Psychology and Aging
2008, Vol. 23, No. 4, 859 – 872
Copyright 2008 by the American Psychological Association
0882-7974/08/$12.00 DOI: 10.1037/a0014218
Aging and Emotional Memory: Cognitive Mechanisms Underlying
the Positivity Effect
Julia Spaniol
Andreas Voss
Ryerson University
Albert-Ludwigs-Universität Freiburg
Cheryl L. Grady
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Rotman Research Institute at Baycrest and University of Toronto
Younger adults tend to remember negative information better than positive or neutral information
(negativity bias). The negativity bias is reduced in aging, with older adults occasionally exhibiting
superior memory for positive, as opposed to negative or neutral, information (positivity bias). Two
experiments with younger (N ⫽ 24 in Experiment 1, N ⫽ 25 in Experiment 2; age range: 18 –35 years)
and older adults (N ⫽ 24 in both experiments; age range: 60 – 85 years) investigated the cognitive
mechanisms responsible for age-related differences in recognition memory for emotional information.
Results from diffusion model analyses (R. Ratcliff, 1978) indicated that the effects of valence on
response bias were similar in both age groups but that Age ⫻ Valence interactions emerged in memory
retrieval. Specifically, older adults experienced greater overall familiarity for positive items than younger
adults. We interpret this finding in terms of an age-related increase in the accessibility of positive
information in long-term memory.
Keywords: aging, emotion, recognition memory, reaction time, diffusion model
Leigland et al., 2004, word condition; Thomas & Hasher, 2006).
The resulting Age ⫻ Valence interaction is commonly referred to
as an age-related positivity effect. Although this phenomenon has
been attributed to age-related improvements in emotion regulation
(e.g., Carstensen, Isaacowitz, & Charles, 1999), the underlying
cognitive mechanisms are not fully understood. In addition, a
number of recent studies have failed to detect a positivity effect
(Comblain, D’Argembeau, Van der Linden, & Aldenhoff, 2004;
Denburg, Buchanan, Tranel, & Adolphs, 2003; Grady et al., 2007;
Grühn, Smith, & Baltes, 2005; Kensinger et al., 2002; Kensinger,
Garoff-Eaton, & Schacter, 2007; Mather & Carstensen, 2003,
Experiment 1), suggesting that the effect may depend on particular
task or stimulus parameters. The goal of this study was to explore
the specific cognitive mechanisms sensitive to age differences in
long-term memory retrieval of neutral and emotional information
and to test their generality across different stimulus materials and
encoding conditions.
Numerous laboratory studies have shown that younger adults
remember negative information better than neutral information.
This negativity bias has been demonstrated in different laboratory
tasks (e.g., old–new recognition and recall; e.g., Charles, Mather,
& Carstensen, 2003) and materials (e.g., Charles et al., 2003;
Grady, Hongwanishkul, Keightley, Lee, & Hasher, 2007; Kensinger, Brierley, Medford, Growdon, & Corkin, 2002). In recent
years, studies of emotional memory in younger and older adults
have suggested that older adults may show a reduced memory
advantage for negative information (Charles et al., 2003; Grady et
al., 2007; Leigland, Schulz, & Janowsky, 2004, face condition),
and may in some instances demonstrate a positivity bias—a memory advantage for positive information (Charles et al., 2003, Experiment 1, recall condition; Knight, Maines, & Robinson, 2002;
Julia Spaniol, Department of Psychology, Ryerson University, Toronto,
Ontario, Canada; Andreas Voss, Institut für Psychologie, Albert-LudwigsUniversität Freiburg, Freiburg, Germany; Cheryl L. Grady, Rotman
Research Institute at Baycrest, Toronto, Ontario, Canada, and Departments of Psychology and Psychiatry, University of Toronto, Toronto,
Ontario, Canada.
This research was supported by Canadian Institutes of Health Research
Grant MOP14036, the Canada Research Chairs program, the Ontario
Research Fund, the Canadian Foundation for Innovation, and Ryerson
University. Experiment 1 was presented at the 2006 Meeting of the
Psychonomic Society in Houston, Texas. We are grateful to Hua Han,
Kimberly Chiew, Scott Blackwood, Magdalena Wojtowicz, and Homan
Allami for technical assistance.
Correspondence concerning this article should be addressed to Julia
Spaniol, Department of Psychology, Ryerson University, 350 Victoria Street,
Toronto, Ontario M5B 2K3, Canada. E-mail: [email protected]
Mechanisms Underlying Age ⫻ Valence Interactions on
Memory Retrieval
Which cognitive mechanisms underlie the positivity effect in
older adults’ long-term memory? Socioemotional selectivity theory (SST; Carstensen et al., 1999; see also Carstensen, Mikels, &
Mather, 2006) posits that older adults deploy cognitive control
mechanisms to avoid negative stimuli and to seek out positive,
emotionally rewarding information. Age ⫻ Valence interactions at
retrieval may thus follow from goal-directed attentional selection
at encoding. Emotional goals could also affect memory at the
retrieval stage. Just as older adults may be reluctant to encode
negative information, they may be less inclined than younger
859
SPANIOL, VOSS, AND GRADY
860
adults to accept negative memories and to reject positive memories. That is, the age-related positivity effect in memory could
result from age differences in response bias. Alternatively, it could
have mnemonic causes. For example, the relative accessibility of
positive long-term memories may be greater for older adults than
for younger adults (see, e.g., Mather & Carstensen, 2005).
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Signal Detection Analysis of Emotional Memory in
Younger and Older Adults
Several researchers have used signal detection theory (Green &
Swets, 1966) to pinpoint the memory mechanisms responsive to
emotional valence in younger and older adults (e.g., Budson et al.,
2006; Charles et al., 2003; D’Argembeau & Van der Linden, 2004;
see also Windmann & Kutas, 2001). The findings have been
mixed, allowing no clear conclusions about age differences in the
effects of valence on response bias versus sensitivity. However,
the use of signal detection theory may be problematic when it does
not involve analysis of receiver-operating-characteristic curves
(e.g., Dougal & Rotello, 2007; Hertzog, 1980). To our knowledge,
only one study to date has used this analysis to compare memory
for neutral and emotional information in younger and older adults
(Kapucu, Rotello, Ready, & Seidl, 2008). In this study, age and
emotional valence affected response bias but not sensitivity.
A shortcoming of receiver-operating-characteristic analysis and
other accuracy-based modeling approaches is the fact that they do
not account for reaction time (RT) data. One prominent model that
overcomes this limitation is the diffusion model (Ratcliff, 1978), a
sequential-sampling model of two-choice decisions.
The Diffusion Model
The diffusion model (Ratcliff, 1978; Ratcliff & Smith, 2004;
Ratcliff, Van Zandt, & McKoon, 1999) assumes that the duration
of two-choice decisions reflects the joint influence of noisy latent
processes, both nondecisional (perceptual-motor) and decisional.
Figure 1 illustrates the decisional RT component for old–new
recognition. Model parameter ␯, the drift rate, is the systematic
influence that propels the decision process from a starting point
(parameter z) toward either of two response boundaries. The higher
the quality of the accumulating information, the higher the (absolute) drift rate, as illustrated by a steeper approach toward one of
the boundaries. The drift rate thus captures memory strength, or
discriminability, and is related to the signal detection parameter d⬘.
However, unlike d⬘, drift also captures the speed of a decision.
In our example, the upper decision boundary represents the
threshold for an old response, whereas the lower boundary represents the threshold for a new response. Once either boundary is
reached, the motor response (e.g., button press) is initiated. Boundary separation parameter a, representing the distance between the
upper and lower boundaries, reflects speed–accuracy tradeoff: The
farther apart the two boundaries, the longer the duration of an
average decision.
The relative position of the starting point z between the two
response boundaries regulates response bias. If z is closer to the
upper boundary, the individual is biased in favor of old responses,
whereas if z is closer to the lower boundary, the individual is
biased in favor of new responses. The relative placement of the
starting point, which can be expressed as z/a, is thus similar to the
signal detection parameter c, but z/a also affects the speed with
which responses are made.
