Exp Brain Res DOI 10.1007/s00221-015-4338-1

RESEARCH ARTICLE

Neural correlates underlying insight problem solving: Evidence from EEG alpha oscillations Zhipeng Cao1,2 · Yadan Li1,2 · Glenn Hitchman1 · Jiang Qiu1,2 · Qinglin Zhang1,2 

Received: 5 April 2015 / Accepted: 26 April 2015 © Springer-Verlag Berlin Heidelberg 2015

Abstract  Previous studies on insight problem solving using Chinese logogriphs as insight problems only investigated the time- and phase-locked changes of electrocortical responses triggered by Chinese logogriphs, but did not focus on what kind of brain state facilitates individuals to solve insight problems. To investigate this, we focused on participants’ alpha activities (8–12 Hz) that closely correlates with insight problem solving and defocused attention while they were solving Chinese logogriphs. Results indicated that in the time window of 800–1400 ms after the presentation of target logogriphs, alpha power over parieto-central electrodes decreased relative to the reference interval in both the successful and unsuccessful logogriphs solving conditions. However, alpha power increased at parieto-occipital electrode sites in successful conditions compared with that in unsuccessful condition. The decrease in alpha activity in both conditions may reflect the cognitive demands in solving the target logogriphs. Furthermore, difference in alpha power between the successful and unsuccessful conditions at parieto-occipital electrode

Zhipeng Cao and Yadan Li contributed equally to this work. Electronic supplementary material  The online version of this article (doi:10.1007/s00221-015-4338-1) contains supplementary material, which is available to authorized users. * Jiang Qiu [email protected] * Qinglin Zhang [email protected]; [email protected] 1

Faculty of Psychology, Southwest University, Chongqing, China

2

Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing, China



sites is associated with the process of heuristic information. Alpha synchronization observed in the successful condition compared to the unsuccessful condition might reflect a state of defocused attention that facilitates insight problem solving. Keywords  Alpha activity · Insight problem solving · Defocused attention · Heuristic information

Introduction Chinese logogriphs are riddles about a Chinese character, which might be phrases, Chinese proverbs and sayings or sentences in a poem. They are considered as a proxy for insight problem and have been used in previous studies on insight problem solving (Luo et al. 2004, 2011; Mai et al. 2004; Qiu et al. 2008a, b, 2010; Wang et al. 2009; Tian et al. 2011; Zhao et al. 2011; Xing 2012; Li et al. 2013). Chinese logogriphs problems are classified as “insight problems” as they share some features with insight problems (Schooler et al. 1993): (1) riddles always contain some misleading information, which is likely to produce an impasse and make logogriphs difficult to solve; (2) people without special knowledge and expertise could solve the logogriphs ultimately; and (3) one can gain an “Aha” experience when he/she guesses the answer to the given logogriph successfully, which is demonstrated by previous studies (Luo et al. 2004; Mai et al. 2004; Qiu et al. 2010). To guess the Chinese characters that are formed by strokes or composed of some simple characters, people need to discover the implicit meanings of the riddles which are a cognitive process similar to breaking mental sets. Then, they can get the answer by splitting, removing or combining the components of the characters based on the implicit

