Impact Of Stereotype Threat On Brain Activity During Memory Tasks in Older Adults Part 2
Sep 11, 2023
2.4. Physiological data acquisition and design
Parasympathetic cardiac control was measured with high-frequency heart rate variability (HF HRV) derived from the ECG. HF HRV is the fluctuation in heart rate between beats within the respiratory frequency band (0.12 – 0.40 Hz) that has been demonstrated to be an index of parasympathetic cardiac control (Berntson et al., 2017). HF HRV is the natural log of the heart period variance in the respiratory band (in ms 2 ). The ECG was obtained using a Bionex system (Mindware Technologies LTD, Gahanna, OH). Mindware software derived HF HRV by spectral analysis of the interbeat interval series from the ECG.
Parasympathetic nervous system activity is closely related to memory performance. A large number of experiments have proven that the activity of the parasympathetic nervous system has a significant improvement effect on the human body's cognitive and memory abilities. In addition, the activity state of this system is also related to the plasticity of the cerebral cortex. In other words, its appropriate stimulation can promote the synaptic connections in the brain and improve neurological function.
When we are in a relaxed state, our parasympathetic nerves remain active, which causes the brain to release memory-enhancing chemicals such as acetylcholine. Acetylcholine is a catalyst that promotes the transmission of information between neurons and helps us learn and remember better. In addition, proper breathing and heart rate in a relaxed state are more conducive to blood circulation in the brain, thereby fully supplying oxygen to the brain and improving brain function.
Therefore, relaxing muscles, slow breathing, and a comfortable environment can help us relax, thereby adjusting the excitement of the parasympathetic nerves and promoting learning and memory. In addition, diet, exercise, and sleep all need to fully consider the influence of the parasympathetic nervous system. People should pay attention to maintaining a good lifestyle to keep their bodies and brainss in optimal condition.
In short, the parasympathetic nervous system has an important impact on human health and memory performance. Therefore, in our daily life, it is very necessary to maintain an appropriate relaxation state. Only by relaxing your mind and allowing your body and mind to relax can you achieve better learning and memory effects. It can be seen that we need to improve our memory, and Cistanche deserticola can significantly improve our memory because Cistanche deserticola is a traditional Chinese medicinal material that has many unique effects, one of which is to improve memory. The efficacy of minced meat comes from the various active ingredients it contains, including acid, polysaccharides, flavonoids, etc. These ingredients can promote brain health in various ways.
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2.5. fMRI acquisition
Images were acquired using a 3-T Philips Achieva scanner at the University of Chicago MRI Research Center. Functional scans were acquired using a T2*-weighted echo planar imaging sequence (repetition time [TR] = 2 s, echo time [TE] = 25 ms, field of view [FOV] = 192 mm; flip angle = 77°, matrix size = 64×63 mm, in-plane resolution = 3 × 3 mm2 ). 34 interleaved slices (4 mm thickness, 0.5 mm skip between slices) were acquired for whole-brain coverage. During each of the 5 functional runs, we applied z-shimming on 4 slices across the orbital frontal region along the superior/inferior direction for compensation gradient to regain signal loss due to nasal cavity artifact (Du et al., 2007). Structural scans were acquired last using high resolution T1 - weighted structural Turbo Field Echo (TR = 8 ms, TE = 3.8 ms, flip angle = 8°, FOV = 224 mm, matrix = 224×224 mm2, in-plane resolution = 1 × 1 mm2 ).
2.6. fMRI data processing and analysis
Our MRI tasks had two phases: a blocked task phase which alternated between the episodic memory encoding, working memory, and countdown tasks, followed by an event-related memory test for the episodic memory items. Our stereotype manipulation preceded both phases, so an impact of stereotype activation on participants’ overall approach to the various tasks – or their mindset during the experimental session – should have been present in each phase. Because blocked designs tend to have more power to detect group differences in performance compared to event-related designs, these were our primary focus and we only report MRI methods and analyses of the blocked design in the sections that follow. Additional methods and analyses for the event-related recognition task are described in the Supplemental Materials
Preprocessing and data analysis were conducted using SPM12 (Wellcome Trust Centre for Neuroimaging, London) implemented in MATLAB R2015a (The Mathworks Inc., Natick, MA, USA). Standard preprocessing was performed on the functional data, including using field maps to unwarp the images, realigning the time series using a least squares approach and a six-parameter (rigid body) spatial transformation aligning to the first image in the series, anatomical coregistration, segmentation of the anatomical scan into gray matter, white matter, and cerebrospinal fluid, and spatial smoothing (using a 6-mm full-width half-maximum isotropic Gaussian kernel). Normalization to the Montreal Neurological Institute (MNI space) template (resampling at 2 mm cubic voxels) was implemented for group analyses.