With other things being equal, an increase in the nondecisional
RT component increases RT without affecting accuracy, whereas
an increase in boundary separation boosts accuracy slightly and
increases RT. Finally, an increase in the drift rate leads to an
increase in accuracy and a decrease in RT. For an explanation of
the role of the variance parameters (s␯, sz, and st), the reader is
Correct RT
distribution
(Old items)
Response “Old”
a
ν
Sample path 2
Sample path 1
z
Time
Sample path 3
0
Error RT
distribution
(New items)
Response “New”
Figure 1. Illustration of the diffusion process for an old item in an old–new recognition task. The diffusion
process starts at point z and is driven toward the upper boundary a (old response) by a positive drift rate, ␯. A
single drift rate is shown, although drift rates are assumed to be normally distributed across trials. Three sample
paths illustrate random variation in the information accumulation process. Sample Paths 1 and 2 result in the
correct (old) response, whereas sample Path 3 drifts toward the lower boundary 0 (new response), resulting in
an error. RT ⫽ reaction time.
AGING AND EMOTIONAL MEMORY
referred to Ratcliff and Rouder (1998) and Ratcliff and Tuerlinckx
(2002).
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Rationale and Hypotheses
The goal of this study was to test hypotheses about possible age
differences in mechanisms underlying long-term memory performance for negative, neutral, and positive stimuli. In particular, we
were interested in determining the degree to which age and emotional valence affect two distinct cognitive mechanisms: response
bias and memory bias. SST (Carstensen et al., 1999) can be used
to derive predictions for both types of bias, and the diffusion model
provides measures for these biases that are informed by both
accuracy and RTs.
Mather et al., 2004). Inclusion of more than one material type in
Experiment 1 therefore served to test the generality across materials of potential age and valence effects on response bias and
memory bias.
Influences of Mood and Personality
We included a paper–pencil mood and personality battery to
allow us to explore the possibility of age differences in mood (e.g.,
Barrick, Hutchinson, & Deckers, 1989; Gross et al., 1997; Mroczek & Kolarz, 1998) and personality (e.g., McCrae et al., 1999;
Zonderman, Siegler, Barefoot, Williams, & Costa, 1993).
General Method
Motivational (Response Bias) Hypothesis
Response bias refers to a preference for one of the two decision
outcomes (i.e., “old” or “new” responses). Under the assumption
that recognition of negative stimuli is an aversive experience,
whereas recognition of positive stimuli is emotionally rewarding,
SST leads to a response bias hypothesis: Older adults should prefer
to endorse rather than reject positive test stimuli. At the same time,
they should be less inclined to endorse neutral and, particularly,
negative stimuli. Younger adults, by contrast, should demonstrate
a response bias favoring negative over neutral and positive stimuli.
In terms of diffusion model parameters, response bias is conceptualized as the relative position of the starting point (parameter z)
between the two decision boundaries. If the starting point is closer
to one boundary than to the other, the participant shows a response
bias in favor of the response associated with the nearer boundary.
Mnemonic (Memory Bias) Hypothesis
Memory bias refers to the overall tendency, across target and
distractor stimuli, to extract mnemonic information that favors
either an “old” or a “new” response. For ease of description, we
refer to these tendencies as familiarity bias and novelty bias,
respectively.1 An age-related increase in the general accessibility
of positive pre-experimental memories, consistent with SST, could
lend a mnemonic advantage to positive experimental items for
older adults. According to the memory bias hypothesis, older
adults should thus show a stronger tendency to extract familiarity
information for positive stimuli, compared with negative and neutral stimuli.
In terms of diffusion model parameters, memory bias is the sum
of the drift rates across target and distractor items.2 If familiarity
and novelty signals are equally strong, their sum is zero because
target items elicit positive drift rates and distractor items elicit
negative drift rates (see Figure 1). Thus, positive scores indicate
familiarity bias, and negative scores indicate novelty bias.
861
Participants
All participants gave written informed consent for the study,
which was approved by the ethics committees of the Rotman
Research Institute at Baycrest and Ryerson University. Different
groups of 24 younger adults (12 women) and 24 older adults (12
women) participated in Experiment 1, conducted at the Rotman
Research Institute, and in Experiment 2, conducted at Ryerson
University. Participant characteristics are shown in Table 1. All
participants were right-handed native English speakers who were
recruited from the local community and were compensated for
their time. All participants were screened for health problems
(history of neurological and psychiatric illness, current depression
and anxiety disorders, use of psychotropic medication, uncorrected
vision and hearing deficits) and scored 27 or higher on the MiniMental State Examination (Folstein, Folstein, & McHugh, 1975).
In Experiment 1, older adults scored significantly higher on the
Mill Hill Vocabulary scale (Raven, 1982) than younger adults,
t(46) ⫽ 3.49, p ⬍ .001, ␩2 ⫽ .21; there was no age difference in
vocabulary scores in Experiment 2. In both experiments, the age
groups were matched on mean years of education. Scores on the
60-item NEO Five-Factor Inventory (Costa & McCrae, 1989) and
the 20-item Toronto Alexithymia scale (Taylor, Bagby, Ryan, &
Parker, 1990) did not differ significantly for younger and older
adults in both experiments (see Table 1). One exception, for
participants in Experiment 2 only, was the Neuroticism scale.
Here, older adults scored significantly lower than younger adults,
t(47) ⫽ 3.07, p ⬍ .004, ␩2 ⫽ .17. Furthermore, scores on the
negative mood scale of the Positive and Negative Affect Schedule
(Watson, Clark, & Tellegen, 1988) were higher for younger adults
than for older adults in Experiment 1, t(24.2) ⫽ 2.46, p ⬍ .021,
␩2 ⫽ .20, after Satterthwaite correction for unequal variances, and
Experiment 2, t(47) ⫽ 11.72, p ⬍ .001, ␩2 ⫽ .21. By contrast,
positive mood scores did not differ significantly for the two age
groups.
Generality Across Stimulus Types
As mentioned previously, the literature on age differences in
emotional memory has yielded mixed findings. Some of this
heterogeneity may be due to the use of different stimulus materials,
including faces, scenes, and words. These stimuli are known to
elicit activation in different brain regions, which may vary in their
sensitivity to age-related change (e.g., Leigland et al., 2004;
1
We use the term familiarity loosely, without specific reference to its
role in dual-process models of recognition memory (e.g., Yonelinas, 1994).
2
This procedure is of course similar to comparing the absolute values of
drift for old and new items. However, results are easier to interpret because
a single measure of memory bias is obtained that is independent of the
overall level of memory.
SPANIOL, VOSS, AND GRADY
862
Table 1
Participant Characteristics by Experiment and Age Group
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Experiment 1
Experiment 2
Characteristic
Younger adults
(N ⫽ 24)
Older adults
(N ⫽ 24)
Younger adults
(N ⫽ 25)
Older adults
(N ⫽ 24)
Age (years)
Age range
Education (years)
Vocabulary
MMSE
Neuroticism
Extraversion
Openness
Agreeableness
Conscientiousness
Positive mood
Negative mood
TAS-20
22.54 (2.12)
19–28
15.88 (1.03)
16.92 (3.94)a
29.38 (0.77)
22.42 (11.00)
29.67 (6.10)
28.83 (6.38)
32.08 (5.47)
31.13 (7.60)
29.71 (5.95)
12.92 (6.23)a
46.25 (9.03)
67.54 (4.06)
60–75
15.04 (3.20)
21.33 (4.78)
29.00 (1.06)
17.42 (8.14)
27.58 (6.18)
29.46 (7.81)
34.42 (5.87)
35.25 (6.53)
31.79 (6.16)
9.75 (1.00)
43.42 (10.37)
22.32 (3.96)
18–32
15.48 (1.83)
20.92 (19.67)
29.52 (0.65)
20.44 (7.33)a
31.24 (5.25)
32.52 (4.95)
31.44 (6.95)
30.76 (7.02)
27.64 (7.12)
11.44 (2.69)a
41.48 (8.46)
71.75 (6.51)
61–85
16.33 (2.70)
23.74 (3.58)
28.92 (1.14)
14.33 (6.55)
27.96 (7.51)
32.33 (6.36)
34.67 (6.10)
32.88 (7.60)
30.83 (7.85)
9.71 (1.33)
43.50 (7.56)
Note. Vocabulary is the raw score (maximum of 33) on the Mill Hill Vocabulary scale. Neuroticism,
extraversion, openness, agreeableness, and conscientiousness are from the revised NEO Five-Factor Inventory.
