Article critique paper apa format use the provided article

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1. Title page: 1 page (4 points)

· Use APA style to present the appropriate information: 

o A Running head must be included and formatted APA style 

§ The phrase “Running head” is at the top of the title page followed by a short title of your creation (no more than 50 characters) that is in ALL CAPS. This running head is left-justified (flush left on the page). Note that the “h” in head is all lower case! Look at the first page of these instructions, and you will see how to set up your Running head. 

§ There must be a page number on the title page that is right justified. It is included in the header

o Your paper title appears on the title page. This is usually 12 words or less, and the first letter of each word is capitalized. It should be descriptive of the paper (For this paper, you should use the title of the article you are critiquing. The paper title can be the same title as in the Running head or it can differ – your choice) 

o Your name will appear on the title page

o Your institution will appear on the title page as well

o For all papers, make sure to double-space EVERYTHING and use Times New Roman font. This includes everything from the title page through the references. 

o This is standard APA format. ALL of your future papers will include a similar title page

2. Summary of the Article: 1 ½ page minimum, 3 pages maximum – 14 points)

An article critique should briefly summarize, in your own words, the article research question and how it was addressed in the article. Below are some things to include in your summary. 

· The summary itself will include the following: (Note – if the article involved more than one experiment, you can either choose to focus on one of the studies specifically or summarize the general design for all of the studies)

1. Type of study (Was it experimental or correlational? How do you know?)

2. Variables (What were the independent and dependent variables? How did they manipulate the IV? How did they operationally define the DV? Be specific with these. Define the terms independent and dependent variable and make sure to identify how they are operationally defined in the article)

3. Method (What did the participants do in the study? How was it set up? Was there a random sample of participants? Was there random assignment to groups?). How was data collected (online, in person, in a laboratory?). 

4. Summary of findings (What were their findings?)

§ Make sure that: 

1. The CAPS portion of your running head should also appear on the first page of your paper, but it will NOT include the phrase “Running head” this time, only the same title as the running head from the first paper in ALL CAPS. Again, see the example paper. There is a powerpoint presentation on using Microsoft Word that can help you figure out how to have a different header on the title page (where “Running head” is present) and other pages in the paper (where “Running head” is NOT present). You can also find how-to information like this using youtube!

2. If you look at the header in pages 2 through 5 (including THIS current page 4 that you are reading right now!), you will see “Running head” omitted. It simply has the short title (ARTICLE CRITIQUE PAPER INSTRUCTIONS) all in caps, followed by the page number. 

3. The same title used on the title page should be at the top of the page on the first actual line of the paper, centered. 

4. For this paper, add the word “Summary” below the title, and have it flush left. Then write your summary of the article below that.

3. Critique of the study: 1 ½ pages minimum – 3 pages maximum – 16 points)

· This portion of the article critique assignment focuses on your own thoughts about the content of the article (i.e. your own ideas in your own words). For this section, please use the word “Critique” below the last sentence in your summary, and have the word “Critique” flush left. 

· This section is a bit harder, but there are a number of ways to demonstrate critical thinking in your writing. Address at least four of the following elements. You can address more than four, but four is the minimum.  

· 1). In your opinion, how valid and reliable is the study? Why? (make sure to define what reliable and valid mean, and apply these definitions to the study you are critiquing. Merely mentioning that it is valid and reliable is not enough – you have to apply those terms to the article. That is, how do you know it is reliable? How do you know it is valid? 

· 2). Did the study authors correctly interpret their findings, or are there any alternative interpretations you can think of?

· 3). Did the authors of the study employ appropriate ethical safeguards?