For each participant, the BOLD response was modeled using an unbiased whole-brain approach under the assumptions of the general linear model (GLM) (Friston et al., 1995). In these analyses, blocked event types of interest and covariates of no interest (a mean for the functional run, a linear trend for the functional run, and six movement parameters derived from realignment) were used to compute parameter estimates at each voxel. A high-pass filter of 66 s was used to remove low-frequency drifts. Second-level analyses were conducted using a 2 (Group: stereotype, control) by 3 (Task: episodic encoding, working memory, countdown) mixed factorial design. This ANOVA yielded overall task-related differences in brain activity (main effect of task: encoding vs. working memory vs. countdown), overall stereotype-related differences in brain activity (main effect of group: stereotype vs. control), and any differences in the effect of stereotype on brain activity associated with these tasks (interaction between task and group). For completeness, the countdown task also was used as a low-difficulty control task to identify brain activity associated with the memory tasks, and we report contrasts for episodic encoding > countdown and working memory > countdown. All the results were reported at p < .05, corrected for multiple comparisons using Monte Carlo simulations via 3dClustSim on AFNI.
In addition to whole-brain analyses, region of interest (ROI) studies were conducted. Anatomical ROIs were selected based on the literature to further characterize the BOLD response during the different cognitive tasks and possible group differences. ROIs linked to the executive-control interference hypothesis were based on prior stereotype threat studies in younger adults (Krendl et al., 2008; Wraga et al., 2007), and included the bilateral amygdala, ACC, MFG, and left IFG/BA 47. ROIs linked to the regulatory fit hypothesis were based on prior studies of prevention focus (Johnson et al., 2006; Mitchell et al., 2009) or processing aging stereotype words (Colton et al., 2013) and included MCC and PCC. ROIs were based on the neuromorphometric atlas in SPM12 and contrast estimates were extracted using SPM12 tools. Contrast estimates were then entered into two different Group × Condition MANOVAs to separately test the executive-control interference hypothesis and the regulatory fit hypothesis.
We conducted brain-behavior correlations using partial least squares regression (PLS-R) via the ExPosition package in R (Beaton et al., 2014). This method was chosen for several reasons. First, behavioral performance measures often are correlated with one another as is true for brain activity across regions and PLS techniques capitalize on the shared variance across factors to explain most of the covariance in the data. Second, the multivariate nature of this technique allows simultaneous estimation of many measures, including multiple brain regions and multiple behavioral measures. Because the covariance in the variables was analyzed together in a single analysis, no corrections for multiple comparisons were needed. The brain and behavioral variables of interest were used to create two matrices: one that represented the brain measures for each participant and one that represented the behavioral measures. The cross-product of these two matrices was decomposed into mutually orthogonal latent variables using singular value decomposition. The latent variable scores represented the weights of the brain factors that contributed to higher or lower performance for each behavioral measure. Pearson correlations between the latent variable scores from each X and Y matrix were used to determine the significance and effect size of each resulting factor.
3. Results
3.1. Manipulation check
Of the 69 participants in the study, all 33 participants in the stereotyped group remembered reading the aging stereotype passage, whereas only 7 of 36 (19%) participants in the control group reported thinking that the study was about cognitive decline with aging. Each of these 7 participants said they did not get this message from the researcher or the passage but guessed the purpose of the study was related to aging or memory through the self-report questionnaires that were given at the very end of the survey before the manipulation check. None of the control participants reported thinking about aging stereotypes before or during the fMRI scanning. These results suggest that our procedures successfully activated aging stereotypes in the stereotype group only.
