmed by post-hoc pairwise tests—300 ms vs. 100 ms, *F(*1*,* <sup>25</sup>*)* = 27*.*25, *p <* 0*.*0001; 300 ms vs. 900 ms, *F(*1*,* <sup>25</sup>*)* = 89*.*65, *p <* 0*.*0001; 100 ms vs. 900 ms, *F(*1*,* <sup>25</sup>*)* = 32*.*61, *p <* 0*.*0001. The interaction between Task 2 difficulty and Task 2 emotion was significant, *<sup>F</sup>(*1*,* <sup>25</sup>*)* <sup>=</sup> <sup>4</sup>*.*48, *<sup>p</sup> <sup>&</sup>lt;* <sup>0</sup>*.*05, <sup>η</sup><sup>2</sup> *p* = 0*.*15; Angry faces elicited a larger P1 effect than happy faces in the easy condition (3.218 μV vs. 2.909 μV, respectively) but a similar P1 in the difficult condition (3.160 μV vs. 3.203μV, respectively). This pattern was further qualified by a Group × Difficulty <sup>×</sup> Emotion interaction, *<sup>F</sup>(*1*,* <sup>25</sup>*)* <sup>=</sup> <sup>6</sup>*.*65, *<sup>p</sup> <sup>&</sup>lt;* <sup>0</sup>*.*05, <sup>η</sup><sup>2</sup> *p* = 0*.*21 (younger adults: easy: angry = 3.67μV, happy = 3.08μV, difficult: angry = 3.45μV, happy = 3.64μV; older adults: easy: angry = 2.77μV, happy = 2.74 μV, difficult: angry = 2.87μV, happy = 2.77μV).

To interpret this three-way interaction, we ran separate simple effects analyses by Task 2 difficulty. For easy trials, the Group × Emotion interaction was significant, *F(*1*,* <sup>25</sup>*)* = 4*.*67, *p <* 0*.*05, so younger adults did show a larger emotion effect than older adults. For difficult trials, though, the Group × Emotion interaction was not significant, *F(*1*,* <sup>25</sup>*)* = 2*.*10, *p* = 0*.*16. To clarify the Group × Emotion simple effect for easy trials, we ran separate analyses across age group. For younger adults, there was a simple effect of emotion, *F(*1*,* <sup>13</sup>*)* = 5*.*13, *p <* 0*.*05 (angry = 3.67μV, happy = 3.08μV), but the simple effect for emotion was not significant for older adults (*p* = 0*.*77) (angry = 2.77μV, happy = 2.74μV). This means that younger adults showed significantly higher amplitude P1 components for angry faces than for happy faces, but that there was no difference in emotional valance for older adults.

There was also a Difficulty × Hemifield interaction, *F(*1*,* <sup>25</sup>*)* = 10*.*16, *p <* 0*.*01, η<sup>2</sup> *<sup>p</sup>* = 0*.*29. This interaction occurred because for the left hemifield (the electrode O1), difficult trials showed higher amplitudes (3.355μV) than easy trials (3.092 μV), but for the right hemifield (the electrode O2), easy (3.284μV) and difficult (3.308 μV) trials showed similar amplitudes. No other effects reached statistical significance. Finally, because our prediction was that older adults should be especially likely to show lower amplitudes at the 100 ms SOA than at longer SOAs, and we did observe a Group × SOA interaction that approached significance, *F(*1*,* <sup>25</sup>*)* = 2*.*37, *p* = 0*.*11, we analyzed the data separately by SOA. We observed simple effect of age that approached significance at the 100 ms SOA, *F(*1*,* <sup>25</sup>*)* = 3*.*47, *p* = 0*.*07 (younger = 4.20μV, older = 2.82μV), but not at the 300 ms SOA (*p* = 0*.*41) (younger = 5.88μV, older = 4.84μV) or the 900 ms SOA (*p* = 0*.*55) (younger = 0.67μV, older = 1.07μV).

# **DISCUSSION**

The present study provided both ERP evidence for age-related sparing of attentional capacity (i.e., automatic processing) and for an age-related deficit in the processing of angry faces on trials with more pronounced emotional expressions (i.e., "easy" trials—in contrast to trials with faces with less pronounced emotional expressions, or "difficult" trials—these stimuli were closer to neutral). That is, younger adults showed significantly higher P1 amplitude for angry, easy trials than for happy easy trials, but older adults showed almost identical P1 amplitudes for both angry and happy easy trials. These ERPs results, then, replicate the results of St. Jacques et al. (2010) who used fMRI methods and observed a functional connection deficit for older adults (in the circuit connecting the top–down feedback loop from the amygdala to the early visual processing areas (primary and secondary cortices). On the other hand, we observed typical behavioral effects for a PRP task. We will first discuss the behavioral results and then the ERP data.

