Consciousness and Cognition 34 (2015) 43–51
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Consciousness and Cognition journal homepage: www.elsevier.com/locate/concog
An illustrated heuristic prototype facilitates scientific inventive problem solving: A functional magnetic resonance imaging study Dandan Tong a,b,1, Wenfu Li c,1, Chaoying Tang d, Wenjing Yang a,b, Yan Tian a,b, Lei Zhang e, Meng Zhang f, Jiang Qiu a,b, Yijun Liu a,b,⇑, Qinglin Zhang a,b,⇑ a
Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, PR China School of Psychology, Southwest University (SWU), Chongqing 400715, PR China Mental Health Department of Jining Meidical University, Jining, Shandong Province 272013, PR China d Management School of University of Chinese Academy of Sciences, Beijing 100101, PR China e Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, PR China f Department of Psychology, Xinxiang Medical University, Xinxiang, Henan 453003, PR China b c
a r t i c l e
i n f o
Article history: Received 19 September 2014
Keywords: Scientific invention Heuristic prototypes Visual image fMRI
a b s t r a c t Many scientific inventions (SI) throughout history were inspired by heuristic prototypes (HPs). For instance, an event or piece of knowledge similar to displaced water from a tub inspired Archimedes’ principle. However, the neural mechanisms underlying this insightful problem solving are not very clear. Thus, the present study explored the neural correlates used to solve SI problems facilitated by HPs. Each HP had two versions: a literal description with an illustration (LDI) and a literal description with no illustration (LDNI). Thirty-two participants were divided randomly into these two groups. Blood oxygenation level-dependent fMRI contrasts between LDI and LDNI groups were measured. Greater activity in the right middle occipital gyrus (RMOG, BA19), right precentral gyrus (RPCG, BA4), and left middle frontal gyrus (LMFG, BA46) were found within the LDI group as compared to the LDNI group. We discuss these results in terms cognitive functions within these regions related to problem solving and memory retrieval. Ó 2015 Elsevier Inc. All rights reserved.
1. Introduction Creativity is imperative for the progress of human civilization and is crucial throughout cultural life (Fink et al., 2010; Luo & Niki, 2003) and is characterized by the formation of something that is both novel and useful (Jung, Mead, Carrasco, & Flores, 2013; Runco & Jaeger, 2012; Stein, 1953). Throughout the history of insightful problem solving, creative behavior
Abbreviations: SI, scientific invention/innovation; HPs, heuristic prototypes; fMRI, functional magnetic resonance imaging; LDI, literal description with illustration; LDNI, literal description with no illustration; RMOG, right middle occipital gyrus; RPCG, right precentral gyrus; LMFG, left middle frontal gyrus; LPFC, left anterior prefrontal cortex; LIFG, left inferior frontal gyrus; LDLPFC, left dorsolateral prefrontal cortex; WCAT, Williams’ creativity aptitude test; BOLD, blood oxygenation level-dependent. ⇑ Corresponding authors at: School of Psychology, Southwest University (SWU), Chongqing 400715, PR China. E-mail addresses: yijunliu@ufl.edu (Y. Liu), [email protected] (Q. Zhang). 1 Dandan Tong and Wenfu Li contributed equally to this work. http://dx.doi.org/10.1016/j.concog.2015.02.009 1053-8100/Ó 2015 Elsevier Inc. All rights reserved.
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seems to appear when analogized from some heuristic knowledge when devising real-life, scientific innovations. One example was Rutherford’s discovery of an atom’s structure. In this case, Rutherford made a solar system analogy to devise an understanding of the atom. Other examples were Archimedes’ discovery of the law of buoyancy and Kekule’s discovery of benzene’s molecular structure. Previous studies also have highlighted the important role of analogy during a scientist’s insightful problem solving activities (Boden, 1993; Finke, 1990; Green, Kraemer, Fugelsang, Gray, & Dunbar, 2010). Recent investigations have utilized brain-imaging techniques, such as fMRI, to study the neural correlates associated with analogical problem solving. Luo and Niki (2003), using different types of analogical tasks, found that bilateral activation in prefrontal regions (right BA 11/BA 47 and left BA 45), the hippocampus, the left postero-superior temporal area, and the fusiform gyrus were involved during the integration of information. Krawczyk, McClelland, Donovan, Tillman, and Maguire (2010), using four-term analogical problems, found that the left inferior frontal gyrus (LIFG), left middle frontal gyrus (LMFG), and the left dorsolateral prefrontal cortex (LDLPFC) might be involved in this type of analogical reasoning. Green, Kraemer, Fugelsang, Gray, and Dunbar (2012), using analogical mapping with varied semantic distances, revealed frontopolar (BA9/10) activity during integration of semantically distant information in order to resolve analogical reasoning problems (similar findings can be found in Green et al. (2010). Although these aforementioned findings aid an understanding of the analogical process, certain gaps remain. For instance, few studies have investigated insight problem solving during a scientist’s insightful problem solving activities using real-life scientific innovations. Hence, we need to employ experimental methods to better assess this ‘‘knowledge-rich’’ field. In two recent studies, Luo, Li, et al. (2013) proposed heuristics were crucial to SI problem solving by way of bionic imitation. Using a brain imaging technique, (Luo, Li, et al., 2013) contrasted novel with older scientific innovations to explore the neural basis of SI induced by HPs. Hao et al. (2013) explored the neural basis of novel SI problem solving after learning the prototype. Although these studies dealt with actual innovations, until now, no study has examined the role of visual information processing during insightful problem solving, real-world SI problems. Sas, Luchian, and Ball (2010), using abstract and representational analogical cues, found representational cues were more beneficial when solving the ‘‘eight coin problem.’’ This indicated that visual imagery facilitated problem solving during the scenario. Dunbar (1997) noted that scientists frequently use biological analogies during innovative problem solving. This is an effective way to improve ecological validity among analogical research by combining abundant real-world SI examples and laboratory studies (Thomas, 2007). Real-life SIs (including technical problems and heuristic prototypes) in the present study included bionic cases in which insight is stimulated through activation of a related prototype. The term ‘‘prototype’’ here is distinct from its use in traditional cognitive psychology. We defined the term ‘‘heuristic prototype’’ as the object containing heuristic information to solve an insight problem but is superficially irrelevant to the insight problem (Ming, Tong, Yang, Qiu, & Zhang, 2014). Each scientific innovation consists of three parts: SI problem, HP, and the reference answer. Take the technical problem of how to design submarine as an example. The HP that the surface of the shark’s skin has a unique
Fig. 1. Two examples of materials used for the LDI condition. Left, illustration of the heuristic prototype; right, corresponding literal description.
