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Creative potential, attention, and speed of information processing
Personality and Individual Differences 43 (2007) 1470–1480 www.elsevier.com/locate/paid
Creative potential, attention, and speed of information processing Oshin Vartanian *, Colin Martindale, Jonna Kwiatkowski University of Maine, Orono, Maine, USA Received 22 September 2006; received in revised form 30 March 2007; accepted 17 April 2007 Available online 14 June 2007
Abstract Despite the conceptual overlap between intelligence and creativity, little systematic work exists on the link between creativity and speed of information processing. We hypothesized that differential focusing of attention determines the relationship between creative potential and speed of information processing. Supporting our predictions, we found negative correlations between creative potential and reaction time on tasks not involving interference (Hick Task, Concept Verification Task), but positive correlations between creative potential and reaction time on tasks requiring the inhibition of interfering information (Negative Priming, Global Precedence). This pattern of results suggests that the relation between creative potential and reaction time is a function of the differential focusing of attention. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Attention; Creativity; Inhibition; Intelligence; Interference; Reaction time
1. Introduction There is a rich tradition in psychology on the relation between intelligence and speed of information processing (Jensen, 1982). In contrast, although there is evidence that intelligence *
Corresponding author. Address: Defence Research and Development Canada, 1133 Sheppard Avenue West, P.O. Box 2000, Toronto, Ontario, Canada M3M 3B9. Tel.: +1 416 635 2000x3008. E-mail address: [email protected] (O. Vartanian). 0191-8869/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.paid.2007.04.027
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and creativity are related constructs (Sternberg & O’Hara, 1999), there has been little work on the link between creativity and speed of information processing. A cognitive process that has been hypothesized to play a critical role in creativity and has the potential to shed light on this relation is attention. Creative people have been characterized by more defocused attention than noncreative people (Mendelsohn, 1976). Such defocused attention may facilitate ‘‘attentional capture’’ (von Muhlenen, Rempel, & Enns, 2005), enabling concepts that are deemed irrelevant to a problem to capture their attention and in some cases provide the clues or building blocks for solutions. In turn, defocused attention may be caused by reduced cognitive inhibition, where inhibition is understood as a mechanism that can restrict the flow of information into the focus of attention. The consequence of reduced cognitive inhibition is that more information is allowed into the focus of attention for processing. In support of this view, there is evidence that the tendency not to inhibit seemingly irrelevant information leads to better problem solving ability in creative people (Carson, Peterson, & Higgins, 2003; Peterson & Carson, 2000; Peterson, Smith, & Carson, 2002). Martindale (1999) has hypothesized that rather than having a stable disposition toward defocused attention, creative people are better at adjusting their focus of attention depending on task demands (see Ansburg & Hill, 2003; Rawlings, 1985), and that this adjustment is automatic or reactive rather than one involving self-control. In essence, creative people exhibit differential rather than reduced focusing of attention. In earlier phases of problem solving when the problem is relatively ill-defined, creative people are more likely to defocus attention. This tendency makes the central task more susceptible to interference by seemingly irrelevant information, some of which may provide the building blocks for solutions. However, this widening of attention comes at the cost of slowing down processing on the task. In later stages of problem solving when creative people are verifying developed ideas, performance will benefit through the inhibition of irrelevant stimuli and added focus on the task. This narrowing of attention speeds up processing on the task. Evidence from EEG studies has offered indirect support for Martindale’s theory. Martindale and Hines (1975) measured EEG alpha wave activity—an inverse measure of cortical arousal—while participants completed creativity and intelligence tests. In creative people there was a lower level of cortical arousal while they were engaged in the creativity test, but higher arousal during the intelligence test. In contrast, people lower in creativity exhibited equally high levels of arousal across creativity and intelligence tests. These results suggest that creative people may be more defocused—as characterized by lower cortical arousal—only when they are engaged in creative production, but not otherwise. This feature distinguishes Martindale’s theory from Eysenck’s (1995) and Mendelsohn’s (1976) theories that proposed similar ideas but argued that in creative people defocused attention is a stable trait rather than a variable state. We tested predictions derived from Martindale’s (1999) theory of creativity using four reaction time (RT) tasks. Martindale predicted positive correlation between creative potential and RT when the potential for interference by distracting information is high, and the reverse when interference by distracting information is low. We selected the Hick Task (Hick, 1952) and the Concept Verification Task (Knorr & Neubauer, 1996) as tasks not involving interference. The Hick Task is an elementary RT task that places minimal cognitive demands on the participant (Neubauer, Riemann, Mayer, & Angleitner, 1997). On each trial the participant is instructed to press a button upon detecting a stimulus. Given that there is no buildup of inhibition or interference within or across trials, we predicted a negative correlation between creative potential and RT in this task.
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Each trial in the Concept Verification Task starts by the presentation of a rule (e.g., ‘‘ORANGE and SQUARE’’), and the participant must press a button upon understanding it. Then, an object appears on the screen and the participant must press a button indicating whether it matches the rule. RT is recorded for both judgments. Again, given that there is no buildup of inhibition or interference within or across trials, we predicted that there would be a negative correlation between creative potential and RT in this task. In summary, we predicted that in subjects with higher creative potential, narrowing of the focus of attention in the context of the Hick Task and the Concept Verification Task would lead to faster processing. We selected Negative Priming (Claridge, Clark, & Beech, 1992) and Global Precedence (Navon, 1977) as tasks requiring the inhibition of interfering information. In Negative Priming participants are presented with successive pairs of words written in different colors. Participants are instructed to indicate the color of the second word in each pair. Normally, participants are slower on those trials where the name of the first word in the pair is the same as the color of the second word in the pair (e.g., the word RED written in blue followed by the word GREEN written in red), because inhibiting responses to the first word (i.e., the word RED) slows down responding to the color of the second word (i.e., red). Because of the buildup of interference across trials, we predicted that there would be a positive correlation between creative potential and RT in all conditions of this task. In Global Precedence participants are presented with large letters made up of smaller letters. Some trials require that participants inhibit local features and identify the big letter, whereas other trials require that they inhibit global features and identify the small letters. Evidence suggests that global information is processed before local information because of global advantage in response time and global interference with local processing (Navon, 1977; Navon & Norman, 1983). Because interference is maximal when global features must be inhibited in favour of local features, we predicted that there would be a positive correlation between creative potential and RT specifically on those trials where participants are instructed to identify the small letters. In summary, we predicted that in subjects with higher creative potential, widening of the focus of attention in the context of Negative Priming and those Global Precedence trials where global features must be inhibited in favour of local features would lead to slower processing.