3.2. Pre-Post changes in stress response and anxiety by group
Pre and post-changes in parasympathetic cardiac response and anxiety were measured as a function of the stereotype activation manipulation with resting HF HRV and self-reported state anxiety, respectively (see Table 1). Higher resting HF HRV is associated with more optimal frontal functioning and cognitive control in response to challenging tasks (Colzato and Steenbergen, 2017), whereas lower resting HF HRV tends to be associated with suboptimal stress responses (Porges, 2001; Thayer et al., 2009), and lower HRV has been associated with the negative impact of stereotype threat on performance in younger adults (Williams et al., 2019). Three-time points were measured for HF HRV: pre-passage, during the passage, and post-passage. Analyses on HF HRV yielded a main effect of time (F (2, 114) = 7.99, p < .001), reflecting an increase in HF HRV in both groups with no effect of group and no interaction (ps > 0.87). Pre and post-measures of state anxiety also were gathered. This analysis yielded a main effect time (F (1, 66) = 79.38, p < .001), reflecting an increase in state anxiety in both groups with no effect of group and no interaction (ps > 0.71). These data are inconsistent with the idea that the stereotype passage would make participants feel more anxious or threatened than the control passage, which was predicted by the executive-control interference hypothesis but not the regulatory fit hypothesis (which assumes strategy shifts, but not necessarily anxiety or stress in response to aging stereotypes). Instead, these findings suggest that reading a passage about the study’s goals in preparation for taking the tasks during MRI elevated anxiety, with no group differences in this anticipatory response.
3.3. Cognitive tasks
Participants performed well on the cognitive tasks and could distinguish between targets and lures effectively (see Table 2). At the group level, there were minimal differences in performance on either task that could be attributed to stereotype activation. For episodic memory, a 2 (Group: Stereotype vs. Control) × 2 (Session: Lab vs. fMRI) repeated measure ANOVA was conducted on each of the two measures of accuracy, revealing a significant group effect on old/related accuracy (F (1,67) = 4.88, p = .031). This effect indicates that the stereotyped group outperformed the control group, but this effect did not interact between the sessions, providing any evidence that stereotype activation (in the second session) impacted performance. No other group-level effects were found on episodic memory or working memory performance (all ps > 0.22). For completeness, Table 2 also reports separate group comparisons of each hit rate, false alarm rate, and accuracy score, as well as a corrected false recognition score on the episodic memory task (false alarms to related lures minus unrelated lures), which assesses the impact of category relatedness on false alarms independent from response bias effects that should impact all lure types.
Following prior work suggesting individual differences may mask stereotype activation effects in episodic memory (e.g., Smith et al., 2017), we investigated whether retirement status (retired, working) and years of education may moderate the impact of stereotype activation on episodic and working memory accuracy. For episodic memory, this analysis revealed that stereotype activation increased old/new accuracy in highly educated and retired individuals (p < .019). This result was driven by reduced false alarms to new items, potentially reflecting a switch to more conservative category-based monitoring and responding in the stereotyped group, which could increase old/new accuracy in the current task (see Supplemental Material; Fig. S1). Although prior work has been mixed (see Smith et al., 2017), the current result is consistent with two prior studies that also tested episodic memory for associated words, which found that stereotype activation reduced false alarms under task procedures that encouraged error-prevention as in the current task (e.g., warnings against errors, Barber and Mather, 2013b; Wong and Gallo, 2016), consistent with the regulatory fit hypothesis. For working memory, although the three-way interaction was in the same general direction, none of the effects reached significance (ps > 0.61).
3.4. fMRI whole-brain univariate
As discussed in the methods section, the stereotype manipulation preceded the MRI tasks, and so any general impact of stereotype threat on participants’ mindset when taking the various tasks (and associated differences in brain activity) should have been observed across all our task phases. Because blocked designs tend to be more powerful than event-related designs, these were our primary focus and we only report analyses of the blocked design in the sections that follow. Nevertheless, analyses of brain activity during the event-related phase supported the key results, and these are reported in the Supplementary Material.