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meanings the riddles involve, which is similar to forming novel associations (Luo et al. 2011). Electroencephalography (EEG), which involves recording electrical brain activity along the scalp and provides neural information of high temporal resolution (de Haan and Thomas 2002), has been used extensively in cognitive and psychophysiological research. EEG research mainly concentrates on several parameters such as functional connectivity, event-related potentials (ERPs) and power changes in different frequency bands (Fink and Benedek 2012). Several studies combining Chinese logogriphs tasks with ERPs have provided increasing insights into the neural underpinnings of insight problem solving (Mai et al. 2004; Qiu et al. 2008a, b; Wang et al. 2009; Luo et al. 2011; Zhao et al. 2011; Xing 2012; Li et al. 2013). These studies have revealed electrophysiological correlates underlying “aha” experience (Mai et al. 2004), breaking mental sets and forming novel associations (Qiu et al. 2008b; Wang et al. 2009; Luo et al. 2011; Zhao et al. 2011; Xing 2012), mental preparation for insight problem solving (Qiu et al. 2008a) and emotional and competitive influences on insight problem solving (Li et al. 2013). For instance, insightful answers evoked a more negative ERP deflection than did non-insightful answers in the 300–500-ms time window after the onset of the answer (Luo et al. 2004; Xing 2012), and a more positive ERP deflection in a later time window of 600–1100 ms (Xing 2012). Moreover, Qiu et al. found that 1000 ms before onset of the target logogriphs, the mental preparation for successful logogriphs evoked a more positive-going ERP component than unsuccessful logogriphs (Qiu et al. 2008a). Proceeding research on Chinese logogriphs adopted a learning–testing experimental paradigm to investigate insight problem solving in which five heuristic logogriphs with corresponding answers were presented in learning stage and then five target logogriphs were presented in testing stage (Qiu et al. 2008a, b, 2010; Tian et al. 2011; Li et al. 2013). Participants were required to try to understand the heuristic logogriphs in the learning stage and solve the target logogriphs in the testing stage. These studies have suggested that the heuristic logogriphs provided heuristic information indicating a potential solution to the corresponding target logogriphs which, once activated and utilized successfully, enabled the target logogriphs to be solved in a few seconds (Qiu et al. 2008a, b, 2010; Tian et al. 2011; Li et al. 2013). To solve one target logogriph, participants need to activate and utilize an appropriate heuristic information learned in the proceeding learning stage successfully. But they learn five heuristic logogriphs in the learning stage, and more than one piece of heuristic information is represented while solving a certain target logogriph. It is not clear that what kind of brain state could facilitate individuals to activate and utilize an appropriate

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heuristic information that is vital for solving the target logogriphs from multiple heuristic information learnt in previous learning stage. Previous studies have demonstrated that in a state of defocused attention (refers a state in which one can focus on multiple stimuli or events simultaneously rather than just one at a time), an individual’s attentional span is higher, allowing more information to be processed, which results in more possibilities of combining different concepts or remote associations to generate new ideas (Gabora 2000, 2002; Vartanian et al. 2007; Dorfman et al. 2008; Rossmann and Fink 2010; Jauk et al. 2012). Furthermore, Gabora et al. proposed that in the state of defocused attention, there are more memory locations where features of stimuli or experiences currently being attended to get stored which allows evoked information to flow into streams of thought, regardless of whether associations between the information are apparently logical or not (Gabora 2000, 2002). In addition, defocused attention allows both remote and close associates in semantic memory to be activated to approximately the same degree, whereas typically similar concepts are activated more strongly than remote ones (Kounios et al. 2008). Thus, it is possible that when solving target logogriphs, an individual’s defocused attention to heuristic information could increase the possibility of activation and utility of matching heuristic information, which further increases the possibility of solving the target logogriphs. But studies on insight problem solving using Chinese logogriphs as insight problems only investigated the time- and phase-locked changes of electrocortical responses triggered by Chinese logogriphs, revealing ERP components correlated with cognitive processes like breaking mental set or forming novel association in insight problem solving (Qiu et al. 2008b; Luo et al. 2011), and did not focus on the defocused attention that would facilitate individuals to solve the target logogriphs. Event-related synchronization/desynchronizaiton (ERS/ ERD), reflecting the specified frequency band power changes (increases or decreases) during the performance of tasks compared with the reference interval, represents the changes of synchrony of the underlying neuronal populations which control oscillations in neuronal networks (Pfurtscheller and Lopes da Silva 1999). Within the spectral content of EEG, increases in event-related alpha oscillations in the 8–12 Hz band frequency have been demonstrated to be associated with defocused attention (Martindale and Mines 1975; Martindale and Hasenfus 1978; Martindale 1999; Razumnikova 2007; Benedek et al. 2011). For example, Martindale reported that highly creative individuals showed a state of defocused attention indexed by an increase in alpha activity while they were performing a creativity test or generating a creative story (Martindale and Mines 1975; Martindale 1999). Moreover,

