Positive mood

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Psychological Science
21(12) 1770 –1776
© The Author(s) 2010
Reprints and permission:
DOI: 10.1177/0956797610387441
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: rnadler@uwo.ca
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
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.
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
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
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.
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.
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
Average subject
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.
1772Nadler 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
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.
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 µ f µ o σ f 2 σ o 2 cov f,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 33293 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 + (x f /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 rt io n C or re ct RD Category Set II Category Set .0 .2 .4 .6 .8 1.0 P ro po rt io n C or re ct .0 .2 .4 .6 .8 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. 1774Nadler 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 ro po rt io n F it by O pt im al M od el .0 .2 .4 .6 .8 1.0 P ro po rt io n F it by O pt im al M od el .0 .2 .4 .6 .8 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). References Ashby, F.G. (1992a). Multidimensional models of categorization. In F.G. Ashby (Ed.), Multidimensional models of perception and cognition (pp. 449–483). Hillsdale, NJ: Erlbaum. Ashby, F.G. (1992b). Multivariate probability distributions. In F.G. Ashby (Ed.), Multidimensional models of perception and cogni- tion (pp. 1–34). Hillsdale, NJ: Erlbaum. Ashby, F.G., Alfonso-Reese, L.A., Turken, A.U., & Waldron, E.M. (1998). A neuropsychological theory of multiple systems in cat- egory learning. Psychological Review, 105, 442–481. Ashby, F.G., & Ell, S.W. (2001). The neurobiology of human cat- egory learning. Trends in Cognitive Sciences, 5, 204–210. Ashby, F.G., & Gott, R. (1988). Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimen- tal Psychology: Learning, Memory, and Cognition, 14, 33–53. Ashby, F.G., Isen, A.M., & Turken, A.U. (1999). A neuropsychologi- cal theory of positive affect and its influence on cognition. Psy- chological Review, 106, 529–550. Ashby, F.G., & Maddox, W.T. (2005). Human category learning. Annual Review of Psychology, 56, 149–178. Gable, P., & Harmon-Jones, E. (2008). Approach-motivated positive affect reduces breadth of attention. Psychological Science, 19, 476–482. Gable, P., & Harmon-Jones, E. (2010). The blues broaden, but the nasty narrows. Psychological Science, 21, 211–215. Isen, A.M. (1990). The influence of positive and negative affect on cognitive organization: Some implications for development. In N.L. Stein, B. Leventhal, & T.R. Trabasso (Eds.), Psychological and biological approaches to emotion (pp. 75–94). Hillsdale, NJ: Erlbaum. Isen, A.M. (1999). On the relationship between affect and creative problem solving. In S.W. Russ (Ed.), Affect, creative experience, 1776Nadler et al. and psychological adjustment (pp. 3–17). Philadelphia, PA: Taylor & Francis. Isen, A.M., Daubman, K.A., & Nowicki, G.P. (1987). Positive affect facilitates creative problem solving. Journal of Personality and Social Psychology, 52, 1122–1131. Isen, A.M., Johnson, M.M.S., Mertz, E., & Robinson, G.F. (1985). The influence of positive affect on the unusualness of word associations. Journal of Personality and Social Psychology, 48, 1413–1426. Maddox, W.T., & Ashby, F.G. (1993). Comparing decision bound and exemplar models of categorization. Perception & Psychophysics, 53, 49–70. Maddox, W.T., & Ashby, F.G. (2004). Dissociating explicit and pro- cedural-learning based systems of perceptual category learning. Behavioral Processes, 66, 309–332. Maddox, W.T., Ashby, F.G., Ing, A.D., & Pickering, A.D. (2004). Disrupting feedback processing interferes with rule-based but not information-integration category learning. Memory & Cognition, 32, 582–591. Maddox, W.T., Baldwin, G., & Markman, A. (2006). A test of the regulatory fit hypothesis in perceptual classification learning. Memory & Cognition, 34, 1377–1397. Minda, J.P., & Miles, S. (2010). The influence of verbal and nonver- bal processing on category learning. In B. Ross (Ed.), The psy- chology of learning and motivation (pp. 117–162). Burlington, VT: Academic Press. Mitchell, R., & Phillips, L. (2007). The psychological, neurochemi- cal and functional neuroanatomical mediators of the effects of positive and negative mood on executive functions. Neuropsy- chologia, 45, 617–629. Nomura, E.M., Maddox, W.T., Filoteo, J.V., Ing, A.D., Gitelman, D.R., Parrish, T.B., et al. (2007). Neural correlates of rule-based and information-integration visual category learning. Cerebral Cortex, 17, 37–43. Pierce, J. (2007). PsychoPy—psychophysics software in Python. Journal of Neuroscience Methods, 162, 8–13. Smith, J.D., Tracy, J.I., & Murray, M.J. (1993). Depression and cat- egory learning. Journal of Experimental Psychology: General, 122, 331–346. Watson, D., Clark, L., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063–1070. Zeithamova, D., & Maddox, W. (2006). Dual-task interference in per- ceptual category learning. Memory & Cognition, 34, 387–398. ARTICLE CRITIQUE INSTRUCTIONS 1 ARTICLE CRITIQUE INSTRUCTIONS 2 Article Critique Instructions (60 points possible) Ryan J. Winter Florida International University Purpose of The Article Critique Paper 1). Psychological Purpose This paper serves several purposes, the first of which is helping you gain insight into research papers in psychology. As this may be your first time reading and writing papers in psychology, one goal of Paper I is to give you insight into what goes into such papers. This article critique paper will help you learn about the various sections of an empirical research report by reading at least one peer-reviewed articles (articles that have a Title Page, Abstract*, Literature Review, Methods Section, Results Section, and References Page—I have already selected some articles for you to critique, so make sure you only critique one in the folder provided on Canvas) This paper will also give you some insights into how the results sections are written in APA formatted research articles. Pay close attention to those sections, as throughout this course you’ll be writing up some results of your own! In this relatively short paper, you will read one of five articles posted on Canvas and summarize what the authors did and what they found. The first part of the paper should focus on summarizing the design the authors used for their project. That is, you will identify the independent and dependent variables, talk about how the authors carried out their study, and then summarize the results (you don’t need to fully understand the statistics in the results, but try to get a sense of what the authors did in their analyses). In the second part of the paper, you will critique the article for its methodological strengths and weaknesses. Finally, in part three, you will provide your references for the Article Critique Paper in APA format. 