· 4). Briefly describe a follow-up study you might design that builds on the findings of the study you read how the research presented in the article relates to research, articles or material covered in other sections of the course

· 5). Describe whether you feel the results presented in the article are weaker or stronger than the authors claim (and why); or discuss alternative interpretations of the results (i.e. something not mentioned by the authors) and/or what research might provide a test between the proposed and alternate interpretations

· 6). Mention additional implications of the findings not mentioned in the article (either theoretical or practical/applied)

· 7). Identify specific problems in the theory, discussion or empirical research presented in the article and how these problems could be corrected. If the problems you discuss are methodological in nature, then they must be issues that are substantial enough to affect the interpretations of the findings or arguments presented in the article. Furthermore, for methodological problems, you must justify not only why something is problematic but also how it could be resolved and why your proposed solution would be preferable.

· 8). Describe how/why the method used in the article is either better or worse for addressing a particular issue than other methods 

4. Brief summary of the article: One or paragraphs (6 points)

· Write the words “Brief Summary”, and then begin the brief summary below this

· In ONE or TWO paragraphs maximum, summarize the article again, but this time I want it to be very short. In other words, take all of the information that you talked about in the summary portion of this assignment and write it again, but this time in only a few sentences. 

· The reason for this section is that I want to make sure you can understand the whole study but that you can also write about it in a shorter paragraph that still emphasizes the main points of the article. Pretend that you are writing your own literature review for a research study, and you need to get the gist of an article that you read that helps support your own research across to your reader. Make sure to cite the original study (the article you are critiquing). 

5. References – 1 page (4 points)

· Provide the reference for this article in proper APA format (see the book Chapter 14 for appropriate referencing guidelines or the Chapter 14 powerpoint). 

· If you cited other sources during either your critique or summary, reference them as well (though you do not need to cite other sources in this assignment – this is merely optional IF you happen to bring in other sources). Formatting counts here, so make sure to italicize where appropriate and watch which words you are capitalizing!

6. Grammar and Writing Quality (6 points)

· Few psychology courses are as writing intensive as Research Methods (especially Research Methods Two next semester!). As such, I want to make sure that you develop writing skills early. This is something that needs special attention, so make sure to proofread your papers carefully. 

· Avoid run-on sentences, sentence fragments, spelling errors, and grammar errors. Writing quality will become more important in future papers, but this is where you should start to hone your writing skills. 

· We will give you feedback on your papers, but I recommend seeking some help from the FIU writing center to make sure your paper is clear, precise, and covers all needed material. I also recommend asking a few of your group members to read over your paper and make suggestions. You can do the same for them! 

· If your paper lacks originality and contains too much overlap with the paper you are summarizing (i.e. you do not paraphrase appropriately or cite your sources properly), you will lose some or all of the points from writing quality, depending on the extent of the overlap with the paper. For example, if sentences contain only one or two words changed from a sentence in the original paper, you will lose points from writing quality. 

Please note that you do not need to refer to any other sources other than the article on which you have chosen to write your paper. However, you are welcome to refer to additional sources if you choose. 

Psychological Science
21(12) 1770 –1776
© The Author(s) 2010
Reprints and permission:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0956797610387441
http://pss.sagepub.com

A well-established finding is that mood interacts with cogni-
tive processing (for a review, see Isen, 1999), with executive
functioning implicated as a possible source of the effects of
this interaction (Mitchell & Phillips, 2007). Positive mood
leads to enhanced cognitive flexibility,1 whereas negative
mood may reduce (or may not affect) cognitive flexibility (for
a review, see Ashby, Isen, & Turken, 1999). Category learning
has also been associated with cognitive flexibility (Ashby
et al., 1999; Maddox, Baldwin, & Markman, 2006), making cat-
egory learning well suited to the study of the effects of mood
on cognition. For example, Ashby, Alfonso-Reese, Turken,
and Waldron (1998) predicted that depressed subjects should
be impaired in learning rule-described (RD) category sets.
Smith, Tracy, and Murray (1993) supported this prediction and
also found that depressed subjects were not impaired when
learning non-RD categories. However, the more general ques-
tion of how induced positive and negative mood states influ-
ence category learning remains unanswered. We addressed
this question by using two kinds of categories, one in which
learning is thought to be enhanced by cognitive flexibility and
one in which learning is not thought to be enhanced by cogni-
tive flexibility (Maddox et al., 2006).

