Chain free association, creativity, and the default mode network

Neuropsychologia 118 (2018) 40–58

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Neuropsychologia journal homepage: www.elsevier.com/locate/neuropsychologia

Chain free association, creativity, and the default mode network a,b,⁎

c,d,e

Tali R. Marron , Yulia Lerner , Ety Berant Talma Hendlerc,d,e,f, Miriam Fausta,b

a,1

c,2

T c,3

, Sivan Kinreich , Irit Shapira-Lichter ,

a

Department of Psychology, Bar-Ilan University, Ramat Gan 5290002, Israel Gonda Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel Functional Brain Center, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv 64239, Israel d Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 64239, Israel e Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 64239, Israel f School of Psychological Sciences, Tel Aviv University, Tel Aviv 64239, Israel b c

A R T I C LE I N FO

A B S T R A C T

Keywords: Free association Creative cognition Default mode network fMRI Divergent thinking Executive processes

Research on creativity shows that creative thinking entails both executive (controlled) and associative (spontaneous) processes. Yet standard creativity tasks cannot reliably isolate these two types of cognitive processes, making it difficult to understand the relation between the two and the roles of their corresponding brain networks in creative cognition. In this study we used a behavioral and neuroimaging approach in an effort to establish chain free association (FA) tasks as a relevant method for directly investigating spontaneous associative thinking and its role in creative cognition. We further examined the relation between performance on such tasks and intelligence. Participants completed common creativity tasks and then underwent fMRI scanning while producing FA chains. Instructions to participants that emphasized the spontaneous nature of the task, coupled with proper control conditions that were balanced for difficulty, enabled us to uncover spontaneous (as opposed to controlled) processes. To examine whether behavioral measures that can be derived from FA chains (associative fluency, associative flexibility and semantic remoteness between associations) are indicative of unconstrained spontaneous associative processing and are related to different aspects of verbal creativity and intelligence, scores on these measures were correlated with scores on creativity tasks and on an intelligence task, and with brain activity. We found that: (1) the Default Mode Network (DMN), a network involved in selfgenerated and internally-directed thought, was more involved in chain FA than in other tasks expected to reflect more controlled forms of internally-directed thought, suggesting that the DMN involvement might be related to the unconstrained spontaneous nature of chain FA. Higher involvement of the left IFG, SFG, MFG under chain FA was also revealed; (2) higher scores on different behavioral measures from FA chains were related to higher activation of the DMN and to reduced activation of the left IFG, a major node in the executive function network; (3) behavioral measures from FA chains were correlated with different aspects of creative performance but not with intelligence. Taken together, these findings lend support to the hypothesis that chain FA involves associative spontaneous thinking. They further suggest that behavioral measures derived from chain FA could indicate patterns of unconstrained associative thinking, related to reduced cognitive control, that are relevant for creative ideation, and might be able to serve as a measure of these patterns.

1. Introduction It is becoming commonly accepted that creative cognition entails a unique coupling of associative processes together with executive processes (Beaty et al., 2014b; Silvia et al., 2013). Broadly, these two types of processes can be said to correspond, respectively, to the spontaneous

⁎

unfolding of creative ideas, and to top-down exertion of control over thoughts through active switching between ideas and inhibition of mundane ones (Beaty et al., 2014b). This conceptualization of creative cognition is supported by brain research, which has consistently shown that both the Default network, functionally associated with self-generated thought and spontaneous thinking (Andrews-Hanna et al., 2010a,

Correspondence to: Bar-Ilan University, Department of Psychology and Gonda Brain Research Center, Ramat Gan 5290002, Israel. E-mail address: [email protected] (T.R. Marron). 1 Present address: Department of Psychology, Baruch Ivcher School of Psychology, Interdisciplinary Center (IDC), Herzliya 46150, Israel. 2 Present address: Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 3 Present address: The Functional MRI Center, Cognitive Neurology Clinic and Department of Neurology, Beilinson Hospital, Rabin Medical Center, Petah Tikva 4941492, Israel. https://doi.org/10.1016/j.neuropsychologia.2018.03.018 Received 4 October 2017; Received in revised form 12 March 2018; Accepted 13 March 2018

Available online 17 March 2018 0028-3932/ © 2018 Elsevier Ltd. All rights reserved.

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processes that are less controlled and more spontaneous in nature. We suggest that these associative processes—which we refer to as spontaneous associative processes (though they, too, are likely to involve some level of external or internal control)—are more elusive but are highly important to cognition in general and specifically to creative thinking (Andrews-Hanna et al., 2017; Christoff et al., 2016). Spontaneous associative processes related to creative thinking include, among others, a propensity to engage in mind wandering (Baas et al., 2008; Nijstad et al., 2010), disinhibition of cognitive control mechanisms (Eysenck, 1995; Radel et al., 2015), response chaining (Benedek and Neubauer, 2013; Levin, 1978), and retrieval from episodic (i.e., personal experiences) and semantic (i.e., conceptual knowledge stores) memory (Gilhooly et al., 2007; Madore et al., 2015). Executive processes related to creative thinking include, among others, use of working memory (Benedek et al., 2014b; De Dreu et al., 2012; Vartanian et al., 2014), processing speed (Dorfman et al., 2008; Kwiatkowski et al., 1999), switching, and inhibition of salient but irrelevant responses (Benedek et al., 2012a; Zabelina et al., 2012). Though the two types of processes are theoretically of a conflicting nature, both are relevant for each aspect of creative ideation, and are deployed in accordance with task requirements or the participant's phase of solution generation (Zabelina et al., 2012). The idea that creative cognitions involve a combination of associative and executive processes is supported by brain research, which shows that brain networks relevant for associative processes and areas relevant for executive processes couple up during creative ideation (Beaty et al., 2016, 2015; Jung et al., 2013; Mok, 2014). Associative thinking has been widely related to activity within the Default Mode Network (DMN) (Bar et al., 2007; Shapira-Lichter et al., 2013). This network comprises the posterior cingulate cortex (PCC), the precuneus, the medial prefrontal cortex (mPFC), the bilateral inferior parietal lobes (IPL)/ temporal parietal junctions (TPJ), the middle temporal gyrus (MTG) and the medial temporal lobe (MTL) (Binder et al., 2009; Buckner et al., 2008; Laird et al., 2009; Raichle et al., 2001). Although the exact roles of DMN involvement in human cognition have not yet been determined, accumulating empirical evidence has consistently shown that it is involved in inward directed and self-generated thought (Andrews-Hanna et al., 2014; Axelrod et al., 2017; Benedek et al., 2016; Buckner et al., 2008; Raichle et al., 2001) that includes search in memory (Shapira-Lichter et al., 2013) as well as spontaneous thinking and mind wandering (Andrews-Hanna et al., 2010a, 2010b; Christoff et al., 2016; Ellamil et al., 2016; Fox et al., 2015; Zabelina and Andrews-Hanna, 2016). Although several studies point to substantial involvement of the DMN (among other networks) specifically in spontaneous associative thinking (Andrews-Hanna et al., 2017; Christoff et al., 2016; Fox et al., 2015; Zabelina and Andrews-Hanna, 2016), researchers are only beginning to understand the manner in which different hubs and sub networks of the DMN support spontaneous ideation in general (Andrews-Hanna et al., 2010a, 2010b; AndrewsHanna et al., 2017; Christoff et al., 2016; Fox et al., 2015; Zabelina and Andrews-Hanna, 2016), and specifically spontaneous associative aspects of creative ideation (for an in-depth review see Marron and Faust, 2018). Executive processes, in turn, such as selection of semantic information and working memory during creative tasks, have been related to reduced activity in the DMN and high activity in prefrontal regions that are part of the executive function network: e.g., the left Inferior frontal gyrus (IFG) and dorsal lateral prefrontal cortex (DLPFC) (Beaty et al., 2015; Benedek et al., 2014a; Goel and Vartanian, 2005; Gonen-Yaacovi et al., 2013; Vartanian et al., 2014). Recent research also emphasizes the importance of intelligence to creative thinking. In particular, several studies have shown that intelligence levels might explain some of the variance in the relationships between executive and associative processes and common measures of creativity (Beaty et al., 2014b; Benedek et al., 2014b, 2012a; Lee and Therriault, 2013; Nusbaum and Silvia, 2011; Silvia et al., 2013). Thus far, the contribution of intelligence to this variance has mainly been

2010b; Benedek et al., 2016; Christoff et al., 2016; Ellamil et al., 2016; Fox et al., 2015), and the executive network, functionally related to executive control processes (Friedman and Miyake, 2017), are involved in creative idea production (Beaty et al., 2015; Christoff et al., 2009; Mok, 2014). Notably, however, this current knowledge regarding the mechanisms of creative cognition is not adequately reflected in behavioral tasks commonly used to measure creativity. These tasks constitute fundamental tools in a vast stream of creativity research, which seeks to shed light on the elusive construct of creativity and to develop interventions to enhance it. Currently, most behavioral tasks that measure creativity assess simultaneously the two types of cognitive processes involved in creative cognition (e.g., Mednick, 1968; see Runco et al., 2016). As a result, with these standard tests, it is effectively impossible to reliably isolate the role of spontaneous, associative processes, as opposed to controlled processes, in contributing to creative performance. Although many researchers consider the efficiency of coupling the two processes to be the primary factor characterizing creativity (Andrews-Hanna et al., 2017; Beaty et al., 2015, 2014b; Christoff et al., 2016; Ellamil et al., 2012; Gabora, 2010), we suggest that the ability to differentiate between these processes can contribute considerably to the understanding of creative thinking and cognition in general (Fox et al., 2015). For example, in developing interventions for enhancing creativity, it is possible that different techniques might affect each type of process in different ways (e.g., brainstorming might affect associative processes to a greater extent compared with executive processes; e.g., Nijstad and Stroebe, 2006; Shah et al., 2013). Thus, separate measures of each process might provide a more accurate indication of the effects of a given intervention compared with a single measure that combines the two. Indeed, several creativity studies have measured executive aspects of creative thinking, using common tasks that isolate executive control processes (e.g., Benedek et al., 2014b, 2012a; Bott et al., 2014; Vartanian et al., 2014). However, the capacity to measure spontaneous associative processes is much more limited, given that few tasks have been developed for this purpose, and to our knowledge, few have been robustly verified to isolate these processes (one exception is O’Callaghan et al., 2015). The current study is one of the first to successfully use both behavioral and neuroimaging means to provide initial evidence that a straightforward behavioral task—namely, chain free association, discussed below—can be used to indicate characteristics of undirected and spontaneous associative processes that are relevant for creative performance. 1.1. The involvement of associative and executive processes in creativity A creative idea is commonly defined as one that is novel and unique, and that is also adequate and useful (e.g., Fink et al., 2009). Creative thinking entails many cognitive processes (or “creative cognitions”) that facilitate generation of ideas that meet these criteria. The most widely recognized creative cognitions include the following: ideational combination of remote semantic concepts (Faust and Mashal, 2007; Jung-Beeman, 2005; Kenett, 2018; Mednick, 1962; Vartanian et al., 2009), divergent thinking (DT; producing multiple unique ideas from diverse domains), and convergent thinking (used to find a single “correct”/appropriate solution) (Benedek et al., 2012b; Christoff et al., 2008; Lee and Therriault, 2013; Runco and Acar, 2012). Behavioral creativity research suggests that each of these creative cognitions involves a specific combination of both associative and executive processes (Andrews-Hanna et al., 2017; Beaty et al., 2015, 2014b; Christoff et al., 2016; Ellamil et al., 2012). The dichotomy between the two is not entirely clear-cut: In particular, some associative processes are controlled and goal-related and involve executive functions such as selection (e.g., in semantic fluency tasks in-which participants are required to generate associations from a certain category; Lee and Therriault, 2013). Herein, however, we focus on associative 41