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posterior alpha activity observed during the performance of remote association tests in which participants are required to find a new word related to three given words is also interpreted as evidence of defocused attention (Razumnikova 2007). In the present study, in order to investigate that defocused attention facilitates individuals to solve insight problems, we adopted a learning–testing experimental paradigm using Chinese logogriphs as insight problem tasks (Qiu et al. 2010; Li et al. 2013) and focused on alpha band power (8–12 Hz) which has been suggested to be closely related to insight problem solving and defocused attention in creative tasks (Martindale and Mines 1975; Martindale and Hasenfus 1978; Martindale 1999; Jung-Beeman et al. 2004; Fink et al. 2006, 2009a, b, 2011; Fink and Neubauer 2006; Razumnikova 2007; Kounios et al. 2008; Sandkühler and Bhattacharya 2008; Benedek et al. 2011, 2014; Jauk et al. 2012; Kounios and Beeman 2014). Chinese logogriphs solving involves complex cognitive processes including understanding superficial meaning of the target logogriphs, forming rich association, breaking mental set, forming novel associations, etc., that activate multiple regions of brain (Luo et al. 2004, 2011; Qiu et al. 2008b, 2010; Wang et al. 2009; Xing 2012). For example, activities in the anterior cingulate cortex (ACC) and the prefrontal cortex (PFC) were associated with insightful riddle solving (Luo et al. 2004). In addition, it has been demonstrated that decreased alpha power is observed during cognitive task performance which may reflect active cognitive processing in different brain regions (Klimesch 1999, 2012; Pfurtscheller and Lopes da Silva 1999; Krause et al. 2000; Stipacek et al. 2003; Klimesch et al. 2007) and is a consistent pattern during insight problem solving (Jung-Beeman et al. 2004; Kounios et al. 2006, 2008; Razumnikova 2007; Sandkühler and Bhattacharya 2008; Dietrich and Kanso 2010). Thus, we hypothesized that cognitive loads and activities in related brain region would increase after the presence of the target logogriphs regardless whether participants successfully solve the target logogriphs or not, which could be indicated by task-related alpha desynchronization from the reference interval. Moreover, in a state of defocused attention, an individual’s attentional span is higher, allowing more information to be processed (Gabora 2000, 2002; Vartanian et al. 2007; Dorfman et al. 2008; Rossmann and Fink 2010; Jauk et al. 2012). When participants are in a state of defocused attention, more heuristic information can be processed while solving the target logogriphs, which increases the possibility of activation and utility of matching heuristic information and further increases the possibility of solving the target logogriphs. Thus, we hypothesized that if defocused attention could facilitate insight problem solving, when we contrast the alpha oscillation in successful and unsuccessful logogriph solving conditions,

increases in alpha power indicating defocused attention would be observed in successful condition.

Materials and methods Participants Sixteen right-handed undergraduates (8 females) aged 21–23 years (mean age 21.6 years) from Southwest University, China, participated in the experiment as paid volunteers. All participants were healthy, had normal or corrected-to-normal vision and had no history of neurological or psychiatric illness. All participants gave written informed consent to participate, and this study was approved by the Administrative Committee of Psychological Research of Southwest University. Experimental stimuli Based on our previous pilot experiment (Qiu et al. 2008b), we selected 150 target logogriphs from the 180 available whose difficulty and ability to provoke interest in participants were prescreened. For each of the 150 target logogriphs, a corresponding heuristic logogriph containing heuristic information or indicating a potential solution to the target logogriph was made. For example (Fig. 1), “yǒu kǒu nán yán” ( ) was the heuristic logogriph for the target logogriph “yǒu yǎn nán jiàn” ( ). Participants first learned the heuristic logogriph and received heuristic information for solving the matching target logogriph. The heuristic logogriph, “yǒu kǒu nán yán” (“having a mouth but being unable to speak”) refers to the Chinese character “yǎ” ( ) (meaning mute). In Chinese, “yǎ” is composed of

Fig. 1  Procedure of stimulus presentation. Examples of experimental materials are presented along with the experimental procedure. Red and blue lines indicate the time interval chosen in time–frequency domain analyses. The red line denotes the reference interval (color figure online)