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Fig. 2. Two examples of materials used for the LDNI condition.
structure: it consists of billions of tiny, raised, microscopic sections that prevent sea plants and organisms from adhering to it, allowing sharks to swim faster. Afterwards, the unsolved SI problem involves determining how to enable the hull of a submarine to move faster and more efficiently. Here, scientists were likely inspired by the feather and function of shark skin and used that comparison to help devise the submarine. Therefore, the present study selected real-life SI problems (same as in previous studies; see Dandan, Haixue, et al., 2013; Hao et al., 2013; Luo, Li, et al., 2013) and used the ‘‘learning–testing experimental paradigm’’ to investigate whether an illustrated visual prototype (see Fig. 1 for an example) facilitated SI problem solving. In this ‘‘learning–testing experimental paradigm,’’ participants were asked to learn several prototypes and then resolve the same number of SI problems. Based on the effects of visual images (illustrations) on insight and creative problem solving (Luchian, 2011), we predicted that HP illustrations might facilitate SI problem resolution. Thus, we asked participants to solve SI problems under two conditions: literal description with illustration (LDI) (Fig. 1) or literal description without illustration (LDNI) (Fig. 2). In order to solve the SI problem, participants should activate the correct heuristic prototype. The illustration, presented together with the literal description of the prototype in the LDI condition, should assist participants in forming a novel association between heuristic information and the SI problem. Considering the prefrontal cortex (PFC) is consistently involved in analogical reasoning and visuospatial insight problem solving (Aziz-Zadeh, Liew, & Dandekar, 2013; Bunge, Wendelken, Badre, & Wagner, 2005; Waltz, Lau, Grewal, & Holyoak, 2000), we hypothesized that successful SI problem resolution in the LDI condition would activate PFC regions (e.g., MFC or DLPFC). Based on the role of visual imagery (illustrations) and fronto-parietal regions on SI problem solving and magical ideation or creativity (Badzakova-Trajkov, Häberling, & Corballis, 2010; Hao et al., 2013), we hypothesized that increased activation might be found in the right MOG in the LDI condition relative to the LDNI condition. 2. Materials and methods 2.1. Participants A total of 32 participants were recruited from Southwest University (Chongqing, China) through poster or online advertisements. Participants were randomly divided into two groups (n = 16 for each) and asked to memorize HPs for certain inventions using either a LDI (Fig. 1) or LDNI. The LDI group comprised 8 male and 8 female participants (age, 20–24 years; mean, 21.8 years), and the LDNI group comprised 9 male and 7 female participants (age, 20–25 years, mean, 22.4 years). All participants were right-handed, as assessed by the Edinburgh Handedness Inventory, (Oldfield, 1971) and had no reported neurological disorders, significant physical illness, head injury, or history of alcohol/drug abuse. The local ethics committee of Southwest University, Chongqing, China, approved the study procedure. All participants signed the informed consent form before taking part in this study and received monetary compensation for their participation. 2.2. Materials Real-world scientific inventions have been used widely in previous research in our laboratory (Dandan, Haixue, et al., 2013; Dandan, Wenfu, et al., 2013; Hao et al., 2013; Luo, Du, et al., 2013; Luo, Li, et al., 2013). Although scientists had already solved these problems in the real world, naïve participants could hardly be expected to solve these problems without a corresponding prototype. Thus, a solution inspired by a prototype would be novel for a participant. Subjects in our study just required to report the general method of resolving the problem, rather than specifying the individual steps or concrete processes. For the above example, the correct solution would be to ‘‘imitate the structure of the shark’s skin, producing a special hull that is allowing the submarine to move faster’’. Boden (1994) defined a solution as creativity that is new to the solver regardless of ‘‘how many times other people have already had the same idea.’’ It is important to note that these materials would not be suitable for future study if readers were exposed to them; thus, they will not be listed here. In order to
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determine novelty of selected SIs and usefulness of the prototype, we conducted a pilot study where a group of participants was asked to resolve 40 SI problems without HPs. Another group resolved these problems after learning the prototypes. Results showed that most participants could not solve these problems without the prototypes (accuracy, LDNI MOG PCG MFG
R R L
19 4 46
Y 48 33 42
t
Cluster size (mm3)
cohen’d
3.577 3.648 3.618
537 1503 396
1.227 1.251 1.242
Z 58 28 35
8 52 19
Note: H = hemisphere; BA = brodmann area; R = right; L = left. MOG = middle occipital gyrus; PCG = precentral gyrus; MFG = middle frontal gyrus.