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produce ideas from diverse concepts. Several researchers emphasize the spontaneous aspects of this tendency and thus refer to it as spontaneous flexibility (Frick et al., 1959; May and Metcalf, 1965; Runco, 1986). In chain FA, associative flexibility is measured according to the number of discriminable concepts included in the word association chains that an individual generates (Frick et al., 1959). Fluency scores on FA chains (i.e., the overall number of word associations generated) can also serve as an indicator of associative flexibility, as these scores tend to be highly correlated with the number of discriminable concepts (Beaty et al., 2014b; Benedek et al., 2012b). Importantly, a clear relationship has been established between creativity and flexibility scores on chain FA tasks. These scores have been shown to predict both originality and fluency scores on common creativity tasks (Beaty et al., 2014b; Benedek et al., 2012b; Frick et al., 1959; Silvia et al., 2013). However, it has yet to be confirmed that the relationship between chain FA performance and creativity is driven by spontaneous associative processes. The assumption that measures of chain FA performance can serve as an indication of spontaneous associative processes (Frick et al., 1959; Spence et al., 2009) and are related to the corresponding brain networks (Bar et al., 2007), as opposed to executive processes, has thus far been based primarily on intuition. In fact, some claim that measures of chain FA performance are largely attributable to executive processes, and specifically broad retrieval ability (i.e., ability to selectively and strategically retrieve knowledge from memory) (Beaty et al., 2014b; Silvia et al., 2013). The seeming disparity between the two perspectives might be related to nuances in implementation of the chain FA task, and specifically, in instructions provided to participants. In more recent studies participants received instructions to produce chains that were as long and as diverse as possible (Beaty et al., 2014b; Benedek et al., 2012b). It is highly likely that these instructions to switch themes and produce long chains elicited more executive functions (e.g., top-down switching, working memory speed) than would be active without these instructions, or even with instructions aimed at calming participants and ensuring them that all answers are acceptable. Investigating the neural mechanisms underpinning chain FA would enable researchers to decipher the opposing views on the cognitive mechanisms of chain FA that relate to creativity. Yet, surprisingly little brain research has been done in this regard. To our knowledge, only two studies have used brain imaging to examine chain FA. In a recent functional magnetic resonance imaging (fMRI) study, researchers compared chains of FA that participants produced in relation to different types of emotionally valenced sentences (i.e., cue sentences that aroused neutral, negative, or conflicting emotions) (Kehyayan et al., 2013). It was found that the anterior cingulate cortex (ACC) was activated when chain FA followed cues that elicited emotional conflict. Given the lack of more informative control conditions (e.g., more controlled word-generation tasks that are less likely than chain FA to involve spontaneous associative process), not much can be learned from this study on the neural underpinnings of the spontaneous aspects of chain FA and their relation to creativity. The second brain study examining chain FA is that of Spence et al. (2009), who used fMRI to compare chain FA to semantic (words belonging to semantic categories) and phonemic fluency (words that begin with a certain letter). These control conditions are more useful in isolating the "free" and spontaneous aspect of chain FA: Free verbalization of words that come to mind might rely not only on spontaneous associative processes but also on processes that are relevant for any kind of word-generation task (e.g., working memory), and specifically ones that involve access to semantic or phonemic relationships between words; thus, the control conditions can be used to control for the brain network activation that corresponds to these processes. Phonological fluency is associated with enhanced activity in the left IFG (Costafreda et al., 2006) and left premotor cortex (left middle frontal cortex {MFG}; Birn et al., 2010; Shapira-Lichter et al., 2013), while semantic fluency also engages the left MFG and the IFG (Costafreda et al., 2006) as well as areas in the DMN: left MTG, left

studied in relation to executive processes (e.g. working memory, switching; Beaty et al., 2014a, 2014b; Benedek et al., 2014b, 2012a; Nusbaum and Silvia, 2011; Silvia et al., 2013) and less in relation to spontaneous associative processes (one exception is the work of Benedek et al., 2012b). The difference in the scope of research dedicated to the two types of processes might be due to the dearth of simple measures to assess spontaneous associative processes, in contrast to the numerous cognitive tasks that test for executive processes (e.g., Miyake et al., 2000). 1.2. Distinguishing associative from executive processes in measurements of creative cognition As discussed above, standard tasks used to measure creative thinking (e.g., the Alternative Uses Task; see Section 2.3) tend to make no distinction between executive and associative processes. However, some researchers have attempted to isolate the two types of processes (associative: Beaty et al., 2014a, 2014b; Benedek et al., 2012b; executive: Beaty et al., 2014a, 2014b; Benedek et al., 2012a; Silvia et al., 2013) when evaluating creative cognition. For example, Beaty et al. (2014a, 2014b) attempted to measure associative abilities by requiring participants to list synonyms for cue words, and calculating the average semantic distance of each synonym from the cue word. Recently, researchers have begun to focus on free association (FA) tasks as a potential key to measuring associative processes involved in creative thinking (Benedek et al., 2012b). There are three major types of free association tasks, which are used in a variety of applications: single-word associations, continuous association, and chain free association. All three tasks involve verbalization of a spontaneous (i.e., “free”) thought that arises in the individual's mind, without having strict task criteria (e.g., “say a word that comes to mind” as opposed to “say a word that begins with a specific letter”; (for an in-depth review see Marron and Faust, 2018). Research has identified relationships between performance on FA tasks and creative abilities (Beaty et al., 2014b; Benedek et al., 2012b; Benedek and Neubauer, 2013; Kenett et al., 2014; Silvia et al., 2013). Given that FA tasks, almost by definition, might provide a window into individuals’ spontaneous and associative thinking processes (Bollas, 2009; Freud, 1958), it indeed seems plausible that these tasks can be used to measure spontaneous associative processes related to creativity. Notably, however, it has not actually been proven that FA elicits associative or spontaneous cognition, or that the relationship between FA task performance and creativity is driven by spontaneous associative cognitive processes rather than by executive processes. The current study provides empirical support for these relationships with regard to chain FA. 1.3. Chain free association Chain FA is a task in which participants are required to verbalize a “chain” of single-word associations that come to mind, each association relating to the previous one (e.g., wax, candle, fire, hot, summer, love) (Benedek et al., 2012b). Though each of the three major FA tasks is in some sense "free" and has the potential to relate to creative thinking (see Marron and Faust, 2018), we suggest that chain FA, which is characterized by very low constraints and simple instructions (e.g., there is no requirement for each association to relate back to an original cue word as in continuous association) is particularly likely to capture the natural dynamic associative unfolding of spontaneous thought in general (Andrews-Hanna et al., 2017; Spence et al., 2009), and specifically aspects of this unfolding that are relevant for creative cognition (Beaty et al., 2014a, 2014b; Benedek et al., 2012b). Indeed, several researchers have suggested that chain FA, and specifically the quality of associative flexibility captured in this task, can be used to measure associative processes that are activated during creative cognition (Beaty et al., 2014a, 2014b; Benedek et al., 2012b). In general, associative flexibility refers to the tendency or ability to flexibly 42

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superior temporal gyrus (STG), left IPL, hippocampus, parahippocampus, areas in the mPFC (Binder et al., 2009; Birn et al., 2010; Glikmann-Johnston et al., 2015; Shapira-Lichter et al., 2013; Whitney et al., 2011) and the PCC (Binder et al., 2009; Shapira-Lichter et al., 2013). Despite the adequate control conditions, this research did not reveal higher activation of areas known to be involved in unconstrained or spontaneous thinking (i.e., the DMN) during chain FA (as compared with semantic and phonemic fluency). We suggest that this outcome is likely to be attributable to the fact that the researchers focused on prefrontal areas, and also to the fact that their imaging design did not capture the natural spontaneous nature of FA (e.g., because of technical challenges, each participant had to be cued to produce each word in the chain, instead of freely generating associations uninterrupted; Spence et al., 2009). Spence et al. did find that chain FA elicited brain activity in the left IFG and left anterior prefrontal cortex (aPFC). As these areas are widely recognized for their function in executive control and inhibition (Koechlin and Hyafil, 2007; Swick et al., 2008), the authors suggested that this activity was related to inhibition during FA, perhaps due to embarrassment (Spence et al., 2009). We note, however, that activity in the left IFG can also be attributed to switching between concepts on chain FA (Hirshorn and Thompson-Schill, 2006), if such switching is indeed indicative of executive broad retrieval (Beaty et al., 2014b; Silvia et al., 2013). In this study we attempt to understand more fully the role of the left IFG in chain FA.