2. Method Our participants were 104 male undergraduates (18–26 years) who were offered course credit for participation. This study consisted of two parts. In the first part participants completed a battery of paper-and-pencil tests in a number of group sessions. Participants who completed the first part of the study were subsequently booked for individual sessions during which they completed our battery of tasks. 2.1. Paper-and-pencil tests The paper-and-pencil tests included psychometric measures of creative potential and a measure of intelligence. To determine creative potential, participants completed the Remote Associates Test (Mednick, 1962), the Alternate Uses Test (Wallach & Kogan, 1965), and the Creative
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Personality Scale (Gough, 1979). The Remote Associates Test consists of 30 items. Each item includes three words (e.g., blood, music, cheese) that can be linked through a fourth associated word (i.e., blue). Participants were given 30 minutes to solve as many items as possible. The final score consisted of the total number of solved items. The Alternate Uses Test involves generating as many uses as possible for three common objects (brick, shoe, newspaper). Participants are allowed three minutes per object. The final score consisted of adding the total number of uses generated across the three objects (fluency). Although performance on the Alternate Uses Test can be measured in other ways (e.g., flexibility, originality), fluency accounts for almost all of the variance on divergent thinking tests (Plucker & Renzulli, 1999). The Creative Personality Scale (Gough, 1979) consists of 30 items that have positive or negative weights. Items with positive weights are characteristic of people with creative potential, whereas items with negative weights are not. The total score consisted of adding the weights of the selected adjectives. Because measurement of creative potential is more reliable when it is based upon multiple tests (see Dailey, Martindale, & Borkum, 1997; Martindale, 1999; Martindale, Anderson, Moore, & West, 1996; Martindale & Dailey, 1996), we standardized scores on each of the three measures and then averaged across the three standardized scores to create a composite measure called creative potential for each participant. Our measure of intelligence was the vocabulary subset of the Shipley Institute of Living Test which consists of 40 multiple-choice items (Zachary, 1986). For each item the participant must choose one of four words that is closest in meaning to a target word (its synonym). Participants are allowed 10 minutes to complete the test. Scores on the Shipley Institute of Living Test correlate .80 with scores on the Wechsler Adult Intelligence Scale—Revised (Zachary, 1986; see also Mason, Lemmon, Wayne, & Schmidt, 1991). 2.2. Reaction time tasks The computer programs were written in Visual Basic (Fig. 1). The order of tasks was randomized for each participant. Our battery of tasks included six tasks of vaying length, only four of which are relevant to our hypotheses and reported here. This explains differences in the number of participants who completed each task. Before starting each task, participants were given detailed verbal instructions by the experimenter followed by written, interactive instruction during a practice session. All programs recorded response accuracy, and only RTs for correct responses were analyzed. To counteract expectancy effects, the inter-stimulus interval (ISI) between successive trials in all tasks was random, and varied from one to four seconds. 2.2.1. Hick Task Hick (1952) measured latencies for detecting a stimulus, and for reacting to the detected stimulus. The initial computer window presented a horizontal row of one, three, or five unlabeled buttons centered in the top half of the screen and two buttons labeled ‘‘See It’’ and ‘‘Ready’’ (Fig. 1). Participants started each trial by clicking on the ‘‘Ready’’ button. They were instructed to click the ‘‘See It’’ button upon seeing a yellow circle appear above any unlabeled button. Then, they were to click the button appearing below the yellow circle. This ended the trial. The number of buttons that appeared on each trial (one, three, or five) was randomized. Latencies were recorded for clicking on the ‘‘See It’’ button (detection of light) and for clicking on the button below the yellow circle (reaction to stimulus).
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Fig. 1. Computer interfaces for the RT tasks used in this study. Note. The interface during a three-button trial in the Hick Task (a); the interface during an ‘‘AND’’ trial in the Concept Verification Task (b); the interface on a trial from Negative Priming where the participant must press ‘‘Green’’ in response to the color of the displayed word (c); the interface on a trial from Global Precedence where the participant must press ‘‘H’’ either in response to identifying the big letter or the small letters (d).
2.2.2. Concept Verification Task The Concept Verification Task (Knorr & Neubauer, 1996) measured latencies for understanding a rule and for deciding if an object satisfied the rule. At the start of each trial participants were presented with a written rule centered in the top half of the computer window (Fig. 1). Participants were instructed to click on the ‘‘Understand’’ button once the rule was understood, which was then followed by the appearance of a geometric shape in the space where the rule had appeared before. The geometric shape either matched or did not match the rule. For example, if the rule said ‘‘RED and SQUARE’’, a matching shape would be a red square and a shape that did not match the rule would be a green square. The participants were instructed to click on the ‘‘Match’’ or ‘‘No Match’’ button as soon as they had decided if the shape matched the rule or not. This ended the trial. Latencies were recorded for clicking the ‘‘Understand’’ button and for clicking the ‘‘Match’’ or ‘‘No Match’’ button. There were five types of rules: SINGLE WORD
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(e.g., ‘‘ORANGE’’), AND (e.g., ‘‘ORANGE and SQUARE’’), OR (e.g., ‘‘ORANGE or SQUARE’’), AND NOT (e.g., ‘‘ORANGE and not SQUARE’’), and BUT NOT BOTH (e.g., ‘‘ORANGE or SQUARE, BUT NOT BOTH’’). 2.2.3. Negative Priming In Negative Priming (Claridge et al., 1992) participants were presented with successive pairs of color words written in different colors (Fig. 1). Participants were instructed to click on the button that indicated the color of the second word in each pair. There were four types of relationships between the words. The first type, ‘‘negative priming’’, paired words based on color. The name of the first word in the pair was the same as the color of the second word in the pair (e.g., the word RED written in blue followed by the word GREEN written in red). The second type, ‘‘distractor’’, paired words randomly except when that led to a negative priming relationship. The third type, ‘‘same’’, paired the same first word (in all trials) with randomly selected second words except when that led to a negative priming relationship. The fourth type, ‘‘X condition’’, randomly paired sets of the letter ‘X’ of varying length and color. Each type was presented 12 times over the course of the task in random order. 2.2.4. Global Precedence In Global Precedence (Navon, 1977) participants were presented with large letters made up of smaller letters (Fig. 1). Participants were instructed to respond to either the big letter or the small letters that made up the big letter. For the ‘‘big letter’’ condition, participants were presented with a random selection of the letters ‘‘H’’ and ‘‘S’’ made up of ‘‘H’’, ‘‘S’’, or little squares. For the ‘‘little letters’’ condition, participants were presented with a random selection of ‘‘H’’, ‘‘S’’, and squares made up of either ‘‘H’’ or ‘‘S’’ but not little squares. Participants responded by clicking on the button labeled with the appropriate letter.