Our starting point was the competition between verbal and
implicit systems (COVIS) theory, which posits the existence

of separate explicit and implicit category-learning systems
(Ashby et al., 1998). The explicit system enables people to
learn RD categories and is associated with the prefrontal cor-
tex (PFC) and the anterior cingulate cortex (ACC). RD cate-
gory learning uses hypothesis testing, rule selection, and
inhibition to find and apply rules that can be verbalized, and it
is influenced by cognitive flexibility. The implicit system
enables people to learn non-RD categories, relies on connec-
tions between visual cortical areas and the basal ganglia, and
is not affected by cognitive flexibility. This system is likely
procedural in nature and dependent on a dopamine-mediated
reward signal (Maddox, Ashby, Ing, & Pickering, 2004). RD
and non-RD category sets have been dissociated behaviorally
(for a review, see Maddox & Ashby, 2004) and neurobiologi-
cally (Nomura et al., 2007), making them appropriate for the
study of mood effects.

We argue that positive mood increases cognitive flexibility,
and this effect enhances the explicit category-learning system

Corresponding Author:
Ruby T. Nadler, The University of Western Ontario, Department of
Psychology, Social Science Centre, Room 7418, 1151 Richmond St., London,
Ontario, Canada N6A 5C2
E-mail: [email protected]

Better Mood and Better Performance:
Learning Rule-Described Categories Is
Enhanced by Positive Mood

Ruby T. Nadler, Rahel Rabi, and John Paul Minda
The University of Western Ontario

Abstract

Theories of mood and its effect on cognitive processing suggest that positive mood may allow for increased cognitive flexibility.
This increased flexibility is associated with the prefrontal cortex and the anterior cingulate cortex, both of which play crucial
roles in hypothesis testing and rule selection. Thus, cognitive tasks that rely on behaviors such as hypothesis testing and rule
selection may benefit from positive mood, whereas tasks that do not rely on such behaviors should not be affected by positive
mood. We explored this idea within a category-learning framework. Positive, neutral, and negative moods were induced in
our subjects, and they learned either a rule-described or a non-rule-described category set. Subjects in the positive-mood
condition performed better than subjects in the neutral- or negative-mood conditions in classifying stimuli from rule-described
categories. Positive mood also affected the strategy of subjects who classified stimuli from non-rule-described categories.

Keywords

frontal lobe, emotions, hypothesis testing, selective attention, response inhibition

Received 4/7/10; Revision accepted 6/28/10

Research Report

Better Mood and Better Performance 1771

mediated by the PFC (Ashby et al., 1999; Ashby & Ell, 2001;
Minda & Miles, 2010). We base our predictions on two lines
of research. First, Ashby et al. (1999) hypothesized that posi-
tive affect is associated with enhanced cognitive flexibility as
a result of increased dopamine in the frontal cortical areas of
the brain. Second, the COVIS theory predicts that increased
dopamine in the PFC and ACC should enhance learning on
RD tasks, and reduced dopamine should impair learning on
RD tasks (Ashby et al., 1998). Thus, positive mood should be
associated with enhanced RD category learning, an important
prediction that has not to our knowledge been tested directly.

We induced a positive, neutral, or negative mood in sub-
jects and presented them with one of two kinds of category
sets that have been widely used in the category-learning litera-
ture (Ashby & Maddox, 2005). These sets consisted of sine-
wave gratings (Gabor patches) that varied in spatial frequency
and orientation. The RD set of Gabor patches required learners
to find a single-dimensional rule in order to correctly classify
the stimuli on the basis of frequency but not orientation, and
the non-RD, information-integration (II) set of Gabor patches
required learners to assess both orientation and frequency.
Subjects in the RD condition were able to formulate a verbal
rule to ensure optimal performance, but subjects in the II con-
dition were not able to form a rule that could be easily
verbalized.