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two simpler characters, “kǒu” ( ) (meaning mouth which is the semantic-code radical signifying that the word concerns mouth) and “yà” ( ) (denoting the sound or pronunciation of the word). In other words, when the simpler character “kǒu” (mouth) is combined with “yà,” the combined, emergent character is “yǎ” (mute). The riddle is “having a mouth but being unable to speak,” and when “kǒu” (mouth) is combined with “yà,” the character produced (yǎ) is mute. Therefore, the answer to this riddle is “yǎ”. The crucial process in solving the riddle is first thoroughly understanding the surface meaning of the riddle and then obtaining the answer by analyzing the Chinese character into component radicals (i.e., component, simpler characters). When the target logogriph appeared, participants could easily guess the answer to the logogriph “yǒu yǎn nán jiàn” (“having eyes but being unable to see”) because the target logogriph resembled the heuristic logogriph in a key aspect. The superficial meaning, “yǒu yǎn nán jiàn,” is “having eyes but being unable to see”. The answer to this riddle is a Chinese character “máng” ( , meaning blind). In Chinese, “máng” is composed of two simpler characters, “wáng” ( , is a phonetic radical, and pronounced “máng” in old Chinese) and “mù” ( , is a semantic radical signifying that the word concerns eyes). By understanding the surface meaning of the riddle and isolating the Chinese character “mù” from the character “máng” in which “mù” is embedded, the answer “máng” can be obtained. A similar example of English counterparts is: “It has an egg in it, but when you have it. You can eat no egg”. [Heuristic riddle (answer): Eggplant]; “It has an apple in it, but when you have it. You can eat no apple”. [Target Riddle (answer): pineapple]. Most logogriphs were between two and six characters in length, while all answers were a single character. The characters that appeared in both the questions and answers were of high frequency and presented in a size 16 Song Ti font. The presentation and timing of all stimuli were administrated by the E-Prime software (Psychology Software Tools, Inc. Pittsburgh, PA). Experimental procedure The present experiment adopted a Chinese logogriph guessing task by using a learning–testing paradigm (Fig. 1). In the learning stage, a fixation was firstly presented for 500 ms. Subsequently, one heuristic logogriph (including its answer) was presented in the center of the screen for 8000 ms, during which participants were asked to try to understand the heuristic information. As soon as they understood the heuristic information, they could press the 1 key, which would terminate the heuristic screen. If they did not respond, the screen would be displayed for the entire 8000 ms. After the five heuristic logogriphs were presented, the testing stage began in which participants were asked to

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guess the answers to five target logogriphs and then judge whether the guess they made was the same as the correct answers. The testing stage began with an 800-ms fixation which was followed by the presentation of a target logogriph. The target logogriphs were displayed for 4000 ms or until participants guessed the answer. Participants could press the 1 key as soon as they believed that they had guessed the “correct” answer to the target logogriph, which would terminate the screen, or make no response if they could not find the answer. Subsequently, a fixation was displayed for 500 ms, before the correct answer to the target logogriph was presented for 2000 ms. Participants were instructed to press the 1 key if they guessed the correct answer, or make no response if their guess was incorrect or they failed to solve the logogriph. The inter-trial interval was 1500 ms. The whole experiment consisted of 30 blocks of 5 pairs of logogriph, and each block consisted of one learning stage and one testing stage. In the learning and testing stages, logogriphs were presented in a random order. Participants received three practice blocks before entering the formal experimental phase. The participants were seated at about 80 cm from a 19-in. (5:4) computer screen. They were instructed to avoid unnecessary body movements and keep their eyes fixated on the center of the screen while performing the tasks. Participants took a break after every five blocks. Electrophysiological recording and analysis Brain electrical activity was recorded from an elastic cap with electrodes at 66 scalp sites according to the 10–20 system (Brain Product, Munchen, Germany), with the reference electrodes placed on the left and right mastoids. The vertical electrooculogram (VEOG) was recorded by using electrodes placed approximately 1 cm above and below the participant’s right eye, and the horizontal electrooculogram (HEOG) was measured by electrodes placed at the outside rims of their eyes. All electrode impedances were maintained below 5 kΩ. The EEG and EOG signals were amplified and digitized using a band pass of 0.05–80 Hz and continuously sampled at 500 Hz for off-line analysis. The EEGs went through the following steps of offline preprocessing using EEGLAB (Delorme and Makeig 2004) and MATLAB functions. The EEGs were band-pass filtered between 0.1 and 30 Hz. EEG epochs for analysis (500 ms prestimulus and 2500 ms poststimulus) were extracted and then corrected using the prestimulus interval (−500–0 ms) baseline. Eye movement artifacts (eye blinks and movements) were corrected with an infomax independent component analysis algorithm (Makeig et al. 1997; Jung et al. 2001; Delorme and Makeig 2004). The independent components related to eye movements which had a large EOG