unconstrained internal search. (See Sections 2.8.2 and 2.8.3 for further details.) We note that, as a means of ensuring that any observed patterns of brain activation during chain FA would not merely reflect mental effort (e.g., Singh and Fawcett, 2008), we balanced the difficulty levels across conditions (see Section 2.6.2). Our analysis involved correlating behavioral measures derived from FA chains (measures of flexibility, fluency, and semantic remoteness) with scores on creativity tasks, with intelligence, and with values of brain activity in regions that are involved in chain FA, in order to shed light on the role of spontaneous associative processing in creative cognition. We expected to obtain the following outcomes, which would lend support to our general hypothesis that chain FA tasks constitute a relevant method for directly investigating spontaneous, unconstrained associative processes and their role in creative cognition:

1.4. The present study

H2. Higher scores on the behavioral measures derived from chain FA (FA fluency, FA flexibility, and FA semantic remoteness) will be related to enhanced involvement of the DMN and to lower activation of executive-related neural processes (left IFG and aPFC; Spence et al., 2009) during FA, suggesting that these measures are related to spontaneous associative as opposed to executive processes (see Section 1.3). Specifically, we suggest that higher DMN involvement on longer and more diverse FA chains indicates elevated levels of internal search (Shapira-Lichter et al., 2013) and could specifically point to the unconstrained and dynamic unfolding of ideas in FA (Andrews-Hanna et al., 2017), while lower activation of executive related neural processes (left IFG and the aPFC) indicates reduced executive control (i.e., a reduction of strong constraints on content and transition between ideas (Radel et al., 2015)).

H1. The DMN will be more involved in chain FA than in more goalrelated internal searches (Bar et al., 2007), due to the fact that FA involves less restricted internal search, and relies to a greater extent on spontaneous cognitive processes compared with control tasks (see Sections 2.8.2 and 2.8.3 for further details). Additionally, chain FA will elicit higher activation of the executive network (e.g., left IFG and left aPFC; Spence et al., 2009), indicating either inhibition on spontaneous associative processes (Spence et al., 2009) or executive broad retrieval (Beaty et al., 2014b; Silvia et al., 2013).

In this study, we hypothesized that chain FA is a relevant method for investigating different characteristics of spontaneous and unconstrained associative processes and their relevance for creative cognition. To investigate this hypothesis, we instructed participants to complete common creativity tasks and then subjected them to fMRI scanning while they produced chains of single-word FAs. To facilitate spontaneous processes, we provided participants with instructions that emphasized that they should produce chain FA with as little internal inhibition as possible on the words produced. Specifically, the instructions did not provide any restrictions or specific demands; they encouraged participants to try to say everything and anything that comes to mind even if it does not make sense, and emphasized that there are no right or wrong associations (Bollas, 2009). This approach refines that of previous studies, which provided stricter instructions with regard to the nature of the associations to be produced (Beaty et al., 2014a, 2014b; Benedek et al., 2012b). Moreover, we designed the fMRI FA task to enable associations to be produced at a self-paced natural rate, without interruption (in contrast to previous studies, in which participants were cued to produce each association in the chain; Spence et al., 2009). Our control conditions included semantic and phonemic fluency tasks, as in Spence et al. (2009), with an addition of a laboratory-based episodic memory task (i.e., memory for stimuli encoded in the laboratory; Burianova et al., 2010; Burianova and Grady, 2007; ShapiraLichter et al., 2013; see Section 2.5.2), which has also been found to involve the DMN (Kim, 2010; Rugg and Vilberg, 2014; Shapira-Lichter et al., 2013). These three control conditions were chosen to enable us to isolate the "free", externally unconstrained aspect of the unfolding of FA. Each of our control conditions involved production of associations that require internally-directed cognition by search processes that are likely to be involved in production of FA as well (i.e., a person might relate one association to the next by drawing from semantic memory features, phonemic attributes, or relations through episodic memory). We assume here that the main difference between FA and the control conditions is that, in the control conditions, word production is based on goal-directed search triggered by selection criteria, whereas in the FA condition there are no selection criteria. Thus, the FA condition is expected to capture a state of lower utilization of control processes, such as evaluation and verification, and more utilization of spontaneous

H3. Scores on each of the behavioral measures derived from chain FA (FA fluency, FA flexibility, and FA semantic remoteness) will be differentially related to different measures of creative ability (i.e., different measures of performance on common creativity tasks). Expected outcomes regarding specific correlations corresponding to specific creativity tasks are presented in the Methods section (Section 2.7). 2. Methods 2.1. Participants The original sample consisted of 36 healthy students from Bar-Ilan University, from a wide range of majors. Participants received either cash payment or a combination of course credit and cash payment for their involvement in the study. Fifteen participants were excluded from the imaging analysis due to excessive head movement during the scan (3 participants > 3 mm) and due to technical problems related to articulation (see Section 2.8.1; 12 participants). One additional participant was excluded from both imaging and behavioral analysis due to malfunction in the sound recording. Thus, the final sample for behavioral analysis comprised 35 participants (29 females; mean age: 22.8 years, age range: 19–26), and the final sample for brain imaging analysis comprised 20 participants (19 females; mean age: 22.4 years, age range: 19–25). We note that the gender distribution was not equal; possible implications are discussed 43

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between the two words in the pair can either be literal (L; broken vase), conventional metaphoric (CM; bright student), novel metaphoric taken from poetry (NM; mercy blanket), or nonexistent (“unrelated”, UR; boot laundry). Participants are asked to decide whether the two words constitute a semantically meaningful expression or not by press of a button (meaningful/not meaningful) (Faust, 2012). Two creativity scores were derived from this task: the percentage of correct recognition of NM as being plausible—reflecting flexibility of access to remote semantic categories—and response time of correct recognition of NM—reflecting associative speed (reviewed in Faust, 2012; Faust and Mashal, 2007; Gold et al., 2012; Zeev-Wolf et al., 2015, 2014). We note that the percentage of correct responses for “unrelated” word pairs enables the experimenter to identify potential concerns regarding false positive responses resulting from a general tendency to consider any pair of words as being related, which may influence NM recognition scores. In our case, respondents correctly identified 83% of unrelated word pairs as such, suggesting that false positive responses were not a salient problem in our sample. In this study, we administered two versions of the creativity tests, such that half the participants completed one version (version 1), whereas the other half completed the other version (version 2). The two versions comprised the same types of tasks but differed in the stimuli presented to participants. No differences in performance were found between versions, F(3,36) = 0.14, p = 0.93. The rationale for using two versions relates to a larger study that the current research is a part of. In that study, after undergoing the experimental procedure described herein, each participant participated in a creativity intervention and subsequently took another creativity test. For counterbalance purposes, half the participants completed version 1 before the intervention and version 2 after the intervention, whereas the other half completed the two versions in the opposite order. Thus, in the current research, which took place before the intervention, half the participants completed version 1, and the other half completed version 2.

in Section 4.5. The procedure was approved by the Ethics Committee of the Tel Aviv Sourasky Medical Center (TASMC) as well as by Bar-Ilan University's Ethics Committee, and all participants provided written informed consent prior to participation. All participants were right-handed, as assessed by a standard selfreport questionnaire (Oldfield, 1971), with normal or corrected-tonormal vision. Exclusion criteria included: participation in any type of psychological therapy or any major psychiatric illness [evaluated by the Brief Symptom Inventory (BSI; Derogatis and Melisaratos, 1983)]. 2.2. Overview of procedure After screening, participants completed a battery of questionnaires: socio-demographic characteristics and behavioral tests, including three creativity tasks, as elaborated below (see Section 2.3). Questionnaires were administered in random order and took about 90 min to complete. On a separate day, in the two days following the behavioral tests, each participant underwent an fMRI scan. Before the fMRI session, each participant received an in-depth explanation on the paradigm and tasks, and participated in a short practice session (20 min) with stimuli that did not appear in the actual scan. Participants were required to study two types of word lists. These words were used later in the episodic memory task in the scanner (see fMRI paradigm Section 2.5.2). The scanning session lasted for ~ 50 min. 2.3. Behavioral assessment: creativity tasks To measure participants' creative thinking, we subjected them to three creativity tasks: the Alternative Uses Task (AUT; Part of the TelAviv Creativity Test; Milgram and Milgram, 1976), the Remote Associates Test (RAT; Mednick, 1968), and the Comprehension of Metaphors task (CoM; Faust, 2012). The AUT is commonly used to measure divergent thinking (DT; Benedek et al., 2013; Runco and Acar, 2012). The RAT is widely used as a measure of creative potential (Dietrich and Kanso, 2010), and is supposed to test ability to combine remote associates (Mednick, 1968), convergent thinking, and insight problem solving (Kenett et al., 2014; Madore et al., 2015). The CoM measures verbal creativity in terms of the ability to access and combine remote semantic associates (Faust, 2012). In the AUT, participants were required to list creative uses for four common objects (e.g., newspaper). Each participant's performance was scored along three dimensions: creative quality, flexibility, and fluency. Creative quality and flexibility were each rated by two independent judges. For the creative quality dimension, using a 5-point Likert scale, each judge assigned a creativity score, (1 = uncreative, 5 = very creative) to each idea produced by the participant (Silvia et al., 2013); the raters were instructed that this score should reflect the idea's originality, unusualness and appropriateness (e.g., Benedek et al., 2013; Runco and Acar, 2012). Inter-rater reliability on these scores ranged from 0.83 to 0.87. Then, for each participant, the three ideas rated as most creative (per item) were selected and their scores were averaged into a single DT creativity score. This measure of creativity has been shown to be fully distinct from fluency of responses (Benedek et al., 2017, 2013). For the flexibility dimension, judges rated the number of categories or themes used by the participant in his or her answers (e.g., Kühn et al., 2014a); inter-rater reliability on these scores was 0.95–0.98. DT fluency was measured as the number of ideas produced by the participant (Takeuchi et al., 2012; Vartanian et al., 2009). In the RAT (Mednick, 1968), participants were required to find an associated fourth word that is related to three seemingly unrelated stimulus words (e.g., stimulus: pine, crab, sauce; answer: apple). We used the validated Hebrew version of the RAT (Nevo and Levin, 1978), which consists of 25 items and lasts 15 min. Scores reflected the number of correct answers. The CoM (Faust, 2012) is an online task in which participants are presented with word-pairs in Hebrew, where the semantic relations