echo planar images per volume using blood oxygenation level-dependent (BOLD) contrast (TR = 2000 ms; TE = 30 ms; 3 3 mm in-plane resolution; field of view [FOV] = 220 220; flip angle = 90°; acquisition matrix = 64 64; voxel size = 3.4 mm 3.4 mm 3.0 mm). Slices were acquired interleaved and oriented parallel to the anterior–posterior commissure plane (AC-PC plane), with a thickness of 3 mm and a 1-mm gap. High-resolution T1-weighted 3D fast-field echo sequences were obtained for each participant as anatomical reference (176 slices, TR = 1900 ms; TE = 2.52 ms; slice thickness = 1 mm; FOV = 256 256; voxel size = 1 1 1 mm). 2.5. Imaging data analysis Data were analyzed using the Brain Voyager QX v2.0 software (Brain Invention, The Netherlands). Due to T1 saturation (the first volume scans in a functional run contain data with very high-intensity values), the first five scans were skipped in the analysis. Then, functional data were converted into Brain Voyager’s FMR format (Vingerhoets, 2008). Pre-processing of functional scans included slice-scan time correction using cubic spine (sinc) interpolation, 3D motion correction, spatial smoothing and temporal filtering. 3D motion correction using ‘sinc interpolation’ was applied to minimize the effects of head motion on analyses. No participant’s head motion exceeded 3 mm. Spatial smoothing was achieved by applying a full-width at half maximum (FWHM = 6 mm), and temporal filtering was achieved by using a high-pass filter (frequencies lower than 3 cycles per run were removed) for drift removal. Following these processes, the 3D anatomical data for each subject were converted into Brain Voyager’s VMR format (Vingerhoets, 2008). Pre-processing of 3D scans included co-registration of functional and 3D anatomical data, transformation of the recorded 3D data set into Talairach space, and Talairach transformation of functional data. First, the functional data were co-registered with the 3D anatomical data of each subject’s high-resolution 3D anatomical scan by using an intensity-driven alignment algorithm. Second, the recorded 3D anatomical data were rotated to align with the AC-PC plane and normalized into the Talairach space (Talairach & Tournoux, 1988) by transformation. Finally, Talairach transformation of functional data resulted in a normalized four-dimensional (4-D) volume time-course (VTC) data set for each functional run (including re-sampling of voxels to 3 3 3 mm3). Multiple regression analysis of BOLD signal changes for individual participants was convolved with a canonical hemodynamic response function (double-gamma) to form covariates in a general linear model (Vingerhoets, 2008; Xue, Lu, Levin, & Bechara, 2011). Whole brain analysis of correct SI problem solving in the LDI group and LDNI group was carried out in a random effect model for group analysis. For multiple comparison correction, the voxel-wise intensity threshold was set at P < .005, and a cluster-level threshold was calculated using the ‘‘Cluster-Level Statistical Threshold Estimator’’ program in the plug-in menu of Brain Voyager QX v2.0, with Monte Carlo simulation. Effects were deemed to be significant when the cluster size was greater than 14 voxels (378 mm3), in which case the probability of a type I error was less than 0.05 (Luo, Li, et al., 2013; Morrison, Björnsdotter, & Olausson, 2011). Information about the regions in the statistical maps is shown in Table 1. To investigate the cognitive implication of the regions we identified, the mean b-values within each region were extracted separately from each subject and subjected to correlation analysis with creative tendency scores, using SPSS 13.0 (SPSS Inc., Chicago, IL, USA). 3. Results Only trials to which students responded correctly in the questionnaires as well as in the scanner were considered as correct responses. According to the behavioral response in the scanner and written questionnaire answers, the mean accuracy rate in the LDI group (72.8% [SD = 0.060]) was significantly higher than that in the LDNI group (58.8% [SD = 0.084]) (t [31] = 5.470, p < .0001). In addition, the mean reaction time for coming to the correct solution was significantly shorter in the LDI group (10,057 ms [SD = 1745]) than in the LDNI group (11,980 ms [SD = 1260]) (t [31] = 3.574, p < .001). Contrasting the experimental conditions (LDI > LDNI) revealed that the successful solving of SI problems was associated with significantly stronger brain activation in the RMOG (BA19), RPCG (BA4) and LMFG (BA46) (see Fig. 4). In order to explore whether the greater activation under LDI condition were correlate to subjects creative tendency, Pearson correlations were carried out to examine the relationships between: the mean b-values of the greater activation regions and the score of imagination, curiosity, challenge, risk-taking and total score of SWCAT. The results showed that
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Fig. 4. Neural activations in the LDI and LDNI conditions (activation threshold: p < .05, corrected). Activation profiles with parameter estimates are shown for each region during both conditions.
Table 2 Correlations between RMOG, RPCG, and LMFG b-values and creativity subscale scores in the LDI group. Brain regions
Risk-taking
RMOG RPCG LMFG
.058 .153 .163
Curiosity .073 .219 .109
Imagination
Challenging
.502* .678** .096
.418 .014 .418
Creativity tendency .401 .212 .401
Note: RMOG: right middle occipital gyrus; RPCG: right precentral gyrus; LMFG: left middle frontal gyrus. * Indicate significance (p < .05). ** Indicate significance (p < .01).