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channel contribution and a frontal scalp distribution were removed, and then additional baseline correction using the prestimulus time interval was performed. For the time–frequency analysis, a Fourier transformation with a fixed Hanning window (250 ms) was chosen to transform the single trial data corresponding to each condition into the time–frequency domain. The windowed Fourier transformation may give a good time–frequency resolution when we focus on a certain range of frequencies (Freeman and Quiroga 2013), and the parameter has been shown to represent a good tradeoff between time resolution and frequency resolution (Zhang et al. 2012; Hu et al. 2014). We used the task-related power (TRP) changes in the EEG which had widely been used in previous studies (Fink et al. 2006, Fink et al. 2009a, b, 2011; Fink and Neubauer 2006; Benedek et al. 2011, 2014; Jauk et al. 2012) to measure the participant’s brain activity while they were performing the experimental tasks. For each frequency and electrode, the TRP was calculated by subtracting the power (log-transformed) during the reference interval (−450 to −50 ms) from the power (log-transformed) at each time point according to the following formula: TRPt,f = log[At,f] − log[Rf], where At,f is the signal power at a given time (t) and at a given frequency (f), and Rf is the signal power averaged within the reference interval. Therefore, negative values denote decreases in power from the reference (i.e., desynchronization), while positive values suggest task-related increases in power (i.e., synchronization). The logarithmic calculation for TRP is recommended for the comparison of two short-time (within 1000 ms) power spectra to determine subject-specific frequency bands (Pfurtscheller and Lopes da Silva 1999; Pfurtscheller 2001). TRP at each time point was calculated and then analyzed. The alpha frequency band (8–12 Hz) was chosen for further analysis due to our experimental hypothesis, and we did not conduct analysis on sub-band alpha power since a previous study demonstrated that the upper (8–10 Hz) and lower (10–12 Hz) sub-bands can be aggregated to a single band in a creative task (Jauk et al. 2012). EEG signals after 800 ms were chosen for further analyses as participants were understanding the superficial meaning of the target logogriphs before 800 ms (Qiu et al. 2008b), and alpha power before 800 ms was separated and short in time. We selected EEG data before the 1400-ms time period that is out of three times of the SD of mean response time for the successful logogriphs in order to exclude influences from button presses or motor preparation as much as possible. To select electrodes, alpha power changes in successful and unsuccessful conditions were averaged on each electrode (Cohen 2014). From electrodes that showed a decrease in alpha power over two conditions averaged together, eight parieto-central electrode sites (CP3, CP1, P1, CPz, Pz, P2, CP2 and CP4) and fourteen

parieto-occipital electrode sites (P7, P3, PO7, PO5, PO3, O1, POz, Oz, O2, PO4, PO6, PO8, P4 and P8) were selected (see Figure 2 in Supplementary Materials). Mean TRP changes were then measured accordingly and respectively. For all statistical analyses, data were computed using repeated measures ANOVAs with the Greenhouse–Geisser correction applied to p values whenever necessary.