On the blocked MRI data, we conducted a 2 (Group: stereotype, control) × 3 (Task: episodic encoding, 2-back, countdown) factorial ANOVA to identify overall group differences between the stereotype and the control groups and Group × Task interactions on brain activity. Concerning task-related regions, these analyses revealed robust patterns of brain activity that have previously been associated with these kinds of cognitive tasks (Fig. 2). To further investigate these task-specific effects, we conducted post hoc contrasts for each task relative to the countdown task (Table S1). For episodic memory encoding, brain activity was lateralized to the left hemisphere, including the left temporal cortex, PFC, and lateral parietal cortex - both typically associated with episodic encoding using verbal materials with visual presentation (Spaniol et al., 2009; Kelley et al., 1998). No regions showed the opposite pattern (countdown > encoding) at the set threshold. For working memory, brain activity was found in bilateral insula, PFC, and inferior parietal cortex - all commonly found in N-back tasks (Owen et al., 2005; Nee et al., 2013). No regions showed the opposite pattern (countdown > 2-back) at the set threshold.
Concerning stereotype and control group differences, no main effects of the group were found but a significant group × task interaction was observed in three clusters: right dorsal PCC, right precuneus, and right postcentral sulcus (see Fig. 3 and Table 3). Follow-up analyses were conducted to determine the direction of these interaction effects and to compare the size of the effect across tasks. These analyses indicated that the interaction in each cluster was due to the stereotype group exhibiting higher activity in the memory encoding task than the control group (t(67)=4.16, SE = 0.13, p < .001, t(67)=3.17, SE =0.21, p = .002, and t(67)=3.11, SE = 0.16, p = .003), whereas smaller group differences were found between the two groups in the 2-back task (t (67)=2.64, SE = 0.13, p = .01, t(67)=1.96, SE = 0.21, p = .06, and t(67)=2.52, SE = 0.16, p = .014) and the control task (t(67)=2.24, SE = 0.13, p = .028, t(67)=1.56, SE = 0.21, p = .12, and t(67)=1.07, SE = 0.16, p = .29). Controlling for age did not qualitatively change these results. This activity in mostly posterior midline regions is consistent with previous research that identified the involvement of similar regions in prevention-focus processing (Johnson et al., 2006; Mitchell et al., 2009) and processing aging stereotype words for self-relevance (Colton et al., 2013). As can be seen in Fig. 3, while these results demonstrate the strongest effects in the episodic memory task, the effects were largely consistent across the three different tasks. To parallel the memory performance analysis, a MANOVA investigating the Group × Education × Retirement interaction was conducted on brain activity during the memory task in these three clusters. This analysis did not yield the same significant three-way interaction (p = .37).
3.5. fMRI ROIs
We also analyzed brain activity in anatomical ROIs selected based on the two primary hypotheses guiding the stereotype threat literature: the executive-control interference hypothesis (bilateral amygdala, ACC, MFG, and left IFG/BA 47, see Krendl et al., 2008; Wraga et al., 2007) and a prevention focus under the regulatory fit hypothesis (MCC and PCC, see Johnson et al., 2006; Mitchell et al., 2009). We conducted two separate 2 (Group: stereotype, control) × 3 (Task: episodic encoding, 2-back, countdown) MANOVAs targeting each set of ROIs. The MANOVA on ROIs linked to the executive-control interference hypothesis did not yield a significant multivariate effect of Group or a Group × Task interaction (ps > 0.45), and activity in these ROIs was quite similar across the two groups (see Fig. S2). In contrast, the MANOVA on ROIs linked to the regulatory fit hypothesis yielded a significant effect of Group (Pillai’s Trace = 0.11, F(4, 198) = 5.89, p < .001) but no interaction (p = .99). To test which ROIs contributed to this overall effect, individual ANOVAs were conducted on each ROI, revealing significant group effects in right PCC (F(1201) = 13.52, p < .001), and left PCC (F(1201) = 10.18, p < .001), as shown in Fig. 4, but not the right MCC (p = .28) nor the left MCC (p = .09, see Fig. S2). These PCC effects indicated greater brain activity in the stereotyped group than the control group and were numerically strongest for brain activity in the episodic memory encoding task. These PCC patterns are quite similar to those found in the whole-brain analysis. Moreover, as seen in Fig. 4, participants in the control group showed the typical de-activation pattern in the PCC during the task blocks, consistent with a primary focus on the tasks as opposed to more inward, self-referential thoughts (e.g., Mak et al., 2017). In contrast, stereotype participants showed minimal de-activation, suggesting they remained self-focused throughout the tasks.