the mean b-values of RMOG (BA19) and RPCG (BA4) both correlated significantly with scores of the imagination factor (Table 2). 4. Discussion The present study measured brain activation patterns while participants resolved real-world scientific inventions. Results showed that HPs with illustrations facilitated greater activation in the RMOG (BA19), RPCG (BA4), and LMFG (BA46) as compared to HPs without illustrations. Interestingly, activation in the RMOG and the RPCG was related to scores on the WCAT imagination dimension of creative potential. Previous studies have demonstrated the important role of visual imagery during analogical problem solving (Finke, 1990; LeBoutillier & Marks, 2003). Luo, Niki, and Knoblich (2006) further suggested that visual processes could provide crucial information for representation and re-arrangement during insightful problem solving. Moreover, previous studies also showed the MOG was involved in relevant visual imagery. Vandenberghe, Price, Wise, Josephs, and Frackowiak (1996) suggested that modality-specific activations unrelated to semantic processing occur in the right middle occipital gyrus in response to pictures. Bentall, Murray, and Howard (2004) indicated activation observed in the medial occipital cortex might represent visual imagery involved in the formation, maintenance, and comparison of two competing probabilistic hypotheses. Hao et al. (2013) also found the right MOG (BA 19) might be involved in visual imagery during application of novel functional associations toward a problem solution. In the current study, during the learning phase, illustrations promoted understanding and memory of biological functional features of heuristic prototypes. In other words, participants produced a clearer visual representation of the HP in their brain. However, it was difficult to gain these biological functional features during the LDNI condition. Thus, during the testing phase, participants were more able to form the functional association
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between the HP and the scientific problem. Therefore, activation in the RMOG suggests visual processes might be crucial for scientific invention in a way that is different from basic, literal representations of HPs. Fink, Graif, and Neubauer (2009) reported the PCG was involved in creativity tasks, such as the alternate uses task, which focuses on forming novel associations among words. More creative individuals show greater activation in the RPCG when engaged in assimilation of sensory information and learning new motor programs and motor imagery (Malouin, Richards, Jackson, Dumas, & Doyon, 2003). Chavez, Graff-Guerrero, Garcia-Reyna, Vaugier, and Cruz-Fuentes (2004) found a positive correlation between scores on figural and verbal creativity (measured by the Torrance Tests of Creative Thinking) and cerebral blood flow (CBF) in the RPCG (BA 6). In addition, Chávez-Eakle, Graff-Guerrero, García-Reyna, Vaugier, and Cruz-Fuentes (2007) further found high creative performance was associated with greater CBF activity in the RPCG. Based on these findings, it could be concluded the RPCG is involved in imagery, association processes, and forming novel and divergent thinking. Qiu et al. (2010) suggested the formation of novel associations necessitates the cognitive process of connecting heuristic information to a new idea. Therefore, activation in the RPCG might reflect the formation of a novel association between the SI problem and information provided by the HP. Imagination has been suggested as a good predictor for creative tendencies (Liu, Hu, Adey, Cheng, & Zhang, 2013). Other studies indicate active imagination is a trait characteristic common to both creative artists and scientists (Amabile, 1996; Feist, 1999). Interestingly, further correlation analyses in the present study suggested mean RMOG and RPCG b-values were correlated significantly with WCAT imagination scores. These findings may likely indicate these regions are involved in the imaginary process of representing illustrations and associations between the HP and SI problem. Thus, participants could utilize their visual imagination to facilitate analogical imaginary. With regard to LMFG involvement, Mashal, Faust, Hendler, and Jung-Beeman (2007) suggested the LMFG was associated with novel and creative information processing. Seger, Desmond, Glover, and Gabrieli (2000) also observed increased activation in the bilateral MFG when participants generated unusual verbs as compared to generating usual ones. Other studies suggested greater activation in the LMFG was related to a vivid, conscious experience of feelings among more creative individuals (Camacho, Vives-Rocabert, & Solís, 1983; Chávez & del Carmen Lara, 2000; Chávez-Eakle et al., 2007; Dabrowski, Kawczak, & Piechowski, 1970). Additionally, Luo (2004) suggested the spatial neural network (including the left and right middle temporal/occipital gyrus and left middle frontal gyrus) mediated representational changes in insight. An fMRI study on semantic divergence and creative story generation showed activity in the right PFC was involved in higher cognitive control of stringent monitoring for a creative solution (Howard-Jones, Blakemore, Samuel, Summers, & Claxton, 2005). In the present study, after forming a novel association between the HP and SI problems, participants probably needed to use novel and unusual words to organize and express their solution. Therefore, the LMFG might be involved in increased generation and monitoring of novel word expressions to solve a problem. Other studies have shown efficient memory retrieval enhanced by cognitive stimulation facilitates generation of creative ideas (Fink et al., 2010, 2012). Additionally, others suggest pictures or illustrations could improve performance during text learning (Carney & Levin, 2002; Peeck, 1993) as well as verbal memory retrieval (Kaschel et al., 2002; Lwin, Morrin, & Krishna, 2010). As mentioned above, in the present study, the illustrated prototype might have enhanced problem solving by improving an understanding of heuristic prototypes and facilitate memory recall of the relevant prototype. In other words, the SI problem could only be solved by means of information retrieval gained during the learning phase and a combination between retrieved information and the SI problem. Thus, improved recall of the relevant HP contributed to SI problem solving during the LDI condition. 5. Advantages and deficiencies There are two overarching theories dealing with insightful problem solving: the Representation Change (RC) Theory (Kaplan & Simon, 1990) and the Progress Monitoring (PM) Theory (Knoblich, Ohlsson, Haider, & Rhenius, 1999). The former proposes that when people change their search strategy space from the wrong problem space to the right problem space, insight may occur. The latter theory argues that people need to seek alternative moves to shorten the distance from the current state to the goal state in order to solve problems. However, the RC Theory does not clearly state how people find the right problem space, and the PM Theory does not elaborate on which way people detect new and more efficient means for solving a problem. Therefore, based on a previous study (Luo, Du, et al., 2013; Ming et al., 2014), we proposed the heuristic prototype theory. The heuristic prototype theory explores the RC and PM theories further and states that insight should occur as soon as critical heuristic information contained in a prototype is obtained. This prototype helps orient individuals during the process of finding the right problem search space in order to solve the problem. The present study implemented fMRI and real-world SIs to investigate the neural basis of visual illustrated prototype facilitation for SI problem solving. Our results suggest the right middle occipital gyrus, right precentral gyrus, and left middle frontal gyrus were more active when an illustration was provided compared to when it was absent. These findings deepen our understanding regarding neural correlates underlying scientific invention and the role of visual information during insightful problem solving. The measure (WCAT) used in the present study is reliable, its split half reliability and Cronbach alpha coefficient is good. However, it is a 3-point Likert scale which does not permit a large rang of responses and not sensitive to the construct of interest, even it is reliable. This is a limitation of present investigation. Therefore, we will pay much attention on this in future studies.