2.4. Intelligence assessment Participants’ intelligence was assessed on the basis of self-reported Psychometric Entrance Test (PET) scores, the Israeli equivalent of the SAT scores (Nevo and Oren, 1986). PET scores have been shown to be strongly correlated (r = 0.81) with scores on the Wechsler Adult Intelligence Scale-Revised (Nevo and Sela, 2003) and have been used as a measure of intelligence in prior research (e.g., Meiran et al., 2016). 2.5. Functional MRI 2.5.1. fMRI data acquisition Whole brain imaging was performed on a 3.0 T Siemens MRI scanner, (MAGNETOM Prisma, Germany) using a 64-channel head coil, at TASMC. Functional T2*-weighted images were obtained using field of view = 220 mm, matrix size = 96 × 96, repetition time = 3000 ms, echo time = 35 ms, flip angle = 90°, 44 axial slices of 3-mm thickness, and gap = 0. We also obtained a high- resolution, Axial T1-weighted MPRAGE structural scan (a 3D spoiled gradient echo sequence, voxel size = 0.9 × 0.9 × 0.9 mm, gap = 0). Visual stimuli were presented using the software Presentation (Neurobehavioral Systems, Albany, CA; version 18.0). Participants’ overt verbal responses were recorded by an MR-compatible noise reduction audio system (OptoAcoustics Ltd) and analyzed offline using Audacity software (version 2.1). 2.5.2. fMRI paradigm The fMRI paradigm was based on an extensively-studied wordgeneration task (Shapira-Lichter et al., 2013), which was extended to include an FA condition (e.g., Spence et al., 2009). Our paradigm included five conditions, in which participants overtly generated words: 2 conditions of free association, and 3 control conditions in which participants responded to phonemic (letter), semantic (category), or 44

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recall words that they had studied during a training session prior to scanning as part of a word list learning and recall task (see Section 2.2). The prior task involved studying two lists of words: a list of words presented visually, and a list of words presented auditorily. Accordingly, participants were evaluated on two modes of episodic retrieval: instructions in the scanner were either to "recall words that you heard" or to "recall words that you read". Each of the two episodic memory cues was repeated three times. Participants were allowed to recall the same word more than one time across blocks (but not within a single block). This task is considered to be a laboratory episodic memory task: recollection of events that are experienced in the context of a laboratory as compared to autobiographical retrieval, which involves recollection of more personally relevant events (Burianova et al., 2010; Burianova and Grady, 2007; Shapira-Lichter et al., 2013). 2.6. Behavioral assessment of free association chains during the scanning session Fig. 1. fMRI paradigm. Each of the five conditions was performed six times. Task blocks of 18 s were separated by visual fixation of 6–9 s. In each block, an instruction and a stimulus word appeared for the first two seconds of the block, and then a black screen appeared for the remaining 16 s during which the participants produced overt responses.

2.6.1. Derivation of FA performance measures: associative fluency, associative flexibility, and semantic distance between associations All FA chains produced by participants during the scan were recorded and transcribed to a spreadsheet. For further analysis only the blocks chosen during the balance procedure were used (see Section 2.6.2). Each chain was assigned a fluency score reflecting the number of associations. Next, each chain was scored by four independent expert judges for associative flexibility and semantic distance between associations. A chain's associative flexibility score was calculated as the number of discriminable concepts included in the chain (see Benedek et al., 2012b; Frick et al., 1959); inter-rater reliability ranged from 0.73 to 0.74. As associative flexibility in chain free association tasks has been found to be highly correlated with the number of words produced (Benedek et al., 2012b), after the judges' scoring, we divided each chain's score by the participant's fluency score of that chain. To obtain a final FA flexibility score for the participant, we averaged the individual flexibility scores of the eight FA chains he or she had produced. Additionally, for each pair of consecutive words in a chain, the judges rated the semantic distance between the two words on a 6-point Likert scale, ranging from 1 (= minimal semantic distance) to 6 (= high semantic distance; i.e., the words are far apart). Inter-rater reliability was 0.82. We calculated a semantic remoteness score for each chain by averaging the rated semantic distances for the chain. We then averaged between chains to obtain a semantic remoteness score for the participant.

episodic (previously studied word list) cues, respectively (See Fig. 1 for a scheme of the paradigm). Each of the five conditions was performed six times (with different stimuli words each time), in a pseudo-randomized order. Task blocks of 18 s were separated by visual fixation of 6–9 s. In each block, an instruction and a stimulus word appeared for the first two seconds of the block, and then a black screen appeared for the remaining 16 s (during the participants' overt response) (see Fig. 1). Stimuli for the different conditions were chosen after a behavioral pretest (N = 30) to ensure similar levels of difficulty (i.e., similar numbers of answers produced; Shapira-Lichter et al., 2013). Similarly to the creativity tasks, two versions of the paradigm were administered for counterbalance purposes (see Section 2.3 for further explanation). Participants’ performance did not differ significantly between the versions (F(4,20) = 1.3 p = 0.3). As noted in Section 2.2, participants received training on the paradigm prior to the scan. a) Free association tasks (FA): Participants were instructed to generate single words that arose in their minds following a given stimulus word, in an association chain manner (i.e., only the first association should relate to the presented stimulus, whereas each subsequent association should relate to the previous associative response). The participants were instructed to produce associations freely—whatever comes to their mind—with as little inhibition as possible (see Section 1.4). In one FA condition the stimulus word was emotional and in the other neutral. All stimulus words in the FA conditions were taken from a network analysis of the Hebrew lexicon (Rubinsten et al., 2005). The stimulus words were nouns controlled for concreteness, familiarity, orthographic neighbors and naming time. The emotional valence of stimulus words was rated in a pretest (N = 30), and words with the highest emotional valence were chosen for the emotional FA condition. In the present research, emotional and neutral FA conditions did not differ from each other in terms of behavioral results and brain activity and hereafter are pooled together in the analysis. b) Phonemic fluency task (PFT): In each PFT block, participants were presented with a Hebrew letter and instructed to produce as many words as possible that begin with that letter. c) Semantic fluency task (SFT): Participants produced as many words as possible that correspond to a given category (e.g., animals with four legs, electronic devices). d) Episodic memory task (EMT): Participants were asked to freely

2.6.2. Balance procedure To address the possibility of confounds associated with task difficulty and articulation effects, performance in the four conditions was controlled at the individual participant level by balancing the numbers of responses per word-generation condition for each participant. Although different conditions might generally involve different levels of effort, we designed the experiment such that all categories were open, and responses would not reach a ceiling effect in the 18 s task block. Thus, choosing blocks with equivalent numbers of responses (i.e., similar levels of performance) between conditions provides an adequate control for effort (e.g., Shapira-Lichter et al., 2013). Specifically, for each of the five word-generation conditions, we selected four of the six blocks, such that the average number of responses in each block did not diverge by more than two from the average number of responses across all blocks corresponding to the condition (see also Shapira-Lichter et al., 2013). The unselected blocks were not included in additional analysis (see Appendix A, Table A1, for the mean numbers of responses before and after the balance procedure, and see Appendix B, Table B1, for inter-correlations between the numbers of responses before the balance procedure). Statistical analysis confirmed that in version 1, across participants, this balancing approach yielded similar fluency 45

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levels across the five experimental conditions [one-way repeated measure analysis of variance (ANOVA), five levels: emotional FA, neutral FA, PFT, SFT, and EMT, F(4,11) = 2.84, P = 0.11, N = 12]. In version 2, a difference was found among conditions F(4,9) = 5.16, P = 0.04, N = 8]. Post-hoc pair-wise Bonferroni comparisons showed that the PFT condition was characterized by fewer responses in relation to both FA conditions as well as the SFT condition.

participants (Leys et al., 2013) and (2) we observed no variance of activity between brain regions and between conditions on the specific time course of each participant. Twelve participants were removed in this manner. Notably, brain activation patterns before excluding participants were highly similar to those after removal, but with less noise. Appendix C (Fig. C1) shows a conjunction analysis of 32 participants (before removal).

2.7. Behavioral data analysis

2.8.2. General linear model (GLM) analysis Participants’ GLM defined six predictors uniformly representing fixation (excluded as 'rest'), in addition to balanced FA, phonemic, semantic, and episodic blocks (see balance procedure described above in Section 2.6.2). The unbalanced blocks were included as confound predictors. Additionally, 6 motion parameters were entered as confound predictors after undergoing Z-transformation. All predictors were modeled as boxcar functions convolved with the hemodynamic response function. Based on an average normalized 3D anatomy, a mask file was defined to anatomically restrict the RFX GLM analysis. Singlesubject analysis was followed by a Z-transformed multi-subject analysis computed with random effects. To identify brain regions that uniquely contributed to the FA condition, a conjunction analysis was performed on the contrasts FA > PFT, FA > SFT, and FA > EMT. As mentioned in Section 1.4, we assume that cognitive processes involved in FA may overlap to some degree with those involved in verbalization of words characterized by phonemic, semantic, or episodic relations. Evidence for such overlap can be observed in the correlations between the control conditions and FA in terms of the number of responses generated by participants (see Appendix B, Table B1). These correlations suggest that similar processes characterize an individual's ability to generate different types of associations (e.g., speed of working memory). We note, however, that we carried out a within-subject comparison across conditions before averaging across participants—which means that the common processes shared by all three control conditions and by FA were "cleaned". Additionally, the balance procedure cleaned elements related to differences in effort (see Section 2.6.2). Thus, in general, any differences between FA-related brain activity and the brain activity corresponding to the control conditions are expected to relate to lower utilization of executive control processes in the FA condition. Still, even with this design, there is likely to be some overlap between the processes characterizing FA and those characterizing each one of the control conditions (but not the others; see Section 2.8.3 for a detailed discussion of the specific processes that FA is expected to share with each control task). Thus, we chose to conduct correlations of behavioral measures of FA (fluency, flexibility, and semantic remoteness) with specific contrasts (instead of averaging the three control conditions together) because it is possible that each control condition involves different levels of activity in different brain regions—such that averaging the three conditions would lead to a loss of important information. More generally, comparing the FA condition with each control condition individually enables us to isolate a different aspect of FA that can shed light on the nature of the correlations involving behavioral measures of FA, which are also hypothesized to each be related to different constructs of cognition. The conjunction analysis was held at a statistical threshold of p < 0.002 using FDR correction for multiple comparisons (Benjamini and Yekutieli, 2001; Genovese et al., 2002) and a minimal cluster size of 5 × 33 voxels. T-values for each region of interest (ROI) that emerged in the conjunction analyses were based on a ‘conjunction null distribution’ derived from all contrasts used in the conjunction (Nichols et al., 2005).