Positive mood and negative mood are Positive and Negative Affect Schedule scores. Standard deviations are in
parentheses. MMSE ⫽ Mini-Mental State Examination; TAS-20 ⫽ 20-Item Toronto Alexithymia scale.
a
Significant age group difference ( p ⬍ .05).
Stimuli and Apparatus
The stimuli consisted of 132 faces, 132 scenes, and 132 words,
with one third of each set previously rated as negative, neutral, or
positive, respectively. For each type, additional practice items
were used but not included in any of the analyses. The face stimuli
were black-and-white photographs from the Japanese and Caucasian Facial Expressions of Emotion and Neutral Faces (Matsumoto
& Ekman, 1988) and other sources (for details, see Grady et al.,
2007). The scenes were black-and-white photographs of indoor
and outdoor scenes, each containing at least one person, from the
International Affective Picture System (Ito, Cacioppo, & Lang,
1998) and other sources. The words were taken from the Affective
Norms for English Words (Bradley & Lang, 1999) and included a
subset of 117 words that had previously been rated similarly by
younger and older adults (Wurm, Labouvie-Vief, Aycock, Rebucal, & Koch, 2004). The words in each valence category did not
differ significantly on Kučera and Francis (1967) word frequency
(M ⫽ 46.06, SD ⫽ 75.21) and word length (M ⫽ 5.99, SD ⫽ 1.86).
All three stimulus types were used in Experiment 1, whereas only
scenes were used in Experiment 2.
One half of the stimuli of each material type were used as target
items in the recognition task, and the other half were used as
distractor items. To ensure that each stimulus was used equally
often as a target and as a distractor, each set of materials was
randomly divided into two sets of 66 stimuli, List A and List B.
The assignment of List A and List B items to target and distractor
status was counterbalanced across participants. For each material,
Lists A and B were equated on mean valence and arousal ratings,
as well as on mean frequency and length in the case of words.
The experimental tasks were created in E-Prime (Psychology
Software Tools, Inc., Pittsburgh, PA). Stimulus presentation was
controlled by a 2.8-GHz Pentium 4 laptop computer with a 15-in.
(38.10-cm) flat-panel LCD monitor. Viewing distance was approx-
imately 50 cm. All study and test stimuli were presented centrally
against a black background.
Experiment 1
The experimental session comprised an incidental study phase,
a 20-min. filled retention interval, a recognition test, and a ratings
task. Participants completed the Positive and Negative Affect
Schedule before beginning the study phase. During the study
phase, participants viewed a series of 72 faces, 72 scenes, and 72
words. The order of the three stimulus sets was counterbalanced
across participants. Within each set, stimulus order was randomized for each participant, with the exception of the first six stimuli,
which were identical for all participants and served as practice
items that were excluded from the analyses. Each stimulus was
presented for 2 s and followed by a 1-s blank screen. Participants
were instructed to rate the emotional valence of each item by
pressing one of three response keys: left arrow (“negative”), downward arrow (“neutral”), and right arrow (“positive”). If no response occurred during the stimulus presentation, the poststimulus
blank screen remained active until a response was made or until 4 s
had passed. Participants were unaware that their memory for the
items would be tested later.
During the 20-min. retention interval, participants completed the
60-item NEO Five-Factor Inventory and the Toronto Alexithymia
scale. During the recognition test, participants viewed studied and
unstudied faces, scenes, and words. The order of the three materials was the same as during the study phase. Each set contained 72
studied and 72 unstudied stimuli. Stimulus order within sets was
random, except for the first 12 stimuli (6 studied, 6 unstudied) of
each set, which served as practice items for all participants. Participants used the X and comma keys to indicate old–new decisions. The assignment of response keys to the old and new responses was counterbalanced across participants. Each stimulus
AGING AND EMOTIONAL MEMORY
stayed on the screen until a response occurred or until 5 s had
passed. After the recognition test, participants provided 5-point
Likert scale ratings of valence and arousal for all stimuli using
self-assessment manikins (e.g., Lang, Bradley, & Cuthbert, 1999).
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Results
All analyses included only those 22 younger and 21 older adults
whose data were fit well by the diffusion model (see Model Fit
section in Experiment 1 for details). RTs were excluded from the
analyses if they were either less than 100 ms or greater than 3,000
ms. The proportion of excluded responses was slightly greater for
older adults (M ⫽ .03, SD ⫽ .03) than for younger adults (M ⫽
.01, SD ⫽ .02), t(33.8) ⫽ 2.02, p ⬍ .053, after Satterthwaite
correction for unequal variances. A detailed discussion of the
effects of outlier RTs on the estimation of diffusion models was
provided elsewhere (Ratcliff & Tuerlinckx, 2002). For all analyses
of variance (ANOVAs) involving within-subjects factors, we used
multivariate tests (Wilks’s ␭) to correct for violation of the sphericity assumption (O’Brien & Kaiser, 1985). In these cases we
report 1 ⫺ ␭ as the multivariate equivalent of the univariate effect
size measure ␩2 (Tabachnick & Fidell, 1996).
Accuracy. We calculated the sensitivity index d⬘ from each
participant’s hit and false alarm rates (see Table 2) and performed
a 2 ⫻ 3 ⫻ 3 split-plot ANOVA on d⬘ with age (younger adults vs.
older adults) as a between-subjects variable and material (faces,
scenes, or words) and valence (negative, neutral, or positive) as
within-subjects variables. The omnibus analysis yielded a significant effect of material, F(2, 40) ⫽ 16.58, p ⬍ .001, 1 ⫺ ␭ ⫽ .45,
and a significant Material ⫻ Valence interaction, F(4, 38) ⫽ 3.25,
p ⫽ .022, 1 ⫺ ␭ ⫽ .25. No effects involving age were significant.
Additional contrasts indicated a significant effect of valence on d⬘
for faces, F(2, 40) ⫽ 7.21, p ⫽ .002, 1 ⫺ ␭ ⫽ .26, and a marginally
863
significant effect for scenes, F(2, 40) ⫽ 2.97, p ⫽ .063, 1 ⫺ ␭ ⫽
.13, but no significant effect for words. For faces, d⬘ was greater
for negative items than for neutral items and greater for neutral
items than for positive items, both F(1, 41) ⱖ 8.09, p ⱕ .007, ␩2 ⱖ
.16. For scenes, d⬘ was greater for negative items than for neutral
items, F(1, 41) ⫽ 6.09, p ⫽ .047, ␩2 ⫽ .12. However, d⬘ did not
differ significantly between neutral and positive scenes.
Reaction time. RT data are presented in Table 3. We conducted 2 ⫻ 3 ⫻ 3 split-plot ANOVAs with factors age, material,
and valence, separately for correct and incorrect RT. The age
effect was significant for both correct RT, F(1, 41) ⫽ 14.84, p ⬍
.001, ␩2 ⫽ .27, and error RT, F(1, 41) ⫽ 12.28, p ⬍ .001, ␩2 ⫽
.23, indicating age-related slowing on both measures. No other
effects were significant.
Diffusion Models
Parameters of Ratcliff’s (1978) diffusion model were estimated
with the fast-dm method (Voss & Voss, 2007, 2008; see also Voss,
Rothermund, & Voss, 2004). With fast-dm, the model’s parameters are estimated by optimizing the fit between empirical and
predicted cumulative RT distributions with the KolmogorovSmirnov (KS) test statistic. Unlike the more widely used ␹2-based
estimation procedures (Ratcliff & Tuerlinckx, 2002), the KS
method does not require binning response times and thereby ensures maximal information use. This feature of the KS method is
particularly valuable when the number of responses per model is
small (e.g., if models are fitted individually for each participant).