# **BEHAVIORAL FINDINGS**

For Task 2, both age groups showed PRP effects for RT2, although older adults showed larger PRP effects than did younger adults. PRP effects are thought to measure the delay in access to Task 2 response selection while individuals are completing processing on Task 1 response selection at short SOAs (Pashler, 1984). Thus, the larger PRP effect for older adults than younger adults reflects the central bottleneck to be larger for older adults than for younger adults, which is quite common (e.g., Allen et al., 2002, 2009; Lien et al., 2006). Another finding from the RT2 data was that older adults were faster in responding to happy than to angry faces, but younger adults were faster in responding to angry than to happy faces. This is a slight departure from earlier studies on single-task, facial emotional discrimination such as Baena et al. (2010) who observed that both age groups showed faster responses to happy faces than to angry faces, but that the effect was more exaggerated for older adults. Finally for Task 2, we observed additivity for RT2 between SOA and task difficulty, with a trend toward underadditivity—a result that replicated Tomasik et al. (2009). This is tempered behavioral evidence that processing stage before response selection for Task 2 emotions cannot be processed in parallel with response selection for Task 1 tones (because of the trend toward underadditivity). However, the trend toward underadditivity for RT along with similar P1 amplitudes at short and long SOAs do suggest that information for Task 1 and Task 2 can be processing simultaneously. Also, behavioral indices of performance may be controlled by later dorsal attentional stream processes, and it may not be possible to eliminate the structural bottleneck when both tasks must be processed with the dorsal attentional stream.

For RT1, there was a backward correspondence effect (Lien and Proctor, 2000). That is, RT1 was affected by Task 2 emotion type (RT1 was slower if Task 2 involved an angry face compared to a happy face). Backward correspondence effects are considered to be evidence of parallel processing (Lien and Proctor, 2000). The slight increase in RT1 as SOA decreased was offset by the reverse effect for Task 1 errors. Thus, there appeared to be no appreciable effect for SOA on Task 1.

# **ERP FINDINGS**

As noted in the Introduction, Shaw et al. (2011) found that the N2pc effect (a measure of spatial attention) was not modulated by SOA (and all amplitudes were significantly higher than zero). Also, these investigators found a stronger effect for angry faces than for happy faces (although this was a between-subjects effect). This was interpreted by Shaw et al. of evidence of automatic emotional processing. In the present study, we used the P1 component because of evidence of its association to amygdalar activation in epilepsy patients (Rotshtein et al., 2010). The PRP logic for whether central attention is required for Task 2 emotion perception, though, is the same for both Shaw et al. (N2pc) and the present study (P1). The present Task 2 still involved faces with different emotions and we used three different SOAs between Task 1 and Task 2, except the present study tested both younger and older adults (the Shaw et al. study presented two adjacent faces and asked participants to make a gender discrimination of a given emotion type, or the location discrimination of a given emotion type rather than directly making an emotional discrimination). In the present study, the amplitude for the 100 ms SOA was significantly higher than the amplitude from the 900 ms SOA. This finding suggests that participants were apparently able to process emotional faces at the 100 ms SOA. This leaves two unresolved issues, though. First, why was the amplitude for the 300 ms SOA higher than the amplitude for the 100 ms SOA? One possibility is that automaticity is graded (Pessoa et al., 2002). That is, perhaps at the 100 ms SOA individuals have enough amygdalar activation to carry out facial discrimination, but that at the 300 ms SOA there is an even stronger level of amygdalar activation. However, this graded interpretation is complicated by the fact that the P1 effect was significantly larger at the 100 ms SOA than the 900 ms SOA. Finally, and probably most perplexing, why was the P1 effect so small at the 900 ms SOA? An understanding of this drop in the P1 effect at 900 ms SOA relative to 100 ms and 300 ms SOAs will take additional empirical work to interpret, although we did observe clear evidence of Task 2 P1 effects at a short SOA (100 ms) that are consistent with the idea that individuals can process Task 2 stimuli simultaneously with the processing of Task 1 information for certain tasks (also see Shaw et al., 2011).

Another important issue to consider is why the behavioral data showed just a trend toward underadditivity (evidence for automatic emotion perception), but the ERP data showed stronger evidence consistent with emotion perception without central attention. As noted in Shaw et al.(2011), ERP components may be more sensitive than behavioral measures because they are a more direct measure of early emotional processing. We believe that this finding provides additional evidence of the efficacy of using ERPs to study attention and perception. These results are similar to those observed by Shafer et al. (2012). These investigators also observed evidence of both automatic emotional processing and non-automatic emotional processing in the same study (using fMRI methods).