Results Behavioral results One participant who had a strong tendency to press the key 1 in the whole experiment (88 % pressing the 1 key when responses were needed) was excluded from further analyses. Participants failed to respond to an average of 28.5 ± 10.1 heuristic logogriphs in the learning stage. Thus, their corresponding target logogriphs were excluded from further analyses. In the testing stage, the target logogriphs to which the participants guessed the right answer (i.e., pressed the 1 key during both the presentation of the target logogriphs and the correct answers) were counted as successful logogriphs, otherwise they were counted as unsuccessful logogriphs. The mean numbers of trials for successful logogriphs and unsuccessful logogriphs were 58.1 ± 10.1 (38.7 ± 6.7 %) and 63.4 ± 12.6 (42.3  ± 8.4 %), respectively. The mean response time for the successful logogriphs was 2299.0 ± 200.2 ms. EEG results Figure  2 shows the differences between successful and unsuccessful trials in terms of frequency spectrum, amplitude of alpha power and topographical maps. The topographic maps demonstrate alpha desynchronization occurred in both successful and unsuccessful conditions at the parieto-central electrode sites. For the parieto-central electrodes, the grand-averaged TRP changes in the alpha band during the 800–1400-ms time window for the successful and the unsuccessful logogriphs were −0.146 ± 0.333 logμV2 and −0.192  ± 0.242 logμV2, which suggested decreases in the alpha power (i.e., alpha desynchronization) compared with the reference interval in both conditions. Furthermore, TRP changes in the alpha band were analyzed using a 2 (condition: successful vs. unsuccessful)  × 8 (electrode site) repeated measures ANOVA. The results suggested that the main effects of condition and electrode site did not reach statistical significance [F(1, 14)  = 0.557, p  = 0.468, η2p  = 0.038; F(7, 98) = 0.600, p  = 0.600, η2p  = 0.041, respectively], nor did the interaction between the two factors [F(7, 98) = 0.418, p = 0.670, η2p = 0.029]. The amplitude of alpha TRP power changes at

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Fig.  2  a Frequency spectrum. Difference (successful logogriphs minus unsuccessful logogriphs) in the frequency spectrum (0–15 Hz) at P7 and PO7 electrode sites. The dotted rectangle indicates the time window (800–1400 ms) and frequency band (8–12 Hz) used in

analyses. b Alpha power changes. The amplitude of alpha TRP power changes at the P7 and PO7 electrode sites. c Topographical maps. Topographical maps of the distribution of alpha TRP power changes in the 800–1400-ms time window

the CP1 and CP2 electrode sites is presented in Supplementary Materials. In addition, for the parieto-occipital electrodes, the TRP changes in the alpha band were analyzed using a 2 (condition: successful vs. unsuccessful) × 14 (electrode site) repeated measures ANOVA. The results revealed that the main effect of condition was significant [F(1, 14) = 12.516, p = 0.003, η2p = 0.472]. However, the main effect of electrode site and its interaction with condition were not significant [F(13, 182) = 1.929, p = 0.117, η2p = 0.121; F(13,

182) = 1.559, p = 0.213, η2p = 0.100, respectively]. These results mainly suggested that alpha power changes were significantly different between successful and unsuccessful logogriph conditions. As some of studies we reported used different methods to measure task-related power changes, alpha power changes were also calculated according to the following formula: TRPt,f % = [At,f  −  Rf]/Rf, where At,f is the signal power at a given time (t) and at a given frequency (f), and Rf is the signal power averaged within the reference