We estimated individual diffusion models for each participant
and material type (faces, scenes, words). Separate drift rates (␯)
and starting values (z) were estimated for each level of valence
(negative, neutral, and positive), yielding a total of 14 parameters
per participant and material type: nondecision time (t0); boundary
Table 2
Mean Hit Rates, False Alarm Rates, and d⬘
Hit rate
Item
Younger
adults
False alarm rate
Older
adults
Younger
adults
d⬘
Older
adults
Younger
adults
Older
adults
Experiment 1
Faces
Negative
Neutral
Positive
Scenes
Negative
Neutral
Positive
Words
Negative
Neutral
Positive
.63 (.12)
.57 (.16)
.46 (.16)
.61 (.20)
.61 (.20)
.59 (.23)
.25 (.16)
.21 (.10)
.28 (.13)
.31 (.27)
.41 (.21)
.48 (.25)
1.07 (0.52)
0.96 (0.41)
0.46 (0.34)
0.84 (0.49)
0.57 (0.42)
0.31 (0.32)
.81 (.10)
.77 (.14)
.71 (.13)
.82 (.11)
.85 (.12)
.77 (.16)
.08 (.07)
.14 (.08)
.11 (.11)
.18 (.15)
.19 (.14)
.29 (.20)
2.25 (0.60)
1.89 (0.67)
1.87 (0.60)
1.97 (0.51)
2.01 (0.71)
1.42 (0.61)
.81 (.11)
.77 (.12)
.85 (.11)
.74 (.16)
.70 (.21)
.78 (.18)
.18 (.10)
.12 (.08)
.18 (.12)
.18 (.18)
.11 (.13)
.21 (.19)
1.82 (0.58)
1.90 (0.56)
1.99 (0.67)
1.68 (0.81)
1.82 (0.74)
1.74 (0.77)
.22 (.12)
.16 (.10)
.30 (.20)
1.71 (0.54)
1.30 (0.39)
1.31 (0.54)
1.51 (0.54)
1.31 (0.53)
1.15 (0.69)
Experiment 2
Scenes
Negative
Neutral
Positive
Note.
.66 (.16)
.56 (.13)
.59 (.16)
.76 (.14)
.62 (.18)
.70 (.16)
Standard deviations are in parentheses.
.11 (.09)
.12 (.08)
.16 (.15)
SPANIOL, VOSS, AND GRADY
864
Table 3
Mean Median Reaction Times (in Milliseconds) for Correct and Error Responses
Correct reaction time
Item
Younger
adults
Error reaction time
Older adults
Younger
adults
Older adults
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Experiment 1
Faces
Negative
Neutral
Positive
Scenes
Negative
Neutral
Positive
Words
Negative
Neutral
Positive
950 (136)
956 (163)
938 (138)
1,164 (250)
1,198 (284)
1,179 (293)
1,035 (248)
984 (220)
1,012 (226)
1,267 (291)
1,278 (353)
1,260 (379)
950 (163)
962 (162)
1,008 (182)
1,187 (241)
1,181 (267)
1,231 (304)
1,116 (335)
1,060 (261)
1,106 (299)
1,512 (505)
1,420 (529)
1,320 (401)
913 (175)
914 (194)
885 (139)
1,080 (238)
1,037 (166)
1,063 (208)
1,042 (240)
977 (222)
1,115 (425)
1,326 (396)
1,274 (378)
1,379 (529)
1,119 (246)
1,156 (354)
1,124 (254)
1,590 (286)
1,502 (328)
1,536 (362)
Experiment 2
Scenes
Negative
Neutral
Positive
Note.
1,045 (187)
1,068 (194)
1,096 (203)
1,372 (277)
1,406 (327)
1,395 (261)
Standard deviations are in parentheses.
separation (a); starting points for negative, neutral, and positive items
(zNeg, zNeu, and zPos); drift for negative, neutral, and positive old
(target) items (␯Old/Neg, ␯Old/Neu, and ␯Old/Pos); drift for negative,
neutral, and positive new (distractor) items (␯New/Neg, ␯New/Neu,
␯New/Pos); and variance in nondecision time (st), starting point (sz),
and drift (s␯). The upper boundary was associated with old responses, and the lower boundary was associated with new responses (see Figure 1); consequently, positive drift rates were
expected for studied items, and negative drift rates were expected
for nonstudied items. We report model fit before describing the
analyses of the model parameters.
Model Fit
The probability of the KS statistic served as an index of model
fit (Voss et al., 2004). Separate models were estimated for each
combination of participant and material. Parameter z was also
allowed to vary as a function of valence, and parameter ␯ was
allowed to vary as a function of valence and old–new item status.
As a result, for each participant–material combination, we estimated six submodels (three levels of valence by two levels of item
status) and thus obtained six fit indices. The product of these six p
values—that is, the combined probability that all six models fit the
data—served as a global fit index for each participant–material
combination.3 A significant global fit index ( p ⬍ .05) indicated
poor model fit. Under this rationale, 3 older and 2 younger participants with poor model fit were excluded from the analyses. The
model provided a good fit for the remaining 22 younger and 21
older participants. With respect to background variables, the participants whose data were not fit well by the model did not differ
significantly from the rest of the sample, with one exception: Older
adults with poor model fit had significantly fewer years of education (M ⫽ 12.0, SD ⫽ 0) than older adults with good model fit
(M ⫽ 14.03, SD ⫽ 3.19), t(20) ⫽ ⫺5.0, p ⬍ .001, after Satterthwaite correction for unequal variances.
Model Parameters
Group means of the diffusion parameters are presented in Table 4.
The first set of analyses tested for effects of age on nondecision
time and boundary separation. As mentioned earlier, the presence
of age differences in these parameters has been well established,
with older adults typically displaying longer nondecision times and
increased boundary separation compared with younger adults (for
a review, see Spaniol, Madden, & Voss, 2006). We expected to
replicate these findings in the current data. In addition, we tested
for effects of material, and Age ⫻ Material interactions, on nondecision time and boundary separation. Valence was not a factor in
these analyses because we did not estimate nondecision time and
boundary separation separately for each level of valence. The
second set of analyses tested the research hypotheses of age and
valence effects, and possible material-related modulation of these
effects, on response bias and memory bias.
Nondecision time and boundary separation. A split-plot
ANOVA on nondecision time (parameter t0) with age as a
between-subjects variable and material as within-subjects variable
yielded a significant effect of age, F(1, 41) ⫽ 11.93, p ⬍ .001,
␩2 ⫽ .23. No effects involving material were significant. On
average, t0 was 114 ms longer for older adults than for younger
adults. The same analysis for boundary separation (parameter a)
yielded significant effects of age, F(1, 41) ⫽ 7.04, p ⫽ .011, ␩2 ⫽
3
The fit index p is not exact because the KS test is biased when the
tested distribution is matched in shape to the empirical data, as is the case
here.
AGING AND EMOTIONAL MEMORY
865
Table 4
Mean Diffusion Parameter Values
Parameter
Younger adults
Older adults
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Experiment 1
Faces
t0
a
z
Neg
Neu
Pos
␯Old
Neg
Neu
Pos
␯New
Neg
Neu
Pos
st
sz
s␯
Scenes
t0
a
z
Neg
Neu
Pos
␯Old
Neg
Neu
Pos
␯New
Neg
Neu
Pos
st
sz
s␯
Note.
