Divergent thinking and differential focusing of perceptual attention in visual serial search tasks

Learning and Individual Differences 44 (2015) 25–32

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Divergent thinking and differential focusing of perceptual attention in visual serial search tasks Leonid Dorfman ⁎, Vera Gassimova ⁎⁎ Department of Psychology, Perm State Academy of Art and Culture, 18 Gazeta Str., Perm 614000, Russia

a r t i c l e

i n f o

Article history: Received 12 February 2014 Received in revised form 21 May 2015 Accepted 29 May 2015 Keywords: Divergent thinking Perceptual attention Differential focusing of attention Focused and defocused attention Intelligence Elementary cognitive task Cognitive inhibition task

a b s t r a c t Seminal creativity theories developed by Eysenck and Martindale bring distinguishing predictions to bear on relations between divergent thinking and attention. Drawing upon these theories, the current study was intended to investigate whether the differential focusing of perceptual attention accommodated within visual serial search tasks relates to divergent thinking. An elementary cognitive task was employed to simulate focused perceptual attention and a cognitive inhibition task to simulate defocused perceptual attention. The data obtained lend support to both theories: The attentional distraction scores were more consistent with Martindale's theory, and the attentional selection scores with Eysenck's theory. The theories of Martindale and Eysenck are considered as complementary rather than mutually exclusive, relative to the differential focusing of perceptual attention in visual serial search tasks with respect to divergent thinking. © 2015 Elsevier Inc. All rights reserved.

1. Introduction The view that creativity includes various angles is broadly accepted (e.g., Eysenck, 1995; Martindale, 1999; Mumford & Gustafson, 1988; Runco, 2008; Simonton, 1999). For example, creativity is examined in relation to divergent thinking (e.g., Runco, 1999, 2008), intelligence (e.g., Sternberg & O'Hara, 1999), attention (e.g., Eysenck, 1995; Martindale, 2002), intuition (e.g., Simonton, 1980, 1999), and some personality traits such as psychoticism (Eysenck, 1995) and persistence (e.g., Simonton, 1999). The present study attempts to examine relationships of divergent thinking with attention in visual serial search tasks. The view that creativity and attention are related constructs dates back at least to the 1960s. After Mendelsohn and Griswold (1964) pioneered discovering a link of creativity with incidental stimuli, attention was acknowledged a prominent topic providing guidelines for research relative to the cognitive basis of creativity. Yet it took several decades before an important theoretical breakthrough arose in this domain. Eysenck (1995) and Martindale (1995, 1999) proposed competing seminal theories animating interest in creativity and attention. Attention is not a unitary concept (Schweizer, 2010; Stankov, 1983). It embraces, for instance, mental concentration, search, selective and ⁎ Correspondence to: L. Dorfman, Department of Psychology, Perm State Academy of Art and Culture, 18 Gazeta Str., Perm 614000, Russia. ⁎⁎ Corresponding author. E-mail address: [email protected] (L. Dorfman).

http://dx.doi.org/10.1016/j.lindif.2015.05.007 1041-6080/© 2015 Elsevier Inc. All rights reserved.

divided attention, and vigilance (e.g., Moray, 1969). More generally, attention can be defined as the appropriate allocation of processing resources to relevant stimuli (Coull, 1998). Therefore, the most preferred way of studying creativity and attention consists in treating the latter as focused and defocused, or even predominantly defocused (e.g., Ushakov, 2006). Thus, adopting the concept of divergent thinking as articulated by Guilford (1956; 1968) seems appropriate for studying relations between creativity and focused/defocused attention. Indeed, since divergent thinking is the ability to generate many diverse ideas in various paths (e.g. Runco, 2008), it would, at least theoretically, correspond to defocused attention as deviating to some degree from an accurate focus. This does not mean that creative people are always in a state of defocused attention. Rather, they are more capable of switching between focused and defocused attention (e.g., Martindale, 2002; Zabelina & Beeman, 2013), and studying this flexibility can facilitate the understanding of the relationship between these states of attention. Certainly, divergent thinking is neither synonymous with nor sufficient for creativity. Divergent thinking is a kind of creative thinking, but the latter extends the former (Runco, 2008). Both Eysenck (1995) and Martindale (1995, 1999) enter into the controversy about how attention relates to creativity. This debate continues until the present. The key point is that both Eysenck and Martindale apply data which are grounded on the target combined with distractor stimuli tasks to simulate focused and defocused attention. But they disagree about whether attention is “overinclusive” and then sticks to the target ignoring the distractors (Eysenck, 1995,

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pp. 245–248), or distractive and then gets off target to the distractors (Martindale, 1995, 1999). As will be shown below, there is supportive evidence in favor of both assumptions. Another issue is that attention can be divided at both conceptual and perceptual levels (Friedman, Fishbach, Förster, & Werth, 2003; Martindale, 1995). Besides, the perceptual level of attentional allocation for creativity is theoretically salient but empirically least examined and understandable. Actually, very few studies have obtained correlations of perceptual focused and defocused attention with divergent thinking. Moreover, the results are difficult to compare since the methodologies of the studies differ. Our concern in this article was therefore with investigating the relationship between a differential focusing of perceptual attention and divergent thinking. Predictions were derived from the creativity theories of Eysenck and Martindale, and our study attempted to some extent to make an empirical effort to resolve their disagreement. The aim of the current study was therefore to investigate the relationship between focused and defocused perceptual attention accommodated within serial visual search tasks and divergent thinking.

2. Background We place great importance on three crucial questions. First, the clarification and therefore articulation of focused and defocused attention are necessary. Second, the attention measurements should be considered with their theoretical meaning. Third, the relative paucity of empirical evidence sharpens the problem of how defocused and focused attention relates to divergent thinking. Identifying possible answers to these questions is an important step towards clearing the ground on which further study of the relationship between focused/defocused attention and divergent thinking can be raised.