It is important to note that the present P1 data cannot be easily accounted for by anything other than an emotional effect because emotion type interacted with age group and task difficulty. One might be concerned with the possibility that something like an early perceptual effect for Task 2 or even Task 1 modulation was driving the P1 effect. However, early perceptual effects for Task 2 were constant across different stimuli—the only thing that varied was emotion type. Also, Task 1 was a non-emotional task (pure vs. fuzzy tones). Thus, it is unclear how Task 1 could have modulated emotion type effects in Task 2.

# **THEORIES OF AGING AND EMOTION PERCEPTION**

As noted earlier, there are different theories of aging and emotion regulation. Clearly the most widely studied theory is the SST of Carstensen et al. (1999). This model proposes that older adults change their emotional regulation system to emphasize positive emotions and inhibit negative emotions. The present behavioral data seem to be partially consistent with this idea. Namely, younger adults processed angry faces faster than happy faces, but older adults showed the reverse effect. This is essentially the same pattern of results observed by Mather et al. (2004) and LeClerc and Kensinger(2008) using an fMRI paradigm. Thus, one possibility is that younger adults are maximally sensitive to the threat perception aspects of negatively valenced emotional stimuli (e.g., Allen et al., 2008), but that older adults are better able to block out negatively valenced stimuli (this would likely reflect an example of greater VMPFC executive control on the part of older adults). However, the electrophysiological ERP data from the present study showed a much more complicated picture of the processing dynamics of emotional facial discrimination. In particular, older adults showed no difference in P1 amplitude for happy and angry faces, but younger adults observed the more typical pattern of significantly larger P1 amplitude for angry faces than for happy faces on easy trials (e.g., see Holmes et al., 2008, 2009a,b). If older adults were better at emotional regulation such that positive emotional content was able to pass through the system more efficiently than negative emotional content, then one would predict higher-amplitude P1 effects for happy than for angry faces. One possibility is that the emotional regulation observed by Mather et al. (2004) and LeClerc and Kensinger (2008) occurs after the time period measured by the present P1 ERP component. Indeed, this view is consistent with Mather and Carstensen (2005) who claim an emotional regulation locus rather than an emotional arousal/activation locus of older adults' late positivity effect.

Another finding was that older adults, in general, showed a trend toward lower P1 amplitudes—especially at the 100 ms SOA (*p <* 0*.*07). These results seem to be more consistent with a more general drop in perceptual object activation in older adults (both in angry and happy faces). However, it is important to note that this trend toward a sensory deficit on the part of older adults cannot explain the emotional valance age difference observed for more pronounced emotional expressions (i.e., the Age Group × Difficulty × Emotion Type interaction).

## **ARE THERE AGE DIFFERENCES IN AMYGDALAR PROCESSING?**

The present observation of emotional valence modulation of the P1 perceptual ERP effect for younger adults but not for older adults is consistent with the notion that younger adults exhibit amygdalar modulation of the visual cortices, but that older adults do not (see Rotshtein et al., 2010). This evidence is consistent with the fMRI functional connectivity results of St. Jacques et al. (2010) in which older adults appeared to exhibit a functional connectivity deficit in this circuit between the amygdala and the visual cortices. It is also consistent with the amygdalar volumetric loss for older adults that Fjell et al. (2009) observed using longitudinal methods. However, this view is not necessarily consistent with the meta-analysis results of Nashiro et al. (2012). Nachiro et al. reviewed the fMRI literature and failed to find evidence of an appreciable amygdalar decline with increased adult age. As illustrated in **Figure 2**, though, the amygdala is part of multiple systems involved in emotional processing. It can directly act on incoming stimulus information (the posterior portion of our model illustrated in **Figure 2**—emotional arousal/activation) as well as receiving top–down feedback from the prefrontal mechanism (the anterior portion of the model typically referred to as emotional regulation). Because the hemodynamic response used in fMRI takes 2 s to develop per stimulus (Buckner, 1998), fMRI research is likely measuring the amygdalar activation involving the top-down modulation by prefrontal mechanisms (the anterior portion of **Figure 2**) instead of the arousal of the amygdala by the visual cortices, and interactive modulation of the visual cortices by the amygdala that occurs in less than 200 ms (Cornwell et al., 2008; also see the anterior portion of **Figure 2**). Thus, it will take additional research on this topic to clarify the precise time course of potential age differences in amygdalar function. However, there is now DTI tractography evidence that the neural circuit connecting the amygdala with the fusiform gyrus and the visual cortices is the inferior longitudinal fasciculous (ILF) (Catani et al., 2003). Thus, this functional connectivity age difference observed by St. Jacques et al. (2010) and the differential modulation of emotional valence on visual perception across age observed on the P1 data in the present study could be a white-matter integrity deficit in the ILF neural pathway more than a deficit in amygdalar function, *per se*.