3. Results Means and standard deviations for all paper-and-pencil measures are presented in Table 1. Pearson correlation coefficients between all paper-and-pencil measures are presented in Table 2. The Vocabulary subset of the Shipley Institute of Living Test was intended for use as a covariate in all correlational analyses, but this procedure was not undertaken for two reasons. First, the correlation between vocabulary scores and creative potential was not significant, r(102) = .07.
Table 1 Means and standard deviations for measures of creative potential and intelligence (N = 104) Instrument
M
SD
Alternate Use Test (fluency) Remote Associates Test Creative Personality Scale Shipley Institute of Living (vocabulary)
32.71 8.92 10.71 24.47
10.90 3.45 5.65 10.35
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Table 2 Pearson correlation coefficients between creative potential measures and scores on the vocabulary section of the Shipley Institute of Living Test (N = 104) RAT AUT CPS SYN
RAT
AUT
CPS
SYN
– .10 (.20) .20* (.42) .22* (.45)
– .24* (.51) .04 (.07)
– .02 (.03)
–
* Indicates
p < .05. RAT = Remote Associates Test, AUT = Alternate Uses Test, CPS = Creative Personality Note: Scale, SYN = Vocabulary (synonyms) subscale score on the Shipley Institute of Living Test. Effect sizes are indicated in p parentheses and were calculated using the formula d = 2r/ (1 r2) (Cohen, 1988).
Second, the correlation between vocabulary scores and RT did not reach significance on any component of any task (Table 3). We tested our main hypothesis by computing Pearson correlation coefficients between RT and creative potential for the four tasks. Note that testing our predictions did not require testing for significant differences between correlations. We predicted a negative correlation between creative potential and RT on the Hick Task and the Concept Verification Task. The Hick Task measures latencies for noticing and reacting to stimuli. Supporting our prediction there was a significant negative correlation between creative potential and RT for reacting to the light (r[61] = .27, p < .05), but not for noticing the light (r[61] = .18). The Concept Verification Task measures latencies for understanding a rule and making a decision about it. Supporting our prediction there was a significant negative correlation between creative potential and RT for understanding the rule (r[42] = .42, p < .01), but not for making a decision based on the rule (r[42] = .24). In contrast, we predicted a positive correlation between creative potential and RT on Negative Priming, and when global features had to be inhibited in favour of local features in Global Precedence. Supporting both predictions, there was a significant positive correlation between creative potential and RT across all trials in Negative Priming (r[49] = .28, p < .05), and a positive correlation between creative potential and RT for reacting to local features in Global Precedence (r[102] = .32, p < .001).
4. Discussion Based on Martindale’s (1999) theory, we predicted that there would be a negative correlation between creative potential and RT in the Hick Task and the Concept Verification Task, but a positive correlation between creative potential and RT in Negative Priming and when global features had to be inhibited in favour of local features in Global Precedence. Overall, the results supported this prediction. We next turn to a more detailed analysis of the results. The Hick Task measures how quickly one can notice a perceptual event and react to it. Neither step involves the introduction of interference by task-irrelevant information. There was a negative correlation between creative potential and RT for reacting to a perceptual event. Closer inspection of the trend for correlations between creative potential and RT for reacting to the perceptual event across the one (r[61] = .34, p < .05), three (r[61] = .26, p < .05), and five (r[61] = .13,
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Table 3 Pearson correlation coefficients between RT, creative potential, and vocabulary scores on the Shipley Institute of Living Test Task
Condition
Hick Task (N = 63)
Noticing the light Across all conditions One button Three buttons Five buttons Clicking the lit button Across all conditions One button Three buttons Five buttons
CVT (N = 44)
Understanding the rule Across all conditions SINGLE WORD AND OR AND NOT BUT NOT BOTH Deciding Match/No Match Across all conditions SINGLE WORD AND OR AND NOT BUT NOT BOTH
NP (N = 51)
Across all conditions Negative priming Random Same XXX
GP (N = 104)
Big letter (global feature) Across all conditions H made up of small H H made up of small S S made up of small H S made up of small S H made up of small squares S made up of small squares Small letters (local feature) Across all conditions Small H making up big H Small H making up big S Small S making up big H Small S making up big S Small H making up a square Small S making up a square
Creative potential
d
SYN
r(61) = .18 r(61) = .21 r(61) = .19 r(61) = .08
.36 .44 .38 .15
.07 .10 .04 .07
r(61) = .27* r(61) = .34* r(61) = .26* r(61) = .13
.57 .77 .56 .25
.15 .18 .12 .11
r(42) = .42** r(42) = .20 r(42) = .44** r(42) = .31* r(42) = .38** r(42) = .38*
1.02 .42 1.08 .69 .89 .89
.02 .05 .06 .02 .01 .04
r(42) = .24 r(42) = .27 r(42) = .42** r(42) = .12 r(42) = .12 r(42) = .08
.51 .57 1.02 .23 .23 .15
.09 .05 .11 .14 .05 .23
r(49) = .28* r(49) = .23 r(49) = .28* r(49) = .29* r(49) = .28*
.61 .49 .61 .62 .61
.08 .08 .08 .07 .04
r(102) = .12 r(102) = .03 r(102) = .12 r(102) = .11 r(102) = .22* r(102) = .16 r(102) = .01
.23 .05 .24 .21 .45 .33 .01
.05 .02 .06 .06 .04 .15 .03
r(102) = .32*** r(102) = .24** r(102) = .29** r(102) = .29** r(102) = .27** r(102) = .34*** r(102) = .24**
.70 .51 .62 .62 .57 .77 .51
.06 .02 .10 .11 .11 .02 .03
Note: CVT = Concept Verification Task, NP = Negative Priming, GP = Global Precedence, SYN = Vocabulary (synonyms) p subscale score on the Shipley Institute of Living Test. Effect sizes were calculated using the formula d = 2r/ (1 r2) (Cohen, 1988). * Indicates p < .05, ** indicates p < .01, *** indicates p < .001.
