Apa sample paper experiment paper assignment help

Running head: EFFECTS OF AGE ON DETECTION OF EMOTION 1
Effects of Age on Detection of Emotional Information
Christina M. Leclerc and Elizabeth A. Kensinger
Boston College
Author Note
This research was supported by National Science Foundation Grant BCS 0542694
awarded to Elizabeth A. Kensinger.
Correspondence concerning this article should be addressed to Christina M. Leclerc,
Department of Psychology, Boston College, McGuinn Hall, Room 512, 140 Commonwealth
Avenue, Chestnut Hill, MA 02467. Email: christina.leclerc.1@bc.edu
Christina M. Leclerc and Elizabeth A. Kensinger, Department of Psychology,
Boston College.
Author Note
arch was supported by National Science Foundation Grant BCS 0542694
beth A. Kensinger.
ndence concerning this article should be addressed to Christina M. Leclerc,
sychology, Boston College, McGuinn Hall, Room 512, 140 Commonwealth
ut Hill, MA 02467. Email: christina.leclerc.1@bc.edu
M. Leclerc and Elizabeth A. Kensinger, Department of Psychology,
Writing the abstract, 2.04
Establishing a title, 2.01; Preparing the
manuscript for submission, 8.03
Formatting the author name (byline) and
institutional affiliation, 2.02, Table 2.1
Double-spaced manuscript,
Times Roman typeface,
1-inch margins, 8.03
Elements of an author note, 2.03
EFFECTS OF AGE ON DETECTION OF EMOTION 2
Abstract
Age differences were examined in affective processing, in the context of a visual search task.
Young and older adults were faster to detect high arousal images compared with low arousal and
neutral items. Younger adults were faster to detect positive high arousal targets compared with
other categories. In contrast, older adults exhibited an overall detection advantage for emotional
images compared with neutral images. Together, these findings suggest that older adults do not
display valence-based effects on affective processing at relatively automatic stages.
Keywords: aging, attention, information processing, emotion, visual search
Figure 2.1. Sample One-Experiment Paper (The numbers refer to numbered
sections in the Publication Manual.)
Paper adapted from “Effects of Age on Detection of Emotional Information,” by C. M. Leclerc and E. A. Kensinger,
2008, Psychology and Aging, 23, pp. 209–215. Copyright 2008 by the American Psychological Association.

Effects of Age on Detection of Emotional Information
Frequently, people encounter situations in their environment in which it is impossible to
attend to all available stimuli. It is therefore of great importance for one’s attentional processes to
select only the most salient information in the environment to which one should attend. Previous
research has suggested that emotional information is privy to attentional selection in young
adults (e.g.,
& Tapia, 2004; Nummenmaa, Hyona, & Calvo, 2006), an obvious service to evolutionary drives
to approach rewarding situations and to avoid threat and danger (Davis & Whalen, 2001; Dolan
& Vuilleumier, 2003; Lang, Bradley, & Cuthbert, 1997; LeDoux, 1995).
For example, Ohman, Flykt, and Esteves (2001)presented participants with 3 × 3visual
arrays with images representing four categories (snakes, spiders, flowers, mushrooms). In half
the arrays, all nine images were from the same category, whereas in the remaining half of the
arrays, eight images were from one category and one image was from a different category (e.g.,
eight flowers and one snake). Participants were asked to indicate whether the matrix included a
discrepant stimulus. Results indicated that fear-relevant images were more quickly detected than
fear-irrelevant items, and larger search facilitation effects were observed for participants who
were fearful of the stimuli. A similar pattern of results has been observed when examining the
attention-grabbing nature of negative facial expressions, with threatening faces (including those
not attended to) identified more quickly than positive or neutral faces (Eastwood, Smilek,&
Merikle, 2001; Hansen & Hansen, 1988). The enhanced detection of emotional information is
not limited to threatening stimuli; there is evidence that any high-arousing stimulus can be
detected rapidly, regardless of whether it is positively or negatively valenced (Anderson, 2005;
Anderson, 2005; Calvo & Lang, 2004; Carretie, Hinojosa, Marin-Loeches, Mecado,
ant stimulus. Results indicated that fearr-rr relevant images were more quickly detected than
elevant items, and larger search facilitation effects were observed for participants whoaa
arful of the stimuli. A similar pattern of results has been observed when examining the
n-grabbing nature of negative facial expressions, with threatening faces (including those
nded to) identified more quickly than positive or neutral faces (Eastwood, Smilek,&
e, 2001; Hansen & Hansen, 1988). The enhanced detection of emotional information is
ited to threatening stimuli; there is evidence that any high-arousing stimulus can be
d rapidly, regardless of whether it is positively or negatively valenced (Anderson,(( 2005;55
Calvo & Lang, 2004; Carretie et al., 2004; Juth, Lundqvist, Karlsson, & Ohman, 2005;
Nummenmaa et al., 2006).
From this research, it seems clear that younger adults show detection benefits for
arousing information in the environment. It is less clear whether these effects are preserved
across the adult life span. The focus of the current research is on determining the extent to which
aging influences the early, relatively automatic detection of emotional information.
Regions of the brain thought to be important for emotional detection remain relatively
intact with aging (reviewed by Chow& Cummings, 2000). Thus, it is plausible that the detection
of emotional information remains relatively stable as adults age. However, despite the
preservation of emotion-processing regions with age (or perhaps because of the contrast between
the preservation of these regions and age-related declines in cognitive-processing regions; Good
et al., 2001; Hedden & Gabrieli, 2004; Ohnishi, Matsuda, Tabira,Asada, & Uno, 2001; Raz,
2000; West, 1996), recent behavioral research has revealed changes that occur with aging in the
regulation and processing of emotion. According to the socioemotional selectivity theory
(Carstensen, 1992), with aging, time is perceived as increasingly limited, and as a result, emotion
regulation becomes a primary goal (Carstensen, Isaacowitz, & Charles, 1999). According to
socioemotional selectivity theory, age is associated with an increased motivation to derive
emotional meaning from life and a simultaneous decreasing motivation to expand one’s
knowledge base. As a consequence of these motivational shifts, emotional aspects of the
Writing the introduction, 2.05
Ordering citations within
the same parentheses, 6.16
Selecting
the correct
tense, 3.18
Continuity in presentation
of ideas, 3.05
Citing one
work by six
or more
authors, 6.12
No capitalization in
naming theories, 4.16
Numbers
expressed
in words,
4.32
Numbers that represent
statistical or mathematical
functions, 4.31
Use of hyphenation for
compound words, 4.13,
Table 4.1
Figure 2.1. Sample One-Experiment Paper (continued)