0.62 (0.09)
1.49 (0.29)
0.77 (0.19)
1.69 (0.38)
0.79 (0.31)
0.73 (0.29)
0.70 (0.24)
0.87 (0.46)
0.85 (0.32)
0.96 (0.43)
0.50 (0.72)
0.35 (0.71)
⫺0.11 (0.57)
0.39 (0.67)
0.44 (0.85)
0.27 (0.98)
⫺1.02 (0.86)
⫺1.10 (0.72)
⫺0.75 (0.61)
0.29 (0.13)
0.23 (0.12)
0.34 (0.17)
⫺0.42 (0.86)
⫺0.28 (0.71)
⫺0.07 (1.06)
0.38 (0.19)
0.25 (0.15)
0.31 (0.23)
0.64 (0.09)
1.58 (0.37)
0.72 (0.17)
1.93 (0.43)
0.83 (0.35)
0.81 (0.39)
0.88 (0.30)
1.17 (0.45)
1.07 (0.49)
1.19 (0.37)
1.80 (1.55)
1.49 (1.15)
0.74 (0.73)
1.10 (0.99)
1.34 (1.03)
0.97 (0.89)
⫺1.88 (1.04)
⫺1.67 (1.57)
⫺1.80 (1.00)
0.26 (0.10)
0.23 (0.11)
0.39 (0.46)
⫺1.28 (0.74)
⫺1.12 (0.60)
⫺0.82 (0.78)
0.36 (0.19)
0.23 (0.15)
0.31 (0.18)
Parameter
Words
t0
a
z
Neg
Neu
Pos
␯Old
Neg
Neu
Pos
␯New
Neg
Neu
Pos
st
sz
s␯
Scenes
t0
a
z
Neg
Neu
Pos
␯Old
Neg
Neu
Pos
␯New
Neg
Neu
Pos
st
sz
s␯
Younger adults
Older adults
0.60 (0.10)
1.50 (0.40)
0.70 (0.13)
1.70 (0.37)
0.79 (0.26)
0.78 (0.33)
0.81 (0.26)
0.89 (0.30)
0.83 (0.33)
1.02 (0.30)
1.31 (0.94)
1.50 (1.16)
1.52 (0.93)
0.76 (0.65)
0.73 (1.06)
1.11 (1.15)
⫺1.36 (0.90)
⫺1.83 (0.79)
⫺1.31 (0.76)
0.18 (0.09)
0.34 (0.22)
0.37 (0.21)
Experiment 2
⫺1.45 (1.14)
⫺2.26 (1.31)
⫺1.31 (0.96)
0.26 (0.16)
0.28 (0.13)
0.40 (0.19)
0.67 (0.15)
1.87 (0.48)
0.80 (0.17)
2.27 (0.52)
4.09 (0.46)
1.03 (0.49)
1.13 (0.50)
1.48 (0.51)
1.32 (0.46)
1.37 (0.39)
0.56 (1.05)
0.19 (0.42)
0.18 (0.48)
0.50 (0.67)
0.18 (0.81)
0.45 (0.71)
⫺1.70 (0.78)
⫺1.41 (0.73)
⫺1.37 (0.73)
0.25 (0.19)
0.53 (0.26)
0.41 (0.26)
⫺1.01 (0.68)
⫺1.12 (0.69)
⫺0.74 (0.83)
0.39 (0.27)
0.55 (0.28)
0.37 (0.24)
Standard deviations are in parentheses. Neg ⫽ negative; Neu ⫽ neutral; Pos ⫽ positive.
.15, and material, F(2, 40) ⫽ 6.44, p ⫽ .004, 1 ⫺ ␭ ⫽ .24. Older
adults had greater values of a than younger adults, indicating more
conservative response settings. In addition, planned contrasts indicated that a was greater for scenes than for faces, F(1, 41) ⫽
12.39, p ⬍ .001, ␩2 ⫽ .22, and words, F(1, 41) ⫽ 6.38, p ⫽ .013,
␩2 ⫽ .15.
Response bias. We used z/a as a measure of response bias (see
Spaniol et al., 2006; Voss et al., 2004), with values greater than 0.5
indicating a bias to classify items as old and values less than 0.5
indicating a bias to classify items as new. In a split-plot analysis
with between-subjects factor age and within-subjects factors valence and material, only valence showed a significant effect, F(2,
40) ⫽ 6.64, p ⫽ .003, 1 ⫺ ␭ ⫽ .25. Planned contrasts revealed that
response bias was greater for emotional (negative and positive)
items than for neutral items, F(1, 41) ⱖ 3.89, p ⱕ .055, ␩2 ⱖ .09,
indicating that participants were more strongly biased toward old
responses for emotional items than for neutral items. Despite the
nonsignificant Age ⫻ Valence interaction, F(2, 40) ⫽ 1.15, p ⫽
.327, 1 ⫺ ␭ ⫽ .05, theoretical interest in this measure led us to
conduct additional contrasts to test for age differences in response
bias at each level of valence. There were no significant age
differences in response bias for negative and neutral items, F(1,
41) ⱖ .30, p ⱕ .589, ␩2 ⱕ .02, but there was a significant age
difference for positive items, F(1, 41) ⫽ 5.01, p ⫽ .030, ␩2 ⫽ .11,
indicating that older adults were more inclined to endorse positive
items than younger adults. Group means of the response bias
measure, averaged across materials, are presented in Figure 2.
Memory bias. The sum of the drift rates for studied and
unstudied items (␯Old ⫹ ␯New) served as a measure of memory
bias. Positive values of memory bias indicate familiarity bias,
whereas negative values indicate novelty bias. In a split-plot analysis with between-subjects factor age and within-subjects factors
valence and material, the following interactions were significant:
Age ⫻ Valence, F(2, 40) ⫽ 3.40, p ⫽ .043, 1 ⫺ ␭ ⫽ .15; Age ⫻
Material, F(2, 40) ⫽ 7.69, p ⫽ .002, 1 ⫺ ␭ ⫽ .28; and Valence ⫻
Material, F(4, 38) ⫽ 2.95, p ⫽ .033, 1 ⫺ ␭ ⫽ .24. None of the
main effects reached statistical significance. The Age ⫻ Valence
interaction held the most interest in the context of our hypotheses.
We probed this interaction by testing for age differences at each
level of valence. A marginally significant age difference was
SPANIOL, VOSS, AND GRADY
866
Response bias
Younger
Older
0.70
z/a
0.65
“Old”
Old
0.60
0.55
Negative
Neutral
Positive
0
“New”
Memory Bias
“Old”
0.10
νOld + νNew
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0 50
0.50
0
-0.10
-0.20
“New”
-0.30
-0.40
Negative
Neutral
Positive
Figure 2. Mean parameter values for response bias (z/a) and memory bias (␯Old ⫹ ␯New) for younger and older
adults in Experiment 1, across materials. Error bars represent standard errors.
present only for positive items, F(1, 41) ⫽ 3.79, p ⫽ .058, ␩2 ⫽
.08, indicating that older adults had higher values of memory bias
for positive items than younger adults. Younger adults showed
significant novelty bias for positive items, t(21) ⫽ 4.06, p ⬍ .001,
␩2 ⫽ .44, whereas older adults showed no significant memory bias
for these items, t(20) ⫽ .17, p ⫽ .865, ␩2 ⬍ .01. The results of the
omnibus analysis did not change when we included negative mood
scores as covariates. Group means of the memory bias measure,
averaged across materials, are presented in Figure 2.
We probed the interactions that involved the material variable
by performing separate ANOVAs of age and valence on the
memory bias measure for each material. The age effect was significant for faces, F(1, 41) ⫽ 6.20, p ⫽ .017, ␩2 ⫽ .13, and words,
F(1, 41) ⫽ 5.43, p ⫽ .025, ␩2 ⫽ .12, and marginally significant for
scenes, F(1, 41) ⫽ 3.55, p ⫽ .067, ␩2 ⫽ .08. For faces and scenes,
older adults were less biased to retrieve evidence favoring new
responses than younger adults, across all levels of valence. For
words, this pattern was reversed: Older adults were more biased to
retrieve evidence favoring new responses than younger adults.
Across the two age groups, valence had a significant effect on
memory bias for scenes only, F(2, 40) ⫽ 3.99, p ⬍ .026, 1 ⫺ ␭ ⫽
.17. Post hoc comparisons indicated that mean values of memory
bias were not significantly different for negative and neutral scenes
but were significantly lower (i.e., more negative) for positive
scenes, F(1, 41) ⫽ 5.57, p ⫽ .023, ␩2 ⫽ .11.
Variance parameters. The variability of nondecision times
(parameter st) was greater for older adults than for younger adults,
F(1, 41) ⫽ 7.60, p ⫽ .009, ␩2 ⫽ .16. There was also a significant
effect of material on st, F(2, 40) ⫽ 10.00, p ⬍ .001, 1 ⫺ ␭ ⫽ .33.
Additional analyses indicated that st did not differ for faces and
scenes but was significantly smaller for words, F(1, 41) ⫽ 12.19,
p ⬍ .001, ␩2 ⫽ .23. Starting point variability (parameter sz) and
drift rate variability (parameter s␯) showed no significant effects of
age, material, or their interaction.