We predicted that subjects in a positive mood, compared
with those in a neutral or negative mood, would perform better
when learning RD categories. It was unclear whether a nega-
tive mood would impair RD learning relative to a neutral
mood, as the effects of negative mood on cognitive processing
are variable and difficult to predict (for a review, see Isen,
1990). Because the PFC and the ACC do not mediate the
implicit system, we did not expect mood to affect II category
learning.

Method
Subjects

Subjects were 87 university students (61 females and 26
males), who received $10.00 or course credit for participation.
Subjects were randomly assigned to one of the three mood-
induction conditions and one of the two category sets. Six sub-
jects who scored below 50% on the categorization task were
excluded from data analysis.

Materials
We used a series of music clips and video clips from YouTube2
to establish affective states. We verified that these clips evoked
the intended emotions by conducting a pilot study. After each
viewing or listening, subjects in the pilot study (7 graduate
students, who did not participate in the main experiment) rated
how the clip made them feel on a 7-point scale, which ranged
from 1 (very sad) to 4 (neutral) to 7 (very happy). Table 1

shows the complete list of clip selections and the average rat-
ings by pilot subjects; it also denotes the clips selected for the
main experiment. As a manipulation check during the main
experiment, we queried subjects with the Positive and Nega-
tive Affect Schedule (PANAS) after using the selected clips to
induce moods. The PANAS assesses positive and negative
affective dimensions (Watson, Clark, & Tellegen, 1988).

The Gabor patches used in the main experiment were gen-
erated according to established methodologies (see Ashby &
Gott, 1988; Zeithamova & Maddox, 2006). For each category
(RD and II), we randomly sampled 40 values from a multivari-
ate normal distribution described by that category’s parame-
ters (shown in Table 2). The resulting structures for the RD
and II category sets are illustrated in Figure 1.3 We used the
PsychoPy software package (Pierce, 2007) to generate a Gabor
patch corresponding to each coordinate sampled from the mul-
tivariate distributions.

Procedure
In the main experiment, subjects were assigned randomly to
one of three mood-induction conditions (positive, neutral, or
negative), as well as to one of two category sets (RD or II).

Table 1. Music and Video Clips Used in the Pilot Study

Selection
Average subject

rating

Positive music
Mozart: “Eine Kleine Nachtmusik—Allegro”* 6.57
Handel: “The Arrival of the Queen of Sheba” 5.00
Vivaldi: “Spring” 6.14
Neutral music
Mark Salona: “One Angel’s Hands”* 3.86
Linkin Park: “In the End (Instrumental)” 4.14
Stephen Rhodes: “Voice of Compassion” 3.29
Negative music
Schindler’s List Soundtrack: “Main Theme”* 2.00
I Am Legend Movie Theme Song 2.71
Distant Everyday Memories 2.57
Positive video
Laughing Baby* 6.57
Whose Line Is It Anyway: Sound Effects 6.43
Where the Hell Is Matt? 6.00
Neutral video
Antiques Roadshow Television Show* 4.14
Facebook on 60 Minutes 3.71
Report About the Importance of Sleep 4.29
Negative video
Chinese Earthquake News Report* 1.43
Madison’s Story (About Child With Cancer) 1.71
Death Scene From the Film The Champ 1.86

Note: Clips were taken from the YouTube Web site. Asterisks denote clips
that were used in the main experiment.

1772 Nadler et al.

Subjects were presented with the clips (music first, then video)
from their assigned mood condition and then completed the
PANAS so we could ensure that the mood induction was
successful.