To maintain positive affect in the face of negative age-related change (e.g., limited time
remaining, physical and cognitive decline), older adults may adopt new cognitive strategies. One
such strategy, discussed recently, is the positivity effect (Carstensen &Mikels, 2005), in which
older adults spend proportionately more time processing positive emotional material and less
time processing negative emotional material. Studies examining the influence of emotion on
memory (Charles, Mather, & Carstensen, 2003; Kennedy, Mather, & Carstensen, 2004) have
found that compared with younger adults, older adults recall proportionally more positive
information and proportionally less negative information. Similar results have been found when
examining eye-tracking patterns: Older adults looked at positive images longer than younger
adults did, even when no age differences were observed in looking time for negative stimuli
(Isaacowitz, Wadlinger, Goren, & Wilson, 2006). However, this positivity effect has not gone
uncontested; some researchers have found evidence inconsistent with the positivity effect (e.g.,
Grühn, Smith, & Baltes, 2005; Kensinger, Brierley, Medford, Growdon, & Corkin, 2002).
Based on this previously discussed research, three competing hypotheses exist to explain
age differences in emotional processing associated with the normal aging process. First,
emotional information may remain important throughout the life span, leading to similarly
facilitated detection of emotional information in younger and older adults. Second, with aging,
emotional information may take on additional importance, resulting in older adults’ enhanced
detection of emotional information in their environment. Third, older adults may focus
principally on positive emotional information and may show facilitated detection of positive, but
not negative, emotional information.
The primary goal in the present experiment was to adjudicate among these alternatives.
To do so, we employed a visual search paradigm to assess young and older adults’ abilities to
motional processing associated with the normal aging process. First,
n may remain important throughout the life span, leading to similarly
of emotional information in younger and older adults. Second, with aging,
n may take on additional importance, resulting in older adults’ enhanced
al information in their environment. Third, older adults may focus
e emotional information and may show facilitated detection of positive, but
nal information.
goal in the present experiment was to adjudicate among these alternatives.
ed a visual search paradigm to assess young and older adults’ abilities to
rapidly detect emotional information. We hypothesized that on the whole, older adults would be
slower to detect information than young adults would be (consistent with Hahn, Carlson, Singer,
& Gronlund, 2006; Mather & Knight, 2006); the critical question was whether the two age
groups would show similar or divergent facilitation effects with regard to the effects of emotion
on item detection. On the basis of the existing literature, the first two previously discussed
hypotheses seemed to be more plausible than the third alternative. This is because there is reason
to think that the positivity effect may be operating only at later stages of processing (e.g.,
strategic, elaborative, and emotion regulation processes) rather than at the earlier stages of
processing involved in the rapid detection of information (see Mather & Knight, 2005, for
discussion). Thus, the first two hypotheses, that emotional information maintains its importance
across the lifespan or that emotional information in general takes on greater importance with
age, seemed particularly applicable to early stages of emotional processing.
Indeed, a couple of prior studies have provided evidence for intact early processing of
emotional facial expressions with aging. Mather and Knight (2006)examined young and older
adults’ abilities to detect happy, sad, angry, or neutral faces presented in a complex visual array.
Mather and Knight found that like younger adults, older adults detected threatening faces more
quickly than they detected other types of emotional stimuli. Similarly, Hahn et al. (2006)also
found no age differences in efficiency of search time when angry faces were presented in an
array of neutral faces, compared with happy faces in neutral face displays. When angry faces,
compared with positive and neutral faces, served as nontarget distractors in the visual search
arrays, however, older adults were more efficient in searching, compared with younger adults,
Capitalization of words
beginning a sentence after
a colon, 4.14
Using the colon between
two grammatically
complete clauses, 4.05
Using the semicolon to
separate two independent
clauses not joined by
a conjunction, 4.04
Using the comma between
elements in a series, 4.03
Punctuation with citations
in parenthetical material,
6.21
Citing references in text,
inclusion of year within
paragraph, 6.11, 6.12
Hypotheses and their
correspondence to research
design, Introduction, 2.05
Prefixes and
suffixes that
do not require
hyphens,
Table 4.2