2.1. Focused and defocused attention The ability to focus attention is generally explained as being the ability to inhibit or filter out irrelevant stimuli and thoughts in order to be able to focus on relevant stimuli and thoughts. The more stimuli are in the focus of attention the less attention to be focused (Martindale, 2002). Focused attention captures relevant information whereas defocused attention extends to both relevant and irrelevant information. Consequently, focused attention is narrowing as compared to defocused attention, which is widening. The assumption is that cognitive inhibition is a mechanism limiting the flow of information to the focus of attention. Thus, attention variation is usually obtained by using different task demands (Dorfman, Martindale, Gassimova, & Vartanian, 2008). To simulate focused and defocused attention conditions, elementary cognitive tasks and cognitive inhibition tasks are ordinarily employed. Experimentally, elementary cognitive tasks (focused attention condition) include a simple target stimulus with no irrelevant information. Elementary cognitive tasks put minimal requirements on the participants. They perform simple mental operations with a target stimulus (relevant information) under conditions where no distractor stimuli (irrelevant information) are presented. Conversely, cognitive inhibition tasks (defocused attention condition) are intended to administer tasks so that attention is spread over several sources or stimuli, one of which is a target stimulus and the others are distractors (Martindale, 2002; Vartanian, 2009). The negative priming paradigm is an example of how a priming stimulus may inhibit the reaction to a target stimulus, like in the Stroop effect (Stroop, 1935). The priming is an irrelevant stimulus (referred to as a distractor), which participants are required to ignore while focusing on a target stimulus. If the distractor impairs performance in target detection, the distractor is said to have also been selected by attention.

2.2. Measures of focused and defocused attention The speed of information processing is the most widely acknowledged measure of attention. Stankov (1983) points out that during the 1970s reaction time (RT) was a preferred method, proven to be sensitive to registering attention. In subsequent decades, psychologists have continued using this method. Interestingly, an inverse relation was found between the size of the focus and the efficiency of processing stimuli based on RT measures (e.g. Benso, Turatto, Mascetti, & Umilta, 1998; Kent, Howard, & Gilchrist, 2012). RT-based attention tasks are also used in creativity studies (e.g., Ansburg & Hill, 2003; Dorfman et al., 2008; Eysenck, 1995; Vartanian, Martindale, & Kwiatkowski, 2007). The findings support the view that information processing speed is indicated by faster RT under the condition of focused attention and by slowed-down RT under the condition of defocused attention. In some studies, measures of response accuracy indicating attention adhere to the RT paradigm (e.g., Kent et al., 2012; Liu, Wolfgang, & Smith, 2009; Smith & Ratcliff, 2009; Treisman, 1977), and in other studies perceptual accuracy is involved instead of response speed (e.g., Pack, Carney, & Klein, 2013; van Damme, Crombez, & Notebaert, 2008). RT is a speed measure, but speed and attention cannot be equated. Confounding them may conceal the nature of attention itself (Stankov, 1983). This view opens the door to some advantages of information-processing tasks which require selective allocation of some limited processing resource (e.g., Anderson, 2014; Broadbent, 1958; Cowan, Fristoe, Elliott, Brunner, & Saults, 2006; Neill, 1977; Norman & Bobrow, 1975; Thornton & Gilden, 2007). However, we leave behind our interest in theorizing about capacity and limited resources as such, including their classification (e.g., Neill, 1977). Instead, we turn to the performance of tasks which can be taken to reflect the capacity or scope of attention. This can be indicated by the quantity of information processed and the accuracy of the processing, although RT is also widely used (e.g., Cowan et al., 2006; Hearns & Moss, 1968; Mendelsohn & Griswold, 1964; Shurtleff & Marsetta, 1968; Verghese & Pelli, 1992). However, the quantitative measures of capacity, has lacked compelling empirical evidence and is so far not elaborated enough as compared with the RT paradigm. 2.3. Attention and creativity Martindale (1989, 2002) proposed a differential relationship of creativity with attention. He suggested that creative people are better than less creative people at shifting between focused and defocused attention as the situation demands. His theory has been tested out (Dorfman et al., 2008; Martindale, 2002; Vartanian et al., 2007). Creative potential (as measured by the Alternate Uses Test, Wallach & Kogan, 1965) and processing speed were positively correlated for interference tasks (Negative Priming tasks by Claridge, Clark, & Beech, 1992, defocused attention condition) indicated in slower RT, and negatively correlated for noninterference tasks (Concept Verification Test by Knorr & Neubauer, 1996, focused attention condition) indicated in faster RT. These data support Martindale's theory. In general, defocused attention involves a larger amount of information to be processed. As a result, creative people slow down their information processing. Conversely, focused attention narrows the amount of information to be processed, thus filtering out unnecessary information. As a result, creative people have a faster RT. In studying attentional priming effects on creativity, Friedman et al. (2003) distinguished between perceptual attention and conceptual attention. They revealed that defocused attention and focused attention are different processes, whereas perceptual and conceptual attention is closely related. In particular, participants who completed the broadly focusing visual search task demonstrated more originality than those who completed the narrowly focusing visual search task. It was demonstrated that situational variations in the scope of perceptual attention might analogously influence the scope of conceptual attention, thereby