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ns) button conditions offers further support for our hypothesis (Table 3). One would expect attention to be at its most focused when a single button was present, as opposed to when three or five were present. Because of the tendency to focus attention in the absence of distraction one would expect a decreasing trend for the strength of the correlation between creative potential and RT as the number of buttons increased, which is precisely what happened. Thus, it seems that creative participants are fastest when distraction is minimal, thus allowing maximal focus. This may also explain why there was no correlation between creative potential and RT for noticing a light, which required distributed attention across five potential target locations. The Concept Verification Task measures the speed with which one can understand a rule and for making a decision based on that rule. Like the Hick Task, this task does not introduce interference by task-irrelevant information into either step. There was a significant negative correlation between creative potential and RT for understanding rules, but not for making a decision based on rules, and this pattern was present across four of five rule categories (Table 3). This indicates that in the absence of interfering information creative people are faster in internalizing a rule, but having understood the rule they may be no faster than noncreative participants in making a decision based on that rule. Having understood the rule both creative and noncreative subjects may invariably focus attention for making the ‘‘Match’’ or ‘‘No Match’’ decision, thus minimizing individual differences in focus of attention. For Negative Priming we predicted that people with higher creative potential should be slower in all conditions because the nature of interference varies on a trial-by-trial basis, and this variance itself introduces an added level of ambiguity to the task. The results supported our prediction (Table 3). Incidentally, Eysenck (1995) hypothesized that due to lower buildup of inhibition to the first word, creative people would be faster in indicating the color of the second word on negative priming trials, thus exhibiting shorter RT specifically when inhibitory interference is maximal. Our results do not support Eysenck’s (1995) hypothesis, and instead suggest that when confronted with stimuli that interfere with the central task, people with higher creative potential will be slower than those with lower creative potential. Finally, results from Global Precedence supported our predictions as well. In Global Precedence interference is maximal in trials where global features must be inhibited in favour of local features (i.e., where interference is higher and inhibition more difficult), but not when local features had to be inhibited in favour of global features (i.e., where interference is lower and inhibition less difficult). The fact that global-level information is processed before local-level information has been attributed to global advantage in response time, and global interference with local processing (Navon, 1977; Navon & Norman, 1983), a pattern repeated in our study where global features (M = 652 ms, SD = 109) were processed faster than local features (M = 677 ms, SD = 118). The significant positive correlation between creative potential and RT for reacting to local but not global features indicates that higher creative potential leads to longer latencies specifically under those conditions where interreference is maximal, but not otherwise. It is noteworthy that scores on the vocabulary subset of the Shipley Institute of Living Test were unrelated to creative potential or RT in any of the four tasks (Table 3). This may be because vocabulary scores do not measure fluid intelligence, and that a measure of fluid intelligence may have shown higher correlations with RT and creative potential than did vocabulary scores. Notwithstanding this possibility, our results suggest that at least in the context of our study the link
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between creative potential and RT was not a function of how those variables are related to crystallized intelligence. Overall, our results demonstrate that the link between creative potential and speed of information processing is a function of the extent to which focus of attention is adjusted in relation to task demands. However, a critical question that remains is how the focus of attention is adjusted flexibly in people with higher creative potential. Martindale (1999) has argued that this adjustment is automatic or reactive rather than involving self-control. Potentially, this bottom-up process may be sensitive to the granularity or ambiguity of features in the problem space. In contrast, this flexible adjustment process may be driven by controlled as well as spontaneous processes in relation to working memory capacity (Dietrich, 2004), or by top-down processes in the service of strategy change (Haider, Frensch, & Joram, 2005). Given our results, the logical next step would be an examination of mechanisms that control this variability in people with higher creative potential. References Ansburg, P. I., & Hill, K. (2003). Creative and analytic thinkers differ in their use of attentional resources. Personality and Individual Differences, 34, 1141–1152. Carson, S. H., Peterson, J. B., & Higgins, D. M. (2003). Decreased latent inhibition is associated with increased creative achievement in high-functioning individuals. Journal of Personality and Social Psychology, 85, 499–506. Claridge, G. S., Clark, K. H., & Beech, A. R. (1992). Lateralization of the negative priming effect: Relationships with schizotypy and with gender. British Journal of Psychology, 83, 13–23. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Erlbaum. Dailey, A., Martindale, C., & Borkum, J. (1997). Creativity, physiognomic perception, and synaesthesia. Creativity Research Journal, 10, 1–8. Dietrich, A. (2004). The cognitive neuroscience of creativity. Psychonomic Bulletin and Review, 11, 1011–1026. Eysenck, H. J. (1995). Genius: The natural history of creativity. Cambridge: Cambridge University Press. Gough, H. G. (1979). A creative personality scale for the adjective checklist. Journal of Personality and Social Psychology, 37, 1398–1405. Haider, H., Frensch, P. A., & Joram, D. (2005). Are strategy shifts caused by data-driven processes or by voluntary processes? Consciousness and Cognition, 14, 495–519. Hick, W. E. (1952). On the rate of gain of information. Quarterly Journal of Experimental Psychology, 4, 11–26. Jensen, A. R. (1982). Reaction time and psychometric g. In H. J. Eysenck (Ed.), A model for intelligence (pp. 93–132). Berlin: Springer. Knorr, E., & Neubauer, A. C. (1996). Speed of information processing in an inductive reasoning task and its relationship to psychometric intelligence. Personality and Individual Differences, 20, 653–660. Martindale, C. (1999). Biological bases of creativity. In R. J. Sternberg (Ed.), Handbook of creativity (pp. 137–152). New York: Cambridge University Press. Martindale, C., Anderson, K., Moore, K., & West, A. N. (1996). Creativity, oversensitivity, and rate of habituation. Personality and Individual Differences, 20, 423–427. Martindale, C., & Dailey, A. (1996). Creativity, primary process cognition, and personality. Personality and Individual Differences, 20, 409–414. Martindale, C., & Hines, D. (1975). Creativity and cortical activation during creative, intellectual, and EEG feedback tasks. Biological Psychology, 3, 71–80. Mason, C. F., Lemmon, D., Wayne, K. S., & Schmidt, R. (1991). Shipley Institute of Living Scale: Formulas for abstraction quotients from a normative sample of 580. Sex and socioeconomic status considered as additional moderating variables. Psychological Assessment: A Journal of Consulting and Clinical Psychology, 3, 412–417. Mednick, S. A. (1962). The associative basis of the creative process. Psychological Review, 69, 220–232. Mendelsohn, G. A. (1976). Associative and attentional processes in creative performance. Journal of Personality, 44, 341–369.