negative stimuli were not of equivalent arousal levels (fearful faces typically are more arousing
than happy faces; Hansen & Hansen, 1988). Given that arousal is thought to be a key factor in
modulating the attentional focus effect (Hansen & Hansen, 1988; Pratto & John, 1991; Reimann
& McNally, 1995), to more clearly understand emotional processing in the context of aging, it is
necessary to include both positive and negative emotional items with equal levels of arousal.
In the current research, therefore, we compared young and older adults’ detection of four
categories of emotional information (positive high arousal, positive low arousal, negative high
arousal, and negative low arousal) with their detection of neutral information. The positive and
negative stimuli were carefully matched on arousal level, and the categories of high and low
arousal were closely matched on valence to assure that the factors of valence (positive, negative)
and arousal (high, low) could be investigated independently of one another. Participants were
presented with a visual search task including images from these different categories (e.g., snakes,
cars, teapots). For half of the multi-image arrays, all of the images were of the same item, and for
the remaining half of the arrays, a single target image of a different type from the remaining
items was included. Participants were asked to decide whether a different item was included in
the array, and their reaction times were recorded for each decision. Of primary interest were
differences in response times (RTs) based on the valence and arousal levels of the target
categories. We reasoned that if young and older adults were equally focused on emotional
information, then we would expect similar degrees of facilitation in the detection of emotional
stimuli for the two age groups. By contrast, if older adults were more affectively focused than
were younger adults, older adults should show either faster detection speeds for all of the
emotional items (relative to the neutral items) than shown by young adults or greater facilitation
g y , g ,
single target image of a different type from the remaining
were asked to decide whether a different item was included in
were recorded for each decision. Of primary interest were
) based on the valence and arousal levels of the target
ung and older adults were equally focused on emotional
t similar degrees of facilitation in the detection of emotional
contrast, if older adults were more affectively focused than
should show either faster detection speeds for all of the
utral items) than shown by young adults or greater facilitation
for the arousing items than shown by the young adults (resulting in an interaction between age
and arousal).
Method
Participants
Younger adults (14 women, 10 men, Mage = 19.5 years, age range: 18–22 years) were
recruited with flyers posted on the Boston College campus. Older adults (15 women, nine men,
Mage = 76.1 years, age range: 68–84 years) were recruited through the Harvard Cooperative on
Aging (see Table 1, for demographics and test scores).1
Participants were compensated $10 per
hour for their participation. There were 30 additional participants, recruited in the same way as
described above, who provided pilot rating values: five young and five old participants for the
assignment of items within individual categories (i.e., images depicting cats), and 10 young and
10 old participants for the assignment of images within valence and arousal categories. All
participants were asked to bring corrective eyewear if needed, resulting in normal or corrected
to normal vision for all participants.
Materials and Procedure
The visual search task was adapted from Ohman et al. (2001). There were 10 different
types of items (two each of five Valence × Arousal categories: positive high arousal, positive low
arousal, neutral, negative low arousal, negative high arousal), each containing nine individual
exemplars that were used to construct 3 × 3 stimulus matrices. A total of 90 images were used,
each appearing as a target and as a member of a distracting array. A total of 360 matrices were
presented to each participant; half contained a target item (i.e., eight items of one type and one
target item of another type) and half did not (i.e., all nine images of the same type). Within the
Prefixed words that
require hyphens,
Table 4.3
Using abbreviations, 4.22; Explanation
of abbreviations, 4.23; Abbreviations
used often in APA journals, 4.25;
Plurals of abbreviations, 4.29
Elements of the Method
section, 2.06; Organizing
a manuscript with levels
of heading, 3.03
Using numerals to express
numbers representing age, 4.31
Identifying
subsections
within the
Method
section, 2.06
Participant (subject)
characteristics,
Method, 2.06

Running head: EFFECTS OF AGE ON DETECTION OF EMOTION 10
selected such that the arousal difference between positive low arousal and positive high arousal
was equal to the difference between negative low arousal and negative high arousal.
Similarity ratings. Each item was rated for within-category and between-categories
similarity. For within-category similarity, participants were shown a set of exemplars (e.g., a set
of mushrooms) and were asked to rate how similar each mushroom was to the rest of the
mushrooms, on a 1 (entirely dissimilar) to 7 (nearly identical) scale. Participants made these
ratings on the basis of overall similarity and on the basis of the specific visual dimensions in
which the objects could differ (size, shape, orientation). Participants also rated how similar
objects of one category were to objects of another category (e.g., how similar the mushrooms
were to the snakes). Items were selected to assure that the categories were equated on within-
category and between-categories similarity of specific visual dimensions as well as for the
overall similarity of the object categories (ps > .20). For example, we selected particular
mushrooms and particular cats so that the mushrooms were as similar to one another as were the
cats (i.e., within-group similarity was held constant across the categories). Our object selection
also assured that the categories differed from one another to a similar degree (e.g., that the
mushrooms were as similar to the snakes as the cats were similar to the snakes).
Procedure
Each trial began with a white fixation cross presented on a black screen for 1,000 ms; the
matrix was then presented, and it remained on the screen until a participant response was
recorded. Participants were instructed to respond as quickly as possible with a button marked yes
if there was a target present, or a button marked no if no target was present. Response latencies
and accuracy for each trial were automatically recorded with E-Prime (Version 1.2) experimental
Running head: EFFECTS OF AGE ON DETECTION OF EMOTIONRR
selected such that the arousal difference between positive low arousal and positi
was equal to the difference between negative low arousal and negative high arou
Similarity ratings. Each item was rated for within-category and between
similarity. For within-category similarity, participants were shown a set of exem
of mushrooms) and were asked to rate how similar each mushroom was to the re
mushrooms, on a 1 (entirely dissimilar) to 7 (nearly identical(( ) scale. Participants
ratings on the basis of overall similarity and on the basis of the specific visual di
which the objects could differ (size, shape, orientation). Participants also rated h
objects of one category were to objects of another category (e.g., how similar the
were to the snakes). Items were selected to assure that the categories were equate
category and between-categories similarity of specific visual dimensions as well
overall similarity of the object categories (p(( s > .20). For example, we selected pa
h d ti l t th t th h i il t
matrix. Within the 180 target trials, each of the five emotion categories (e.g., positive high
arousal, neutral, etc.) was represented in 36 trials. Further, within each of the 36 trials for each
emotion category, nine trials were created for each of the combinations with the remaining four
other emotion categories (e.g., nine trials with eight positive high arousal items and one neutral
item). Location of the target was randomly varied such that no target within an emotion category
was presented in the same location in arrays of more than one other emotion category (i.e., a
negative high arousal target appeared in a different location when presented with positive high
arousal array images than when presented with neutral array images).
The items within each category of grayscale images shared the same verbal label (e.g.,
mushroom, snake), and the items were selected from online databases and photo clipart
packages. Each image depicted a photo of the actual object. Ten pilot participants were asked to
write down the name corresponding to each object; any object that did not consistently generate
the intended response was eliminated from the set. For the remaining images, an additional 20
pilot participants rated the emotional valence and arousal of the objects and assessed the degree
of visual similarity among objects within a set (i.e., how similar the mushrooms were to one
another) and between objects across sets (i.e., how similar the mushrooms were to the snakes).
Valence and arousal ratings. Valence and arousal were judged on 7-point scales (1 =
negative valence or low arousal and 7 = positive valence or high arousal). Negative objects
received mean valence ratings of 2.5 or lower, neutral objects received mean valence ratings of
3.5 to 4.5, and positive objects received mean valence ratings of 5.5 or higher. High arousal
objects received mean arousal ratings greater than 5, and low arousal objects (including all
neutral stimuli) received mean arousal ratings of less than 4. We selected categories for which
both young and older adults agreed on the valence and arousal classifications, and stimuli were
Latin abbreviations, 4.26
Numbers expressed in words
at beginning of sentence, 4.32
Italicization of anchors
of a scale, 4.21