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generating innovative alternatives. The authors suggest that varieties of attentional narrowing and broadening share the same underlying mechanism: they differ in content (perceptual vs. conceptual), not process. Mendelsohn and Griswold (1964) examined how creative and less creative people process information pertaining to focal and peripheral incidental cues. It was found that, compared with less creative people, creative people use more of both focal and peripheral incidental cues. For the creatives, in addition, the number of focal anagram solutions was significantly greater than that of peripheral anagram solutions. The results were interpreted as reflecting a wider deployment of attention by the high-creatives during problem solving relative to the lowcreatives whose attentional focus was narrowing. Both the abovementioned RT and number measures demonstrate that creative people tend to have less focused attention than uncreative people do. It is worth noting that two possible, competing mechanisms of attention sound in the literature on creativity. Martindale's (1995) assumption is based on a neural network model of cognition. He introduces the notion of cognitive disinhibition. It corresponds to defocused attention and a lower level of activation spread across a larger number of mental representations. The defocused attention of creative people is considered as capturing relevant information alongside irrelevant information which cannot be suppressed or ignored. Thus, the speed of information processing slows down. In turn, focused attention is seen as the strong activation of only a few proximal nodes. In this condition, no irrelevant information would interfere with relevant information processing, and the latter is thus indicated by faster speed. A competing view of the defocused attention of creative people arises from neuropsychological studies investigating inhibition on experimental work. Eysenck (1995) addresses negative priming (e.g., Claridge et al., 1992) and latent inhibition (e.g., Lubow, Ingberg-Sachs, Zalstein-Orda, & Gewirtz, 1992) paradigms. Usually, a balancing-out of the facilitatory processing of task-relevant stimuli and the inhibition of task-irrelevant ones happens. For instance, in the Stroop color naming task (1935) the prime makes it more difficult (i.e. it takes longer) to respond to the target stimulus. However, patients with schizophrenia not only show a failure of negative priming, but also of positive priming effects, namely, facilitatory rather than inhibitory effects. The inhibition reduction was treated a dysfunction in the attentional system (for reviews, see Maruff & Currie, 1996; Nestor & O'Donnel, 1998). Instead of distractor stimuli to be inhibited, they were ignored. Consistent with this assumption, Eysenck (1995) suggested that the failure of inhibitory processes links to overinclusiveness, that is to say processing more target stimuli than is necessary. Eysenck (1995) made a prediction that subjects high in psychoticism and schizotypal normal subjects would show a similar lack of cognitive inhibition. This hypothesis was supported in data obtained by Gray, Fernandez, Wiliams, Ruddle, and Snowden (2002). They revealed that the inhibition reduction (within the latent inhibition paradigm) has been associated with the impulsive non-conformity scale of the Oxford–Liverpool Inventory of Feelings and Experiences evaluating schizotypal traits in healthy participants (Mason, Claridge, & Jackson, 1995). Gray et al. (2002) noted that this scale has much in common with the psychoticism scale of the Eysenck Personality Questionnaire (Eysenck & Eysenck, 1975). The psychoticism scale does not relate to the symptoms of schizophrenia, but would be more akin to a measure of the impulsive non-conformity scale of psychopathy (Gray et al., 2002). Thus, as Eysenck (1995) suggests, lack of cognitive inhibition, as measured by negative priming or latent inhibition, may concern creativity because it is closely related to psychoticism. Later on, Carson, Peterson, and Higgins (2003) revealed that decreased latent inhibition is associated with the originality score of the divergent thinking tasks as well as with the overall divergent thinking in high-functioning people. Thus, the Eysenckian proposition that reduced latent inhibition links to creativity was supported by empirical evidence (but see Burch, Hemsley, Corr, & Pavelis, 2005). Eysenck (1995) has shown that RT is the main response measure in negative priming tasks. In latent inhibition experiments, RT has not

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given positive results. Instead, ease of association, such as percentage of correct answers or number of trials to reach a criterion has been employed. Consequently, the predicted links of creativity with latent inhibition reduction can rely on either of the experimental paradigms mentioned above or on one of them only. Comparing Martindale's (1995) and Eysenck's (1995) neuropsychological grounds of attention and creativity, it can be clearly seen that they result in different predictions. According to Martindale's theory, an underlying assumption of defocused attention is that creative people fail to suppress or ignore irrelevant information. Then cognitive disinhibition occurs and the speed of information processing slows down. In contrast, Eysenck's theory forecasts that the attention (overinclusiveness) of creative people links to inhibition reduction, indicated in that irrelevant information is ignored rather than distracting a target stimulus from being processed. It results in that of significant differences in RT for interference tasks and noninterference tasks take no place. Moreover, even faster RT for interference tasks can be registered as compared to noninterference tasks (negative priming paradigm). Within the latent inhibition paradigm, we may also expect that the number of correct trials can be unchanging or even growing despite the presence of inhibitors. 3. The present study The objective of the present study is to investigate whether focused and defocused perceptual attention accommodated within serial visual search tasks relates to divergent thinking. Attention was investigated in visual serial search tasks wherein a single target character was embedded in a display containing other characters. To some extent, we attempted to take into account that a serial visual search class is distinguishable from a parallel visual process class (e.g., Thornton & Gilden, 2007). Questions were raised as to the extent to which distractor characters are processed and yield effects on the target character detection while a serial visual search is performed. Further, a closer look at defocused and focused attention is in order. When the attention is under complex demand (example: mark the target if a given combination of three characters precede the target), the participant has to direct his or her attention to the required preceding characters and then to address the target. Thus the attention is widening because it should capture both the preceding characters and the target. We consider this condition a simulation of defocused attention. When the attention is under simple demand (example: mark the target), the participant has to concentrate his or her attention on the target only. Then the attention is narrowing. We consider this condition a simulation of focused attention. Certainly, the above conditions are relative. The defocused attention condition appears as compared to the focused attention condition and vice versa. In our opinion, inhibition tasks can be employed to examine whether irrelevant stimuli (distractors) draw attention to themselves or not in addition to the focus on the target. That is to say, inhibition tasks can be employed to examine defocused attention. But how can the concept “inhibition” be operationalized? There is evidence that extraneous characters can impair the processing of the designated target character. This effect can arise in interference tasks where the same elements of stimuli appear both in the target and distractor stimuli as in Stroop (1935) tasks. For instance, the word “blue” written in red is a priming stimulus for the target stimulus word “green” written in blue, where the correct response to the target is the color of the latter word (i.e., blue) (e.g., Martindale, 2002). However, extraneous characters can also impair the processing of the designated target character even if the former and the latter do not interfere as they do in the above-mentioned Stroop tasks. In some of the flanker tasks, for instance, people find it difficult to completely exclude the flankers from processing the target (e.g., Eriksen & Eriksen, 1974; Yantis & Johnston, 1990). Here interference tasks as in Stroop do not appear, but flankers do inhibit the target processing. For this reason, inhibition tasks in our study are not designated to yield Stroop interference effects. Rather, we expect effects like flankers to