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Acknowledgments This work was supported by the National Natural Science Foundation of China (31470981; 31271087; 61431013), Key Technology Research Project of Henan Province (142102310048), the humanities and social science research Project of Henan Colleges and Universities (2015-QN-367) and the Fundamental Research Funds for the Central Universities (SWU1209101). The authors thank the anonymous reviewers for their helpful comments. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.concog. 2015.02.009. References Amabile, T. M. (1996). Creativity in context. New York: Westview. Aziz-Zadeh, L., Liew, S.-L., & Dandekar, F. (2013). Exploring the neural correlates of visual creativity. Social Cognitive and Affective Neuroscience, 8(4), 475–480. Badzakova-Trajkov, G., Häberling, I. S., & Corballis, M. C. (2010). Cerebral asymmetries in monozygotic twins: An fMRI study. 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Contents lists available at ScienceDirect
Consciousness and Cognition journal homepage: www.elsevier.com/locate/concog
An illustrated heuristic prototype facilitates scientific inventive problem solving: A functional magnetic resonance imaging study Dandan Tong a,b,1, Wenfu Li c,1, Chaoying Tang d, Wenjing Yang a,b, Yan Tian a,b, Lei Zhang e, Meng Zhang f, Jiang Qiu a,b, Yijun Liu a,b,⇑, Qinglin Zhang a,b,⇑ a
Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, PR China School of Psychology, Southwest University (SWU), Chongqing 400715, PR China Mental Health Department of Jining Meidical University, Jining, Shandong Province 272013, PR China d Management School of University of Chinese Academy of Sciences, Beijing 100101, PR China e Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, PR China f Department of Psychology, Xinxiang Medical University, Xinxiang, Henan 453003, PR China b c
a r t i c l e
i n f o
Article history: Received 19 September 2014
Keywords: Scientific invention Heuristic prototypes Visual image fMRI
a b s t r a c t Many scientific inventions (SI) throughout history were inspired by heuristic prototypes (HPs). For instance, an event or piece of knowledge similar to displaced water from a tub inspired Archimedes’ principle. However, the neural mechanisms underlying this insightful problem solving are not very clear. Thus, the present study explored the neural correlates used to solve SI problems facilitated by HPs. Each HP had two versions: a literal description with an illustration (LDI) and a literal description with no illustration (LDNI). Thirty-two participants were divided randomly into these two groups. Blood oxygenation level-dependent fMRI contrasts between LDI and LDNI groups were measured. Greater activity in the right middle occipital gyrus (RMOG, BA19), right precentral gyrus (RPCG, BA4), and left middle frontal gyrus (LMFG, BA46) were found within the LDI group as compared to the LDNI group. We discuss these results in terms cognitive functions within these regions related to problem solving and memory retrieval. Ó 2015 Elsevier Inc. All rights reserved.
1. Introduction Creativity is imperative for the progress of human civilization and is crucial throughout cultural life (Fink et al., 2010; Luo & Niki, 2003) and is characterized by the formation of something that is both novel and useful (Jung, Mead, Carrasco, & Flores, 2013; Runco & Jaeger, 2012; Stein, 1953). Throughout the history of insightful problem solving, creative behavior
Abbreviations: SI, scientific invention/innovation; HPs, heuristic prototypes; fMRI, functional magnetic resonance imaging; LDI, literal description with illustration; LDNI, literal description with no illustration; RMOG, right middle occipital gyrus; RPCG, right precentral gyrus; LMFG, left middle frontal gyrus; LPFC, left anterior prefrontal cortex; LIFG, left inferior frontal gyrus; LDLPFC, left dorsolateral prefrontal cortex; WCAT, Williams’ creativity aptitude test; BOLD, blood oxygenation level-dependent. ⇑ Corresponding authors at: School of Psychology, Southwest University (SWU), Chongqing 400715, PR China. E-mail addresses: yijunliu@ufl.edu (Y. Liu), [email protected] (Q. Zhang). 1 Dandan Tong and Wenfu Li contributed equally to this work. http://dx.doi.org/10.1016/j.concog.2015.02.009 1053-8100/Ó 2015 Elsevier Inc. All rights reserved.
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seems to appear when analogized from some heuristic knowledge when devising real-life, scientific innovations. One example was Rutherford’s discovery of an atom’s structure. In this case, Rutherford made a solar system analogy to devise an understanding of the atom. Other examples were Archimedes’ discovery of the law of buoyancy and Kekule’s discovery of benzene’s molecular structure. Previous studies also have highlighted the important role of analogy during a scientist’s insightful problem solving activities (Boden, 1993; Finke, 1990; Green, Kraemer, Fugelsang, Gray, & Dunbar, 2010). Recent investigations have utilized brain-imaging techniques, such as fMRI, to study the neural correlates associated with analogical problem solving. Luo and Niki (2003), using different types of analogical tasks, found that bilateral activation in prefrontal regions (right BA 11/BA 47 and left BA 45), the hippocampus, the left postero-superior temporal area, and the fusiform gyrus were involved during the integration of information. Krawczyk, McClelland, Donovan, Tillman, and Maguire (2010), using four-term analogical problems, found that the left inferior frontal gyrus (LIFG), left middle frontal gyrus (LMFG), and the left dorsolateral prefrontal cortex (LDLPFC) might be involved in this type of analogical reasoning. Green, Kraemer, Fugelsang, Gray, and Dunbar (2012), using analogical mapping with varied semantic distances, revealed frontopolar (BA9/10) activity during integration of semantically distant information in order to resolve analogical reasoning problems (similar findings can be found in Green et al. (2010). Although these aforementioned findings aid an understanding of the analogical process, certain gaps remain. For instance, few studies have investigated insight problem solving during a scientist’s insightful problem solving activities using real-life scientific innovations. Hence, we need to employ experimental methods to better assess this ‘‘knowledge-rich’’ field. In two recent studies, Luo, Li, et al. (2013) proposed heuristics were crucial to SI problem solving by way of bionic imitation. Using a brain imaging technique, (Luo, Li, et al., 2013) contrasted novel with older scientific innovations to explore the neural basis of SI induced by HPs. Hao et al. (2013) explored the neural basis of novel SI problem solving after learning the prototype. Although these studies dealt with actual innovations, until now, no study has examined the role of visual information processing during insightful problem solving, real-world SI problems. Sas, Luchian, and Ball (2010), using abstract and representational analogical cues, found representational cues were more beneficial when solving the ‘‘eight coin problem.’’ This indicated that visual imagery facilitated problem solving during the scenario. Dunbar (1997) noted that scientists frequently use biological analogies during innovative problem solving. This is an effective way to improve ecological validity among analogical research by combining abundant real-world SI examples and laboratory studies (Thomas, 2007). Real-life SIs (including technical problems and heuristic prototypes) in the present study included bionic cases in which insight is stimulated through activation of a related prototype. The term ‘‘prototype’’ here is distinct from its use in traditional cognitive psychology. We defined the term ‘‘heuristic prototype’’ as the object containing heuristic information to solve an insight problem but is superficially irrelevant to the insight problem (Ming, Tong, Yang, Qiu, & Zhang, 2014). Each scientific innovation consists of three parts: SI problem, HP, and the reference answer. Take the technical problem of how to design submarine as an example. The HP that the surface of the shark’s skin has a unique
Fig. 1. Two examples of materials used for the LDI condition. Left, illustration of the heuristic prototype; right, corresponding literal description.