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interval (Pfurtscheller and Lopes da Silva 1999). For the parieto-central electrodes, the grand-averaged TRP changes in the alpha band during the 800–1400-ms time window for the successful and the unsuccessful logogriphs were −0.087  ± 0.232 and −0.140  ± 0.199, which suggested there were decreases in alpha power (i.e., desynchronization) compared with the reference interval in both conditions. Furthermore, TRP changes in the alpha band were analyzed using a 2 (condition: successful vs. unsuccessful)  × 8 (electrode site) repeated measures ANOVA. The results suggested that the main effects of condition and electrode site did not reach statistical significance [F(1, 14)  = 1.131, p  = 0.306, η2p  = 0.075; F(7, 98) = 0.818, p  = 0.501, η2p  = 0.055, respectively], neither did the interaction between the two factors [F(7, 98) = 0.626, p  = 0.558, η2p  = 0.043]. For parieto-occipital electrodes, the TRP changes in the alpha band were analyzed using a 2 (condition: successful vs. unsuccessful) × 14 (electrode site) repeated measures ANOVA. The results revealed that the main effect of condition was significant [F(1, 14)  = 7.710, p  = 0.015, η2p  = 0.355]. However, the main effect of electrode site and its interaction with condition were not significant [F(13, 182) = 1.822, p = 0.132, η2p  = 0.115; F(13, 182) = 1.821, p  = 0.149, η2p  = 0.115, respectively]. These results were similar to those of logarithmic calculation; thus, further analyses used logarithmic calculation for TRP. To explore the hemispheric differences in alpha power during the performance of the tasks, we performed an additional ANOVA analysis using 12 electrodes (excluding the two middle line electrodes from the 14 electrode points). The results of a 2 (condition: successful vs. unsuccessful)  × 6 (electrode site) × 2 (hemisphere: left vs. right) repeated measures ANOVA suggested that the main effect of condition was significant [F(1, 14) = 7.426, p = 0.016, η2p  = 0.347]. However, the interaction between condition and hemisphere was not significant [F(1, 14) = 0.655, p = 0.432, η2p = 0.045], neither was the interaction between condition and electrode site [F(5, 70) = 2.010, p = 0.139, η2p  = 0.126]. Furthermore, the main effects of hemisphere and electrode site were also nonsignificant [F(1, 14)  = 0.414, p  = 0.530, η2p  = 0.029; F(5, 70) = 2.828, p = 0.053, η2p = 0.168, respectively], but there was a significant interaction between them [F(5, 70) = 3.199, p  = 0.039, η2p  = 0.186]. Moreover, the interaction among electrode site, condition and hemisphere was also evident [F(5, 70) = 2.896, p  = 0.047, η2p  = 0.171]. These results indicated that there was no statistical hemispheric difference in alpha power in successful and unsuccessful conditions. In addition, as several main ERP indicators such as P300 and N500 are between 100 and 800 ms, and Fig. 2 also demonstrates that there is an increase in alpha power

before 800 ms during successful condition in comparison with unsuccessful condition, we have conducted additional analyses in the 100–800-ms time interval (see Complementary analysis in the Supplementary Materials). The results indicated that for the parieto-central and parieto-occipital electrodes, there was no significant difference in alpha power changes between successful and unsuccessful conditions during the 100–800-ms time window.

Discussion In the current study, we focused on the task-related alpha power evoked by guessing Chinese logogriphs to explore alpha oscillations underlying insight problem solving. EEG results mainly indicated that in the time window of 800– 1400 ms, task-related alpha power changes over parietocentral electrodes in both successful and unsuccessful logogriphs conditions decreased from the reference interval. In addition, a marked difference in the task-related alpha power desynchronization at parieto-occipital electrode sites was found between the successful and unsuccessful conditions. Successful logogriph solving was accompanied by increased alpha power at parieto-occipital electrode sites compared with unsuccessful logogriph solving. In the 800–1400 time interval, alpha power over parietocentral electrodes decreased from the reference interval in both successful and unsuccessful logogriph conditions. Previously, observed alpha desynchronization had been interpreted as a reflection of cognitive demands during task performance (Pfurtscheller et al. 1996; Klimesch 1999, 2012; Krause et al. 2000; Stipacek et al. 2003; Klimesch et al. 2007). For example, Stipacek and his colleague found alpha ERD increased linearly with ascending memory load, which suggested alpha ERD is a reflection of increasing cognitive load (Stipacek et al. 2003). Thus, it is possible that the alpha desynchronization in both successful and unsuccessful conditions suggests that more cognitive processes such as memory and attention were involved in solving the target logogriphs. Besides, the alpha desynchronization observed in the present study replicated the pattern of decreased alpha activity in previous studies of insight problem solving (Razumnikova 2007; Sandkühler and Bhattacharya 2008), which also suggests it might be a reflection of the task demands of insight problem solving. Furthermore, a previous ERP study suggested that during the time window of approximately 200–600 ms after the presentation of the target logogriphs, participants were understanding the superficial meaning of the target logogriphs (Qiu et al. 2008b) further supporting the notion that cognitive activities during the subsequent 800–1400-ms time window might be correlated with task demands for guessing the target logogriphs. It is interesting to note that there was no