Valence and Arousal Ratings
Mean ratings on the valence and arousal scales for faces, scenes,
and words used in this experiment are presented in the Appendix.
One additional older participant’s arousal ratings were not recorded because of equipment failure. We conducted separate splitplot ANOVAs on mean valence and arousal ratings, with age
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AGING AND EMOTIONAL MEMORY
(younger adults vs. older adults) as a between-subjects variable
and material (faces, scenes, or words) and valence (negative,
neutral, or positive) as within-subjects variables. For valence ratings, the only significant effect was valence, F(2, 45) ⫽ 59.52, p ⬍
.001, 1 ⫺ ␭ ⫽ .73. Planned contrasts indicated that ratings were
significantly lower for negative items than for neutral items, F(1,
46) ⫽ 86.53, p ⬍ .001, ␩2 ⫽ .63, and significantly lower for
neutral items than for positive items, F(1, 46) ⫽ 72.03, p ⬍ .001,
␩2 ⫽ .61. For arousal ratings, age was a significant factor, F(1,
45) ⫽ 8.45, p ⬍ .006, ␩2 ⫽ .16, as was valence, F(2, 44) ⫽ 10.32,
p ⬍ .001, 1 ⫺ ␭ ⫽ .32. These main effects were moderated by a
marginally significant Age ⫻ Material ⫻ Valence interaction, F(4,
42) ⫽ 2.51, p ⫽ .056, 1 ⫺ ␭ ⫽ .19. We probed this interaction by
performing separate ANOVAs for each level of valence, with
factors age and material. Age was a significant factor in each case,
all F(1, 45) ⱖ 4.91, p ⱕ .032, ␩2 ⱖ .10, indicating overall higher
arousal ratings for older adults. For neutral items, this main effect
of age was moderated by an Age ⫻ Material interaction, F(2,
45) ⫽ 6.21, p ⫽ .004, 1 ⫺ ␭ ⫽ .22. Follow-up t tests indicated that
older adults gave higher arousal ratings than younger adults to
neutral faces and words, t(45) ⱖ 1.95, p ⱕ .057, ␩2 ⱖ .08, but
there was no significant age difference in arousal ratings for
neutral scenes.
Discussion
The primary goal of Experiment 1 was to investigate effects of
age and emotional valence on two mechanisms affecting recognition memory: response bias, a motivational mechanism, and memory bias, a mnemonic mechanism. We used diffusion modeling
(Ratcliff, 1978) to measure the influence of these mechanisms on
younger and older adults’ performance in a recognition task that
included three types of stimuli (faces, scenes, and words). The
central result of Experiment 1 was an Age ⫻ Valence interaction
on memory bias. Memory bias was defined as the sum of the
diffusion drift rates for target and distractor items. The Age ⫻
Valence interaction on memory bias reflected the fact that positive
items elicited novelty bias for younger adults but not for older
adults.
The results suggest that older adults experienced greater overall
familiarity for positive items than younger adults. In addition,
although the Age ⫻ Valence interaction on response bias was
nonsignificant, the results offered some evidence that older adults
may have been more inclined than younger adults to endorse
positive items as old.
Experiment 2
The goal of Experiment 2 was twofold. First, we sought to
replicate the major finding from Experiment 1, the age difference
in memory bias for positive items, in an independent participant
sample. Second, we were interested in generalizing the scope of our
investigation to a scenario in which goal-directed control processes
(e.g., Mather & Carstensen, 2005) could operate more freely during
encoding. According to SST, these processes are critical for the
expression of age-related cognitive-affective changes. In Experiment 1, participants had performed a valence rating task during the
incidental encoding phase. This task ensured that younger and
older participants engaged in similar encoding operations across
867
the different stimulus materials. However, the rating task may have
also suppressed older adults’ chronically activated emotionregulation goals during encoding (see Mather & Carstensen,
2005). By contrast, in Experiment 2, participants were instructed to
“lean back and watch the images as you would watch a television”
(e.g., Charles et al., 2003). We anticipated lower levels of memory
performance under these conditions and therefore reduced the list
length, using only scene stimuli, which in Experiment 1 had
produced the highest levels of recognition memory. All other
aspects of the procedure were identical to those in Experiment 1.
Results
Analyses included only those 24 younger and 23 older adults
whose data were fit well by the diffusion model (see Model Fit
section in Experiment 2 for details). As in Experiment 1, RTs were
excluded from the analyses if they were either less than 100 ms or
greater than 3,000 ms. The proportion of excluded responses was
greater for older adults (M ⫽ .09, SD ⫽ .07) than for younger
adults (M ⫽ .03, SD ⫽ .05), t(37.2) ⫽ 3.08, p ⫽ .004, after
Satterthwaite correction for unequal variances.4
Accuracy. Hit rates, false alarm rates, and d⬘ are presented in
Table 2. A 2 ⫻ 3 split-plot ANOVA on d⬘ with age (younger adults
vs. older adults) as a between-subjects variable and valence (negative, neutral, or positive) as a within-subjects variable yielded a
marginally significant effect of valence, F(2, 44) ⫽ 3.03, p ⫽ .058,
1 ⫺ ␭ ⫽ .28. Additional contrasts indicated that across the age
groups, d⬘ was significantly higher for negative scenes than for
neutral and positive scenes, both t(46) ⱖ 3.83, p ⬍ .001, ␩2 ⱖ .24.
There was no significant difference in d⬘ for neutral and positive
scenes.
Reaction time. RT data are presented in Table 3. We conducted 2 ⫻ 3 split-plot ANOVAs with factors age and valence,
separately for correct and incorrect RT. The age effect was significant for both correct RT, F(1, 45) ⫽ 23.36, p ⬍ .001, ␩2 ⫽ .34,
and error RT, F(1, 45) ⫽ 31.11, p ⬍ .001, ␩2 ⫽ .41, indicating
age-related slowing on both measures. No other effects were
significant.
Model Fit
We estimated individual diffusion models for each participant
using the method described for Experiment 1. A significant global
fit index ( p ⬍ .05) was obtained for 1 younger and 1 older adult,
who were thus excluded from all subsequent analyses.
Model Parameters
Group means of the diffusion parameters are shown in Table 4.
The first set of analyses tested for effects of age on nondecision
time and boundary separation. The second set of analyses tested
the research hypotheses of age and valence effects on response
bias and memory bias.
Nondecision time and boundary separation. An independentsamples t test on nondecision time (parameter t0) with age as a
between-subjects variable was significant, t(45) ⫽ 2.78, p ⫽
4
The pattern of results for accuracy and RT did not change if very fast
(⬍100 ms) and very slow (⬎3,000 ms) RTs were included.
SPANIOL, VOSS, AND GRADY
.008, ␩2 ⫽ .15. Nondecision time was, on average, 128 ms
longer for older adults than for younger adults. The same
analysis for boundary separation (parameter a) was also significant, t(45) ⫽ 2.72, p ⫽ .009, ␩2 ⫽ .14. Older adults had greater
values of a than younger adults, indicating more conservative
response settings.
Response bias. A split-plot ANOVA on the response bias
measure z/a with factors age and valence yielded no significant
effects of age, F(1, 45) ⫽ 2.18, p ⫽ .15, ␩2 ⫽ .05, valence, F(2,
44) ⫽ 1.34, p ⫽ .272, 1 ⫺ ␭ ⫽ .06, or their interaction, F(2, 45) ⫽
.49, p ⫽ .615, 1 ⫺ ␭ ⫽ .02. Unlike in Experiment 1, tests for age
differences at each level of valence also failed to reveal significant
effects, t(45) ⱕ 1.69, p ⱖ .10, ␩2 ⱕ .06. Group means of z/a are
presented in Figure 3. To facilitate cross-experiment comparison,
Figure 3 also presents the response bias parameters for the scene
stimuli in Experiment 1. An ANOVA on these parameters also
failed to show significant effects of age, F(1, 41) ⫽ 3.46, p ⫽ .07,
␩2 ⫽ .08, valence, F(2, 40) ⫽ 2.68, p ⫽ .08, 1 ⫺ ␭ ⫽.12, or their
interaction, F(2, 40) ⫽ .16, p ⫽ .85, 1 ⫺ ␭ ⫽ .01.