After receiving instructions, subjects performed a category-
learning task on a computer. On each trial, a Gabor patch
appeared in the center of the screen, and subjects pressed the
“A” or the “B” key to classify the stimulus. Subjects who
viewed the RD category set (Fig. 1a) had to find a single-
dimensional rule to correctly classify the stimuli on the basis
of the frequency of the grating, while ignoring the more salient
dimension of orientation. The optimal verbal rule for such
classification could be phrased as follows: “Press ‘A’ if the
stimulus has three or more stripes; otherwise, press ‘B.’” The
non-RD, II category set (Fig. 1b) required learners to assess
both orientation and frequency. There was no rule for this set

that could be easily verbalized to allow for optimal perfor-
mance. In both conditions, feedback (“CORRECT” or
“INCORRECT”) was presented after each response. Subjects
completed four unbroken blocks of 80 trials each (320 total).
The presentation order of the 80 stimuli was randomly gener-
ated within each block for each subject.

Results
PANAS

Scores on the Positive Affect scale were as follows—positive-
mood condition: 2.89; neutral-mood condition: 2.45; and neg-
ative-mood condition: 2.42. A significant effect of mood on
positive affect was found, F(2, 78) = 3.98, p < .05, η2 = .093.
Positive-mood subjects showed only marginally more positive

Table 2. Distribution Parameters for the Rule-Described and Non-Rule-
Described Category Sets

Category set and category µf µo σf
2 σo

2 covf,o

Rule-described
Category A 280 125 75 9,000 0
Category B 320 125 75 9,000 0
Non-rule-described
Category A 268 157 4,538 4,538 435
Category B 332 93 4,538 4,538 4,351

Note: Dimensions are in arbitrary units; see Figure 1 for scaling factors. The sub-
scripted letters o and f refer to orientation and frequency, respectively.

–200

–100

0

100

200

300

400

500

–100 0 100 200 300 400 500 600

O
rie

nt
at

io
n

Frequency

Rule-Described

–200

–100

0

100

200

300

400

500

–100 0 100 200 300 400 500 600

O
rie

nt
at

io
n

Frequency

Non-Rule-Described
ba

Fig. 1. Structures used in the (a) rule-described category set and (b) non-rule-described, information-integration category set. Category A stimuli are
represented by light circles, and Category B stimuli are represented by dark circles. The solid lines show the optimal decision boundaries between
the stimuli. The values of the stimulus dimensions are arbitrary units. Each stimulus was created by converting the value of these arbitrary units into
a frequency value (cycles per stimulus) and an orientation value (degree of tilt). For both category sets, the grating frequency (f) was calculated as
0.25 + (xf/50) cycles per stimulus, and the grating orientation (o) was calculated as xo × (π/20)°. The Gabor patches are examples of the actual stimuli
seen by subjects.

Better Mood and Better Performance 1773

affect than neutral-mood subjects did (p < .06), but they
showed significantly more positive affect than negative-mood
subjects did (p < .05). These scores indicate that the mood-
induction procedures were effective. Scores on the Negative
Affect scale were as follows—positive-mood condition: 1.15;
neutral-mood condition: 1.18; and negative-mood condition:
2.13. A significant effect of mood on negative affect was
found, F(2, 78) = 30.36, p < .001, η2 = .438, with negative-
mood subjects showing significantly more negative affect than
positive- and neutral-mood subjects did (p < .0001 in both
cases). These results again indicate that the mood-induction
procedures were effective.

Category learning
Figure 2 shows the learning curve (average proportion of cor-
rect responses in Blocks 1–4) for each condition and each cat-
egory set. A mixed analysis of variance revealed main effects
of category set, F(1, 75) = 31.94, p < .001, η2 = .257; mood,
F(2, 75) = 4.40, p < .05, η2 = .071; and block, F(3, 225) =
41.33, p < .001, η2 = .322. It also revealed a significant interac-
tion between mood and category set, F(2, 75) = 4.17, p < .05,
η2 = .067. We conducted two separate analyses of variance
(one for the RD category and one for the II category) to exam-
ine this interaction.