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do that on the target. Thus, we suggest that the characters to be taken into account can produce seeming inhibiting effects on the subsequent target processing, though interference between them is not simulated. We assume that focused and defocused attention conditions can be accommodated within identifying the single target character in a visual serial search. An elementary cognitive task appears if no additional demands are required. Then the elementary cognitive task may be used to simulate the focused attention condition. In its turn, defocused attention can be simulated under the condition that the participants in the study are required first to take into account some preceding characters and then mark the target character. In such a task, the attention addresses the target but seemingly also extends to the distractors. As a result, the attention will be defocused. Then the cognitive inhibition task, but not interference in the vein of Stroop, may be used to simulate the defocused attention condition. Based on the theories of Martindale (Martindale, 1995, 1999) and Eysenck (1995) delineated in the foregoing paragraphs, we may propose some competing predictions with respect to the differential focusing of attention of creative people when involved in visual serial search tasks. As was already mentioned above, within Martindale's theory it was found that in elementary cognitive tasks, creativity facilitates focused attention, indicated by faster RT. In cognitive inhibition tasks, creativity contributes to attention being defocused, which is indicated by sloweddown RT. Applied to these findings, we hypothesized that the differential focusing of attention of creative (divergently thinking) people shows a shift from focused to defocused attention. This would be indicated in that the number of targets processed is lower but attendant errors are incremented. We labeled this hypothesis “Martindale's framework.” Although Eysenck's theory did not yield any prediction on focused attention relative to creativity, his theory clearly predicts that creative people process the target ignoring distractor stimuli. This is indicated since their attentional performance does not decrease or can even be increased. Applying this assumption, we hypothesized that the differential focusing of attention of creative (divergently thinking) people shows a shift from focused to defocused attention. But the attentional shift would be reverse. It can be indicated in that the number of targets processed is incremented but attendant errors are fewer. We labeled this hypothesis “Eysenck's framework.” 4. Method 4.1. Participants Raw data were gathered from a Russian sample consisting of 211 volunteer participants recruited from Perm city high schools. Because of missing data and lack of participation in different aspects of the study, 17 participants (8.06%) were removed from subsequent analyses. The current results are based on raw data from 194 participants (75 boys and 119 girls). Their age ranged from 15 to 17, M = 15.36, SD = .54. The participants received no reward or compensation for taking part in the experiment. 4.2. Materials and procedure The participants completed a test of divergent thinking in a number of group sessions. The participants were subsequently booked for individual sessions during which they completed attentional tasks. 4.3. Paper-and-pencil tasks The paper-and-pencil tasks included psychometric measures of divergent thinking, intelligence, and attention.

4.3.1. Divergent thinking To assess divergent thinking, the participants completed the Alternate Uses Test (Wallach & Kogan, 1965). This test was adapted in Russian by Averina and Scheblanova (1996). The Alternate Uses Test involves generating as many uses as possible for three regular objects (brick, newspaper, pencil). The participants were allowed 3 min per object. The final data consisted of three scores. Fluency is simply the total number of uses generated across the three objects. It is the most common way of scoring the Alternate Uses Test (Plucker & Renzulli, 1999). Flexibility is the total number of categories from which the uses were drawn. The categories were defined according to a comprehensive list of categories suggested by Averina and Scheblanova (1996). Originality was scored according to the scheme offered by Dorfman et al. (2008). The rarest response receives the highest rank, and the most frequent the lowest rank. The final originality score was the total of the originality scores for each use provided by a participant. We standardized scores on each of the three measures of fluency, flexibility, and originality and then averaged across the three standardized scores to create a composite measure of divergent thinking for each participant. 4.3.2. Intelligence Intelligence was measured using the German IST-70 Test Battery (the Intelligence Structure Test) developed by Amthauer (1973) (see also van der Yen, 1992). The IST-70 was adapted in Russian by Senin, Sorokina, and Chirkov (1993). The IST-70 measures verbal, numerical and spatial (visuo-spatial) abilities. Verbal abilities included five subtests, namely, general knowledge, word grouping, word analogies, word-pairing, and memory. Numerical abilities consisted of two subtests, namely, arithmetic reasoning and numerical series. Spatial abilities were composed of figure matching and cubes subtests. The wordpairing subtest consisted of 16 tasks. Each of the remaining subtests consisted of 20 tasks. For each correctly performed task, the participant was given 1 point. Scores on these subtests as well as total IQ were computed for each participant. 4.3.3. Perceptual attention Participants were required to perform a visual search for a specific target character among other characters involved in “Attention in visual serial search tasks.” Because this test is published in Russian (Dorfman, Gassimova, & Bulatov, 2006), below we reveal its specific details. The tasks consist in sequential searching for a target character in a visual characters setting. These tasks fall into an elementary cognitive task – searching for the target while no distractor characters were present – and an inhibitory cognitive task where preceding precise characters were the participants were required to take into account the precise characters preceding the target before they marked it, but to disregard it if the designated preceding characters did not appear. The elementary task was used to narrowly restrict attentional selection to a target character, that is, to focus attention. Conversely, the inhibitory cognitive task was used to broaden attention to both the target character and the distractor characters, namely, to defocus attention condition. Arrays of 40 consonant characters × 40 rows × 4 sheets in one column, a total of 6400 characters, were presented in random order. In each row, the target characters randomly ranged from 0 to 4. 200 target characters were used to assess the focused attention and 184 target characters to assess the defocused attention. (Because none of the participants reached the end of the character arrays in either task, the difference between the amount of target characters for the focused and the defocused attention conditions may be neglectEd.) The arrays were presented on a scrollable computer screen. The rows of characters were exhibited sequentially on the screen sheet by sheet. The participants indicated a target by pressing the keyboard's spacebar. The participant was given 5 min to perform each task. He or she was instructed to perform the task as thoroughly and quickly as possible. In the elementary task, the participant performed a search solely for the target character ‘p’. In the inhibitory cognitive task, the participant

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indicated the target character ‘t’ only if a sequence of three characters ‘d, r, b’ preceded the ‘t’. All characters were in the Cyrillic alphabet. The participants performed both tasks in random order. Attentional selection and attentional distraction measures were specified. Attentional selection was indicated by (a) the response number of target characters and (b) the total number of scanned characters processed in the visual search scope. These scores were computed separately for focused and defocused attention conditions. Attentional distraction was indicated by response errors during the processing of the characters. The errors were referred to the target character when it was missed (in both the elementary and the inhibition tasks), and when a set of distractor characters (in the inhibition task) was incorrectly taken into account or disregarded. The errors were indicated and computed in two scores. The first score was the ratio of errors to the number of target characters processed (in percentage). The second score was the ratio of errors to the total number of scanned characters processed of the visual search scope (in percentage). The error scores were computed separately for focused and defocused attention conditions. Scores of attentional selection and attentional distraction for each participant were recorded and calculated by a designated computer program. 4.4. Data analysis The extreme values on each variable (over X ± 2 SD) were excluded and missing data were replaced with mean scores. Using the Kolmogorov–Smirnov test (D-max statistics), each variable was checked for normality of distribution. Each variable was normally distributed after outliers were removed and replaced with mean scores. Mean and standard deviation were computed for each variable. The t-test was used to assess the differences between the same scores of focused and defocused attention (dependent t), as well as the differences between male and female participants (independent t). For the sake of correlational analyses, the raw values of each variable were converted into z-scores. Pearson correlations were computed between variables of divergent thinking and attention. Instead of computing such correlations for focused and defocused attention tasks separately, we computed them across defocused/focused attention tasks. The following computing procedure was used. Initially, the z-scores of the focused attention variables were subtracted from the z-scores of the defocused attention variables. Then, the resulting z-scores across the defocused/ focused variables indicated the differential focusing of attention. Next, the resulting z-scores and the divergent thinking scores were correlated. Positive correlations indicated a predominance of defocused attention over focused attention scores relative to growing divergent thinking, and negative correlations a predominance of focused attention over defocused attention scores, again, relative to growing divergent thinking. We computed such correlations for controlling intelligence measures.