Memory bias. As in Experiment 1, the sum of the drift rates for
studied and unstudied items (␯Old ⫹ ␯New) served as a measure of
memory bias. Group means of this measure are presented in Figure 3.
In a split-plot analysis with factors age and valence, only the main
effect of age was significant, F(1, 45) ⫽ 10.51, p ⫽ .002, ␩2 ⫽ .19,
indicating significantly higher (i.e., less negative) values of memory
bias for older adults than for younger adults. Although the Age ⫻
Valence interaction was nonsignificant, F(2, 44) ⫽ 1.26, p ⫽ .293,
1 ⫺ ␭ ⫽ .10, we conducted additional contrasts, motivated by the
Experiment 1 finding of a significant age difference in memory bias
for positive stimuli but not for negative or neutral stimuli. Compared
with younger adults, older adults had a significantly higher (i.e., less
negative) value on the memory bias measure for positive scenes,
t(45) ⫽ 3.04, p ⫽ .004, ␩2 ⫽ .17. Separate tests indicated that for
positive scenes, younger adults showed significant novelty bias,
t(23) ⫽ 6.84, p ⬍ .001, ␩2 ⫽ .67, whereas older adults showed no
significant memory bias, t(22) ⫽ 1.20, p ⫽ .242, ␩2 ⫽ .06. There
were no significant age differences in memory bias for negative and
neutral scenes, t(45) ⱕ 1.80, p ⱖ .08, ␩2 ⱕ .07. Including negative
mood and neuroticism scores as covariates in an analysis of covariance did not change the results of the omnibus analysis.
To facilitate cross-experiment comparison, Figure 3 also presents
the memory bias parameters for the scene stimuli in Experiment 1. An
Response bias
0.70
z/a
0.65
0.60
0.55
0.50
0.70
0
Negative
Neutral
Positive
z/a
0 65
0.65
“Old”
0.60
0.55
0.50
0
Negative
Neutral
Positive
“New”
Younger
Older
Memory bias
νOld + νNew
1.00
0.50
Negative Neutral
Positive
0
-0.50
-1.00
1.00
-1.50
“Old”
0 50
0.50
νOld + νNew
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868
0
Negative
Neutral
Positive
-0.50
-1.00
“New”
1.50
Figure 3. Mean parameter values for response bias (z/a) and memory bias (␯Old ⫹ ␯New) for younger and older
adults in Experiment 2 (larger graphs). For comparison, the same parameters are shown for the scene stimuli in
Experiment 1 (smaller graphs). Error bars represent standard errors.
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AGING AND EMOTIONAL MEMORY
ANOVA on these parameters revealed a significant effect of valence,
F(2, 40) ⫽ 3.99, p ⫽ .026, 1 ⫺ ␭ ⫽ .17, but no significant effects of
age, F(1, 41) ⫽ 3.46, p ⫽ .07, ␩2 ⫽ .08, or Age ⫻ Valence, F(2,
40) ⫽ 2.69, p ⫽ .08, 1 ⫺ ␭ ⫽ .12. Additional group comparisons at
each level of valence revealed an age difference for positive scenes,
t(41) ⫽ 2.81, p ⫽ .008, ␩2 ⫽ .16, indicating that older adults showed
significantly higher (i.e., less negative) values of memory bias for
positive scenes, compared with younger adults. Younger adults
showed significant novelty bias for positive scenes, t(21) ⫽ 3.88, p ⬍
.001, ␩2 ⫽ .42, whereas older adults showed no significant memory
bias for positive scenes, t(20) ⫽ .45, p ⫽ .45, ␩2 ⫽ .01. There were
no age differences in memory bias for negative and neutral scenes,
t(41) ⱕ .96, p ⱖ .34, ␩2 ⱕ .02.
Variance parameters. The range of nondecision times (parameter st) was greater for older adults than for younger adults, t(45) ⫽
2.09, p ⫽ .042, ␩2 ⫽ .09. Starting point variability (parameter sz) and
drift rate variability (parameter s␯) showed no significant age differences.
Valence and Arousal Ratings
Mean ratings of valence and arousal for the scenes stimuli, by
participants in Experiment 2, are presented in the Appendix. Separate split-plot ANOVAs of age and valence on mean ratings of
valence and arousal revealed significant effects of valence, both
F(2, 46) ⱖ 21.45, p ⬍ .001, 1 ⫺ ␭ ⱖ .48. Additional contrasts
indicated that valence ratings were significantly higher for positive
scenes, compared with neutral scenes, and for neutral scenes,
compared with negative scenes, all t(46) ⱖ 17.63, p ⬍ .001, ␩2 ⱖ
.87. Arousal ratings were higher for negative scenes, compared
with positive scenes, and for positive scenes, compared with
neutral scenes, all t(46) ⱖ 4.31, p ⬍ .001, ␩2 ⱖ .28. There were
no significant effects of age on valence or arousal ratings.
Discussion
In Experiment 2, participants engaged in incidental encoding
under “free viewing” instructions, which are thought to facilitate
the influence of chronically activated emotional goals on attentional selection and subsequent memory (e.g., Charles et al., 2003).
Despite this change in the procedure, the results of Experiment 2
were largely consistent with those of Experiment 1, in particular
with the results for scene stimuli in Experiment 1. The response
bias measure revealed no significant effects of age or valence. The
memory bias measure failed to show a significant Age ⫻ Valence
interaction, but individual contrasts—motivated by the Experiment
1 results—showed an age-related difference in memory bias for
positive (but not negative or neutral) materials. Similar to the
results of Experiment 1, positive scenes elicited novelty bias in
younger adults but elicited no memory bias in older adults.
869
adults to a diffusion model analyses (Ratcliff, 1978). Diffusion
modeling is more powerful than single-point signal detection analysis because it integrates accuracy and RT data, provides
goodness-of-fit indices for each individual, and permits a more
fine-grained analysis of the cognitive processes underlying retrieval performance (e.g., Spaniol et al., 2006). Our findings support the memory bias hypothesis, whereby age differences in
emotional long-term memory reflect memory retrieval rather than
response bias. Before discussing the implications of these findings
in more detail, we address the results of conventional analyses of
accuracy and RT measures.
Accuracy and RT Patterns
The analyses of the signal detection measure d⬘ yielded no
significant age difference in discriminability. This null effect is not
unusual in old–new recognition tasks, which typically give rise to
smaller age differences in accuracy than do tasks that require the
retrieval of specific contextual information (for a review, see
Zacks & Hasher, 2006). More importantly, we also did not observe
a significant Age ⫻ Valence interaction on d⬘ (for similar findings,
see Comblain et al., 2004; D’Argembeau & Van der Linden, 2004;
Grühn et al., 2005; Kensinger et al., 2002). Both age groups
demonstrated higher discriminability for negative stimuli than for
neutral and positive stimuli; only the verbal stimuli in Experiment
1 failed to give rise to a valence effect on discriminability.
As Mather and Carstensen (2005) suggested, Age ⫻ Valence
effects on memory accuracy may depend on the availability of
goal-directed attentional selection during encoding. In Experiment
1, we provided little opportunity for goal-directed selective encoding, by requiring participants to rate the valence of each stimulus
during the study phase of the experiment. However, in Experiment
2, participants were not distracted by a rating task during encoding.
Nevertheless, we failed to observe age differences in valence
effects even under these circumstances. Power analysis with
GPower (Erdfelder, Faul, & Buchner, 1996) indicates that the
statistical power to detect Age ⫻ Valence interactions in our
design was modest: over .90 for medium-sized effects but only
around .30 for small effects. It would thus be inappropriate to
interpret a null finding too strongly. A meta-analytic approach may
be best suited to shed light on the conditions that do and do not
give rise to Age ⫻ Valence effects on recognition memory.
Median RT showed the typical pattern of age-related slowing,
with no significant effects involving valence (Experiments 1 and
2) or material (Experiment 1). Neither the response bias hypothesis
nor the memory bias hypothesis can be evaluated on the basis of
accuracy and RT results, which confound mnemonic and motivational influences. These influences are difficult to disentangle
without more specific measures such as those provided by the
parameters of the diffusion model, which we discuss next.