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Fig. 2. Two examples of materials used for the LDNI condition.
structure: it consists of billions of tiny, raised, microscopic sections that prevent sea plants and organisms from adhering to it, allowing sharks to swim faster. Afterwards, the unsolved SI problem involves determining how to enable the hull of a submarine to move faster and more efficiently. Here, scientists were likely inspired by the feather and function of shark skin and used that comparison to help devise the submarine. Therefore, the present study selected real-life SI problems (same as in previous studies; see Dandan, Haixue, et al., 2013; Hao et al., 2013; Luo, Li, et al., 2013) and used the ‘‘learning–testing experimental paradigm’’ to investigate whether an illustrated visual prototype (see Fig. 1 for an example) facilitated SI problem solving. In this ‘‘learning–testing experimental paradigm,’’ participants were asked to learn several prototypes and then resolve the same number of SI problems. Based on the effects of visual images (illustrations) on insight and creative problem solving (Luchian, 2011), we predicted that HP illustrations might facilitate SI problem resolution. Thus, we asked participants to solve SI problems under two conditions: literal description with illustration (LDI) (Fig. 1) or literal description without illustration (LDNI) (Fig. 2). In order to solve the SI problem, participants should activate the correct heuristic prototype. The illustration, presented together with the literal description of the prototype in the LDI condition, should assist participants in forming a novel association between heuristic information and the SI problem. Considering the prefrontal cortex (PFC) is consistently involved in analogical reasoning and visuospatial insight problem solving (Aziz-Zadeh, Liew, & Dandekar, 2013; Bunge, Wendelken, Badre, & Wagner, 2005; Waltz, Lau, Grewal, & Holyoak, 2000), we hypothesized that successful SI problem resolution in the LDI condition would activate PFC regions (e.g., MFC or DLPFC). Based on the role of visual imagery (illustrations) and fronto-parietal regions on SI problem solving and magical ideation or creativity (Badzakova-Trajkov, Häberling, & Corballis, 2010; Hao et al., 2013), we hypothesized that increased activation might be found in the right MOG in the LDI condition relative to the LDNI condition. 2. Materials and methods 2.1. Participants A total of 32 participants were recruited from Southwest University (Chongqing, China) through poster or online advertisements. Participants were randomly divided into two groups (n = 16 for each) and asked to memorize HPs for certain inventions using either a LDI (Fig. 1) or LDNI. The LDI group comprised 8 male and 8 female participants (age, 20–24 years; mean, 21.8 years), and the LDNI group comprised 9 male and 7 female participants (age, 20–25 years, mean, 22.4 years). All participants were right-handed, as assessed by the Edinburgh Handedness Inventory, (Oldfield, 1971) and had no reported neurological disorders, significant physical illness, head injury, or history of alcohol/drug abuse. The local ethics committee of Southwest University, Chongqing, China, approved the study procedure. All participants signed the informed consent form before taking part in this study and received monetary compensation for their participation. 2.2. Materials Real-world scientific inventions have been used widely in previous research in our laboratory (Dandan, Haixue, et al., 2013; Dandan, Wenfu, et al., 2013; Hao et al., 2013; Luo, Du, et al., 2013; Luo, Li, et al., 2013). Although scientists had already solved these problems in the real world, naïve participants could hardly be expected to solve these problems without a corresponding prototype. Thus, a solution inspired by a prototype would be novel for a participant. Subjects in our study just required to report the general method of resolving the problem, rather than specifying the individual steps or concrete processes. For the above example, the correct solution would be to ‘‘imitate the structure of the shark’s skin, producing a special hull that is allowing the submarine to move faster’’. Boden (1994) defined a solution as creativity that is new to the solver regardless of ‘‘how many times other people have already had the same idea.’’ It is important to note that these materials would not be suitable for future study if readers were exposed to them; thus, they will not be listed here. In order to
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determine novelty of selected SIs and usefulness of the prototype, we conducted a pilot study where a group of participants was asked to resolve 40 SI problems without HPs. Another group resolved these problems after learning the prototypes. Results showed that most participants could not solve these problems without the prototypes (accuracy, LDNI MOG PCG MFG
R R L
19 4 46
Y 48 33 42
t
Cluster size (mm3)
cohen’d
3.577 3.648 3.618
537 1503 396
1.227 1.251 1.242
Z 58 28 35
8 52 19
Note: H = hemisphere; BA = brodmann area; R = right; L = left. MOG = middle occipital gyrus; PCG = precentral gyrus; MFG = middle frontal gyrus.