software. Before beginning the actual task, participants performed 20 practice trials to assure
compliance with the task instructions.
Results
Analyses focus on participants’ RTs to the 120 trials in which a target was present and
was from a different emotional category from the distractor (e.g., RTs were not included for
arrays containing eight images of a cat and one image of a butterfly because cats and butterflies
are both positive low arousal items). RTs were analyzed for 24 trials of each target emotion
category. RTs for error trials were excluded (less than 5% of all responses) as were RTs that
were ±3SD from each participant’s mean (approximately 1.5% of responses). Median RTs were
then calculated for each of the five emotional target categories, collapsing across array type (see
Table 2 for raw RT values for each of the two age groups). This allowed us to examine, for
example, whether participants were faster to detect images of snakes than images of mushrooms,
regardless of the type of array in which they were presented. Because our main interest was in
examining the effects of valence and arousal on participants’ target detection times, we created
scores for each emotional target category that controlled for the participant’s RTs to detect
neutral targets (e.g., subtracting the RT to detect neutral targets from the RT to detect positive
high arousal targets). These difference scores were then examined with a 2 × 2 × 2 (Age [young,
older] × Valence [positive, negative] × Arousal [high, low]) analysis of variance (ANOVA). This
ANOVA revealed only a significant main effect of arousal, F(1, 46) = 8.41,p = .006, ηp
2
= .16,
with larger differences between neutral and high arousal images (M = 137) than between neutral
and low arousal images (M = 93; i.e., high arousal items processed more quickly across both age
groups compared with low arousal items; see Figure 1). There was no significant main effect for
valence, nor was there an interaction between valence and arousal. It is critical that the analysis
Symbols, 4.45;
Numbers, 4.31
Abbreviations
accepted as
words, 4.24
Numbering and discussing
figures in text, 5.05
Nouns followed
by numerals or
letters, 4.17
Reporting
p values,
decimal
fractions,
4.35
Statistical symbols,
4.46, Table 4.5
Elements of the
Results section, 2.07

revealed only a main effect of age but no interactions with age. Thus, the arousal-mediated
effects on detection time appeared stable in young and older adults.
The results described above suggested that there was no influence of age on the
influences of emotion. To further test the validity of this hypothesis, we submitted the RTs to the
five categories of targets to a 2 × 5 (Age [young, old] × Target Category [positive high arousal,
positive low arousal, neutral, negative low arousal, negative high arousal]) repeated measures
ANOVA.2
Both the age group, F(1, 46) = 540.32,p < .001, ηp
2
= .92, and the ta rget category,
F(4, 184) = 8.98, p < .001, ηp
2
= .16, main effects were significant, as well as the Age Group ×
Target Category interaction, F(4, 184) = 3.59,p = .008, ηp
2
= .07. This interaction appeared to
reflect the fact that for the younger adults, positive high arousal targets were detected faster than
targets from all other categories, ts(23) < –1.90,p < .001, with no other target categories
differing significantly from one another (although there were trends for negative high arousal
and negative low arousal targets to be detected more rapidly than neutral targets (p < .12). For
older adults, all emotional categories of targets were detected more rapidly than were neutral
targets, ts(23) > 2.56, p < .017, and RTs to the different emotion categories of targets did not
differ significantly from one another. Thus, these results provided some evidence that older
adults may show a broader advantage for detection of any type of emotional information,
whereas young adults’ benefit may be more narrowly restricted to only certain categories of
emotional information.
Discussion
As outlined previously, there were three plausible alternatives for young and older adults’
performance on the visual search task: The two age groups could show a similar pattern of
enhanced detection of emotional information, older adults could show a greater advantage for
Elements of the
Discussion section, 2.08
Statistics
in text, 4.44
Capitalize effects
or variables when
they appear with
multiplication
signs, 4.20
Spacing, alignment,
and punctuation of
mathematical copy, 4.46

emotional detection than young adults, or older adults could show a greater facilitation than
young adults only for the detection of positive information. The results lent some support to the
first two alternatives, but no evidence was found to support the third alternative.
In line with the first alternative, no effects of age were found when the influence of
valence and arousal on target detection times was examined; both age groups showed only an
arousal effect. This result is consistent with prior studies that indicated that arousing information
can be detected rapidly and automatically by young adults (Anderson, Christoff,Panitz, De
Rosa, & Gabrieli, 2003; Ohman & Mineka, 2001) and that older adults, like younger adults,
continue to display a threat detection advantage when searching for negative facial targets in
arrays of positive and neutral distractors (Hahn et al., 2006; Mather & Knight, 2006). Given the
relative preservation of automatic processing with aging (Fleischman, Wilson, Gabrieli, Bienias,
& Bennett, 2004; Jennings & Jacoby, 1993), it makes sense that older adults would remain able
to take advantage of these automatic alerting systems for detecting high arousal information.
However, despite the similarity in arousal-mediated effects on detection between the two
age groups, the present study did provide some evidence for age-related change (specifically,
age-related enhancement) in the detection of emotional information. When examining RTs for
the five categories of emotional targets, younger adults were more efficient in detecting positive
high arousal images (as presented in Table 2), whereas older adults displayed an overall
advantage for detecting all emotional images compared with neutral images. This pattern
suggests a broader influence of emotion on older adults’ detection of stimuli, providing support
for the hypothesis that as individuals age, emotional information becomes more salient.
It is interesting that this second set of findings is clearly inconsistent with the hypothesis
that the positivity effect in older adults operates at relatively automatic stages of information
nd neutral distractors (Hahn et al., 2006; Mather & Knight, 2006). Given the66
n of automatic processing with aging (Fleischman, Wilson, Gabrieli, Bienias,
nnings & Jacoby, 1993), it makes sense that older adults would remain able33
f these automatic alerting systems for detecting high arousal information.
espite the similarity in arousal-mediated effects on detection between the two
ent study did provide some evidence for age-related change (specifically,
ment) in the detection of emotional information. When examining RTs for
of emotional targets, younger adults were more efficient in detecting positive
(as presented in Table 2), whereas older adults displayed an overall22
ting all emotional images compared with neutral images. This pattern
nfluence of emotion on older adults’ detection of stimuli, providing support
hat as individuals age, emotional information becomes more salient.
ng that this second set of findings is clearly inconsistent with the hypothesis
ffect in older adults operates at relatively automatic stages of information
processing, given that no effects of valence were observed in older adults’ detection speed. In the
present study, older adults were equally fast to detect positive and negative information,
consistent with prior research that indicated that older adults often attend equally to positive and
negative stimuli (Rosler et al., 2005). Although the pattern of results for the young adults has
differed across studies—in the present study and in some past research, young adults have shown
facilitated detection of positive information (e.g., Anderson, 2005; Calvo & Lang, 2004; Carretie
et al., 2004; Juth et al., 2005; Nummenmaa et al., 2006), whereas in other studies, young adults
have shown an advantage for negative information (e.g., Armony & Dolan, 2002; Hansen &
Hansen, 1988; Mogg, Bradley,de Bono, & Painter, 1997; Pratto & John, 1991; Reimann &
McNally, 1995; Williams, Mathews, &MacLeod, 1996)—what is important to note is that the
older adults detected both positive and negative stimuli at equal rates. This equivalent detection
of positive and negative information provides evidence that older adults display an advantage for
the detection of emotional information that is not valence-specific.
Thus, although younger and older adults exhibited somewhat divergent patterns of
emotional detection on a task reliant on early, relatively automatic stages of processing, we
found no evidence of an age-related positivity effect. The lack of a positivity focus in the older
adults is in keeping with the proposal (e.g., Mather & Knight, 2006) that the positivity effect
does not arise through automatic attentional influences. Rather, when this effect is observed in
older adults, it is likely due to age-related changes in emotion regulation goals that operate at
later stages of processing (i.e., during consciously controlled processing), once information has
been attended to and once the emotional nature of the stimulus has been discerned.
Although we cannot conclusively say that the current task relies strictly on automatic
processes, there are two lines of evidence suggesting that the construct examined in the current
Clear statement of support or
nonsupport of hypotheses,
Discussion, 2.08
Use of an em dash to
indicate an interruption
in the continuity of a
sentence, 4.06;
Description of an
em dash, 4.13