To understand correlations between behavioral data that can be derived from FA chains (fluency, associative flexibility, and semantic distance between associations) and creative performance as well as intelligence, we performed Pearson correlations. We expected that the following correlations would be observed: Correlations between FA performance and AUT performance: (H3a) Fluency on chain FA will correlate with fluency on the AUT (as in Beaty et al., 2014b; Benedek et al., 2012b). (H3b) FA flexibility will correlate with flexibility on the AUT, but not with AUT fluency. This expectation is driven by the fact that our measurement of FA flexibility (see Section 2.6.1) aims to distinguish flexibility from fluency (in contrast to measures of flexibility and fluency in the AUT, which are highly correlated; (Benedek et al., 2012a; De Dreu et al., 2012; Zabelina et al., 2012). (H3c) FA fluency, FA flexibility and FA semantic distance—all of which are assumed to be components of creative ideation—will each correlate with AUT creative-quality. Correlations between FA performance and CoM performance: (H3d) FA flexibility and FA semantic distance, both of which are suggested to involve access to remote semantic networks, will be related to accuracy of identifying novel metaphors on the CoM. (H3e) FA fluency will be related to speed on the CoM. We performed planned comparisons to test each of these expected outcomes. We further examined all additional correlations among the collected measures, and corrected the p-values of these correlations using the false discovery rate (FDR) for multiple comparisons (after correction, comparisons that were correlated at p < 0.05 were considered to be significant). 2.8. fMRI data analysis 2.8.1. Preprocessing The BrainVoyager 2.8 analysis package (Brain Innovation, Maastricht, The Netherlands) was used for the analysis. The first six volumes were discarded to allow for T1 equilibrium. The preprocessing of the functional scans entailed 3D motion correction, slice scan time correction, spatial smoothing [a full width at half maximum (linear trend removal)], and high-pass filtering (fast Fourier transform based with a cutoff of two cycles per time course). Scans in which head movements exceeded 3 mm were discarded (N = 3). We then superimposed the functional images on 2D anatomical images and incorporated them into the 3D datasets through trilinear interpolation. Volume time course (VTC) underwent FWHM 4-mm Gaussian Kernel spatial smoothing. The complete dataset was transformed into Talairach space (Talairach and Tournoux, 1988) and projected on an inflated reconstruction of the cortical surface. We observed excessive noise and abnormal activation on many of the functional images; this noise was assumed to be related primarily to articulation. Thus, we decided to remove scans based on a double criterion: functional outlier maps and manual test of signal variance. To calculate the outlier maps, we used a BrainVoyager plug-in that performs a z-transformation of the time course for each participant, in each voxel. A time course was considered to contain outliers if activity was more than 4 standard deviations above or below the mean activation level in the voxel. A participant was removed from the sample if (1) the total number of voxels showing at least one outlier in the timecourse was higher than the median number of voxels with outliers of all

2.8.3. Selection of ROIs Regions that emerged from the conjunction analysis were considered to be uniquely associated with FA and were defined as ROIs. Additionally, the left IFG and left aPFC cortex were defined as ROIs 46

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following the findings of Spence et al. (2009); to delineate these regions, we used the coordinates referred to in that study. Beta weights were calculated from the ROIs, and a value for each participant was produced for each of the following contrasts: FA > PFT, FA > SFT, FA > EMT. To understand the relation of brain activity to levels of behavioral measures derived from FA chains (fluency, flexibility, and semantic distance), we compared beta weights (based on the differences FA – PFT, FA – SFT, and FA – EMT) to scores of behavioral data derived from the FA chains using Pearson correlations. In examining each difference, we attempted to eliminate the overlapping aspects of the brain activity involved in FA and the activity involved in the corresponding control task. In particular, both FA and PFT are expected to involve brain activity related to phonological search, which most likely involves a serial search based on syllabification of initial letters and recruits brain areas that are relevant for motor programming and articulation (Katzev et al., 2013). Thus, brain activity observed in FA but not in PFT is expected to relate to memory, including semantic and episodic (contextual) associations. SFT, in turn, is expected to overlap with FA in that both tasks are likely to involve retrieval of closely-related factual information and general knowledge (Burianova and Grady, 2007). Thus, observing FA brain activity while controlling for brain activity in the SFT condition may provide insight regarding associative processes that involve access to concepts that are not closely related semantically. Finally, both FA and EMT are likely to involve brain activity that is related to contextual relations between associations. Accordingly, controlling for brain activity in the EMT condition enables us to examine aspects of relationships between associations that are not related to time and place. We note, however, that as our episodic memory task includes recollection of laboratory stimuli, the brain activity observed therein may relate to a specific type of contextual association, perhaps less personal than contextual associations drawn from autobiographical memory. We carried out planned comparisons for behavioral measurements coupled with beta weights corresponding to activity in the IFG and aPFC. All other correlations were corrected using FDR for multiple comparisons (p < 0.05).

Fig. 2. Correlations of FA fluency, FA semantic distance, and FA flexibility with creativity and intelligence measures. FA = free association, CoM = comprehension of metaphors, RAT = remote association task, AUT = alternative uses task, PET = psychometric entrance test. N = 35. *** = significant at pvalue < 0.0001. * = significant at p-value < 0.05.

In line with H3e, FA fluency was negatively correlated with the response time needed to recognize novel metaphors on the CoM task (r = −0.38, p = 0.03). H3d was partially supported: Associative flexibility scores on FA chains were correlated with accuracy level on recognizing novel metaphors on the CoM (r = 0.36, p = 0.03). Yet, no correlation was found between semantic distance scores on chain FA and accuracy of recognizing novel metaphors. No correlation was found between FA measures and RAT scores or intelligence (as measured by PET scores). Interestingly, PET scores were strongly correlated with RAT scores (r = 0.55, p-corrected = 0.003). 3.2. Whole-brain fMRI analysis results To identify brain regions that uniquely contribute to chain FA, we performed a conjunction analysis on the (chain FA > phonological fluency task {PFT}), (chain FA > semantic fluency task {SFT}), and (chain FA > episodic memory {EMT}) contrasts. Whole-brain analysis showed activation of the mPFC, PCC, left superior frontal gyrus (SFG), left middle frontal gyrus (MFG), left TPJ, left IFG, left MTG, right anterior MTG, and right posterior cerebellum (FDR-corrected at voxellevel, p < 0.002) (see Table 1 and Fig. 3). Notably, no significant activation was observed in the aPFC, in contrast to Spence et al.'s (2009) observations.

3. Results 3.1. Behavioral results: correlations of FA fluency, FA semantic distance, and FA flexibility with creativity and intelligence measures To examine how behavioral scores derived from chain FA might be related to creative performance, we correlated participants’ behavioral scores (from the chain FA condition in the fMRI paradigm) with their scores on validated creativity questionnaires, administered previously (N = 35) (see Fig. 2). In line with prior research (Benedek et al., 2012a; De Dreu et al., 2012; Zabelina et al., 2012), AUT fluency and AUT flexibility scores were correlated. Notably, this correlation level was extremely high (r = 0.96, p-corrected < 0.0001); therefore, we omitted AUT flexibility from further analysis, although this measure correlated both with FA fluency (r = 0.68, p < 0.0001) and FA flexibility (r = 0.35, p = 0.04). Additionally, FA flexibility and semantic distance scores on chain FA were also significantly correlated with each other (r = 0.68, p-corrected < 0.0001). FA fluency was not correlated significantly with FA flexibility (r = 0.4, p-corrected = 0.09) or with FA semantic distance (r = 0.23, p-corrected = 3.1). As expected (H3a, H3c), FA fluency scores, defined as the number of words generated, were strongly correlated with fluency scores on the AUT (r = 0.66, p < 0.0001) and with creative-quality score on the AUT (r = 0.42, p = 0.01). As expected, associative flexibility on chain FA, measured by the number of discriminable concepts divided by fluency, was also positively correlated with AUT creative-quality (r = 0.38, p = 0.02; H3c), but not with fluency (r = 0.3, p = 0.07). Contrary to our expectation (H3c), no correlation was found for semantic distance scores on chain FA with creative-quality on the AUT.