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Table 1 Means and standard deviations of divergent thinking, intelligence and t-tests between the scores of the participants, girls and boys. Variable

Total sample

Boys

M

SD

M

SD

Divergent thinking Fluency 15.71 Flexibility 13.92 Originality 52.30

4.24 3.52 17.53

14.53 13.21 45.91

4.23 3.87 16.84

16.46 14.36 56.33

4.10 3.22 16.80

3.16b 2.23a 4.21c

Intelligence Num Spatial Verbal Total IQ

11.02 4.08 9.77 8.16

20.61 19.56 48.48 99.11

11.10 4.37 9.63 7.57

26.61 19.51 52.14 101.46

10.37 13.91 9.64 8.51

3.81c .08 2.58a 1.13

24.29 19.53 50.72 99.94

Girls M

t-test SD

Note: Num = numerical ability, M = mean, SD = standard deviation, ap b .05, bp b .01, and cp b .001.

5.1.2. Focused and defocused attention Average scores and standard deviation on the focused and defocused attention measures are shown in Table 2. As expected, the participants scored substantially higher in processing the target character in a focused attentional task (M = 73.74, SD = 9.89) than in a defocused attentional task (M = 28.84, SD = 3.57). The difference between them was significant, t = 71.71, p b .001. Furthermore, participants yielded less errors while involved in a focused attentional task (M = 9.06, SD = 5.36) than in a defocused attentional task (M = 20.43, SD = 3.57). The difference between them was significant, t = 15.43, p b .001. As predicted, these data demonstrate the inhibitory effect of being required to take into account the preceding characters in addition to the target character. It shows that the defocused attention captures both the target and distractor characters resulting in a decrease of processed target characters and an increase in errors respectively as compared with the focused task. In contrast, the search scope characters processed were significantly fewer in a focused attentional task (M = 955.49, SD = 161.56) than in a defocused attentional task (M = 1193.73, SD = 137.74). But the attendant errors were greater in number in a focused attentional task (M = .71, SD = .40) than in a defocused attentional task (M = .50, SD = .22). The difference between them was significant, t = 7.59, p b .001. As can clearly be seen, there are inverse relations between the target character processing and the search scope characters processed: while the target character processing is greater in the focused attentional task than in the defocused attentional task, the search scope characters processed shows the reverse change. Similarly, inverse relations appear between the ratio of errors of the target character processed and the ratio of errors of the search scope characters processed when their indication is compared between the focused attentional task and the defocused attentional task. These inverse relations are in line with the probability of detecting the target which is inversely related to the number of elements presented in the display (e.g. Eriksen & Eriksen, 1974;

5. Results 5.1. Descriptive statistics

Table 2 Means and standard deviations of focused and defocused attention. Variable

5.1.1. Gender differences The average scores on the three measures of divergent thinking and the four measures of intelligence are shown in Table 1. As can be seen, the scores of total-scale IQ, as well as verbal, spatial, and numerical abilities were modest. Women scored significantly higher than men on all three measures of divergent thinking and on two of the three intelligence test scales but not on total-scale IQ. Although scores on the divergent subscales and several of the intelligence subscales differed for women and men, the correlations for men and women were very similar in all cases, so only the correlations for the entire sample are shown below.

Attentional selection Target letter processed Search scope letters processed Attentional distraction Ratio of errors to target letter processed, % Ratio of errors to search scope letters processed, %

Focused attention

Defocused attention

t-test

M

SD

M

SD

73.74 955.49

9.89 161.56

28.84 1193.73

3.57 137.74

71.71c 20.72c

9.06

5.36

20.43

10.12

15.43c

.71

.40

.50

.22

7.59c

Note: M = mean, SD = standard deviation, ap b .05, bp b .01, and cp b .001.

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Thornton & Gilden, 2007; Verghese & Pelli, 1992). Indeed, if the number of target characters processed is greater, then the number of search scope characters processed is expected to decrease, and vice versa. Our data are consistent with the logic just applied. 5.2. Correlations of divergent thinking and intelligence scores As Table 3 shows, the composite divergent thinking score positively correlated with fluency, flexibility, and originality scores ranged from r(192) = .90 to .95, p b .001. In turn fluency, flexibility, and originality scores were positively intercorrelated, ranging from r(192) = .77 to .88, p b .001. Such high correlations are not surprising. The more uses given (fluency), the greater the probability that they will come from different categories and that at least some of them will be original (e.g., Dorfman et al., 2008; Runco, 1999). Correlations among intelligence subscales were moderate or low. Total-scale IQ scores were positively correlated with verbal, spatial, and numerical abilities scores in the range of r(192) = .47 to .75, p b .001. Verbal, spatial, and numerical ability scores were positively intercorrelated in the range of r(192) = .16, p b .01 to .52, p b .001. To compare, a study by Hakstian and Cattell (1978) found a correlation of .15 between verbal and spatial abilities, whereas the respective variables in our study correlate at r = .16. But correlations of other ability measures in our study and those obtained by Hakstian and Cattell (1978) differ. We surmise that this disagreement for the most part indicates a clear distinction between the concept and measurement of intelligence, although cross-cultural dissimilarities of the samples employed could also cause such differences. Finally, the correlations among verbal and numerical, as well as numerical and spatial ability measures are probably a peculiarity of our sample. Divergent thinking scores positively correlated with total-scale IQ and numerical ability scores. The correlations were low but significant in the range of r(192) = .14, p b .05 to .22, p b .001. Correlations of divergent thinking scores with verbal and spatial ability scores were negligible, p N .05. Our data on correlations of divergent thinking and totalscale IQ scores are consistent with a meta-analysis conducted by Kim (2005). Our data on correlations between divergent thinking and numerical ability scores may be explained through fluid intelligence (e.g., Nisbett et al., 2012) and that a measure of fluid intelligence has shown higher correlations with divergent thinking (e.g., Nusbaum & Silvia, 2011). However, our results failed to reveal a significant correlation between divergent thinking and spatial abilities like numerical ability. Perhaps, these data indicate the heterogeneity of fluid intelligence. Finally, our data on non-significant correlations of divergent thinking and verbal ability may be explained through crystallized intelligence (e.g., Nisbett et al., 2012) and reveal weak relations with divergent thinking (e.g., Nusbaum & Silvia, 2011). Recent evidence on such relations was also obtained by Vartanian, Martindale, and Matthews (2009).