3.6. Brain-behavior relationships
To investigate brain-behavior relationships we used a multivariate method (PLS-R) that allowed the simultaneous assessment of multiple brain regions and multiple cognitive variables. In this PLS-R analysis, the significant clusters from the whole brain analysis were entered into one matrix, and the cognitive measures (see Table 2) were entered as a second matrix except for response times to false alarms in the working memory task due to missing values. For these analyses, we collapsed across group status because even though stereotype activation may have shifted participants’ focus, participants in either group might have had prevention-focused reactions to the experimental procedures. Two independent patterns explained much of the covariance in the data, explaining 86.05% and 10.94%, respectively (Fig. 5). The first latent variable was significant, r (67) = 0.36, p = .002, and indicated that greater brain activity in the right precentral sulcus and PCC (regardless of task) was associated with greater accuracy and lower false alarms across the episodic and working memory tasks. The second latent variable also was significant, r (67) = 0.28, p = .019, and indicated that greater brain activity in the right precuneus (regardless of task) was associated with slower response times and lower false alarms to related lures (after correcting for responding to unrelated lures) in the episodic memory task. Together, the PLS-R results suggest that the greater activation found in brain regions that also were elevated in the Stereotype Group was associated with slower response times and greater accuracy across all participants, suggesting a more conservative, prevention-focus approach that is consistent with the regulatory fit hypothesis.
4. Discussion
Here we report the first fMRI experiment to identify brain activity associated with stereotype activation while older adults took cognitive tasks. Two prominent hypotheses have been proposed to explain alterations in memory due to stereotype activation in older adults. The executive-control interference hypothesis emphasizes negative emotional reactions (performance anxiety) that compete with executive control resources, and this hypothesis has received support in the younger adult behavioral and neuroimaging literature on stereotype threat. The regulatory fit hypothesis instead emphasizes a strategy shift toward an error prevention focus, and this hypothesis has received some support in the older adult behavioral literature on stereotype threat. The behavioral and neuroimaging findings reported here did not support the predictions of the executive-control interference hypothesis, suggesting that stereotype threat works differently in older adults compared to younger adults. Our behavioral and neuroimaging findings instead were more consistent with the regulatory fit hypothesis, although as we discuss below, additional work is now needed to replicate these MRI results and further understand their implications for cognitive processing during stereotype threat.
Several findings argue against the executive-control interference hypothesis, at least concerning stereotype threat effects in older adults under the conditions used in our study. Previous studies have linked stereotype threat activation in younger adults to the anterior cingulate cortex and other regions that have been implicated in emotion regulation (Krendl et al., 2008; Wraga et al., 2007). We did not find group differences in these regions in either the whole-brain analysis or the targeted ROI analyses. The executive-control interference hypothesis also argues that working memory resources are needed to suppress negative emotions while taking cognitive tasks (Beilock et al., 2007; Schmader et al., 2008). Regions in the prefrontal cortex, including ventrolateral PFC, have been implicated in cognitive control resources that might serve such a role. In the current study, the stereotyped group did not recruit these brain regions to a greater extent than the control group. Furthermore, older adults in these two groups did not differ in a physiological assessment of parasympathetic cardiac stress reactivity (HF HRV) or reported anxiety, as would be expected from this hypothesis. Rather, both groups showed comparable increases in anxiety and HF HRV, which may reflect anticipatory responses to the cognitive tests and fMRI portion of the experiment. Finally, there were no differences between the groups in perceived threat from the experimental context itself. Because this questionnaire was given after the stereotype activation manipulation, the executive-control interference hypothesis would have predicted a difference in this measure.