## **GRADED CAPACITY SHARING AND RECRUITMENT**

Another possible explanation of the present results is that Task 1 and Task 2 share processing resources (a graded capacitysharing model). This sort of model would predict that the brain would recruit additional resources at shorter SOAs (when central attention would need to process both Task 1 and Task 2) relative to the longest SOA (900 ms—when Task 1 response selection would be completed before Task 2 was presented). Older adults tend to show more neural recruitment (as measured by PET scanning) than younger adults on some tasks (Grady et al., 1995, 1996; Cabeza et al., 1997; Madden et al., 1999), and this has been taken as evidence that older adults attempt to compensate for less efficient processing by recruiting more neurons. A recruitment model would seem to predict higher amplitudes at the 100 ms SOA than the 300 ms or 900 ms SOAs, and that older adults should show higher amplitudes than younger adults. While this interpretation is theoretically intriguing (because of the aging research on neural recruitment), the present P1 amplitudes for the 300 ms SOA were significantly higher than for the 100 ms SOA. Also, older adults did not exhibit higher P1 amplitudes than younger adults. However, without this disconfirming evidence, one might have claimed that perhaps the P1 component was really showing the attentional capacity allocated to Task 2 rather than to an emotional response (but see Holmes et al., 2008, 2009a,b). Overall, though, it does not appear that the present results are consistent with the attentional capacity (rather than emotional activation) interpretation.

## **GENERAL SENSORY VISUAL DEFICITS**

One potential interpretation of the present results is that older adults simply experienced a general sensory deficit. Indeed, Lindenberger and Baltes (1994) and Baltes and Lindenberger (1997) proposed a "common cause" model of aging in which a general/common sensory deficit mediated all age differences in cognition. However, other studies have provided both crosssectional (e.g., Allen et al., 2001) and longitudinal (e.g., Anstey et al., 2001) evidence that there is unique age-related variance that cannot be accounted for by a common factor. Even though a precise test of this issue requires mediational analyses rather than the moderation analyzes afforded by ANOVA, the present Age Group × Difficulty × Emotion Type interaction does not appear to be consistent with a common-cause interpretation. First, a general sensory decrement in the visual cortices would have no mechanism to respond to emotional valance (the Nim Stim faces are equated on perceptual difficulty). This would seemingly require that the amygdala modulate visual cortex activation levels differentially across emotion type (e.g., Holmes et al., 2008, 2009a,b; Rotshtein et al., 2010). In particular, the finding that younger adults responded differentially to angry and happy lessdistorted faces, but that older adults showed similar activation levels for both emotion types suggests that amygdalar modulation of the visual cortex occurred in the present study.

## **LIMITATIONS AND FUTURE RESEARCH**

The main limitation of the present research is a lack of generalization and replication. For example, does one observe this age deficit for negatively valenced stimuli that are faces, or would it generalize to other familiar perceptual objects (e.g., snakes vs. a mother holding her baby compared to angry vs. happy faces)? Future research that extends the P1 effect to other types of stimuli is needed. The work of Holmes et al. (2008, 2009a,b) does suggest that a similar pattern of results is obtained when one compares non-anxious and anxious participants (instead of younger and older adults), but it will still be important to replicate and extend the present age effects.

# **ACKNOWLEDGMENTS**

We thank Alison Gemperle and Jessica Aubin for running experiments. We also thank Eric Ruthruff for comments on earlier versions of the manuscript.

# **REFERENCES**

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 27 April 2012; accepted: 01 August 2012; published online: 23 August 2012.*

*Citation: Pollock JW, Khoja N, Kaut KP, Lien M-C and Allen PA (2012) Electrophysiological evidence for adult age-related sparing and decrements in emotion perception and attention. Front. Integr. Neurosci. 6:60. doi: 10.3389/fnint. 2012.00060*

*Copyright © 2012 Pollock, Khoja, Kaut, Lien and Allen. This is an openaccess article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

# Impact of negative emotion on the neural correlates of long-term recognition in younger and older adults

#### **Grégoria Kalpouzos <sup>1</sup>\*, Håkan Fischer 1,2, Anna Rieckmann<sup>1</sup> , StuartW. S. MacDonald1,3 and Lars Bäckman<sup>1</sup>**