5.3. Correlations of divergent thinking and performance of differential focusing of attention scores In some cases, intelligence is correlated with creativity (e.g., Sternberg & O'Hara, 1999), and this could possibly account for correlations of divergent thinking and performance of differential focusing of attention on the tasks we used. Furthermore, our data given above demonstrate that only total-scale IQ and numerical ability scores were consistently related to the measures of divergent thinking. Therefore, we computed correlations between divergent thinking and differential focusing of perceptual attention in visual serial search tasks for controlling total-scale IQ and numerical ability measures. As shown in Table 4, the obtained correlations were rather low but significant. Correlations of the target characters processed score with any subscale of the divergent thinking score were positive and ranged at r(192) from .12, p b .10 to .18, p b .001. That is, with growing divergent thinking the target characters processed score increases across focused to defocused attention task. This finding is in line with our prediction labeled “Eysenck's framework.” The ratio of errors to the target character processed correlated positively with the flexibility score at r(192) = .15, p b .05, the fluency score at r(192) = .12, p b .10 and the composite index of divergent thinking at r(192) = 13, p b .10, the last two in trend. In general, these data reveal that with growing divergent thinking the errors score attendant to the target characters processed score increases across the focused to defocused attention task. This finding is in line with our prediction labeled “Martindale's framework.” The search scope characters processed score correlated positively with any subscale of the divergent thinking score and ranged at r(192) from .17, p b .01 to .20, p b .001. That is, the stronger the divergent thinking, the higher the search scope characters processed score across the focused to defocused attention task. This finding is in line with our prediction labeled “Eysenck's framework.” Correlations of the ratio of errors to the search scope characters processed score with any divergent thinking score were negligible. These findings were not predicted. They need a special explanation elsewhere. 6. Discussion We employed attentional perceptual tasks in a visual serial search to examine whether perceptual attention links to divergent thinking as measured with the Alternate Uses Test. Two kinds of attentional tasks were used. The elementary cognitive task consisted in a sequential search for the target character without any preceding distractor characters present. The inhibitory cognitive task was that the target should be marked only if preceded by three characters which an experimenter specified beforehand. As was mentioned above in Section 3, “The present study,” when attention is under complex demand (mark character ‘t’ only if the combination of the three characters ‘d, r, b’ precede the

Table 3 Correlations between divergent thinking and intelligence scores. Divergent thinking Composite index Creative thinking Composite index Fluency Flexibility Originality

Intelligence Fluency

Flexibility

Originality

Total IQ

.95c

.91c .88c

.90c .87c .77c

.19b .19b .14a .22c

Intelligence Total Verbal Numerical Spatial Note: ap b .05, bp b .01; and сp b .001. Nonsignificant correlations are omitted.

Verbal

Num

Spatial

.16b .17b .15a .20c .54c

.75c .52c

.47c .16b .22c

L. Dorfman, V. Gassimova / Learning and Individual Differences 44 (2015) 25–32 Table 4 Correlations of differential focusing of attention and divergent thinking scores for controlling total IQ and numerical ability. Differential focusing of attention

Attentional selection Target letter processed Search scope letters processed Attentional distraction Ratio of errors to target letter processed, % Ratio of errors to search scope letters processed, %

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dimension. We contend that Eysenck's dimension and Martindale's dimension of the differential focusing of attention for creative people can be seen as rather complementary as conflicting.

Divergent thinking Composite index

Fluency

Flexibility

Originality

.18c .19c

.16c .19c

.12a .17c

.18d .20d

.13a

.12a

.15b

Note: ap b .10, bp b .05, cp b .01, and dp b .001. Nonsignificant correlations are omitted.

‘t’), the participant has to direct his or her attention to the characters ‘d, r, b’ and then address the target. Thus, the attention is widening, i.e. defocused as compared to the simple task, which includes the target only. Under the latter condition, the attention is narrowing, i.e. focused. As predicted, our data revealed the clear inhibitory effect of the preceding characters on the subsequent target character. It was indicated by a substantial decrease in the number of target characters processed and an increase of errors as compared with an elementary cognitive task. Thus, a cognitive elementary task actually simulated the focused attention condition and an inhibitory cognitive task the defocused attention condition. The main data obtained were as follows. The data revealed that divergent thinking facilitates perceptual attentional performance, indicated in by increase of the target character processed score from the focused attention task to the defocused attention task. At that, attendant error scores were also increased. In addition, divergent thinking facilitated attentional performance, indicated by an increase of the search scope characters processed score from the focused attention task to the defocused attention task. At that, divergent thinking did not yield any effect on errors attendant to the search scope characters processed. Possibly, the latter is beyond the reach of an inhibitory effect. Our results give priority to neither Martindale's (Martindale, 1995, 2002) theory nor Eysenck's (1995) theory. For instance, data according to which divergent thinking facilitates the differential focusing of attention indicated by incrementing the target character processed or the search scope characters processed, can be relevant to Eysenck's theory. In turn, data according to which divergent thinking would inhibit the differential focusing of attention indicated in increasing the errors attendant to the target character processed, can be relevant to Martindale's theory. It is not unlikely that Martindale's and Eysenck's theories catch different aspects of defocused attention, i.e. they can be treated rather as complementary than as mutually exclusive relative to the differential focusing of perceptual attention in visual serial search tasks relative to divergent thinking. Finally, we come to the suggestion that the differential focusing of attention appears relative to divergent thinking. This would mean that creative (divergently thinking) people are flexible in that they are able to switch from focused to defocused attention and vice versa. For future research, we propose to consolidate the attentional selection and attentional distraction variables of the differential focusing of attention relying on divergent thinking within a unified approach. A step towards this purpose can be to see the attention of creative (divergently thinking) people in the framework of a dual-route account. First, the differential focusing of attention would be considered as “overinclusive,” indicating an increase of the attentional selection scores. It makes sense that creative people are seemingly able to stick to the target despite distractors. This is, so to speak, Eysenck's dimension. Second, the differential focusing of attention would be considered distractive to some extent. It would mean that creative people are sensitive to distractors and are distracted from the target by them. This is, so to speak, Martindale's