General Discussion
The goal of this study was to investigate the cognitive mechanisms underlying age-related differences in the effects of emotional valence on long-term memory retrieval. In particular, we
sought to separate possible effects of age and valence on a motivational mechanism (response bias) from effects on a mnemonic
mechanism (memory bias). To this end, we submitted old–new
recognition data from two experiments with younger and older
Response Bias
According to the motivational hypothesis, older adults should
show a stronger bias to endorse positive stimuli during memory
retrieval than younger adults, to maximize positive affect. This
hypothesis led us to predict an Age ⫻ Valence interaction on
response bias. Response bias was operationalized as the relative
position of the starting point (parameter z) between the two deci-
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870
SPANIOL, VOSS, AND GRADY
sion boundaries of the diffusion model (see Figure 1). In Experiment 1, emotional items, regardless of stimulus material, elicited a
more liberal response tendency than neutral items, similar to
previous findings (Budson et al., 2006; Charles et al., 2003, Experiment 1; Kapucu et al., 2008). Furthermore, older adults
showed a more liberal response bias for positive items, compared
with younger adults, despite a nonsignificant Age ⫻ Valence
interaction. In Experiment 2, there were no significant effects of
age or valence on response bias. The lack of an Age ⫻ Valence
interaction on response bias, in both experiments, suggests that age
differences in motivational influences at retrieval, as captured by
the response bias measure, were not a major modulator of memory
performance in the current study, although this conclusion remains
somewhat tentative given the mixed findings in Experiment 1.
Memory Bias
According to the mnemonic hypothesis, positive and emotionally rewarding long-term memory representations should be particularly accessible for older adults, compared with younger adults.
This hypothesis led us to predict an Age ⫻ Valence interaction on
memory bias—the overall familiarity (or novelty) experienced for
recognition test items. Memory bias was operationalized as the
sum of the diffusion drift rates for target and distractor items.
Because target drift rates are positive and distractor drift rates are
negative (see Figure 1), the two drift rates sum to zero if their
absolute values are equal. Positive values of memory bias indicate
greater efficiency in retrieving evidence in favor of an old response
(familiarity bias), whereas negative values indicate greater efficiency in retrieving evidence in favor of a new response (novelty
bias). Consistent with the mnemonic hypothesis, the results of both
experiments showed an age difference in memory bias for positive
items. Positive items elicited novelty bias for younger adults but
not for older adults. This suggests that older adults experience
greater overall familiarity for positive items than younger adults,
consistent with an age-related increase in the relative accessibility
of positive information in long-term memory.
One question that arises from the analysis of the memory bias
measure is why, across groups and experimental conditions, we
tended to observe novelty bias rather than familiarity bias. In other
words, why were participants generally more efficient in detecting
new stimuli than in recognizing old stimuli? This is not a unique
observation; Ratcliff, Thapar, and McKoon (2004), for example,
also reported greater absolute values of drift for distractors than for
targets in an old–new recognition study with younger and older
adults, across a range of word frequencies. Recent neuroimaging
research (e.g., Rutishauser, Schuman, & Mamelak, 2008) has
identified dissociable brain correlates of familiarity and novelty
detection, suggesting that these may form two dimensions rather
than opposite ends of a continuum. Our finding of novelty bias
could thus be interpreted in terms of greater processing efficiency
of the neurocognitive system underlying novelty processing, compared with the system underlying familiarity processing.
Generalizing Across Materials
Response bias did not differ for the three stimulus materials in
Experiment 1. By contrast, there were significant interactions of
age and material, as well as valence and material, on memory bias,
confirming that this measure showed some sensitivity to differences in the mnemonic processing of faces, scenes, and words.
Across the age groups, valence had a significant impact on memory bias for scenes only, suggesting that emotional modulation of
long-term memory may in part depend on properties such as
imagery and distinctiveness (see also Talmi & Moscovitch, 2004).
Importantly, however, the three-way interaction (Age ⫻ Valence ⫻ Material) failed to reach statistical significance, indicating
that the effects of stimulus modality on the Age ⫻ Valence
interaction on memory bias were small or nonexistent.
Possible Mediators of Age ⫻ Valence Effects
Older participants in Experiment 1 rated the experimental items
more highly on arousal than did younger adults, despite ageequivalent valence ratings. Age differences in arousal could thus
have contributed to the Age ⫻ Valence interaction on memory
bias. However, we consider this unlikely because Experiment 2
yielded a similar pattern of results for memory bias, even though
arousal ratings were not significantly different for younger and
older adults. Furthermore, there was no Age ⫻ Valence interaction
on arousal ratings in Experiment 1.
We also collected measures of personality and mood variables
that could interact with emotion processing and memory (e.g.,
Grady et al., 2007; Mroczek & Kolarz, 1998; Rusting, 1999).
Reported negative mood differed significantly between the groups,
with younger adults reporting more negative mood than older
adults, in both experiments. Neuroticism scores in Experiment 2
also were higher for younger adults than for older adults, replicating previous findings (McCrae et al., 1999; Zonderman et al.,
1993). However, the pattern of age and valence effects on response
bias and memory bias did not change when negative mood and
neuroticism were included as covariates.
Conclusions
The current findings show that even in the absence of an
age-related positivity effect in accuracy and RT measures, a finergrained analysis of younger and older adults’ responses in the
recognition task yields evidence consistent with the predictions of
SST (Carstensen et al., 1999). Specifically, we have demonstrated
differential age-related modulation of memory bias—the overall
tendency to experience items as familiar or as novel—as a function
of emotional valence, across a variety of stimulus materials (Experiment 1) and after both task-directed (Experiment 1) and openended (Experiment 2) encoding. For positive items, younger adults
exhibited novelty bias, whereas older adults did not. Together,
these findings highlight the usefulness of formal modeling and, in
particular, of the diffusion model (Ratcliff, 1978) in investigations
of age-related change in cognitive and affective processing.
Although our results provide no strong support for an agerelated motivational shift at the response bias level, the mnemonic
effects that we observed may be an indirect consequence of an
age-related change in emotional goals, mediated by improved
accessibility of positive long-term memories (see also Conway,
2005). However, the cognitive mechanisms involved in the influence of emotional goals on the retrieval of long-term memory
representations remain to be specified in future research.
AGING AND EMOTIONAL MEMORY
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Appendix
Mean Valence and Arousal Ratings
Valence rating
Item
Younger
adults
Arousal rating
Older
adults
Younger
adults
Older
adults
Experiment 1
Faces
Negative
Neutral
Positive
Scenes
Negative
Neutral
Positive
Words
Negative
Neutral
Positive
1.88 (0.36)
3.00 (0.18)
4.18 (0.21)
1.89 (0.31)
3.98 (0.21)
4.27 (0.51)
2.81 (0.93)
1.60 (0.54)
2.79 (0.85)
3.36 (0.77)
2.28 (0.80)
3.19 (0.93)
1.74 (0.31)
3.04 (0.23)
4.20 (0.30)
1.76 (0.34)
3.02 (0.19)
4.38 (0.36)
3.20 (1.03)
1.61 (0.55)
2.80 (0.77)
3.66 (0.79)
1.80 (0.63)
3.46 (1.06)
1.78 (0.28)
3.02 (0.26)
4.08 (0.28)
1.71 (0.36)
3.06 (0.16)
4.22 (0.31)
2.99 (0.96)
1.68 (0.52)
3.11 (0.53)
3.53 (0.69)
2.01 (0.64)
3.47 (0.79)
3.30 (0.71)
1.41 (0.37)
2.58 (0.75)
3.09 (0.92)
1.51 (0.52)
2.81 (1.08)
Experiment 2
Scenes
Negative
Neutral
Positive
1.85 (0.31)
3.01 (0.08)
4.09 (0.34)
1.89 (0.32)
3.01 (0.11)
4.14 (0.51)
Note. Ratings were obtained on a Likert scale ranging from 1 (negative valence, low arousal) to 5 ( positive valence, high
arousal). Standard deviations are in parentheses.
Received May 24, 2007
Revision received September 18, 2008
Accepted September 18, 2008 䡲