A main effect of mood on overall performance was found
for the RD category set, F(2, 35) = 6.28, p < .001, η2 = .264. A

Tukey’s honestly significant difference test showed that over-
all performance by subjects in the positive-mood condition
(M = .85) was higher than performance by subjects in the neg-
ative-mood condition (M = .73, p < .0001) and subjects in the
neutral-mood condition (M = .73, p < .0001). Performance did
not differ between subjects in the neutral- and negative-mood
conditions (p = .69). No effect of mood on overall perfor-
mance was found for the II category set (p = .71). Overall pro-
portions correct were as follows—positive-mood condition:
.64; negative-mood condition: .66; and neutral-mood condi-
tion: .64.

Computational modeling
For insight into the response strategies used by our subjects,
we fit decision-bound models to the first block of each sub-
ject’s data (for details, see Ashby, 1992a; Maddox & Ashby,
1993). We analyzed the first block of trials because that is
when mood-induction effects are likely to be strongest, and it
is also when cognitive flexibility is most needed. One class of
models assumed that each subject’s performance was based on
a single-dimensional rule (we used an optimal version with a
fixed intercept and a version with the intercept as a free param-
eter). Another class of models assumed that each subject’s per-
formance was based on the two-dimensional II boundary (we
used an optimal version with a fixed intercept and slope, a ver-
sion with a fixed slope, and a version with a freely varying

P
ro

po
rti

on
C

or
re

ct

RD Category Set II Category Set

.0

.2

.4

.6

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1.0

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1.0

Block Block

Positive Mood Neutral Mood
Negative Mood

Positive Mood Neutral Mood
Negative Mood

1 2 3 4 1 2 3 4

Fig. 2. Average proportion of correct responses to stimuli in the three mood conditions as a function of trial block. Subjects were tested on either the
rule-described (RD) category set (left graph) or the non-RD, information-integration (II) category set (right graph). Error bars denote standard errors
of the mean.

1774 Nadler et al.

slope and intercept). We fit these models to each subject’s data
by maximizing the log likelihood. Model comparisons were
carried out using Akaike’s information criterion, which penal-
izes a model for the number of free parameters (Ashby, 1992b).
The proportion of subjects whose responses were best fit by
their respective optimal model is shown in Figure 3. For the
RD categories, .83 of positive-mood subjects, .62 of neutral-
mood subjects, and .54 of negative-mood subjects were fit best
by a model that assumed a single-dimensional rule. For the II
categories, .71 of positive-mood subjects, .40 of neutral-mood
subjects, and .43 of negative-mood subjects were fit best by
one of the II models.

Discussion
In this experiment, positive, neutral, and negative moods were
induced before subjects learned either an RD or a non-RD, II
category set. The RD set required subjects to use hypothesis
testing, rule selection, and response inhibition to achieve opti-
mal performance, and the II set was best learned by associat-
ing regions of perceptual space with responses (Ashby & Gott,
1988). We found that positive mood enhanced RD learning com-
pared with neutral and negative moods. Mood did not seem to
affect II learning. However, a comparison of decision-bound
models suggested that positive-mood subjects displayed a

greater degree of cognitive flexibility compared with neutral-
and negative-mood subjects by adopting an optimal strategy
early in both RD and II learning.

The COVIS theory suggests that people learn categories
using an explicit, rule-based system or an implicit, similarity-
based system (Ashby et al., 1998; Ashby & Maddox, 2005;
Minda & Miles, 2010). The brain areas that mediate these sys-
tems have been well studied, linking the PFC, ACC, and
medial temporal lobes to the explicit system but not to the
implicit system. Our experiment highlights a variable that
facilitates the learning of RD categories using the explicit
system.