7. Conclusions The current study was intended to investigate whether focused and defocused perceptual attention accommodated within visual serial search tasks relate to divergent thinking. An elementary cognitive task was employed to simulate focused perceptual attention and a cognitive inhibition task to simulate the defocused perceptual attention. Instead of using measures of focused and defocused perceptual attention separately, they were unified in measures of the differential focusing of attention indicated by a set of appropriate scores. Martindale's (1995, 1999) and Eysenck's (1995) competing theories served as backgrounds for our study. The data obtained lend support to each theory. The attentional distraction scores were rather consistent with Martindale's theory, whereas the attentional selection scores were consistent with Eysenck's theory. It is suggested that each theory taps different aspects of the differential focusing of perceptual attention relative to divergent thinking. Thus, we put forward a dual-route account of the attention of creative (divergently thinking) people including, both Eysenck's dimension and Martindale's dimension. We propose to see these dimensions as complementary rather than conflicting for creative people. 8. Limitations The findings of the present study are to some extent limited in generalizability. The study was conducted on a Russian sample, and it is open to question whether there are cross-cultural differences on our data or not. The sample was relatively homogeneous with respect to age, in that the tested participants were of traditional high school age. A major concern of this research was done on the visual serial search process. However, additional work is needed to find out if the tasks are grounded on parallel processes in nature. In our study the differential focusing of attention scores were employed, i.e. across both focused and defocused attention. It is not unlikely that if the scores are used on the focused and defocused attention conditions separately, the data will shift to some degree. In our study, the participant performed a search for the target character ‘p’ in an elementary task and a search for the target character ‘t’ if three characters ‘d, r, b’ (the distracting characters) preceded the ‘t’ in an inhibitory cognitive task. At best, these single tasks can only serve as points of departure for further research at depth. In the near future, we intend to develop a computer program where the quantity of either the target character or the distractor characters is multiplied to give variance to the above tasks. We hope this line of work will appreciably contribute to further research. Acknowledgments The authors would like to thank Alexey Popov, Nils Torvald Oesterboe, and also two anonymous reviewers for their constructive, valuable and helpful comments on an earlier draft of the manuscript. References Amthauer, R. (1973). Intelligenz-Struktur-Test L S. T. 70. Handanweisung fur die Durchfahrung und Auswertung. Gottingen: Verlag far Psychologie. Anderson, B.A. (2014). On the precision of goal-directed attentional selection. Journal of Experimental Psychology. Human Perception and Performance, 40(5), 1755–1762. Ansburg, P.I., & Hill, K. (2003). Creative and analytic thinkers differ in their use of attentional resources. Personality and Individual Differences, 34, 1141–1152. Averina, I.S., & Scheblanova, E.I. (1996). The verbal test of creative thinking “the alternate uses”. Moscow: Sobor (In Russian). Benso, F., Turatto, M., Mascetti, G.G., & Umilta, C. (1998). The time course of attentional focusing. European Journal of Cognitive Psychology, 10(4), 373–388.

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Broadbent, D.E. (1958). Perception and communication. New York: Pergamon. Burch, G. St. J., Hemsley, D.R., Corr, P.J., & Pavelis, C. (2005). Personality, creativity and latent inhibition. European Journal of Personality, 19, 1–16. 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, 5(3), 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. Coull, J.T. (1998). Neural correlates of attention and arousal: Insights from electrophysiology, functional neuroimaging and psychopharmacology. Progress in Neurobiology, 55, 343–361. Cowan, N., Fristoe, N.M., Elliott, E.M., Brunner, R.P., & Saults, J.S. (2006). Scope of attention, control of attention, and intelligence in children and adults. Memory & Cognition, 34(8), 1754–1768. Dorfman, L. Ya, Gassimova, V.A., & Bulatov, A.V. (2006). A test “Tasks on attention” and its application in creativity studies. Bulletin of Perm State Pedagogical University. Series 1. Psychology, 2, 34–46 (In Russian). Dorfman, L., Martindale, C., Gassimova, V., & Vartanian, O. (2008). Creativity and speed of information processing: A double dissociation involving elementary versus inhibitory cognitive tasks. Personality and Individual Differences, 44, 1382–1390. Eriksen, B.A., & Eriksen, C.W. (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception and Psychophysics, 16, 143–149. Eysenck, H.J. (1995). Genius: The natural history of creativity. Cambridge: Cambridge UP. Eysenck, H.J., & Eysenck, S.B. (1975). Manual of the Eysenck Personality Questionnaire. London: Hodder and Stoughton. Friedman, R.S., Fishbach, A., Förster, J., & Werth, L. (2003). Attentional priming effects on creativity. Creativity Research Journal, 15(2 & 3), 277–286. Gray, N.S., Fernandez, M., Wiliams, J., Ruddle, R.A., & Snowden, R.J. (2002). Which schizotypal dimensions abolish latent inhibition? British Journal of Clinical Psychology, 41, 271–284. Guilford, J.P. (1956). The structure of intellect. Psychological Bulletin, 53(4), 267–293. Guilford, J.P. (1968). Intelligence, creativity, and their educational implications. San Diego, CA: EDITS. Hakstian, A.R., & Cattell, R.B. (1978). Higher-stratum ability structures on a basis of twenty primary abilities. Journal of Educational Psychology, 70(5), 657–669. Hearns, J.F., & Moss, S.M. (1968). Effect of method of payoff on the detection of targets in a visual search task. Journal of Experimental Psychology, 78(4), 569–573. Kent, C., Howard, C.J., & Gilchrist, I.D. (2012). Distractors slow information accumulation in simple feature search. Journal of Vision, 12(1), 1–12 (13). Kim, K.H. (2005). Can only intelligent people be creative? Journal of Secondary Gifted Education, 16, 57–66. 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. Liu, C.C., Wolfgang, B.J., & Smith, P.L. (2009). Attentional mechanisms in simple visual detection: A speed-accuracy trade-off analysis. Journal of Experimental Psychology. Human Perception and Performance, 35, 1329–1345. Lubow, R.E., Ingberg-Sachs, Y., Zalstein-Orda, N., & Gewirtz, J.C. (1992). Latent inhibition in low and high ‘psychotic prone’ normal subjects. Personality and Individual Differences, 13, 563–572. Martindale, C. (1989). Personality, situation, and creativity. In J.A. Glover, R.R. Ronning, & C.R. Reynolds (Eds.), Handbook of creativity (pp. 211–232). New York: Plenum. Martindale, C. (1995). Creativity and сonnectionism. In S. Smith, T. Ward, & R. Finke (Eds.), The creative cognition approach (pp. 249–268). Cambridge: MIT Press. Martindale, C. (1999). Biological bases of creativity. In R.J. Sternberg (Ed.), Handbook of creativity (pp. 137–152). Cambridge: Cambridge University Press. Martindale, C. (2002). Creativity, attention, and cognitive disinhibition. In R. Tomassoni (Ed.), La psicologia delle arti oggi (pp. 89–99). Milano: Angeli. Maruff, P., & Currie, J. (1996). Neuropsychology of visual attentional deficits in schizophrenia. In C. Pantelis, H.E. Nelson, & T.R.E. Barnes (Eds.), Schizophrenia: A neuropsychological perspective (pp. 87–105). London: Wiley. Mason, O., Claridge, G., & Jackson, M. (1995). New scales for the assessment of schizotypy. Personality and Individual Differences, 18, 7–13. Mendelsohn, G.A., & Griswold, B.B. (1964). Differential use of incidental stimuli in problem solving as a function of creativity. Journal of Abnormal and Social Psychology, 68(4), 431–436. Moray, N. (1969). Attention: Selective processes in vision and hearing. London: Hutchinson. Mumford, M.D., & Gustafson, S.B. (1988). Creativity syndrome: Integration, application, and innovation. Psychological Bulletin, 103, 27–43. Neill, W.T. (1977). Inhibitory and facilitatory processes in selective attention. Journal of Experimental Psychology. Human Perception and Performance, 3(3), 444–450.