In contrast to the executive-control interference hypothesis, several of our neuroimaging and behavioral findings were consistent with the regulatory fit hypothesis. Older adults in the stereotyped group had greater activity in the parietal midline regions than those in the control group. Greater activity in parietal midline regions, such as the PCC, has been associated with the activation of a prevention focus in both younger and older adults (Johnson et al., 2006; Mitchell et al., 2009), consistent with the regulatory fit hypothesis. Interestingly, EEG evidence shows that greater phase-locking associated with posterior midline regions (precuneus, PCC) and other regions in the default mode network (e.g., lateral parietal cortex) can help minimize stereotype threat effects on error monitoring and self-doubt in younger adults (Forbes et al., 2015). This finding suggests that activity in these posterior regions might signal a prevention focus that can help buffer against threat effects. In the current study, the group difference in PCC activity was observed across all the cognitive tasks, as would be expected if stereotype threat activated an error-prevention mindset throughout the experimental session. Additional support for the regulatory fit hypothesis stems from the behavioral findings. A shift to error prevention would arguably be most pronounced in false alarm rates. Although we did not find overall group differences in behavior, we did find that stereotype threat was most likely to impact episodic memory in older adults who are retired and more educated (cf. Smith et al., 2017). Moreover, this interaction was evident in the reduction in false alarms to new items during the episodic memory task. The brain-behavior correlations also showed that, pooling across our two experimental groups, those older adults showing the largest differences in brain activity in PCC also had the highest hit rates, and lowest false alarm rates, and responded more slowly during the episodic memory task. Each of these patterns is consistent with a conservative and error-avoidant strategy.
One important consideration of the current fMRI findings is their generalizability across the different cognitive tasks. The whole-brain analysis yielded a group × task interaction whereas the ROI analysis yielded a main effect of group across tasks, thus appearing to conflict with one another. However, inspecting the two patterns more closely reveals a quite similar pattern: the stereotype group effects were numerically strongest in the episodic memory encoding task, slightly less strong in the working memory task, and weakest in the countdown control task. There also was evidence that the stereotyped group activated PCC more than the control group during the episodic retrieval task (see Supplementary Material). The brain-behavior correlations from the PLS-R analysis also yielded a graded pattern such that associations between PCC activity and performance were strongest for episodic memory tasks and weaker (but in the same direction) on the working memory task. It is unclear why stereotype effects on behavior and brain activity tended to be larger for the episodic memory encoding task than working memory task, but prior behavioral studies also have failed to find effects of stereotype activation on working memory performance in older adults (Hess et al., 2009; Wong and Gallo, 2019). One possible explanation for the current pattern is that our working memory task was more tightly constrained than our episodic memory encoding task, in which participants were asked to memorize the words and but were not given a specific strategy or encoding judgment. Unconstrained tasks might be more sensitive to changes in motivation and strategy as proposed by the regulatory fit hypothesis, although additional work is needed on this point. The more definitive conclusion from the current dataset is that, although the effects of stereotype activation tended to be strongest on the episodic memory task, the primary effects on brain activity appeared to generalize across the tasks, suggesting a global shift in participants’ overall approach to the tasks.
Related to this last point, it is important to realize that although activity in PCC during encoding might reflect a shift towards an error-prevention focus, this pattern does not imply that stereotype threat’s effect on performance derives from its impact at encoding. In principle, explicitly activating stereotypes before the experimental tasks could have activated threat for the duration of the experimental session so that alterations in either encoding or retrieval processes (or both) could have consequences for episodic memory performance. For example, a prevention focus might encourage participants to monitor memory more carefully for missing items from the studied categories, so as not to false alarms about these items, and these processes could have occurred while the categorized items were presented during encoding or retrieval. Depending on how effective older adults were at this kind of monitoring, it also might have caused them to be more sensitive to category membership when making memory judgments at tests, thereby impacting false alarms to unrelated lures as well.