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Creative potential, attention, and speed of information processing Oshin Vartanian *, Colin Martindale, Jonna Kwiatkowski University of Maine, Orono, Maine, USA Received 22 September 2006; received in revised form 30 March 2007; accepted 17 April 2007 Available online 14 June 2007
Abstract Despite the conceptual overlap between intelligence and creativity, little systematic work exists on the link between creativity and speed of information processing. We hypothesized that differential focusing of attention determines the relationship between creative potential and speed of information processing. Supporting our predictions, we found negative correlations between creative potential and reaction time on tasks not involving interference (Hick Task, Concept Verification Task), but positive correlations between creative potential and reaction time on tasks requiring the inhibition of interfering information (Negative Priming, Global Precedence). This pattern of results suggests that the relation between creative potential and reaction time is a function of the differential focusing of attention. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Attention; Creativity; Inhibition; Intelligence; Interference; Reaction time
1. Introduction There is a rich tradition in psychology on the relation between intelligence and speed of information processing (Jensen, 1982). In contrast, although there is evidence that intelligence *
Corresponding author. Address: Defence Research and Development Canada, 1133 Sheppard Avenue West, P.O. Box 2000, Toronto, Ontario, Canada M3M 3B9. Tel.: +1 416 635 2000x3008. E-mail address: [email protected] (O. Vartanian). 0191-8869/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.paid.2007.04.027
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and creativity are related constructs (Sternberg & O’Hara, 1999), there has been little work on the link between creativity and speed of information processing. A cognitive process that has been hypothesized to play a critical role in creativity and has the potential to shed light on this relation is attention. Creative people have been characterized by more defocused attention than noncreative people (Mendelsohn, 1976). Such defocused attention may facilitate ‘‘attentional capture’’ (von Muhlenen, Rempel, & Enns, 2005), enabling concepts that are deemed irrelevant to a problem to capture their attention and in some cases provide the clues or building blocks for solutions. In turn, defocused attention may be caused by reduced cognitive inhibition, where inhibition is understood as a mechanism that can restrict the flow of information into the focus of attention. The consequence of reduced cognitive inhibition is that more information is allowed into the focus of attention for processing. In support of this view, there is evidence that the tendency not to inhibit seemingly irrelevant information leads to better problem solving ability in creative people (Carson, Peterson, & Higgins, 2003; Peterson & Carson, 2000; Peterson, Smith, & Carson, 2002). Martindale (1999) has hypothesized that rather than having a stable disposition toward defocused attention, creative people are better at adjusting their focus of attention depending on task demands (see Ansburg & Hill, 2003; Rawlings, 1985), and that this adjustment is automatic or reactive rather than one involving self-control. In essence, creative people exhibit differential rather than reduced focusing of attention. In earlier phases of problem solving when the problem is relatively ill-defined, creative people are more likely to defocus attention. This tendency makes the central task more susceptible to interference by seemingly irrelevant information, some of which may provide the building blocks for solutions. However, this widening of attention comes at the cost of slowing down processing on the task. In later stages of problem solving when creative people are verifying developed ideas, performance will benefit through the inhibition of irrelevant stimuli and added focus on the task. This narrowing of attention speeds up processing on the task. Evidence from EEG studies has offered indirect support for Martindale’s theory. Martindale and Hines (1975) measured EEG alpha wave activity—an inverse measure of cortical arousal—while participants completed creativity and intelligence tests. In creative people there was a lower level of cortical arousal while they were engaged in the creativity test, but higher arousal during the intelligence test. In contrast, people lower in creativity exhibited equally high levels of arousal across creativity and intelligence tests. These results suggest that creative people may be more defocused—as characterized by lower cortical arousal—only when they are engaged in creative production, but not otherwise. This feature distinguishes Martindale’s theory from Eysenck’s (1995) and Mendelsohn’s (1976) theories that proposed similar ideas but argued that in creative people defocused attention is a stable trait rather than a variable state. We tested predictions derived from Martindale’s (1999) theory of creativity using four reaction time (RT) tasks. Martindale predicted positive correlation between creative potential and RT when the potential for interference by distracting information is high, and the reverse when interference by distracting information is low. We selected the Hick Task (Hick, 1952) and the Concept Verification Task (Knorr & Neubauer, 1996) as tasks not involving interference. The Hick Task is an elementary RT task that places minimal cognitive demands on the participant (Neubauer, Riemann, Mayer, & Angleitner, 1997). On each trial the participant is instructed to press a button upon detecting a stimulus. Given that there is no buildup of inhibition or interference within or across trials, we predicted a negative correlation between creative potential and RT in this task.
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Each trial in the Concept Verification Task starts by the presentation of a rule (e.g., ‘‘ORANGE and SQUARE’’), and the participant must press a button upon understanding it. Then, an object appears on the screen and the participant must press a button indicating whether it matches the rule. RT is recorded for both judgments. Again, given that there is no buildup of inhibition or interference within or across trials, we predicted that there would be a negative correlation between creative potential and RT in this task. In summary, we predicted that in subjects with higher creative potential, narrowing of the focus of attention in the context of the Hick Task and the Concept Verification Task would lead to faster processing. We selected Negative Priming (Claridge, Clark, & Beech, 1992) and Global Precedence (Navon, 1977) as tasks requiring the inhibition of interfering information. In Negative Priming participants are presented with successive pairs of words written in different colors. Participants are instructed to indicate the color of the second word in each pair. Normally, participants are slower on those trials where the name of the first word in the pair is the same as the color of the second word in the pair (e.g., the word RED written in blue followed by the word GREEN written in red), because inhibiting responses to the first word (i.e., the word RED) slows down responding to the color of the second word (i.e., red). Because of the buildup of interference across trials, we predicted that there would be a positive correlation between creative potential and RT in all conditions of this task. In Global Precedence participants are presented with large letters made up of smaller letters. Some trials require that participants inhibit local features and identify the big letter, whereas other trials require that they inhibit global features and identify the small letters. Evidence suggests that global information is processed before local information because of global advantage in response time and global interference with local processing (Navon, 1977; Navon & Norman, 1983). Because interference is maximal when global features must be inhibited in favour of local features, we predicted that there would be a positive correlation between creative potential and RT specifically on those trials where participants are instructed to identify the small letters. In summary, we predicted that in subjects with higher creative potential, widening of the focus of attention in the context of Negative Priming and those Global Precedence trials where global features must be inhibited in favour of local features would lead to slower processing.