research examines relatively automatic processing. First, in their previous work, Ohman et al.
(2001) compared RTs with both 2 × 2 and 3 × 3 arrays. No significant RT differences based on
the number of images presented in the arrays were found. Second, in both Ohman et al.’s (2001)
study and the present study, analyses were performed to examine the influence of target location
on RT. Across both studies, and across both age groups in the current work, emotional targets
were detected more quickly than were neutral targets, regardless of their location. Together,
these findings suggest that task performance is dependent on relatively automatic detection
processes rather than on controlled search processes.
Although further work is required to gain a more complete understanding of the age-
related changes in the early processing of emotional information, our findings indicate that
young and older adults are similar in their early detection of emotional images. The current
study provides further evidence that mechanisms associated with relatively automatic processing
of emotional images are well maintained throughout the latter portion of the life span
(Fleischman et al., 2004; Jennings & Jacoby, 1993; Leclerc & Hess, 2005). It is critical that,
although there is evidence for a positive focus in older adults’ controlled processing of emotional
information (e.g., Carstensen & Mikels, 2005; Charles et al., 2003; Mather & Knight, 2005), the
present results suggest that the tendency to focus on the positive does not always arise when
tasks require relatively automatic and rapid detection of information in the environment.
he early processing of emotional information, our findings indicate that
ults are similar in their early detection of emotional images. The current
her evidence that mechanisms associated with relatively automatic processing
s are well maintained throughout the latter portion of the life span
2004; Jennings & Jacoby, 1993;33 Leclerc & Hess, 2005). It is critical that,
idence for a positive focus in older adults’ controlled processing of emotional
Carstensen & Mikels, 2005; Charles et al., 2003; Mather & Knight, 2005), the
est that the tendency to focus on the positive does not always arise when
ely automatic and rapid detection of information in the environment.
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Use of parallel construction
with coordinating conjunctions
used in pairs, 3.23
Discussion section ending
with comments on
importance of findings, 2.08
Construction of an accurate and
complete reference list, 6.22;
General desciption of references, 2.11

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Example of reference to a
book chapter, print verison,
no DOI, 7.02, Example 25
Digital object identifier as
article identifier, 6.31;
Example of reference to a
periodical, 7.01
134. doi:

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al-
ve
ok
.
empora
negati
doi:
of
andboo
mation.
Gorsuch, I., & Lushene, R. E. (1970). Manual for the State–Trait Inventory.
CA: Consulting Psychologists Press.
). Wechsler Memory Scaler —Revised. San Antonio, TX: Psychological
.
). Technical manual for the Wechsler Adult Intelligence and Memory Scale–
rk, NY: The Psychological Corporation.
An application of prefrontal cortex function theory to cognitive aging.
cal Bulletin, 120, 272–22 292. doi: 10.1037/0033-2909.120.2.272
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22
Footnotes
covariance were conducted with these covariates, with no resulting
influences of these variables on the pattern or magnitude of the results.
2 These data were also analyzed with a 2 × 5 ANOVA to examine the effect of target
category when presented only in arrays containing neutral images, with the results remaining
qualitatively the same. More broadly, the effects of emotion on target detection were not
qualitatively impacted by the distractor category.
EFFECTS OF AGE ON DETECTION OF EMOTION
Analyses of1
Article with more than
seven authors, 7.01,
Example 2
Placement and format
of footnotes, 2.12