3.3. Correlation of brain activity in ROIs and measures of chain FA performance To understand the functional significance of each of the regions that emerged as selective to FA, in addition to the aPFC (identified as an ROI by Spence et al., 2009, though not in our own analysis), we extracted the beta weights corresponding to each region under each condition. We subsequently subtracted the beta weights corresponding to each control condition (PFT, SFT, EMT) from the beta weights corresponding to chain FA. Then, we examined the correlation between the resulting values and the chain FA behavioral measures – FA fluency, FA flexibility, FA semantic distance (see Section 2.8.3). A one-way repeatedmeasures ANOVA confirmed that there were significant differences across the three control conditions (SFT, PFT, EMT) in terms of the brain activity they elicited in each ROI (see Appendix D), supporting our decision not to average the three control conditions in this analysis (see Appendix E, Fig. E1, and Appendix F, Fig. F1 for a visualization of the differences between conditions). FA fluency scores were strongly correlated with brain activity (beta weights from FA – PFT) in the mPFC (r = 0.74, p-corrected = 0.001) and in the left SFG (r = 0.66, p-corrected = 0.03). An additional strong 47

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Table 1 Brain activation for conjunction on the FA > PFT, FA > SFT, and FA > EMT contrasts (FDR-corrected at voxel-level, p < 0.002, N = 20). Region

mPFC PCC SFG MFG TPJ IFG MTG aMTG Posterior cerebellum

Hemisphere

L L L L L L L R R

BA

9 31 6 6 39 45 21 21 –

Peak (Talairach) x

y

z

−13 −3 −9 −42 −45 −51 −45 57 21

56 −52 14 5 −64 20 −31 −10 −79

37 28 61 46 31 19 1 −11 −32

No of voxels

T

p

4047 2104 2754 2570 4481 2966 4119 770 5295

6.06 6.27 5.91 6.77 7.47 7.55 7.55 5.39 6.42

< < < < < < < < <

0.00001 0.00001 0.0001 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

FA = free association, PFT = phonological fluency task, SFT = semantic fluency task, EMT = episodic memory task, BA = brodmann area, L/R = left/right, aMTG = anterior middle temporal gyrus, mPFC = medial prefrontal cortex, PCC = posterior cingulate cortex, SFG = superior frontal gyrus, MFG = middle frontal gyrus, TPJ = temporal parietal junction, IFG = inferior frontal gyrus, MTG = middle temporal gyrus.

was correlated with behavioral measures of chain FA. In addition, we observed correlations between behavioral measures of chain FA performance and other types of creative performance, but no correlations between FA performance measures and intelligence.

correlation was found between FA semantic distance and brain activity (from FA – EMT) in the PCC (r = 0.65, p-corrected = 0.04) (i.e., the greater the distance between associations, the more the PCC was activated). Finally, as expected, a negative correlation was observed between FA flexibility and brain activity (beta weights from FA – PFT) in the left IFG (r = −0.57, p = 0.009). Contrary to our expectation, no correlation was observed between FA fluency and left IFG activity, FA fluency and aPFC activity, or between FA flexibility and aPFC activity. The results are summarized in Fig. 4.

4.1. Brain regions involved in chain FA To the best of our knowledge, this research is the first to establish that the DMN is substantially involved in processes of verbalizing one's train of thought through chain FA. Specifically, whole brain analysis revealed that core regions of the DMN (mPFC, PCC, left TPJ, bilateral MTG), as well as prefrontal executive (left IFG) and motor areas (left MFG, left SFG), are more involved in chain FA than in other forms of language production that are also internal and associative and might involve similar search processes to FA, but are more goal-directed with higher selection criteria. The higher involvement of the DMN in chain FA than in control tasks (after balancing for differences in difficulty level) suggests that chain FA involves more of an unconstrained internal search than the control conditions. Given that areas of the DMN have been reported to

4. Discussion We carried out whole brain imaging of participants engaged in chain FA and analyzed correlations between: (i) behavioral measures of chain FA performance; (ii) measures of performance in creativity and intelligence tasks; and (iii) fMRI measures of brain activity in individuals engaged in chain FA. Our findings reveal that engagement in chain FA elicited heightened DMN activity and left IFG involvement (as compared with control conditions), and that activity in these regions

Fig. 3. Whole brain activation results for a conjunction analysis on the FA > PFT, FA > SFT, and FA > EMT contrasts; presented on an inflated brain of a single participant (FDR-corrected at voxel-level, p < 0.002, N = 20). FA = free association, PFT = phonological fluency task, SFT = semantic fluency task, EMT = episodic memory task, aMTG = anterior middle temporal gyrus, mPFC = medial prefrontal cortex, PCC = posterior cingulate cortex, SFG = superior frontal gyrus, MFG = middle frontal gyrus, TPJ = temporal parietal junction, IFG = inferior frontal gyrus, MTG = middle temporal gyrus.

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Fig. 4. Correlation of brain activity (beta weights) in ROIs revealed in contrasts FA > PFT, FA > SFT, and FA > EMT and free association (FA) dimensions: fluency, flexibility, and semantic distance. N = 20. ** = significant at p-value < 0.01. * = significant at p-value < 0.05.

the production of free associations (see Section 4.2.2 for a detailed discussion on the left IFG). Two additional areas were found to be associated with chain FA: the left SFG and the left MFG. These areas are well known for their involvement in motor functions (e.g., Li et al., 2013; Simmons et al., 2008; Vergani et al., 2014), and have recently been shown to be activated in semantic memory (Binder et al., 2009), internal goal-related cognition such as autobiographical planning (Spreng et al., 2010), rapid generation and articulation of creative outputs (e.g., metaphorical expressions (Benedek et al., 2014a) and music improvisation (Bengtsson et al., 2007; Liu et al., 2012)). Our imaging design and balancing process (Sections 2.5.2 and 2.6.2), in which chain FA was contrasted with other verbal fluencies with similar motor characteristics, provide grounds for the suggestion that the activation of these areas might be related to cognitive aspects associated with chain FA, and not solely to motor effects. The unique coupling of activity in the DMN with activity in executive areas (and also in motor areas, as has been recently observed) has been widely shown to be associated with creative idea production (Bashwiner et al., 2016; Beaty et al., 2015, 2014b; Christoff et al., 2009; Mok, 2014), suggesting that chain FA is a form of creative thought. Nonetheless, the manner in which the DMN, SFG, MFG and left IFG work together in chain FA, along with each area's functional role in relation to FA and creative thinking, is yet to be determined. Our analyses, using measures obtained from chains of FA— specifically FA fluency, FA flexibility, and FA semantic distance—might begin to shed some light on this matter. This analysis is discussed in the following Section (4.2).

be involved in spontaneous cognition (Andrews-Hanna et al., 2017, 2010; Buckner et al., 2008; Christoff et al., 2009; Ellamil et al., 2016; Fox et al., 2015), our results lend support to the idea that chain FA is more spontaneous in nature in comparison to the control conditions. Although the spontaneous nature of chain FA may seem self-evident—and in fact is embedded in the very term “free association” (Bar et al., 2007)—it has never before been empirically established. We acknowledge, however, that the DMN activity observed during FA may be indicative of additional cognitive processes, beyond purely spontaneous associative processes. Recent research and meta-analyses of previous findings point to differential involvement of sub-divisions of the DMN (in addition to areas outside the DMN) in different aspects of spontaneous thinking. Specifically, the activations observed in our whole brain analysis seem to fit with a sub-network named the dorsal medial subsystem, which includes the dorsal mPFC, the TPJ, the MTG, and the lateral superior and inferior prefrontal gyrus. The dorsal medial subsystem has been specifically related to social tasks, particularly those that involve internally reflecting on one's mental state as well as elaboration of spontaneous thoughts (Andrews-Hanna et al., 2010a, 2010b; Andrews-Hanna et al., 2017; Fox et al., 2015; Raichle, 2015). It is possible that the brain activity we observed that was related to chain FA included the metacognitive processes of reflecting and elaborating one's thoughts, processes that would be compatible with the instructions for the FA task (see Section 1.4). Our observations of the involvement of the left IFG in chain FA are in line with findings from previous research on the neural basis for chain FA (Spence et al., 2009). These findings suggest that control and executive processes (Swick et al., 2008) are involved to some extent in 49

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aspects of creativity. To this end, we examined the content of the FA chains and rigorously evaluated the topic domains to which the various associations corresponded. In line with our expectations (see Section 2.7), FA flexibility was related to creative-quality on the AUT (H3c), and not to AUT fluency (H3b). We further found that, in line with H3d, FA flexibility scores were correlated with the ability to access remote semantic concepts, but were not correlated with the speed of doing so. Taken together, these observations support the notion that our measure of FA flexibility is distinct from (AUT) fluency, and that it indeed indicates a form of the cognitive construct of associative flexibility (which is not captured in FA fluency), as we have previously found that the ability to accurately recognize novel metaphors in the CoM is enhanced among individuals with more flexible organization of memory (Kenett et al., 2014). Further supporting the hypothesis that FA fluency and FA flexibility are distinct constructs, we observed substantial differences between the brain regions whose activity correlated with high scores on FA flexibility versus FA fluency. Our results show that activity in the left IFG (and particularly in Brodmann area 45) during FA negatively correlated with FA flexibility scores. The left IFG has become well known for its role in both phonologic processing and semantic processing (Biesbroek et al., 2016; Costafreda et al., 2006; Heim et al., 2008; Katzev et al., 2013; Vigneau et al., 2006). In particular, Brodmann area 45 has been suggested to have a special role in the processing of semantics (meaning), in selecting appropriate meanings, and in suppressing meanings that are inappropriate or irrelevant (Badre and Wagner, 2007; Benedek et al., 2014a). As our findings were obtained for analysis of FA – PFT (i.e., subtracting the beta weight corresponding to PFT from that corresponding to FA), the observed activations can be attributed to associations with relations based on memory (e.g., semantic, episodic) and are not likely to be based on phonological attributes. Thus, it is possible to interpret our observations as follows: The less involved the left IFG is in memory search during FA, the more one is able to shift between different concepts in memory, suggesting that the left IFG might be involved in inhibition of spontaneous flexibility as measured by FA flexibility. This interpretation is compatible with the role of the left IFG in control and suppression of inappropriate or irrelevant meanings, considering that in order to promote flexibility, we encouraged participants to reduce control over the associative process; we instructed participants to say whatever comes to mind, even if it seems irrelevant or inappropriate (see Section 1.4). Moreover, given that we have shown that FA flexibility is correlated with performance on standard creativity tasks, this interpretation lends support to prior research showing that the left IFG inhibits creative idea generation (Mayseless and Shamay-Tsoory, 2015; Pobric et al., 2008). It may seem that our observations regarding the negative correlation between IFG activity and FA flexibility contradict prior creativity studies suggesting that the left IFG contributes to flexible thinking (by switching, selecting, inhibiting mundane "regular" responses/ dissociation from prepotent concepts; Chávez-Eakle et al., 2007; Hirshorn and Thompson-Schill, 2006). We can resolve this seeming contradiction by noting that those studies relied on a different definition of the concept of “flexibility”: Whereas we examined spontaneous associative flexibility—one's tendency to flexibly produce ideas from diverse concepts—those studies investigated adaptive flexibility: the ability to intentionally switch between ideas to meet certain requirements (Frick et al., 1959; May and Metcalf, 1965; Runco, 1986). Taken together, our findings regarding FA flexibility suggest that the construct is related to creative cognition, in a manner that is different from the relation of FA fluency to creative cognition. FA flexibility might serve as an indication of one's ability/tendency to reduce control over thought, thus enabling more diverse ideas to come forth. However, due to the extremely high correlation between AUT flexibility and AUT fluency (which may have resulted, at least in part, from an inherent flaw in the AUT's operationalization of flexibility; see Section 3.1), our data did not enable us to test the correlation of FA flexibility with measures of cognitive