2). APA Formatting Purpose The second purpose of the Article Critique paper is to teach you proper American Psychological Association (APA) formatting. In the instructions below, I tell you how to format your paper using APA style. There are a lot of very specific requirements in APA papers, so pay attention to the instructions below as well as the APA style powerpoint on Canvas. We are using the 7th edition of the APA style manual. 3). Writing Purpose Finally, this paper is intended to help you grow as a writer. Few psychology classes give you the chance to write papers and receive feedback on your work. This class will! We will give you feedback on this paper in terms of content, spelling, and grammar. Article Critique Paper (60 points possible) Each student is required to write an article critique paper based on one of the research articles present on Canvas only those articles listed on Canvas can be critiqued – if you critique a different article, it will not be graded). If you are unclear about any of this information, please ask. What is an article critique paper? An article critique is a written communication that conveys your understanding of a research article and how it relates to the conceptual issues of interest to this course. This article critique paper will include 5 things: 1.Title page: 1 page- 4 points · Use APA style to present the appropriate information: · A Running head must be included and formatted APA style · The running head is 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). 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 in the header on the title page and all subsequent pages. · 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). The title should be bolded. · Your name will appear on the title page, include 2 double spaced lines between the title and your name (see the title page here). Your name and institutional affiliation (the name of your university) should not be bold. · Your institution will appear on the title page as well · 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. · 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: 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?) 3.Critique of the study: 1 ½ pages minimum - 3 pages maximum - 16 points 1. 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. 1. 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, are there any confounding variables in the study (these could be extraneous variables or nuisance variables)? If so, explain what the confound is and specifically how it is impacting the results of the study. A sufficient explanation of this will include at least one paragraph of writing. · 2). Is the sample used in the study an appropriate sample? Is the sample representative of the population? Could the study be replicated if it were done again? Why or why not? · 3). Did they measure the dependent variable in a way that is valid? Be sure to explain what validity is, and why you believe the dependent variable was or was not measured in a way that was valid. · 4). Did the study authors correctly interpret their findings, or are there any alternative interpretations you can think of? · 5). Did the authors of the study employ appropriate ethical safeguards? · 6). 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 · 7). 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 · 8). Mention additional implications of the findings not mentioned in the article (either theoretical or practical/applied) · 9). 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. · 10). 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 Two 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. 7. Self-Rating Rubric (10 points). On canvas, you will find a self-rating rubric. This rubric contains a summary of all the points available to you in this paper. You must submit your ratings for your own paper, using this rubric (essentially, you’ll grade your own paper before you hand it in). You will upload your completed rubric to the “article critique rubric” assignment on Canvas. · Please put effort into your ratings. Do not simply give yourself a 50/50. Really reflect on the quality of your paper and whether you meet all the criteria listed. 1. If it is clear that you have not reflected sufficiently on your paper (e.g., you give a rating of 2/2 for something that is not included in your paper), you will lose points. · This does not mean that you are guaranteed whatever grade you give to yourself. Instead, this will help you to 1) make sure that you have included everything you need to include, and 2) help you to reflect on your own writing. · In fact, we will use this very same rubric when we grade your paper, so you should know exactly what to expect for your grade! Other guidelines for the article critique papers 1. 1). Pay attention to the page length requirements – 1 page for the title page, 1.5 pages to 3 pages for the summary, 1.5 pages to 3 pages for the critique, one or two paragraphs for the brief summary, and 1 page for the references page. If you are under the minimum, we will deduct points. If you go over the maximum, we are a little more flexible (you can go over by half page or so), but we want you to try to keep it to the maximum page. 1. 2). Page size is 8 1/2 X 11” with all 4 margins set one inch on all sides. You must use 12-point Times New Roman font. 1. 3). As a general rule, ALL paragraphs and sentences are double spaced in APA papers. It even includes the references, so make sure to double space EVERYTHING 1. 4). When summarizing the article in your own words, you need not continually cite the article throughout the rest of your critique. Nonetheless, you should follow proper referencing procedures, which means that: 3. If you are inserting a direct quote from any source, it must be enclosed in quotations and followed by a parenthetical reference to the source. “Let’s say I am directly quoting this current sentence and the next. I would then cite it with the author name, date of publication, and the page number for the direct quote” (Winter, 2013, p . 4). 0. Note: We will deduct points if you quote more than once per page, so keep quotes to a minimum. Paraphrase instead, but make sure you still give the original author credit for the material by citing him or using the author’s name (“In this article, Smith noted that …” or “In this article, the authors noted that…”) 3. If you choose to reference any source other than your chosen article, it must be listed in a reference list. 1. 5). Proofread everything you write. I actually recommend reading some sentences aloud to see if they flow well, or getting family or friends to read your work. Writing quality will become more important in future papers, so you should start working on that now! If you have any questions about the articles, your ideas, or your writing, please ask. Although we won’t be able to review entire drafts of papers before they are handed in, we are very willing to discuss problems, concerns or issues that you might have.

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