24
EFFECTS OF AGE ON DETECTION OF EMOTION
Note. Values represent median response times, collapsing across array type and excluding arrays
of the same category as targets (i.e., positive high arousal represents the median RT to respond to
positive high arousal targets, collapsing across positive low arousal, neutral, negative high
arousal, and negative low arousal array categories). The median response time values were
recorded in milliseconds.
Table 2
Raw Response Time (RT) Scores for Young and Older Adults
Category Young group Older group
Positive high arousal 825 1,580
Positive low arousal 899 1,636
Neutral 912 1,797
Negative high arousal 885 1,578
Negative low arousal 896 1,625
24
CTS OF AGE ON DETECTION OF EMOTION
Values represent median response times, collapsing across array type and excluding arrays
same category as targets (i.e., positive high arousal represents the median RT to respond to
e high arousal targets, collapsing across positive low arousal, neutral, negative high
, and negative low arousal array categories). The median response time values were
ed in milliseconds.
2
esponse Time (RT) Scores for Young and Older Adults
oryy Young groupg g p Older groupg p
ve high arousal 825 1,580
ve low arousal 899 1,636
al 912 1,797
ive high arousal 885 1,578
ive low arousal 896 1,625
23EFFECTS OF AGE ON DETECTION OF EMOTION
Note. The Beck Anxiety Inventory is from Beck et al. (1988); the Behavioral Assessment of the
Dysexecutive Syndrome—Dysexecutive Questionnaire (BADS–DEX) is from Wilson et al.
(1996); the State–Trait Anxiety Inventory (STAI) measures are from Spielberger et al. (1970);
and the Digit Symbol Substitution, Digit Span–Backward, and Arithmetic Wechsler Adult
Intelligence Scale—III and Wechsler Memory Scale—III measures are from Wechsler (1997).
Generative naming scores represent the total number of words produced in 60 s each for letter
F, A, and S. The Vocabulary measure is from Shipley (1986); the Mental Control measure is
from Wechsler (1987); the Self-Ordered Pointing measure was adapted from Petrides and Milner
(1982); and the Wisconsin Card Sorting Task (WCST) measure is from Nelson (1976).
Table 1
Participant Characteristics
Younger group Older group
Measure M SD M SD F (1, 46) p
Years of education 13.92 1.28 16.33 2.43 18.62 <.001
Beck Anxiety Inventory 9.39 5.34 6.25 6.06 3.54 .066
BADS–DEX 20.79 7.58 13.38 8.29 10.46 .002
STAI–State 45.79 4.44 47.08 3.48 1.07 .306
STAI–Trait 45.64 4.50 45.58 3.15 0.02 .963
Digit Symbol Substitution 49.62 7.18 31.58 6.56 77.52 <.001
Generative naming 46.95 9.70 47.17 12.98 .004 .951
Vocabulary 33.00 3.52 35.25 3.70 4.33 .043
Digit Span–Backward 8.81 2.09 8.25 2.15 0.78 .383
Arithmetic 16.14 2.75 14.96 3.11 1.84 .182
Mental Control 32.32 3.82 23.75 5.13 40.60 <.001
Self-Ordered Pointing 1.73 2.53 9.25 9.40 13.18 .001
WCST perseverative errors 0.36 0.66 1.83 3.23 4.39 .042
All values represent raw, nonstandardized scores.
Selecting effective
presentation, 4.41;
Logical and effective
table layout, 5.08
Elements of
table notes, 5.16

.
Figure 1. Mean difference values (ms) representing detection speed for each target category
subtracted from the mean detection speed for neutral targets. No age differences were found in the
arousal-mediated effects on detection speed. Standard errors are represented in the figure by the
error bars attached to each column.
Figure legends
and captions, 5.23
Principles of figure use and
construction, types of figures;
standards, planning, and
preparation of figures, 5.20–5.25

INHIBITORY INFLUENCES ON ASYCHRONY 1
Inhibitory Influences on Asychrony as a Cue for Auditory Segregation
Auditory grouping involves the formation of auditory objects from the sound mixture
reaching the ears. The cues used to integrate or segregate these sounds and so form auditory
objects have been defined by several authors (e.g., Bregman, 1990; Darwin, 1997; Darwin &
Carlyon, 1995). The key acoustic cues for segregating concurrent acoustic elements are
differences in onset time (e.g., Dannenbring & Bregman, 1978; Rasch, 1978) and harmonic
relations (e.g., Brunstrom & Roberts, 1998; Moore, Glasberg, & Peters, 1986). In an example of
the importance of onset time, Darwin (1984a, 1984b) showed that increasing the level of a
harmonic near the first formant (F1) frequency by adding a synchronous pure tone changes the
phonetic quality of a vowel. However, when the added tone began a few hundred milliseconds
before the vowel, it was essentially removed from the vowel percept.… [section continues].
General Method
Overview
In the experiments reported here, we used a paradigm developed by Darwin to assess the
perceptual integration of additional energy in the F1 region of a vowel through its effect on
phonetic quality (Darwin, 1984a, 1984b; Darwin & Sutherland, 1984).…[section continues].
Stimuli
Amplitude and phase values for the vowel harmonics were obtained from the vocal-tract
transfer function using cascaded formant resonators (Klatt, 1980). F1 values varied in 10-Hz
steps from 360–550 Hz—except in Experiment 3, which used values from 350–540Hz—to
produce a continuum of 20 tokens.…[section continues].
Listeners
Elements of empirical studies, 1.01
Figure 2.2. Sample Two-Experiment Paper (The numbers refer to num-
bered sections in the Publication Manual. This abridged manu-
script illustrates the organizational structure characteristic of
multiple-experiment papers. Of course, a complete multiple-
experiment paper would include a title page, an abstract page,
and so forth.)
Paper adapted from “Inhibitory Influences on Asychrony as a Cue for Auditory Segregation,” by S. D.
Holmes and B. Roberts, 2006, Journal of Experimental Psychology: Human Perception and Performance, 32,
pp. 1231–1242. Copyright 2006 by the American Psychological Association.