The finding that positive mood enhances performance of
the explicit system posited by the COVIS theory corresponds
with the dopamine hypothesis of positive affect (Ashby et al.,
1999). Our results connect this research with existing work on
category learning, and we view this connection as a substan-
tial step forward in the study of cognition and mood. We sus-
pect that our positive-mood subjects experienced increased
cognitive flexibility, which allowed them to find the optimal
verbal rule faster than negative-mood subjects and neutral-
mood subjects did. Performance on the II category set did not
differ strongly across the different mood conditions. This
result is also in line with the dopamine hypothesis, as positive
mood is not theorized to affect the same brain regions

P
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1.0

Positive Neutral Negative

Mood-Induction Condition

Positive Neutral Negative

Mood-Induction Condition

RD Category Set II Category Set

Fig. 3. Proportion of subjects in each mood-induction condition whose responses best fit the optimal model for the category set to which they
were assigned. Subjects learned either the rule-described (RD) category set (left graph) or the non-RD, information-integration (II) category set
(right graph).

Better Mood and Better Performance 1775

hypothesized by the COVIS theory to be involved with the
learning of non-RD category sets. However, our modeling
results suggest that the cognitive flexibility associated with
positive mood may affect the strategies used in II category
learning. This cognitive flexibility could allow the explicit
system to exhaust rule searches more effectively, even though
performance levels may remain unchanged between the
conditions.

We failed to find an effect of negative mood in RD learn-
ing. This is in line with previous research that reported no dif-
ferences between negative- and neutral-mood subjects on
measures of cognitive flexibility (Isen, Daubman, & Nowicki,
1987). It may be that negative mood does not affect RD cate-
gory learning, although we think it could, given the right cir-
cumstances. One possible explanation of why we did not find
such an effect is that the induced negative mood may not have
been sustained long enough to interfere with performance. We
suspect that subjects in certain negative states will be impaired
in RD category learning. Future work should examine ways of
sustaining mood states and should explore a wider range of
negative mood states.

An intriguing possibility that was not observed is that nega-
tive mood could enhance II category learning. Recent research
suggests that affective states low in motivational intensity
(e.g., amusement, sadness) are associated with broadened
attention, and affective states high in motivational intensity
(e.g., desire, disgust) are associated with narrowed attention
(Gable & Harmon-Jones, 2008, 2010). Thus, for example, sad-
ness may facilitate performance when broadened attention is
beneficial for category learning. We did not find this effect,
either because learning of the II category set used did not ben-
efit from broadened attention or because the induced negative
mood was high in motivational intensity. These interesting
ideas require further research.

Smith et al. (1993) showed that clinically depressed sub-
jects were impaired in RD category learning and unimpaired
in II category learning, but our research is the first to investi-
gate how experimentally induced mood states influence cate-
gory learning. We have shown that positive mood enhanced
the learning of an RD category set, an advantage that was
strong and sustained throughout the task. Positive mood did
not improve the learning of II categories, though there was
evidence that positive mood enhanced selection of the optimal
strategy. By connecting theories of multiple-system category
learning and positive affect, our research suggests that positive
affect enhances performance when category learning benefits
from cognitive flexibility. Future work should examine the
interaction between mood states (motivationally weak com-
pared with intense), valence (positive compared with nega-
tive), and category type (explicit compared with implicit) in
category learning.

Acknowledgments

We thank E. Hayden for many valuable insights on this project.

Declaration of Conflicting Interests

The authors declared that they had no conflicts of interest with
respect to their authorship or the publication of this article.

Funding

This research was supported by Natural Sciences and Engineering
Research Council (NSERC) Grant R3507A03 to J.P.M., an Ontario
Graduate Scholarship award to R.T.N., and an NSERC fellowship to
R.R.

Notes

1. We define cognitive flexibility as the ability to seek out and apply
alternate strategies to problems (Maddox, Baldwin, & Markman,
2006) and to find unusual relationships between items (Isen, Johnson,
Mertz, & Robinson, 1985).
2. The clips can be found by searching for their titles on YouTube
(http://www.youtube.com/), or URLs can be obtained from the first
author.
3. Stimulus parameters and generation were the same as those used
by Zeithamova and Maddox (2006).

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