<sup>1</sup> Aging Research Center, Karolinska Institute and Stockholm University, Stockholm, Sweden

<sup>2</sup> Department of Psychology, Stockholm University, Stockholm, Sweden

<sup>3</sup> Department of Psychology, University of Victoria, Victoria, BC, Canada

#### **Edited by:**

Florin Dolcos, University of Illinois at Urbana–Champaign, USA

#### **Reviewed by:**

Hao Zhang, Duke University Medical Center, USA Florin Dolcos, University of Illinois at Urbana–Champaign, USA Pascal Hot, Université de Savoie, France

#### **\*Correspondence:**

Grégoria Kalpouzos, Aging Research Center, Karolinska Institute and Stockholm University, Gävlegatan 16, 113 30 Stockholm, Sweden. e-mail<EMAIL_ADDRESS>
Some studies have suggested that the memory advantage for negative emotional information over neutral information ("negativity effect") is reduced in aging. Besides the fact that most findings are based on immediate retrieval, the neural underpinnings of long-term emotional memory in aging have so far not been investigated.To address these issues, we assessed recognition of neutral and negative scenes after 1- and 3-week retention intervals in younger and older adults using functional magnetic resonance imaging.We further used an event-related design in order to disentangle successful, false, and true recognition. This study revealed four key findings: (1) increased retention interval induced an increased rate of false recognitions for negative scenes, canceling out the negativity effect (present for hit rates only) on discrimination in both younger and older adults; (2) in younger, but not older, adults, reduced activity of the medial temporal lobe was observed over time for neutral scenes, but not for negative scenes, where stable or increased activity was seen; (3) engagement of amygdala (AMG) was observed in older adults after a 3-week delay during successful recognition of negative scenes (hits vs. misses) in comparison with neutral scenes, which may indicate engagement of automatic processes, but engagement of ventrolateral prefrontal cortex was unrelated to AMG activity and performance; and (4) after 3 weeks, but not after 1 week, true recognition of negative scenes was characterized by more activity in left hippocampus and lateral occipito-temporal regions (hits vs. false alarms). As these regions are known to be related to consolidation mechanisms, the observed pattern may indicate the presence of delayed consolidation of true memories. Nonetheless, older adults' low performance in discrimination of negative scenes could reflect the fact that overall, after long delays of retention, they rely more on general information rather than on perceptual detail in making recognition judgments.

**Keywords: aging, amygdala, emotion, episodic memory, hippocampus, long-term memory, prefrontal cortex, recognition**

# **INTRODUCTION**

Emotion is closely linked to memory because of its importance for survival (e.g., remembering that a previously experienced situation was life-threatening). Hence, negative emotions may enhance memory retrieval and be resistant to forgetting (Weymar et al., 2011). Numerous studies have demonstrated better recognition of negative compared to neutral information in young adults (Kensinger, 2007). This effect may partly reflect reduced forgetting via enhanced consolidation mechanisms (LaBar and Phelps, 1998; Sharot and Phelps, 2004; Sharot and Yonelinas, 2008; Pierce and Kensinger, 2011). This "negativity effect" has been linked to engagement of the amygdala (AMG) in association with the hippocampus (HC) during processing of emotional information, notably at encoding (see Murty et al., 2010; Sabatinelli et al., 2011, for meta-analyses, and Dolcos et al., 2012 for a recent review). Fewer studies have investigated the neural underpinnings of emotional retrieval, especially after long retention intervals. Yet, these studies showed specific or more AMG activity during retrieval of emotional compared to neutral information, for retention

intervals between 5 min and 1 year (Dolan et al., 2000; Dolcos et al., 2005; Kensinger and Schacter, 2005, 2007; Keightley et al., 2011).

Socioemotional selectivity theory postulates increased emotion regulation with advancing adult age. This assertion is based on the perception of remaining lifetime driving motivations and goals (e.g., Carstensen et al., 1999). Some research reveals a "positivity effect," where positive events are enhanced and therefore better remembered, along with a reduced "negativity effect,"where negative events are more likely to be forgotten in older adults (Charles et al., 2003; Mather and Carstensen, 2005; Mather, 2008, 2012; Spaniol et al., 2008; Reed and Carstensen, 2012). However, while findings regarding the positivity effect converge toward a preservation of this effect in aging, results concerning the negativity effect are mixed, as several studies show a memory advantage of negative over neutral information also in older adults (Kensinger et al., 2002; Denburg et al., 2003; Otani et al., 2007; Murty et al., 2009; Gavazzeni et al., 2012). To our knowledge, only Waring and Kensinger (2009) have tested the effect of retention interval on

emotional memory in aging. Despite a general age-related decrease in recognition accuracy, both younger and older adults demonstrated similar memory enhancement for positive and negative scenes. Moreover, this effect was most pronounced for the longest retention interval (1 day vs. a few minutes), suggesting preserved consolidation mechanisms for emotional informating in aging, at least over 1 day of retention.