4.2. Behavioral measures of FA performance and their correlations with creativity scores and brain activity 4.2.1. FA fluency We found that associative fluency scores derived from FA chains (i.e., the number of associations one produces in a given time period) were related to performance on certain aspects of creative cognition: specifically, fluency and creative-quality on a divergent thinking task (AUT), and speed of semantic search (i.e., speed on detecting relationships between remote concepts measured by the CoM; see Fig. 2). These findings are in line with our expectations (H3a, H3c, H3e; see Section 2.7) and replicate previous research that has found similar relations between FA fluency and divergent thinking (Beaty et al., 2014b; Benedek et al., 2012b), and fluency and speed of relating remote concepts (Vartanian et al., 2009). Our results show that high scores on FA fluency were correlated with high activity in the left mPFC and the left SFG. We obtained these findings after we subtracted brain activity values (beta weights) that resulted from the control condition PFT from those of FA (FA-PFT), which enabled us to isolate activations related, among others, to memory search (e.g., episodic, semantic, autobiographical; as discussed in Section 2.8.3). The mPFC has been suggested to have a generative role, specifically of ideas based on associative information (Bar, 2009a), and of nonformulaic concepts (Binder et al., 2009). This generation might be achieved through un-cued self-guided retrieval of semantic knowledge (Binder et al., 2009; Burianova et al., 2010), autobiographic memory (Andrews-Hanna et al., 2014), or episodic memory (Burianova et al., 2010), processes that include selection of ideas (and inhibition of others) from a larger set of possibilities (Benedek et al., 2014a). Thus, our results may suggest that when the mPFC is involved to a greater extent in aspects of FA that are related to memory search, the freeassociating individual generates a larger number of associations. Although numerous studies have linked mPFC activation to creativity (Kühn et al., 2014b; Mayseless et al., 2015; Shamay-Tsoory et al., 2011; Wei et al., 2014), to the best of our knowledge, activation in this region has not been directly related to fluency. Our findings in this regard are compatible with the hypothesis that the mPFC contributes to creative thinking by promoting the generation of associations, perhaps through enhanced un-cued self-guided memory search. As noted above (see Section 4.1), beyond its role in motor function (which we controlled for), the left SFG has been shown to be involved in internal goal-related cognition related to memory (Binder et al., 2009; Spreng et al., 2010) and rapid generation and articulation in creative performance (Liu et al., 2012). Thus, it might be possible that the correlation observed between SFG activity and FA fluency scores—obtained after subtracting PFT beta weights from FA beta weights, indicating activity corresponding to memory-related associations; see Section 2.8.3—was related to memory search and speed of generating associations from memory. These processes are probably beneficial to creating multiple creative ideas (as measured by fluency and creativequality on divergent thinking tasks) in a limited amount of time, as well as accessing remote semantic networks rapidly (measured by response time on the CoM). Taken together, our findings regarding FA fluency might point to a mechanism by which associative fluency contributes to creative cognition: by facilitating the generation of ideas based on un-cued retrieval from inner sources of information (including memory), regardless of the exact content, in a spontaneous and rapid manner. 4.2.2. FA flexibility In this study we aimed to create a refined measure of spontaneous associative flexibility (i.e., the tendency/ability to flexibly produce ideas from diverse concepts) that controls for associative fluency, in order to accurately capture the conceptualization of flexibility and fluency as different cognitive processes that are related to different 50

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flexibility besides comprehension of novel metaphors. Future research should test the ability of FA flexibility to uniquely predict spontaneous flexibility in order to determine whether the informative value of the FA flexibility measure indeed justifies the arduous process of deriving this measure.

years of education (a bias created by university requirements), it might be hypothesized that our results reflect a necessary-but-not-sufficient (Karwowski et al., 2016) relationship between intelligence and creativity. In other words, intelligence may be necessary for creative thinking up to a certain threshold, beyond which other personality measures are more predictive of creative abilities (Jauk et al., 2013).

4.2.3. Semantic distance between associations We did not observe any correlation between our measure of FA semantic distance (reflecting the semantic distance between consecutive associations in an FA chain) and performance on creativity tasks. This lack of correlation might be due to our operationalization of the measure of semantic distance as the average score of the rated semantic distance between any two consecutive associations. This measure can be seen as an average of the semantic distance of many "single word" free-associations. Lack of correlation between this measure and creativity scores is therefore plausible, as it has been found that individuals with high creativity levels do not necessarily produce distant single-word associations (see Marron and Faust, 2018 for a review). However, our analysis revealed a strong correlation between beta weights in the PCC and our measure of FA semantic distance. The beta weights in this correlation are a result of a subtraction of the brain activity elicited by the episodic memory task from the activity elicited by FA, thus revealing activity of associations with relations that are supposedly less characterized by specific context and experience. Previous research shows that both semantic memory and episodic memory involve activation of the PCC (Binder et al., 2009; ShapiraLichter et al., 2013). Thus, this finding can be interpreted as follows: higher PCC involvement beyond that which is related to laboratoryencoded (nonautobiographic) episodic memory is related to more remote semantic relations between associations. This interpretation is in line with studies that have identified PCC activation in creative performance that incorporates production of remote associates (Benedek et al., 2014a; Liu et al., 2012; Mayseless et al., 2015). Moreover, the PCC has recently been shown to be more extensively activated while processing remote semantic associates (i.e., weakly related) than when processing closely-related associates (Krieger-Redwood et al., 2016). Similarly, PCC activation has been shown to be positively related to the extent of demand on semantic retrieval, and specifically the capacity to overcome salient conceptual knowledge (i.e., the known uses of an object on the AUT) (Beaty et al., 2017; Krieger-Redwood et al., 2016). To shed additional light on the role of the PCC in the processing of remote semantic relations, it might be worthwhile to investigate other forms of episodic memory, and particularly memory that has undergone consolidation into long-term memory (e.g., by using an autobiographical memory task).

4.4. Summary and conclusions The present research supports the notion that chain free association tasks offer a "window" into internal spontaneous associative processes (e.g., self-guided generation of associations, unconstrained search and retrieval from memory, naturally shifting between concepts) that are important for creativity. Our observations regarding the brain regions that uniquely contribute to chain FA (as compared to more controlled associative tasks), coupled with our analyses of brain and behavioral data, lend critical support to this conclusion. The brain regions of interest that emerged included the major hubs of the DMN, which are known to be highly relevant for internally directed cognition and spontaneous thinking, along with the left IFG and left motor areas, a combination highly relevant for creative processes (Bashwiner et al., 2016; Beaty et al., 2015, 2014b; Benedek et al., 2016). Correlations of behavioral measures derived from chain FA with creativity tasks suggest that characteristics of an individual's FA chains (e.g., length, diversity) are related to different types of creative performance. Based on our findings that these characteristics are related to different patterns of brain activity, which might be indicative of patterns of spontaneous associative processes (e.g., generative speed, scope of memory search, disinhibition on memory search), we suggest that the behavioral measures derived from chain FA might indicate patterns of spontaneous associative processes that are involved in creative thinking. Chain FA fluency is indicative of an individual's capacity to generate associations, regardless of the associations' exact content. The associative generative abilities that FA fluency points to might include the scope and speed of internally-directed un-cued (spontaneous) retrieval of associations related to memory. This association is supported by the relation of high FA fluency scores to high activation in brain areas relevant for these processes (mPFC and left SFG) in participants engaged in chain FA. We suggest that spontaneous generative abilities as measured by FA fluency are highly relevant for ideational fluency as measured by a DT task and, to some extent, for speed of processing of remote associates and creative quality of ideation. FA flexibility might indicate the ability to naturally shift between contexts and concepts in memory. This ability seems to be related to disinhibition of control processes, allowing access to networks that are more distinct and remote. These mechanisms might underlie the relation between FA flexibility and the production of creative ideas. These propositions are supported by findings of a correlation between high flexibility scores and reduced brain activity in areas that are regularly used for control and inhibition (left IFG) in participants engaged in chain FA. We suggest that FA flexibility measures a different aspect of creativity than FA fluency does, one that is related to flexible access to remote semantic networks. Although semantic distance between consecutive associations was not related to performance in any of the common creativity tasks, brain imaging provided evidence that this measure indicates a separate, but perhaps related construct in creative cognition, one that has to do with very basic processing of remote associates in the PCC. As FA flexibility and FA semantic distance were correlated, we suggest that these constructs may have reciprocal roles in the ability to process and access vast, divergent fields of thought, though the capacity of these processes to find expression might perhaps be hindered by a bottle-neck induced by generation and associative fluency. No correlation was found between intelligence and associative abilities or performance on divergent thinking tasks in our study. We did, however, observe a correlation between intelligence and

4.3. Chain FA and intelligence No correlations were observed between intelligence and the behavioral measures derived from FA chains, or between intelligence and performance on divergent thinking tasks (in terms of fluency, flexibility, or creativity). We did observe a correlation between intelligence scores and scores on the RAT. These findings contradict the idea that intelligence is related to associative processes (Benedek et al., 2012b; Lee and Therriault, 2013) or the research on the contribution of intelligence to divergent thinking (Nusbaum and Silvia, 2011). They do, however, support findings that intelligence might contribute specifically to convergent aspects of creativity (measured by the RAT) that are based on executive processes (Chermahini and Hommel, 2010; Jung and Haier, 2007; Lee et al., 2014; Lee and Therriault, 2013). The latter proposition seems particularly plausible in light of the fact that the RAT is considered to be a test that measures convergent thinking (Beaty et al., 2014a; Lee et al., 2014; Taft and Rossiter, 1966), and indeed performance on it did not correlate with performance on other divergent thinking creativity tasks. That being said, considering that all the participants were of above-average intelligence, with at least 12 51

