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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.

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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.

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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

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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

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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).

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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,

1776 Nadler 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.