echo planar images per volume using blood oxygenation level-dependent (BOLD) contrast (TR = 2000 ms; TE = 30 ms; 3 3 mm in-plane resolution; field of view [FOV] = 220 220; flip angle = 90°; acquisition matrix = 64 64; voxel size = 3.4 mm 3.4 mm 3.0 mm). Slices were acquired interleaved and oriented parallel to the anterior–posterior commissure plane (AC-PC plane), with a thickness of 3 mm and a 1-mm gap. High-resolution T1-weighted 3D fast-field echo sequences were obtained for each participant as anatomical reference (176 slices, TR = 1900 ms; TE = 2.52 ms; slice thickness = 1 mm; FOV = 256 256; voxel size = 1 1 1 mm). 2.5. Imaging data analysis Data were analyzed using the Brain Voyager QX v2.0 software (Brain Invention, The Netherlands). Due to T1 saturation (the first volume scans in a functional run contain data with very high-intensity values), the first five scans were skipped in the analysis. Then, functional data were converted into Brain Voyager’s FMR format (Vingerhoets, 2008). Pre-processing of functional scans included slice-scan time correction using cubic spine (sinc) interpolation, 3D motion correction, spatial smoothing and temporal filtering. 3D motion correction using ‘sinc interpolation’ was applied to minimize the effects of head motion on analyses. No participant’s head motion exceeded 3 mm. Spatial smoothing was achieved by applying a full-width at half maximum (FWHM = 6 mm), and temporal filtering was achieved by using a high-pass filter (frequencies lower than 3 cycles per run were removed) for drift removal. Following these processes, the 3D anatomical data for each subject were converted into Brain Voyager’s VMR format (Vingerhoets, 2008). Pre-processing of 3D scans included co-registration of functional and 3D anatomical data, transformation of the recorded 3D data set into Talairach space, and Talairach transformation of functional data. First, the functional data were co-registered with the 3D anatomical data of each subject’s high-resolution 3D anatomical scan by using an intensity-driven alignment algorithm. Second, the recorded 3D anatomical data were rotated to align with the AC-PC plane and normalized into the Talairach space (Talairach & Tournoux, 1988) by transformation. Finally, Talairach transformation of functional data resulted in a normalized four-dimensional (4-D) volume time-course (VTC) data set for each functional run (including re-sampling of voxels to 3 3 3 mm3). Multiple regression analysis of BOLD signal changes for individual participants was convolved with a canonical hemodynamic response function (double-gamma) to form covariates in a general linear model (Vingerhoets, 2008; Xue, Lu, Levin, & Bechara, 2011). Whole brain analysis of correct SI problem solving in the LDI group and LDNI group was carried out in a random effect model for group analysis. For multiple comparison correction, the voxel-wise intensity threshold was set at P < .005, and a cluster-level threshold was calculated using the ‘‘Cluster-Level Statistical Threshold Estimator’’ program in the plug-in menu of Brain Voyager QX v2.0, with Monte Carlo simulation. Effects were deemed to be significant when the cluster size was greater than 14 voxels (378 mm3), in which case the probability of a type I error was less than 0.05 (Luo, Li, et al., 2013; Morrison, Björnsdotter, & Olausson, 2011). Information about the regions in the statistical maps is shown in Table 1. To investigate the cognitive implication of the regions we identified, the mean b-values within each region were extracted separately from each subject and subjected to correlation analysis with creative tendency scores, using SPSS 13.0 (SPSS Inc., Chicago, IL, USA). 3. Results Only trials to which students responded correctly in the questionnaires as well as in the scanner were considered as correct responses. According to the behavioral response in the scanner and written questionnaire answers, the mean accuracy rate in the LDI group (72.8% [SD = 0.060]) was significantly higher than that in the LDNI group (58.8% [SD = 0.084]) (t [31] = 5.470, p < .0001). In addition, the mean reaction time for coming to the correct solution was significantly shorter in the LDI group (10,057 ms [SD = 1745]) than in the LDNI group (11,980 ms [SD = 1260]) (t [31] = 3.574, p < .001). Contrasting the experimental conditions (LDI > LDNI) revealed that the successful solving of SI problems was associated with significantly stronger brain activation in the RMOG (BA19), RPCG (BA4) and LMFG (BA46) (see Fig. 4). In order to explore whether the greater activation under LDI condition were correlate to subjects creative tendency, Pearson correlations were carried out to examine the relationships between: the mean b-values of the greater activation regions and the score of imagination, curiosity, challenge, risk-taking and total score of SWCAT. The results showed that
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Fig. 4. Neural activations in the LDI and LDNI conditions (activation threshold: p < .05, corrected). Activation profiles with parameter estimates are shown for each region during both conditions.
Table 2 Correlations between RMOG, RPCG, and LMFG b-values and creativity subscale scores in the LDI group. Brain regions
Risk-taking
RMOG RPCG LMFG
.058 .153 .163
Curiosity .073 .219 .109
Imagination
Challenging
.502* .678** .096
.418 .014 .418
Creativity tendency .401 .212 .401
Note: RMOG: right middle occipital gyrus; RPCG: right precentral gyrus; LMFG: left middle frontal gyrus. * Indicate significance (p < .05). ** Indicate significance (p < .01).
