Psychological Science

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: 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

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.

 

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