2. Method Our participants were 104 male undergraduates (18–26 years) who were offered course credit for participation. This study consisted of two parts. In the first part participants completed a battery of paper-and-pencil tests in a number of group sessions. Participants who completed the first part of the study were subsequently booked for individual sessions during which they completed our battery of tasks. 2.1. Paper-and-pencil tests The paper-and-pencil tests included psychometric measures of creative potential and a measure of intelligence. To determine creative potential, participants completed the Remote Associates Test (Mednick, 1962), the Alternate Uses Test (Wallach & Kogan, 1965), and the Creative
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Personality Scale (Gough, 1979). The Remote Associates Test consists of 30 items. Each item includes three words (e.g., blood, music, cheese) that can be linked through a fourth associated word (i.e., blue). Participants were given 30 minutes to solve as many items as possible. The final score consisted of the total number of solved items. The Alternate Uses Test involves generating as many uses as possible for three common objects (brick, shoe, newspaper). Participants are allowed three minutes per object. The final score consisted of adding the total number of uses generated across the three objects (fluency). Although performance on the Alternate Uses Test can be measured in other ways (e.g., flexibility, originality), fluency accounts for almost all of the variance on divergent thinking tests (Plucker & Renzulli, 1999). The Creative Personality Scale (Gough, 1979) consists of 30 items that have positive or negative weights. Items with positive weights are characteristic of people with creative potential, whereas items with negative weights are not. The total score consisted of adding the weights of the selected adjectives. Because measurement of creative potential is more reliable when it is based upon multiple tests (see Dailey, Martindale, & Borkum, 1997; Martindale, 1999; Martindale, Anderson, Moore, & West, 1996; Martindale & Dailey, 1996), we standardized scores on each of the three measures and then averaged across the three standardized scores to create a composite measure called creative potential for each participant. Our measure of intelligence was the vocabulary subset of the Shipley Institute of Living Test which consists of 40 multiple-choice items (Zachary, 1986). For each item the participant must choose one of four words that is closest in meaning to a target word (its synonym). Participants are allowed 10 minutes to complete the test. Scores on the Shipley Institute of Living Test correlate .80 with scores on the Wechsler Adult Intelligence Scale—Revised (Zachary, 1986; see also Mason, Lemmon, Wayne, & Schmidt, 1991). 2.2. Reaction time tasks The computer programs were written in Visual Basic (Fig. 1). The order of tasks was randomized for each participant. Our battery of tasks included six tasks of vaying length, only four of which are relevant to our hypotheses and reported here. This explains differences in the number of participants who completed each task. Before starting each task, participants were given detailed verbal instructions by the experimenter followed by written, interactive instruction during a practice session. All programs recorded response accuracy, and only RTs for correct responses were analyzed. To counteract expectancy effects, the inter-stimulus interval (ISI) between successive trials in all tasks was random, and varied from one to four seconds. 2.2.1. Hick Task Hick (1952) measured latencies for detecting a stimulus, and for reacting to the detected stimulus. The initial computer window presented a horizontal row of one, three, or five unlabeled buttons centered in the top half of the screen and two buttons labeled ‘‘See It’’ and ‘‘Ready’’ (Fig. 1). Participants started each trial by clicking on the ‘‘Ready’’ button. They were instructed to click the ‘‘See It’’ button upon seeing a yellow circle appear above any unlabeled button. Then, they were to click the button appearing below the yellow circle. This ended the trial. The number of buttons that appeared on each trial (one, three, or five) was randomized. Latencies were recorded for clicking on the ‘‘See It’’ button (detection of light) and for clicking on the button below the yellow circle (reaction to stimulus).
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Fig. 1. Computer interfaces for the RT tasks used in this study. Note. The interface during a three-button trial in the Hick Task (a); the interface during an ‘‘AND’’ trial in the Concept Verification Task (b); the interface on a trial from Negative Priming where the participant must press ‘‘Green’’ in response to the color of the displayed word (c); the interface on a trial from Global Precedence where the participant must press ‘‘H’’ either in response to identifying the big letter or the small letters (d).
2.2.2. Concept Verification Task The Concept Verification Task (Knorr & Neubauer, 1996) measured latencies for understanding a rule and for deciding if an object satisfied the rule. At the start of each trial participants were presented with a written rule centered in the top half of the computer window (Fig. 1). Participants were instructed to click on the ‘‘Understand’’ button once the rule was understood, which was then followed by the appearance of a geometric shape in the space where the rule had appeared before. The geometric shape either matched or did not match the rule. For example, if the rule said ‘‘RED and SQUARE’’, a matching shape would be a red square and a shape that did not match the rule would be a green square. The participants were instructed to click on the ‘‘Match’’ or ‘‘No Match’’ button as soon as they had decided if the shape matched the rule or not. This ended the trial. Latencies were recorded for clicking the ‘‘Understand’’ button and for clicking the ‘‘Match’’ or ‘‘No Match’’ button. There were five types of rules: SINGLE WORD
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(e.g., ‘‘ORANGE’’), AND (e.g., ‘‘ORANGE and SQUARE’’), OR (e.g., ‘‘ORANGE or SQUARE’’), AND NOT (e.g., ‘‘ORANGE and not SQUARE’’), and BUT NOT BOTH (e.g., ‘‘ORANGE or SQUARE, BUT NOT BOTH’’). 2.2.3. Negative Priming In Negative Priming (Claridge et al., 1992) participants were presented with successive pairs of color words written in different colors (Fig. 1). Participants were instructed to click on the button that indicated the color of the second word in each pair. There were four types of relationships between the words. The first type, ‘‘negative priming’’, paired words based on color. The name of the first word in the pair was the same as the color of the second word in the pair (e.g., the word RED written in blue followed by the word GREEN written in red). The second type, ‘‘distractor’’, paired words randomly except when that led to a negative priming relationship. The third type, ‘‘same’’, paired the same first word (in all trials) with randomly selected second words except when that led to a negative priming relationship. The fourth type, ‘‘X condition’’, randomly paired sets of the letter ‘X’ of varying length and color. Each type was presented 12 times over the course of the task in random order. 2.2.4. Global Precedence In Global Precedence (Navon, 1977) participants were presented with large letters made up of smaller letters (Fig. 1). Participants were instructed to respond to either the big letter or the small letters that made up the big letter. For the ‘‘big letter’’ condition, participants were presented with a random selection of the letters ‘‘H’’ and ‘‘S’’ made up of ‘‘H’’, ‘‘S’’, or little squares. For the ‘‘little letters’’ condition, participants were presented with a random selection of ‘‘H’’, ‘‘S’’, and squares made up of either ‘‘H’’ or ‘‘S’’ but not little squares. Participants responded by clicking on the button labeled with the appropriate letter.