the mean b-values of RMOG (BA19) and RPCG (BA4) both correlated significantly with scores of the imagination factor (Table 2). 4. Discussion The present study measured brain activation patterns while participants resolved real-world scientific inventions. Results showed that HPs with illustrations facilitated greater activation in the RMOG (BA19), RPCG (BA4), and LMFG (BA46) as compared to HPs without illustrations. Interestingly, activation in the RMOG and the RPCG was related to scores on the WCAT imagination dimension of creative potential. Previous studies have demonstrated the important role of visual imagery during analogical problem solving (Finke, 1990; LeBoutillier & Marks, 2003). Luo, Niki, and Knoblich (2006) further suggested that visual processes could provide crucial information for representation and re-arrangement during insightful problem solving. Moreover, previous studies also showed the MOG was involved in relevant visual imagery. Vandenberghe, Price, Wise, Josephs, and Frackowiak (1996) suggested that modality-specific activations unrelated to semantic processing occur in the right middle occipital gyrus in response to pictures. Bentall, Murray, and Howard (2004) indicated activation observed in the medial occipital cortex might represent visual imagery involved in the formation, maintenance, and comparison of two competing probabilistic hypotheses. Hao et al. (2013) also found the right MOG (BA 19) might be involved in visual imagery during application of novel functional associations toward a problem solution. In the current study, during the learning phase, illustrations promoted understanding and memory of biological functional features of heuristic prototypes. In other words, participants produced a clearer visual representation of the HP in their brain. However, it was difficult to gain these biological functional features during the LDNI condition. Thus, during the testing phase, participants were more able to form the functional association
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between the HP and the scientific problem. Therefore, activation in the RMOG suggests visual processes might be crucial for scientific invention in a way that is different from basic, literal representations of HPs. Fink, Graif, and Neubauer (2009) reported the PCG was involved in creativity tasks, such as the alternate uses task, which focuses on forming novel associations among words. More creative individuals show greater activation in the RPCG when engaged in assimilation of sensory information and learning new motor programs and motor imagery (Malouin, Richards, Jackson, Dumas, & Doyon, 2003). Chavez, Graff-Guerrero, Garcia-Reyna, Vaugier, and Cruz-Fuentes (2004) found a positive correlation between scores on figural and verbal creativity (measured by the Torrance Tests of Creative Thinking) and cerebral blood flow (CBF) in the RPCG (BA 6). In addition, Chávez-Eakle, Graff-Guerrero, García-Reyna, Vaugier, and Cruz-Fuentes (2007) further found high creative performance was associated with greater CBF activity in the RPCG. Based on these findings, it could be concluded the RPCG is involved in imagery, association processes, and forming novel and divergent thinking. Qiu et al. (2010) suggested the formation of novel associations necessitates the cognitive process of connecting heuristic information to a new idea. Therefore, activation in the RPCG might reflect the formation of a novel association between the SI problem and information provided by the HP. Imagination has been suggested as a good predictor for creative tendencies (Liu, Hu, Adey, Cheng, & Zhang, 2013). Other studies indicate active imagination is a trait characteristic common to both creative artists and scientists (Amabile, 1996; Feist, 1999). Interestingly, further correlation analyses in the present study suggested mean RMOG and RPCG b-values were correlated significantly with WCAT imagination scores. These findings may likely indicate these regions are involved in the imaginary process of representing illustrations and associations between the HP and SI problem. Thus, participants could utilize their visual imagination to facilitate analogical imaginary. With regard to LMFG involvement, Mashal, Faust, Hendler, and Jung-Beeman (2007) suggested the LMFG was associated with novel and creative information processing. Seger, Desmond, Glover, and Gabrieli (2000) also observed increased activation in the bilateral MFG when participants generated unusual verbs as compared to generating usual ones. Other studies suggested greater activation in the LMFG was related to a vivid, conscious experience of feelings among more creative individuals (Camacho, Vives-Rocabert, & Solís, 1983; Chávez & del Carmen Lara, 2000; Chávez-Eakle et al., 2007; Dabrowski, Kawczak, & Piechowski, 1970). Additionally, Luo (2004) suggested the spatial neural network (including the left and right middle temporal/occipital gyrus and left middle frontal gyrus) mediated representational changes in insight. An fMRI study on semantic divergence and creative story generation showed activity in the right PFC was involved in higher cognitive control of stringent monitoring for a creative solution (Howard-Jones, Blakemore, Samuel, Summers, & Claxton, 2005). In the present study, after forming a novel association between the HP and SI problems, participants probably needed to use novel and unusual words to organize and express their solution. Therefore, the LMFG might be involved in increased generation and monitoring of novel word expressions to solve a problem. Other studies have shown efficient memory retrieval enhanced by cognitive stimulation facilitates generation of creative ideas (Fink et al., 2010, 2012). Additionally, others suggest pictures or illustrations could improve performance during text learning (Carney & Levin, 2002; Peeck, 1993) as well as verbal memory retrieval (Kaschel et al., 2002; Lwin, Morrin, & Krishna, 2010). As mentioned above, in the present study, the illustrated prototype might have enhanced problem solving by improving an understanding of heuristic prototypes and facilitate memory recall of the relevant prototype. In other words, the SI problem could only be solved by means of information retrieval gained during the learning phase and a combination between retrieved information and the SI problem. Thus, improved recall of the relevant HP contributed to SI problem solving during the LDI condition. 5. Advantages and deficiencies There are two overarching theories dealing with insightful problem solving: the Representation Change (RC) Theory (Kaplan & Simon, 1990) and the Progress Monitoring (PM) Theory (Knoblich, Ohlsson, Haider, & Rhenius, 1999). The former proposes that when people change their search strategy space from the wrong problem space to the right problem space, insight may occur. The latter theory argues that people need to seek alternative moves to shorten the distance from the current state to the goal state in order to solve problems. However, the RC Theory does not clearly state how people find the right problem space, and the PM Theory does not elaborate on which way people detect new and more efficient means for solving a problem. Therefore, based on a previous study (Luo, Du, et al., 2013; Ming et al., 2014), we proposed the heuristic prototype theory. The heuristic prototype theory explores the RC and PM theories further and states that insight should occur as soon as critical heuristic information contained in a prototype is obtained. This prototype helps orient individuals during the process of finding the right problem search space in order to solve the problem. The present study implemented fMRI and real-world SIs to investigate the neural basis of visual illustrated prototype facilitation for SI problem solving. Our results suggest the right middle occipital gyrus, right precentral gyrus, and left middle frontal gyrus were more active when an illustration was provided compared to when it was absent. These findings deepen our understanding regarding neural correlates underlying scientific invention and the role of visual information during insightful problem solving. The measure (WCAT) used in the present study is reliable, its split half reliability and Cronbach alpha coefficient is good. However, it is a 3-point Likert scale which does not permit a large rang of responses and not sensitive to the construct of interest, even it is reliable. This is a limitation of present investigation. Therefore, we will pay much attention on this in future studies.
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