Listeners were volunteers recruited from the student population of the University of
Birmingham and were paid for their participation. All listeners were native speakers of British
English who reported normal hearing and had successfully completed a screening procedure
(described below). For each experiment, the data for 12 listeners are presented.…[section
continues].
Procedure
At the start of each session, listeners took part in a warm-up block. Depending on the
number of conditions in a particular experiment, the warm-up block consisted of one block of all
the experimental stimuli or every second or fourth F1 step in that block. This gave between 85
and 100 randomized trials.… [section continues].
Data Analysis
The data for each listener consisted of the number of /I/ responses out of 10 repetitions
for each nominal F1 value in each condition. An estimate of the F1 frequency at the phoneme
boundary was obtained by fitting a probit function (Finney, 1971) to a listener ’s identification
data for each condition. The phoneme boundary was defined as the mean of the probit function
(the 50% point).…[section continues].
Experiment 1
In this experiment, we used noise-band captors and compared their efficacy with that of a
pure-tone captor. Each noise-band captor had the same energy as that of the corresponding pure-
tone captor and a center frequency equal to the frequency of this tonal captor…[section
continues].
Method
pe e t
iment, we used noise-band captors and compared their efficacy with that of a
ach noise-band captor had the same energy as that of the corresponding pure- -
nter frequency equal to the frequency of this tonal captor…[section
There were nine conditions: the three standard ones (vowel alone, incremented fourth,
and leading fourth) plus three captor conditions and their controls. A lead time of 240 ms was
used for
Results and Discussion
Figure 4 shows the mean phoneme boundaries for all conditions and the restoration effect
for each captor type. The restoration effects are shown above the histogram bars both as a
boundary shift in hertz and as a percentage of the difference in boundary position between the
incremented-fourth and leading-fourth conditions.… [section continues].
Experiment 2
This experiment considers the case where the added 500-Hz tone begins at the same time
as the vowel but continues after the vowel ends.… [section continues].
Method
There were five conditions: two of the standard ones (vowel alone and incremented
fourth), a lagging-fourth condition (analogous to the leading-fourth condition used elsewhere),
and a captor condition and its control. A lag time of 240 ms was used for the added 500-Hz
tone.… [section continues]
Results and Discussion
the added 500-Hz tone.… [section continues].
Policy on metrication, 4.39;
Style for metric units, 4.40
Abbreviating units
of measurement,
4.27, Table 4.4
Plural forms of nouns
of foreign origin, 3.19
Multiple Experiments, 2.09
Figure 2.2. Sample Two-Experiment Paper (continued)

Running head: INHIBITORY INFLUENCES ON ASYCHRONY 5
a perceptual group between the leading portion and the captor tone, on the basis of their common
onset time and harmonic relationship, leaving the remainder of the extra energy to integrate into
the vowel percept… .[section continues].
[Follow the form of the one-experiment sample paper to type references, the author note,
footnotes, tables, and figure captions.]
Running head: INHIBITORY INFLUENCES ON ASYCHRONY
a perceptual group between the leading portion and the captor tone, on the basis o
1984; Roberts & Holmes, 2006). This experiment used a gap between captor offset and vowel
onset to measure the decay time of the captor effect …[section continues].
Method
There were 17 conditions: the three standard ones (vowel alone, incremented fourth, and
leading fourth), five captor conditions and their controls, and four additional conditions
(described separately below). A lead time of 320 ms was used for the added 500-Hz tone. The
captor conditions were created by adding a 1.1-kHz pure-tone captor, of various durations, to
each member of the leading-fourth continuum.…[section continues].
Results
Figure 6 shows the mean phoneme boundaries for all conditions. There was a highly
significant effect of condition on the phoneme boundary values, F(16, 176) = 39.10, p < .001.
Incrementing the level of the fourth harmonic lowered the phoneme boundary relative to the
vowel-alone condition (by 58 Hz, p < .001), which indicates that the extra energy was integrated
into the vowel percept.…[section continues].
Discussion
The results of this experiment show that the effect of the captor disappears somewhere
between 80 and 160 ms after captor offset. This indicates that the captor effect takes quite a long
time to decay away relative to the time constants typically found for cells in the CN using
physiological measures (e.g., Needham & Paolini, 2003).…[section continues].
Summary and Concluding Discussion
Darwin and Sutherland (1984)first demonstrated that accompanying the leading portion
of additional energy in the F1 region of a vowel with a captor tone partly reversed the effect of
the onset asynchrony on perceived vowel quality. This finding was attributed to the formation of
Use of statistical term rather
than symbol in text, 4.45
Figure 2.2. Sample Two-Experiment Paper (continued)

THE SLEEPER EFFECT IN PERSUASION 1
The Sleeper Effect in Persuasion:
A Meta-Analytic Review
Persuasive messages are often accompanied by information that induces suspicions of
invalidity. For instance, recipients of communications about a political candidate may discount a
message coming from a representative of the opponent party because they do not perceive the
source of the message as credible (e.g., Lariscy & Tinkham, 1999). Because the source of the
political message serves as a discounting cue and temporarily decreases the impact of the
message, recipients may not be persuaded by the advocacy immediately after they receive the
communication. Over time, however, recipients of an otherwise influential message may recall
the message but not the noncredible source and thus become more persuaded by the message at
that time than they were immediately following the communication. The term sleeper effect was
used to denote such a delayed increase in persuasion observed when the discounting cue (e.g.,
noncredible source) becomes unavailable or “dissociated” from the communication in the
memory of the message recipients (Hovland, Lumsdaine, & Sheffield, 1949).…[section
Method
Sample of Studies
We retrieved reports related to the sleeper effect that were available by March 2003 by
means of multiple procedures. First, we searched computerized databases, including PsycINFO
(1887–2003), Dissertation Abstracts International (1861–2003), ERIC (1967–2003), and the
Social-Science-Citation-Index (1956–2003), using the keywords sleeper effect, delayed-action,
credibility, source credibility, source expertise, attitude change, discounting cue,attitude
persistence, attitude maintenance, persuasion, propaganda, attitude and memory, attitude and
a delayed increase in persuasion observed when the discounting cue (e.g.,
becomes unavailable or “dissociated” from the communication in the
sage recipients (Hovland, Lumsdaine, & Sheffield, 1949).…[section
Method
d reports related to the sleeper effect that were available by March 2003 by
rocedures. First, we searched computerized databases, including PsycINFO
rtation Abstracts International (1861–2003), ERIC (1967–77 2003), and the
tion-Index (1956–66 2003), using the keywords sleeper effect,tt delayed-action,
redibility, source expertise, attitude change, discounting cue,attitude
maintenance, persuasion, propaganda, attitude and memoryrr , attitude and
retention, attitude and decay, andpersuasion and decay. Because researchers often use the terms
opinion and belief, instead of attitude, we conducted searches using these substitute terms as
well.
Second, … [section continues].
Selection Criteria
We used the following criteria to select studies for inclusion in the meta-analysis.
1. We only included studies that involved the presentation of a communication containing
persuasive arguments. Thus, we excluded studies in which the participants played a role or were
asked to make a speech that contradicted their opinions. We also excluded developmental studies
involving delayed effects of an early event (e.g., child abuse), which sometimes are also referred
to as sleeper effects.…[section continues] .
Moderators
For descriptive purposes, we recorded (a) the year and (b) source (i.e., journal article,
unpublished dissertations and theses, or other unpublished document) of each report as well as
(c) the sample composition (i.e., high-school students, university students, or other) and (d) the
country in which the study was conducted.
We also coded each experiment in terms of .…[section continues].
Studies were coded independently by the first author and another graduate student.
Italicize key terms, 4.21
Description of meta-analysis, 1.02;
Guidelines for reporting meta-analysis,
2.10; see also Appendix
Identification of elements in a
series within a sentence, 3.04
Figure 2.3. Sample Meta-Analysis (The numbers refer to numbered sec-
tions in the Publication Manual. This abridged manuscript illus-
trates the organizational structure characteristic of reports of
meta-analyses. Of course, a complete meta-analysis would
include a title page, an abstract page, and so forth.)
Paper adapted from “The Sleeper Effect in Persuasion: A Meta-Analytic Review,” by G. Kumkale and D.
Albarracin, 2004, Psychological Bulletin, 130, pp. 143–172. Copyright 2004 by the American Psychological
Association.