Nestor, P.G., & O'Donnel, B.F. (1998). The mind adrift: Attentional dysregulation in schizophrenia. In R. Parasuraman (Ed.), The attentive brain (pp. 527–546). Boston, MA: MIT Press. Nisbett, R.E., Aronson, J., Blair, C., Dickens, W., Flynn, J., Halpern, D.F., et al. (2012). Intelligence: New findings and theoretical developments. American Psychologist, 67(2), 130–159. Norman, D.A., & Bobrow, D.G. (1975). On data-limited and resource-limited processes. Cognitive Psychology, 7, 44–64. Nusbaum, E.C., & Silvia, P.J. (2011). Are intelligence and creativity really so different? Fluid intelligence, executive processes, and strategy use in divergent thinking. Intelligence, 39, 36–45. Pack, W., Carney, T., & Klein, S.A. (2013). Involuntary attention enhances identification accuracy for unmasked low contrast letters using non-predictive peripheral cues. Vision Research, 89, 79–89. Plucker, J.A., & Renzulli, J.S. (1999). Psychometric approaches to the study of human creativity. In R.J. Sternberg (Ed.), Handbook of creativity (pp. 35–61). New York: Cambridge University Press. Runco, M.A. (1999). Divergent thinking. In M.A. Runco, & S.R. Pritzker (Eds.), Encyclopedia of creativity. 1. (pp. 577–582). San Diego et al: Academic Press. Runco, M.A. (2008). Commentary: Divergent thinking is not synonymous with creativity. Psychology of Aesthetics Creativity and the Arts, 2(2), 93–96. Schweizer, K. (2010). The relationship of attention and intelligence. In A. Gruszka, G. Matthews, & B. Szymura (Eds.), Handbook of individual differences in cognition attention, memory, and executive control (pp. 247–262). NY, London: Springer. Senin, I.G., Sorokina, O.V., & Chirkov, V.I. (1993). The test of intelllectual abilities. Yaroslavl: Psychodiagnostica (In Russian). Shurtleff, D.A., & Marsetta, M.Y. (1968). Visual search in a letter-cancelling task reexamined. Journal of Experimental Psychology, 77(1), 19–23. Simonton, D.K. (1980). Intuition and analysis: A predictive and explanatory model. Genetic Psychology Monographs, 102, 3–60. Simonton, D.K. (1999). Origins of genius: Darwinian perspectives on creativity. New York: Oxford University Press. Smith, P.L., & Ratcliff, R. (2009). An integrated theory of attention and decision making in visual signal detection. Psychological Review, 116, 283–317. Stankov, L. (1983). Attention and intelligence. Journal of Educational Psychology, 75, 471–490. Sternberg, R.J., & O'Hara, L.A. (1999). Creativity and intelligence. In R.J. Sternberg (Ed.), Handbook of creativity (pp. 251–272). New York: Cambridge University Press. Stroop, J.R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662. Thornton, T.L., & Gilden, D.L. (2007). Parallel and serial processes in visual search. Psychological Review, 114(1), 71–103. Treisman, A. (1977). Focused attention in the perception and retrieval of multidimensional stimuli. Perception & Psychophysics, 22(1), 1–11. Ushakov, D.V. (2006). Psychology of creativity's languages: Yakov Alexandrovich Ponomarev and his scientific school. In D.V. Ushakov (Ed.), Psychology of creativity: The school of Ya. A. Ponomarev (pp. 19–142). Institute of Psychology of Russian Academy of Sciences: Moscow (In Russian). van Damme, S., Crombez, G., & Notebaert, L. (2008). Attentional bias to threat: A perceptual accuracy approach. Emotion, 8, 820–827. van der Yen, A.H.G.S. (1992). Item homogeneity in verbal tests: a Rasch analysis of Amthauer's verbal tests. Educational and Psychological Measurement, 52, 623–641. Vartanian, O. (2009). Variable attention facilitates creative problem solving. Psychology of Aesthetics Creativity and the Arts, 3(1), 57–59. Vartanian, O., Martindale, C., & Kwiatkowski, J. (2007). Creative potential, attention, and speed of information processing. Personality and Individual Differences, 43, 1470–1480. Vartanian, O., Martindale, C., & Matthews, J. (2009). Divergent thinking ability is related to faster relatedness judgments. Psychology of Aesthetics Creativity and the Arts, 3, 99–103. Verghese, P., & Pelli, D.G. (1992). The information capacity of visual attention. Vision Research, 32(5), 983–995. Wallach, M.A., & Kogan, N. (1965). Modes of thinking in young children. New York: Holt, Rinehart, and Winston. Yantis, S., & Johnston, J.C. (1990). On the locus of visual selection: Evidence from focused attention tasks. Journal of Experimental Psychology. Human Perception and Performance, 16(1), 135–149. Zabelina, D.L., & Beeman, M. (2013). Short-term attentional perseveration associated with real-life creative achievement. Frontiers in Psychology, 8, 7 (URL: http://www. frontiersin.org).