Relatively little is known about how the brain processes emotional information in old age (see also Ebner et al., 2012; Pollock et al., 2012). In the following, we review findings from four studies. In all of them, older participants showed a negativity effect, inconsistent with the Socioemotional selectivity theory. Both St Jacques et al. (2009b) and Kensinger and Schacter (2008) showed that AMG was engaged in both younger and older adults during successful encoding of negative pictures. In St Jacques et al., a hemispheric age-related difference was observed: there was greater activity in left AMG in the younger group, and greater activity in right AMG in the older group. By contrast, Fischer et al. (2010) reported age-related differences in brain activity during successful encoding of fearful faces: more AMG and HC activity was found in the younger group, and more activity in the right prefrontal cortex (PFC) was observed in the older group. Murty et al. (2009) scanned younger and older adults during emotional retrieval. Similar to Fischer et al. (2010), the young exhibited more AMG activity, whereas the old showed more PFC activity. Overall, the emerging pattern from these studies is an agerelated reduction of AMG activity coupled with increased PFC activity during encoding and retrieval of emotional information. Some authors have interpreted this shift in patterns of activity as compensatory (Murty et al., 2009), and others have argued that it reflects increased regulation of emotional processes with age-related differences in AMG-PFC functional connectivity (for review, see St Jacques et al., 2009a). However, these interpretations are based on encoding data mostly, while Murty et al.'s encodingretrieval study was a blocked design, showing therefore sustained rather than transient brain activity, the latter allowing to investigate *successful* retrieval. Moreover, in the aforementioned studies, the retrieval session took place from 2 to 45 min after the encoding session, precluding conclusions about neural substrates of longterm emotional memory in aging. Hence, in the present study we sought to uncover transient neural activity during emotional long-term retrieval in aging.

Fischer et al. (2010) noted an important feature of aging effects on recognition memory, namely the tendency to produce more false recognitions of negative emotional items among older adults. Emotionality may result in more false alarms (FA), because of greater semantic cohesiveness of emotional items (e.g., emotional items tend to belong to semantic categories in which instances share more features than for neutral items; Maratos et al., 2000; Marchewka et al., 2008). In this case, individuals may base their recognition decision on general characteristics, or in other words on familiarity processes. Conversely, others have argued that emotionality may reduce false memories, because emotion increases item distinctiveness (Kensinger and Corkin, 2004; for review, see Kensinger, 2012). In this case, recognition would be based on recollection mechanisms (e.g., recognition of specific features of the items). Thus, findings are mixed and the

studies in question used short retention intervals. In a study of younger adults, Howe et al. (2010) found a significant increase of false recognitions for negative, but not for neutral, items from immediate to 1-week retrieval. Older adults tend to rely more on familiarity than on recollection in making recognition judgments (Bastin and van der Linden, 2003; Howard et al., 2006; Prull et al., 2006), which is known to increase false recognitions. Thus, the increase of false recognitions of negative items over time observed by Howe et al. in young adults may be exacerbated in older age, which may contribute to a reduced negativity effect in true recognition (discrimination) after long retention intervals.

In this study, younger and older adults underwent functional Magnetic Resonance Imaging (fMRI) during recognition of negative and neutral scenes at three occasions over 3 weeks. Here, we present behavioral and neural findings associated with recognition of negative and neutral scenes after 1 and 3 weeks of retention in relation to age. We were particularly interested in uncovering the effect of retention interval on the effect of negative emotion on memory in the presence of lures. The elaboration of an eventrelated fMRI design allowed us to study brain activity associated with successful, false, and true recognition.

# **MATERIALS AND METHODS**

# **PARTICIPANTS**

Twenty younger and 20 older adults, all right-handed, were recruited. No subject reported any previous or current psychiatric, neurological or medical disease, and none was taking psychoactive medication or abused any substances. They were paid 1,500 SEK for their participation. The study was approved and conducted in accordance with guidelines established by the regional ethics committee, and written consent was obtained from all participants prior to the start of the study. All subjects initially underwent a cognitive battery. Their performance indicated that they were representative of their age cohorts, with typical negative age differences in fluid cognitive tasks along with no age differences in crystallized cognitive tasks. Because of misunderstanding of the MRI task (*n* = 2), technical issues (*n* = 1), MR findings (*n* = 1), or too few events to be analyzed in the fMRI data (*n* = 2), 19 young and 15 old subjects were retained for analyses. Their characteristics and cognitive scores are shown in **Table 1**.