was satisfactory (Orwin, 1994). We resolved disagreements by discussion and consultation with
colleagues. Characteristics of the individual studies included in this review are presented in
Table 1. The studies often contained several independent datasets such as different messages and
different experiments. The characteristics that distinguish different datasets within a report
appear on the second column of the table.
Dependent Measures and Computation of Effect Sizes
We calculated effect sizes for (a) persuasion and (b) recall–recognition of the message
content. Calculations were based on the data described in the primary reports as well as available
responses of the authors to requests of further information.…[section continues].
Analyses of Effect Sizes
There are two major models used in meta-analysis: fixed-effects and random-
effects.…[section continues].
To benefit from the strengths of both models, we chose to aggregate the effect sizes and to
conduct analyses using both approaches.…[section continues].
Results
The data analysis included a description of the experiments we summarized, an
estimation of overall effects, moderator analyses, and tests of mediation.
Sample of Studies and Datasets
Descriptive characteristics of the datasets included in the present meta-analysis appear in
Table 2.…[section continues].
Overview of the Average Effect Sizes
A thorough understanding of the sleeper effect requires examining (a) the between-
condition differences at each time point as well as (b) the within-condition changes that take
Analyses of Effect Sizes
wo major models used in meta-aa analysis: fixed-effects ands random-
ontinues].
e strengths of both models, we chose to aggregate the effect sizes and to
ing both approaches.…[section continues].
Results
alysis included a description of the experiments we summarized, an
l effects, moderator analyses, and tests of mediation.
and Datasets
characteristics of the datasets included in the present meta-aa analysis appear in
continues].
verage Effect Sizes
understanding of the sleeper effect requires examining (a) the between-
es at each time point as well as (b) the within-condition changes that take
place over time.…[section continues].
In light of these requirements, we first examined whether discounting cues led to a decrease in
agreement with the communication (boomerang effect). Next,.…[section continues].
Ruling out a nonpersisting boomerang effect. To determine whether or not a delayed
increase in persuasion represents an absolute sleeper effect, one needs to rule out a nonpersisting
boomerang effect, which takes place when a message initially backfires but later loses this
reverse effect (see panel A of Figure 1).…[section continues].
Average sleeper effect. Relevant statistics corresponding to average changes in
persuasion from the immediate to the delayed posttest appear in Table 4, organized by the
different conditions we considered (i.e., acceptance-cue, discounting-cue, no-message control,
and message-only control). In Table 4, positive effect sizes indicate increases in persuasion over
time, negative effect sizes indicate decay in persuasion, and zero effects denote stability in
persuasion. Confidence intervals that do not include zero indicate significant changes over time.
The first row of Table 4 shows that recipients of acceptance cues agreed with the message less as
time went by (fixed-effects, d+ = –0.21; random-effects, d+ = –0.23). In contrast to the decay in
persuasion for recipients of acceptance cues, there was a slight increase in persuasion for
recipients of discounting cues over time (d+ = 0.08). It is important to note that change in
discounting-cue conditions significantly differed from change in acceptance-cue conditions,
(fixed-effects; B = –0.29, SE = 0.04), QB(1) = 58.15, p < .0001; QE(123) = 193.82, p <
.0001.…[section continues].
Summary and variability of the overall effect. The overall analyses identified a relative
sleeper effect in persuasion, but no absolute sleeper effect. The latter was not surprising, because
the sleeper effect was expected to emerge under specific conditions.…[section continues].
Use at least
two subheadings
in a section, 3.02
Figure 2.3. Sample Meta-Analysis (continued)

Moderator Analyses
Although overall effects have descriptive value, the variability in the change observed in
discounting-cue conditions makes it unlikely that the same effect was present under all
conditions. Therefore, we tested the hypotheses that the sleeper effect would be more likely (e.g.,
more consistent with the absolute pattern in Panel B1 of Figure 1) when…[section continues].
. . . [references continue]
[Follow the form of the one-experiment sample paper to type the author note, footnotes,
tables, and figure captions.]
References
References marked with an asterisk indicate studies included in the meta-analysis.
Albarracín, D. (2002). Cognition in persuasion: An analysis of information processing in
response to persuasive communications. In M. P. Zanna (Ed.), Advances in experimental
social psychology (Vol. 34, pp. 61–130). doi:10.1016/S0065-2601(02)80004-1
… [references continue]
Johnson, B. T., & Eagly, A. H. (1989). Effects of involvement in persuasion: A meta-analysis.
Psychological Bulletin, 106, 290–314. doi:10.1037/0033-2909.106.2.290
*Johnson, H. H., Torcivia, J. M., & Poprick, M. A. (1968). Effects of source credibility on the
relationship between authoritarianism and attitude change. Journal of Personality and
Social Psychology, 9, 179–183. doi:10.1037/h0021250
*Johnson, H. H., & Watkins, T. A. (1971). The effects of message repetitions on immediate and
delayed attitude change. Psychonomic Science, 22, 101–103.
Jonas, K., Diehl, M., & Bromer, P. (1997). Effects of attitudinal ambivalence on information
processing and attitude-intention consistency. Journal of Experimental Social
Psychology, 33, 190–210. doi:10.1006/jesp.1996.1317
Format for references included
in a meta-analysis with less
than 50 references, 6.26
Figure 2.3. Sample Meta-Analysis (continued)

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