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The uneven gender distribution of our sample might be an additional limitation of this study (see Section 2.1). The distribution arose organically from the participant selection process and might have resulted from the strict exclusion criteria we implemented (e.g., no sign of depression or anxiety, no participation in any type of psychotherapy in the past). These criteria were driven by requirements of a larger study that the current study was a part of (on the effect of an FA-related intervention on creative performance), which required a commitment to participate in 7 additional meetings after those reported here. Although previous studies indicate possible gender differences in creative cognition, across studies, it seems that males and females showed similar behavioral performance (see Baer and Kaufman, 2008 for a review; Runco et al., 2010; Stoltzfus et al., 2011). However, the gender distribution in our sample might have affected the brain activity results, as a recent fMRI study points to gender differences in activation of specific brain regions in creative cognition. The authors suggest that these differences indicate that men and women use different creative strategies (i.e., men use semantic-memory-based cognition, rule learning and decision making, whereas women rely on speech processing and social perception) (Abraham et al., 2014). We note that, in our sample (comprising mostly women), we did not observe activation in the brain regions that were particularly active in women in Abraham et al.'s (2014) study (e.g., posterior STG). Nonetheless, future research should use larger samples, with equal gender distribution. In contrast to prior studies, which measured FA primarily on the basis of fluency scores, we relied on an in-depth, rigorous approach to evaluate qualitative aspects of FA chains (judges' decisions on number of concepts and semantic distance). This approach enabled us to achieve a deeper understanding of associative flexibility than we would have obtained by merely counting responses. Nonetheless, it would be useful to attempt to develop more efficient methods. Additionally, it would be interesting to explore questions related to individual characteristics that might influence the nature of the associations produced: some people might be reminded of a memory and associate all semantic data from that event; some might jump between contexts; some associations might be highly personal; and some associations might be due to the sound and phonetic characteristics of the words. The tendency to access associations from certain domains might inform the relation between behavioral measures of FA and creativity. Future research should entail specific questions about the nature and personal meaning of associations. Finally, given the relationships established herein between chain FA and associative cognitive processes, it would be interesting to carry out research to test whether patterns of chain FA can serve as diagnostic tools to detect malfunctions in those processes. Research has begun to point to the ability of automatic speech semantic patterns to predict schizophrenia (Bedi et al., 2015) and scope of associations to detect depression (Bar, 2009b).

performance on a task more relevant to convergent thinking: the RAT. This finding suggests that spontaneous-associative processes are mainly related to the divergent aspect of creative thinking, whereas intelligence might be related to the more convergent-executive aspects. To conclude, our study suggests that chain FA tasks might be able to serve as a relatively straightforward measure of spontaneous associative fluency and flexibility, both of which are critical for creative thinking. Such tasks can easily be used to test associative abilities, and can also be used as a means of isolating associative abilities from executive abilities when evaluating creative thinking. In addition, we propose that chain FA might serve as a reliable measure to test interventions that aim to enhance creative abilities in general, and associative abilities in particular (including the capacity to overcome mental blocks that might inhibit them; Marron et al., In preparation). Our study also contributes to the larger domain of neurocognitive research on creativity by supporting previous findings regarding cognitive processes related to different brain regions in creative performance (e.g., the mPFC, the left IFG, and the PCC; see Sections 4.2.1, 4.2.2, and 4.2.3, respectively, for extensive discussions of the relevant cognitive processes associated with these regions). Notably, creativity is a complex concept that might operate differently in different domains. As this study used tasks related to verbal creativity, its findings are more likely to be applicable to this specific domain. 4.5. Limitations and future research The present study has several limitations. First, in line with a prior study, we used task blocks with durations of 18 s each (Shapira-Lichter et al., 2013). However, it would be useful to examine whether more flexible, less inhibited behavior emerges as a function of the amount of time spent generating FA. Thus, longer time periods should be tested. Additionally, although conditions among participants who completed version number 2 (N = 8) underwent balancing procedures, the phonological fluency condition appeared to be slightly harder than the chain FA fluency condition, only in this version. This difference might account for some of the activation of the DMN, in the contrast FA > PFT, as DMN activation is reduced when a task is more difficult (Mayer et al., 2010). However, since only 8 out of 20 participants participated in version 2, and our methodological approach was based on a conjunction analysis (we included areas that only exhibited higher activation in all contrasts), this effect was minimized. In addition, we used three different tasks to measure creative abilities (divergent thinking, connecting remote associations, convergent thinking), but not different tasks that represent exactly the same construct. Future studies should evaluate the robustness of our findings across different tasks corresponding to similar creative cognitions, specifically ones that are related to spontaneous flexibility, as our measure of AUT flexibility turned out to be unreliable (see, e.g., Runco et al., 2016). A relevant future direction would be to examine alternative factors (e.g., personality aspects) that might affect the relations between creative performance and behavioral measures from chain FA. Our choice of PET scores as a measure of intelligence might also be a limitation of this study, and have a role in the fact that intelligence was not observed to be correlated with behavioral measures derived from FA chains or with performance on divergent thinking tasks. The PET does not directly measure intelligence, but provides an approximation (given its high correlation with the Wechsler Adult Intelligence ScaleRevised; Nevo and Sela, 2003); see Section 2.4). Moreover, a lack of research on creativity and personality using the PET hinders the ability to test for additional components that affect the relation between measures of creativity and measures from chain FA and intelligence. Future research should use a more direct measure of intelligence to test these relations.

Acknowledgements The authors are grateful to Moshe Bar, Yoed Kenett, Armin Heinecke, and Jackob Nimrod Keynan for their devoted help and knowledge and for their thoughtful feedback and assistance throughout this research, and to Karen Marron for her skillful editing and assistance. This research was supported by the I-CORE Program of The Planning and Budgeting Committee (grant No. 51/11) and The Israel Science Foundation (grant No. 51/11). The funding source had no involvement in the study. Conflicts of interest None.

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Appendix A See Table A1.

Table A1 Mean and standard deviation of responses for each condition of the fMRI paradigm (N = 20), before and after balancing procedure (see Section 2.6.2). Before balance procedure

Version 1

Mean Stdev Mean Stdev Mean Stdev

Version 2 Final Average

After balance procedure

FA

SFT

PFT

EMT

FA

SFT

PFT

EMT

7.01 2.01 7.93 1.40 7.42 1.82

6.82 1.91 8.26 0.95 7.47 1.36

5.95 1.42 5.93 0.95 5.94 1.27

7.20 2.71 8.44 2.82 7.76 2.95

6.81 1.89 7.81 1.23 7.26 1.74

7.00 3.07 7.94 1.16 7.43 1.65

6.14 1.33 6.06 0.75 6.10 1.14

7.11 0.81 8.14 2.44 7.58 2.58

FA = free association, PFT = phonological fluency task, SFT = semantic fluency task, EMT = episodic memory task.

Appendix B See Table B1.

Table B1 Correlations between the fMRI conditions before balance procedure (N = 20).

FA SFT PFT EMT

FA

SFT

PFT

EMT

1 0.5* 0.65* 0.31

1 0.41 0.59*

1 0.1

1

FA = free association, PFT = phonological fluency task, SFT = semantic fluency task, EMT = episodic memory task. * = Significant at p-value < 0.05.

Appendix C See Fig. C1.

Fig. C1. Whole brain activation results for a conjunction analysis on the FA > PFT, FA > SFT, and FA > EMT contrasts; presented on an inflated brain of a single participant [FDR-corrected at voxel-level, p < 0.002, N = 32 (before removal of participants; see Section 2.8.1), FA = free association, PFT = phonological fluency task, SFT = semantic fluency task, EMT = episodic memory task, aMTG = anterior middle temporal gyrus, mPFC = medial prefrontal cortex, PCC = posterior cingulate cortex, SFG = superior frontal gyrus, MFG = middle frontal gyrus, TPJ = temporal parietal junction, IFG = inferior frontal gyrus, MTG = middle temporal gyrus.

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Appendix D We conducted one-way repeated-measures ANOVAs to evaluate whether there are no significant differences among the three control conditions (semantic fluency task, phonologic fluency task, episodic memory task) in terms of the activity they elicit in each ROI. The results of the ANOVAs indicated a significant effect of condition for all of the ROIs: the right MTG (Wilks' Lambda = 0.3, F(2,18) = 20.74, p < 0.0005), the mPFC (Wilks' Lambda = 0.23, F(2,18) = 30.97, p < 0.0005), the PCC (Wilks' Lambda = 0.22, F(2,18) = 31.37, p < 0.0005), the left SFG (Wilks' Lambda = 0.2, F(2,18) = 36.98, p < 0.0005), the left MFG (Wilks' Lambda = 0.17, F(2,18) = 44.54, p < 0.0005), the left TPJ (Wilks' Lambda = 0.19, F (2,18) = 39.76, p < 0.0005), the left IFG (Wilks' Lambda = 0.12, F(2,18) = 66.11, p < 0.0005). Thus, there is significant evidence that the three conditions are different in terms of brain activity. Appendix E See Fig. E1.

Fig. E1. Whole brain activation results for each fMRI condition (free association task, phonological fluency task, semantic fluency task, episodic memory task) > baseline presented on an inflated brain of a single participant. FDR-corrected at voxel-level, p < 0.002, N = 20, mPFC = medial prefrontal cortex, ACC = anterior cingulate cortex, PCC = posterior cingulate cortex, SFG = superior frontal gyrus, MFG = middle frontal gyrus, TPJ = temporal parietal junction, IFG = inferior frontal gyrus.

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Appendix F See Fig. F1.

Fig. F1. Brain activity (beta weights) in ROIs for each of the fMRI paradigm conditions. N = 20. FA = free association, PFT = phonological fluency task, SFT = semantic fluency task, EMT = episodic memory task, aMTG = anterior middle temporal gyrus, mPFC = medial prefrontal cortex, PCC = posterior cingulate cortex, SFG = superior frontal gyrus, MFG = middle frontal gyrus, TPJ = temporal parietal junction, IFG = inferior frontal gyrus, MTG = middle temporal gyrus, aPFC = anterior prefrontal cortex.

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