# **GENERAL PROCEDURE**

The emotional memory task took place at three time points. At Session 1 (S1), subjects underwent intentional encoding of the material and immediate recognition. One week later, they came back and underwent the second recognition session (S2). Three weeks after S1 they undertook the third recognition session (S3). Subjects were scanned at all retrieval sessions (**Figure 1**).

## **MATERIALS AND TASKS**

The task was displayed by means of E-prime (Psychology Software Tools Inc., Pittsburgh, PA, USA) using an IBM ThinkPad 390 computer (IBM, San Jose, CA, USA). The pictures were projected onto a screen in the scanner room using a Philips Hoper HG 20 Impact LCD projector (Philips Corp., Eindhoven, The Netherlands), positioned approximately 3 m in front of the scanner. Stimuli were

#### **Table 1 | Participant characteristics.**

Mean ± SD; \*one missing value.

presented via a mirror system, which was placed on top of the head coil 2–5 cm from the subjects' eyes.

The material consisted of 450 pictures selected from the International Affective Picture System (IAPS, Lang et al., 2008). At encoding, 150 neutral and 150 negative pictures were presented, together with 90 null events (three black crosses on a white background with button press). Subjects were told to memorize the scenes for later retrieval, and to rate the valence of the pictures as neutral or negative. Fifty pictures of each type (neutral, negative) were shown as targets at each recognition session together with 25 lures of each type and 45 null events. The three types of events (targets, lures, null events) were inter-mixed. The order of events was identical for all subjects in order to control for between-person order effects. The pictures were presented during 3.5 s each, and were separated by a cross (on a white background) that lasted for 2–3 s. Participants responded by button presses to indicate whether or not pictures were shown during encoding (yes/no recognition). Overall, negative scenes were more arousing than neutral scenes, and these two categories (negative, neutral) also differed according to the living/non-living dimension. Importantly, within each emotional category of items, the sets of scenes did not differ on valence, arousal, and living/non-living dimensions across sessions of retrieval (no main effect of session and no interaction between emotion and session on valence, arousal, and living/non-living dimensions). Although lures were more arousing than targets, this should not have had any effect on findings as this factor did not interact with emotion or session of retrieval (**Table A1** in Appendix). The material was distributed and balanced across the retrieval sessions to reach the aforementioned criteria according to normative data and results from a pilot study.

# **NEUROIMAGING**

The study was carried out on a GE 1.5 T MRI scanner using an eight-channel head coil. For the event-related functional scanning, an EPI (echo planar imaging) sequence was used with the following parameters: Repetition time (TR) = 2,500 ms (32 axial slices acquired in an interleaved order), echo time (TE) = 40 ms, flip angle = 90˚, field of view (FOV) = 22 cm, slice thickness = 4.5 mm, in-plane resolution = 3.4 mm × 3.4 mm, interslice spacing = 0.5 mm. To avoid signals arising from progressive saturation, four dummy scans were performed prior to image acquisition. Each recognition task (S1, S2, and S3) was run within one session of 19.5 min, resulting in 482 EPI volumes per session. Structural T1-weighted images were also collected with the following parameters: TR = 24 ms (124 coronal slices acquired), TE = 6 ms, flip angle = 35˚, FOV = 22 cm, slice thickness = 1.5 mm, in-plane resolution = 0.86 mm × 0.86 mm, no gap.

# **ANALYSES**

# **Behavioral data**

Hits (H) and FA were calculated following the procedure described by Snodgrass and Corwin (1988), where a correction was applied by adding +0.5 to H (and FA) and +1 to the number of targets (and lures), e.g., H = (*N* hits + 0.5)/(*N* targets + 1). Discrimination was calculated by subtracting FA from H. Analyses of variance were conducted on Hits, FA, and Discrimination with the factors Age (Younger, Older), Session (S1, S2, S3), and Emotion (Neutral, Negative).

## **Neuroimaging data**

SPM8 (Statistical Parametric Mapping, Wellcome Trust Centre for Neuroimaging, London)<sup>1</sup> implemented in Matlab 7.13 (Mathworks, Inc.,MA, USA) was used to analyze the imaging data.