3. Results Means and standard deviations for all paper-and-pencil measures are presented in Table 1. Pearson correlation coefficients between all paper-and-pencil measures are presented in Table 2. The Vocabulary subset of the Shipley Institute of Living Test was intended for use as a covariate in all correlational analyses, but this procedure was not undertaken for two reasons. First, the correlation between vocabulary scores and creative potential was not significant, r(102) = .07.
Table 1 Means and standard deviations for measures of creative potential and intelligence (N = 104) Instrument
M
SD
Alternate Use Test (fluency) Remote Associates Test Creative Personality Scale Shipley Institute of Living (vocabulary)
32.71 8.92 10.71 24.47
10.90 3.45 5.65 10.35
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Table 2 Pearson correlation coefficients between creative potential measures and scores on the vocabulary section of the Shipley Institute of Living Test (N = 104) RAT AUT CPS SYN
RAT
AUT
CPS
SYN
– .10 (.20) .20* (.42) .22* (.45)
– .24* (.51) .04 (.07)
– .02 (.03)
–
* Indicates
p < .05. RAT = Remote Associates Test, AUT = Alternate Uses Test, CPS = Creative Personality Note: Scale, SYN = Vocabulary (synonyms) subscale score on the Shipley Institute of Living Test. Effect sizes are indicated in p parentheses and were calculated using the formula d = 2r/ (1 r2) (Cohen, 1988).
Second, the correlation between vocabulary scores and RT did not reach significance on any component of any task (Table 3). We tested our main hypothesis by computing Pearson correlation coefficients between RT and creative potential for the four tasks. Note that testing our predictions did not require testing for significant differences between correlations. We predicted a negative correlation between creative potential and RT on the Hick Task and the Concept Verification Task. The Hick Task measures latencies for noticing and reacting to stimuli. Supporting our prediction there was a significant negative correlation between creative potential and RT for reacting to the light (r[61] = .27, p < .05), but not for noticing the light (r[61] = .18). The Concept Verification Task measures latencies for understanding a rule and making a decision about it. Supporting our prediction there was a significant negative correlation between creative potential and RT for understanding the rule (r[42] = .42, p < .01), but not for making a decision based on the rule (r[42] = .24). In contrast, we predicted a positive correlation between creative potential and RT on Negative Priming, and when global features had to be inhibited in favour of local features in Global Precedence. Supporting both predictions, there was a significant positive correlation between creative potential and RT across all trials in Negative Priming (r[49] = .28, p < .05), and a positive correlation between creative potential and RT for reacting to local features in Global Precedence (r[102] = .32, p < .001).
4. Discussion Based on Martindale’s (1999) theory, we predicted that there would be a negative correlation between creative potential and RT in the Hick Task and the Concept Verification Task, but a positive correlation between creative potential and RT in Negative Priming and when global features had to be inhibited in favour of local features in Global Precedence. Overall, the results supported this prediction. We next turn to a more detailed analysis of the results. The Hick Task measures how quickly one can notice a perceptual event and react to it. Neither step involves the introduction of interference by task-irrelevant information. There was a negative correlation between creative potential and RT for reacting to a perceptual event. Closer inspection of the trend for correlations between creative potential and RT for reacting to the perceptual event across the one (r[61] = .34, p < .05), three (r[61] = .26, p < .05), and five (r[61] = .13,
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Table 3 Pearson correlation coefficients between RT, creative potential, and vocabulary scores on the Shipley Institute of Living Test Task
Condition
Hick Task (N = 63)
Noticing the light Across all conditions One button Three buttons Five buttons Clicking the lit button Across all conditions One button Three buttons Five buttons
CVT (N = 44)
Understanding the rule Across all conditions SINGLE WORD AND OR AND NOT BUT NOT BOTH Deciding Match/No Match Across all conditions SINGLE WORD AND OR AND NOT BUT NOT BOTH
NP (N = 51)
Across all conditions Negative priming Random Same XXX
GP (N = 104)
Big letter (global feature) Across all conditions H made up of small H H made up of small S S made up of small H S made up of small S H made up of small squares S made up of small squares Small letters (local feature) Across all conditions Small H making up big H Small H making up big S Small S making up big H Small S making up big S Small H making up a square Small S making up a square
Creative potential
d
SYN
r(61) = .18 r(61) = .21 r(61) = .19 r(61) = .08
.36 .44 .38 .15
.07 .10 .04 .07
r(61) = .27* r(61) = .34* r(61) = .26* r(61) = .13
.57 .77 .56 .25
.15 .18 .12 .11
r(42) = .42** r(42) = .20 r(42) = .44** r(42) = .31* r(42) = .38** r(42) = .38*
1.02 .42 1.08 .69 .89 .89
.02 .05 .06 .02 .01 .04
r(42) = .24 r(42) = .27 r(42) = .42** r(42) = .12 r(42) = .12 r(42) = .08
.51 .57 1.02 .23 .23 .15
.09 .05 .11 .14 .05 .23
r(49) = .28* r(49) = .23 r(49) = .28* r(49) = .29* r(49) = .28*
.61 .49 .61 .62 .61
.08 .08 .08 .07 .04
r(102) = .12 r(102) = .03 r(102) = .12 r(102) = .11 r(102) = .22* r(102) = .16 r(102) = .01
.23 .05 .24 .21 .45 .33 .01
.05 .02 .06 .06 .04 .15 .03
r(102) = .32*** r(102) = .24** r(102) = .29** r(102) = .29** r(102) = .27** r(102) = .34*** r(102) = .24**
.70 .51 .62 .62 .57 .77 .51
.06 .02 .10 .11 .11 .02 .03
Note: CVT = Concept Verification Task, NP = Negative Priming, GP = Global Precedence, SYN = Vocabulary (synonyms) p subscale score on the Shipley Institute of Living Test. Effect sizes were calculated using the formula d = 2r/ (1 r2) (Cohen, 1988). * Indicates p < .05, ** indicates p < .01, *** indicates p < .001.