4.1. Limitations and future directions
Several limitations in the current study highlight the need for additional research in this area. First, although the individual differences effects we observed in stereotype effects on episodic memory performance were motivated by prior behavioral work (Smith et al., 2017), this prior work itself has been mixed and our results do not resolve prior discrepancies. Additional behavioral work is needed to understand when and how explicit stereotype activation can impact cognitive performance in older adults. Second, the posterior midline regions that differed between the stereotype and control groups (e.g., PCC) have been implicated in a variety of functions other than prevention-focus (e.g., inward attention more generally), so that activity in PCC alone cannot be taken as the adoption of such a focus (see Poldrack, 2006, for the well-known problem of reverse inference in fMRI). Although our behavioral data in conjunction with the neuroimaging data were more consistent with the regulatory fit hypothesis than the executive-control interference hypothesis, few prior fMRI studies have investigated the neural correlates of adopting a prevention focus (e.g., Johnson et al., 2006; Mitchell et al., 2009). Thus, even though activity in this region is consistent with the regulatory fit hypothesis when considered with the behavioral evidence, additional work is needed to understand the functional role of this region during stereotype activation in older adults. A third limitation is that, although we randomized participants into two groups and they did not differ on neuropsychological assessments (MoCA or Shipley Scales), the stereotyped group was somewhat older and had lower false recognition in the baseline session (before any manipulation). While analyses controlling for age yielded similar results as our primary analyses, the possibility of unintended group differences is always a possibility in a between-subjects design.
A final caveat is that our study does not rule out the possibility that older adults may experience anxiety and executive-control interference from the activation of aging stereotypes in other contexts. For example, in our study, there was evidence that the MRI environment may have been somewhat stressful or anxiety-provoking to older adults in both conditions. This anxiety may have minimized or overshadowed our ability to find group differences in emotional regulation regions associated with stereotype activation predicted by the executive-control interference hypothesis. Although our participants had a pre-MRI baseline session, which should have reduced overall test-taking anxiety during the MRI session, we cannot rule out this possibility. We are unaware of any work aimed at comparing the behavioral effects of stereotype activation in the MRI environment versus other contexts, although one study has shown that memory performance, generally, can be impaired in the MRI compared with a laboratory environment (Gutchess and Park, 2006). Future work could investigate potential interactions between such context effects and stereotype activation effects.
In conclusion, activating aging stereotypes increased brain activity in parietal midline regions associated with the processing of aging stereotypes, the self, and an error-prevention focus. While activity in these brain regions was consistent with the regulatory fit hypothesis, we did not consistently find patterns of brain activity that were predicted by the executive control interference hypothesis. These results add new constraints on current hypotheses of stereotype threat effects as they apply to older adults. Specifically, growing evidence in the behavioral literature suggests that the mechanisms of stereotype threat in older adults may be different from those in younger adults. Our results resonate with this emerging picture, demonstrating that the impact of stereotype threat on brain activity in older adults also is different from what one might expect from the relevant neuroimaging literature in younger adults. Aging stereotype threat seems to operate differently than other kinds of stereotype threat, at both the behavioral and brain levels of analysis.
Supplementary Material
Refer to the Web version on PubMed Central for supplementary material.
Acknowledgments and funding sources
Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Number R21AG049931. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Data/code availability
Neuroimaging and behavioral data used in this study will be freely available on the Open Science Framework upon publication. The software and models that we used to generate the results are stated in appropriate locations in the Methods section.
Appendix.:
Stereotype and Control Passages
Stereotype Passage:
Thank you for participating in our study. Today, you will take memory tasks like the ones that you previously took in the lab. We will measure your performance on these tasks, and we also will take pictures of your brain’s structures and your brain’s activity during the tasks. Scientific studies have shown that these procedures can detect memory decline associated with normal aging, and they also can predict the development of Alzheimer’s disease later in life.
The primary goal of our study is to increase the effectiveness of these measures in detecting memory decline associated with normal aging and Alzheimer’s disease. To achieve this goal, we will compare data from young college students to older adults in your age group. In addition, we will compare data between older adults whose performance puts them at different risk levels for the development of AD later in life. Based on your data from Day 1, our recruiters have determined that you are eligible for this study. Given these goals, you must understand the memory task instructions that I will now give you.
Control Passage:
Thank you for participating in our study. Today, you will take psychological tasks like the ones that you previously took in the lab. We will measure your performance on these tasks, and we also will take pictures of your brain’s structures and your brain’s activity during the lessons. Scientific studies have shown that these procedures can help us understand how the brain supports different psychological processes.
The main goal of this study is to evaluate brain activity associated with individual differences in psychological processes that make each of us unique. To achieve this goal, we will compare data across a large sample of research participants with varying backgrounds and characteristics. Based on your data from Day 1, our recruiters have determined that you are eligible for this study. Given these goals, you must understand the psychology task instructions that I will now give you.
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