*Preprocessing.* The structural T1 images were aligned to the standard MNI template (Montreal Neurological Institute) and voxelbased morphometry was run using the VBM8 toolbox of SPM8 (with DARTEL spatial normalization,Ashburner, 2007), using the default parameters. The normalized gray matter (GM) images were used for volumetric assessment (see Results and HC and AMG Age-Related Atrophy). Furthermore, a binary GM mask was built for use as an explicit mask in the fMRI analyses: using the ImCalc function of SPM, an average GM image was created from the individual normalized GM images of all participants, and this image was transformed into a binary mask. We also built two groupspecific GM masks for within-group analyses (younger, older).

The fMRI dataset was preprocessed using the following steps: first correction for differences in slice timing was applied within each EPI volume (the middle slice was the reference slice), then the images were spatially realigned (the mean EPI image created by SPM was co-registered to the corresponding T1 image, and all volumes were realigned to the mean EPI image) and unwarped. The images were then spatially normalized to the MNI template, resampled to a voxel size of 2 mm<sup>3</sup> , and smoothed with an 8-mm full-width at half-maximum Gaussian filter kernel.

<sup>1</sup>http://www.fil.ion.ucl.ac.uk/spm

*Statistics.* For the fMRI data, single-subject contrasts were set up using the general linear model and group data were analyzed with a random-effects model. All models were convolved with a canonical hemodynamic response function as implemented in SPM. Ten regressors were constructed in order to capture the variance of all events (see bottom panel of **Figure 1**, showing the eight regressors of interest, to which two other regressors were added: baseline and no responses). All events were modeled as delta functions. Covariates of no interest included the six realignment parameters to further account for signal-changes related to inadvertent head motion. Subject analyses were run separately for each retrieval session.

At the group level, statistical parametric maps were generated voxel by voxel using factorial designs. The contrast of interest used to assess brain areas whose activity was related to successful recognition was "H − Misses (M)." The data of Session 1 were not analyzed because of too few M; therefore only the data of S2 and S3 were used in this study. Although the design of the study contains three factors, we conducted separate factorial designs with two factors each as follows: (1) two within-age-group ANOVAs were conducted with the factors Session (S2, S3) and Emotion (Neutral, Negative); (2) two within-session ANOVAs were conducted with the factors Age (Younger, Older) and Emotion (Neutral, Negative); and (3) two within-emotional-valence ANOVAs were conducted with the factors Session (S2, S3) and Age (Younger, Older). This decision was based on the fact that contrasts that do not span all conditions (or cells) at the second-level analyses are not recommended in multifactorial designs. For instance, if the three-way ANOVA was carried out and the interaction of interest (e.g., Emotion × Session) lied within the younger but not the older group, the sphericity assumption (i.e., pooled variance across all factors) may not be fulfilled. Hence, conducting two-way ANOVAs allowed testing all potential interactions without ruling out the sphericity assumption.

Brain areas related to false recognitions were assessed in the older group and only for negative items at S2 and S3. This restriction to the older group and to negative items was due to the number of events: Too few events of this type occurred in the younger group and overall in the neutral condition, precluding meaningful analyses. One older adult produced only four FA at S2 but we decided to keep this subject in the analyses (his presence did not alter the pattern of findings and he was not an outlier at the neuroimaging level). To fathom the neural basis of false recognition of negative scenes, we computed *t*-tests using the contrasts FA vs. correct rejections (CR) and H vs. FA separately for S2 and S3. Therefore, we distinguished the terms "successful recognition" and "true recognition." Successful recognition was assessed with the contrast "H vs. M," regardless of the cognitive processes or confidence with which the subjects made their recognition decision. At the cognitive level, this contrast corresponds to Hits only.

True recognition was assessed with the contrast "H vs. FA"; given the proportion of FA especially in the older adults for the negative items (see Results), it is likely that many negative Hit responses were made based on the same processes or confidence as for negative FA – thus, removing FA-related brain activity from Hit-related activity may show brain areas whose activity was related to true recognition. At the cognitive level, this contrast corresponds to discrimination.

At the voxel level,we used an uncorrected threshold of *p* < 0.001 (cluster threshold *k* > 20 contiguous voxels). For the *a priori* HC (left, right) and AMG (left, right) regions of interest (ROIs) we used a threshold of *p* < 0.0125 (i.e., *p* < 0.05 corrected for four ROIs, and *k* > 20). ROIs were taken from the WFU Pickatlas (Maldjian et al., 2003) and the AAL atlas (Tzourio-Mazoyer et al., 2002). Contrast values as displayed on the graphs were extracted from the clusters of interest (over all voxels of the given clusters) using the eigenvariate tool in SPM.

# **RESULTS**

# **BEHAVIORAL DATA**