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ns) button conditions offers further support for our hypothesis (Table 3). One would expect attention to be at its most focused when a single button was present, as opposed to when three or five were present. Because of the tendency to focus attention in the absence of distraction one would expect a decreasing trend for the strength of the correlation between creative potential and RT as the number of buttons increased, which is precisely what happened. Thus, it seems that creative participants are fastest when distraction is minimal, thus allowing maximal focus. This may also explain why there was no correlation between creative potential and RT for noticing a light, which required distributed attention across five potential target locations. The Concept Verification Task measures the speed with which one can understand a rule and for making a decision based on that rule. Like the Hick Task, this task does not introduce interference by task-irrelevant information into either step. There was a significant negative correlation between creative potential and RT for understanding rules, but not for making a decision based on rules, and this pattern was present across four of five rule categories (Table 3). This indicates that in the absence of interfering information creative people are faster in internalizing a rule, but having understood the rule they may be no faster than noncreative participants in making a decision based on that rule. Having understood the rule both creative and noncreative subjects may invariably focus attention for making the ‘‘Match’’ or ‘‘No Match’’ decision, thus minimizing individual differences in focus of attention. For Negative Priming we predicted that people with higher creative potential should be slower in all conditions because the nature of interference varies on a trial-by-trial basis, and this variance itself introduces an added level of ambiguity to the task. The results supported our prediction (Table 3). Incidentally, Eysenck (1995) hypothesized that due to lower buildup of inhibition to the first word, creative people would be faster in indicating the color of the second word on negative priming trials, thus exhibiting shorter RT specifically when inhibitory interference is maximal. Our results do not support Eysenck’s (1995) hypothesis, and instead suggest that when confronted with stimuli that interfere with the central task, people with higher creative potential will be slower than those with lower creative potential. Finally, results from Global Precedence supported our predictions as well. In Global Precedence interference is maximal in trials where global features must be inhibited in favour of local features (i.e., where interference is higher and inhibition more difficult), but not when local features had to be inhibited in favour of global features (i.e., where interference is lower and inhibition less difficult). The fact that global-level information is processed before local-level information has been attributed to global advantage in response time, and global interference with local processing (Navon, 1977; Navon & Norman, 1983), a pattern repeated in our study where global features (M = 652 ms, SD = 109) were processed faster than local features (M = 677 ms, SD = 118). The significant positive correlation between creative potential and RT for reacting to local but not global features indicates that higher creative potential leads to longer latencies specifically under those conditions where interreference is maximal, but not otherwise. It is noteworthy that scores on the vocabulary subset of the Shipley Institute of Living Test were unrelated to creative potential or RT in any of the four tasks (Table 3). This may be because vocabulary scores do not measure fluid intelligence, and that a measure of fluid intelligence may have shown higher correlations with RT and creative potential than did vocabulary scores. Notwithstanding this possibility, our results suggest that at least in the context of our study the link
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between creative potential and RT was not a function of how those variables are related to crystallized intelligence. Overall, our results demonstrate that the link between creative potential and speed of information processing is a function of the extent to which focus of attention is adjusted in relation to task demands. However, a critical question that remains is how the focus of attention is adjusted flexibly in people with higher creative potential. Martindale (1999) has argued that this adjustment is automatic or reactive rather than involving self-control. Potentially, this bottom-up process may be sensitive to the granularity or ambiguity of features in the problem space. In contrast, this flexible adjustment process may be driven by controlled as well as spontaneous processes in relation to working memory capacity (Dietrich, 2004), or by top-down processes in the service of strategy change (Haider, Frensch, & Joram, 2005). Given our results, the logical next step would be an examination of mechanisms that control this variability in people with higher creative potential. References Ansburg, P. I., & Hill, K. (2003). Creative and analytic thinkers differ in their use of attentional resources. Personality and Individual Differences, 34, 1141–1152. Carson, S. H., Peterson, J. B., & Higgins, D. M. (2003). Decreased latent inhibition is associated with increased creative achievement in high-functioning individuals. Journal of Personality and Social Psychology, 85, 499–506. Claridge, G. S., Clark, K. H., & Beech, A. R. (1992). Lateralization of the negative priming effect: Relationships with schizotypy and with gender. British Journal of Psychology, 83, 13–23. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Erlbaum. Dailey, A., Martindale, C., & Borkum, J. (1997). Creativity, physiognomic perception, and synaesthesia. Creativity Research Journal, 10, 1–8. Dietrich, A. (2004). The cognitive neuroscience of creativity. Psychonomic Bulletin and Review, 11, 1011–1026. Eysenck, H. J. (1995). Genius: The natural history of creativity. Cambridge: Cambridge University Press. Gough, H. G. (1979). A creative personality scale for the adjective checklist. Journal of Personality and Social Psychology, 37, 1398–1405. Haider, H., Frensch, P. A., & Joram, D. (2005). Are strategy shifts caused by data-driven processes or by voluntary processes? Consciousness and Cognition, 14, 495–519. Hick, W. E. (1952). On the rate of gain of information. Quarterly Journal of Experimental Psychology, 4, 11–26. Jensen, A. R. (1982). Reaction time and psychometric g. In H. J. Eysenck (Ed.), A model for intelligence (pp. 93–132). Berlin: Springer. Knorr, E., & Neubauer, A. C. (1996). Speed of information processing in an inductive reasoning task and its relationship to psychometric intelligence. Personality and Individual Differences, 20, 653–660. Martindale, C. (1999). Biological bases of creativity. In R. J. Sternberg (Ed.), Handbook of creativity (pp. 137–152). New York: Cambridge University Press. Martindale, C., Anderson, K., Moore, K., & West, A. N. (1996). Creativity, oversensitivity, and rate of habituation. Personality and Individual Differences, 20, 423–427. Martindale, C., & Dailey, A. (1996). Creativity, primary process cognition, and personality. Personality and Individual Differences, 20, 409–414. Martindale, C., & Hines, D. (1975). Creativity and cortical activation during creative, intellectual, and EEG feedback tasks. Biological Psychology, 3, 71–80. Mason, C. F., Lemmon, D., Wayne, K. S., & Schmidt, R. (1991). Shipley Institute of Living Scale: Formulas for abstraction quotients from a normative sample of 580. Sex and socioeconomic status considered as additional moderating variables. Psychological Assessment: A Journal of Consulting and Clinical Psychology, 3, 412–417. Mednick, S. A. (1962). The associative basis of the creative process. Psychological Review, 69, 220–232. Mendelsohn, G. A. (1976). Associative and attentional processes in creative performance. Journal of Personality, 44, 341–369.
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