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hemispheric lateralization of alpha activity in the current study, which is inconsistent with previous studies which reported hemispheric lateralization of alpha activity in creative ideation tasks (Sandkühler and Bhattacharya 2008; Fink et al. 2009a, b; Fink and Benedek 2012). However, there is also evidence suggesting parietal alpha activity in tasks demanding internal attention (Ray and Cole 1985) or bottom-up processing (Schaefer et al. 2011; Benedek et al. 2014) is not characterized by hemispheric lateralization. Our results also suggested that the unsuccessful logogriph trials evoked more alpha power desynchronization at parieto-occipital electrode sites compared with the successful logogriph trials. The interpretation that decreased alpha power over parieto-central electrodes reflects task demands for guessing the target logogriphs could also account for the differences in alpha power changes at parieto-occipital electrode sites between successful and unsuccessful logogriph trials. In our experimental paradigm, the heuristic logogriphs provided heuristic information indicating a potential solution to the corresponding target logogriphs which enabled the target logogriphs to be solved in a few seconds (Qiu et al. 2008a, b, 2010). The stronger alpha desynchronization in the unsuccessful condition might reflect more cognitive resources being used in inhibiting the unfocused heuristic information or searching for suitable heuristic information. This is probably because in the unsuccessful condition, participants focused on one piece of certain heuristic information learnt from the preceding stage and inhibit others, which might have resulted in the decreased possibility of solving the target logogriphs. Another possible explanation for the significant difference in the task-related alpha power desynchronization at parieto-occipital electrode sites between the successful and unsuccessful conditions lies in the increased alpha power synchronization in the successful condition. When we compared alpha power desynchronization between the successful condition and unsuccessful conditions, stronger alpha power synchronization was observed. According to the previous studies, suggesting that defocused attention is likely to occur during a state of relaxation or lower cortical arousal indexed by stronger alpha oscillation (Martindale and Mines 1975; Martindale 1999; Dorfman et al. 2008), one possible interpretation for the increased alpha power synchronization is that participants were in a state of more relaxation or lower cortical arousal when defocused attention occurred in the successful condition compared with the unsuccessful condition. Defocused attention is generally considered to be closely correlated with creativity (Martindale and Mines 1975; Mendelsohn 1976; Martindale and Hasenfus 1978; Martindale 1999; Gabora 2000, 2002; Dietrich 2004, 2007; Fink and Neubauer 2006; Vartanian et al. 2007; Dorfman et al. 2008; Fink et al. 2009a; Vartanian 2009; Rossmann and Fink 2010; Zabelina and

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Robinson 2010; Benedek et al. 2011; Jauk et al. 2012). Several studies have demonstrated that with a higher attentional span, more information can be processed in a state of defocused attention, which results in more possibilities of combining different concepts or remote associations to generate new ideas (Gabora 2000, 2002; Vartanian et al. 2007; Dorfman et al. 2008; Rossmann and Fink 2010; Jauk et al. 2012). In addition, some theories suggested that defocused attention facilitates insight problem solving by allowing more features of a current stimulus to be stored into memory locations (Gabora 2000, 2002) or more associations (both remote and close in semantic memory) to be activated (Kounios et al. 2008). Thus, it was possible that when participants were in a state of defocused attention, they might have focused on more heuristic information learnt in the preceding stage at the same time, which resulted in a greater possibility of solving the logogriphs because the activation of the matching heuristic information would facilitate the solving of the current target logogriph. The present study revealed the correlation between alpha power and defocused attention in insight problem solving and inferred that the state of defocused attention could facilitate insight problem solving. But it is still unknown that the increased alpha power is the reason or the result of defocused attention in insight problem solving as the defocused attention is difficult to be manipulated in the present study. More elaborative experimental designs will be developed to address these potential problems. In addition, the hypothesis and interpretation in the current study were based on Klimesch’s inhibition theory suggesting that alpha desynchronization means cortical activation and alpha synchronization means cortical deactivation (Klimesch et al. 2000, 2007; Klimesch 2012). However, theories about the explanation of alpha synchronization are controversial. More recent studies suggested that alpha synchronization reflects cortical activation (Basar and Guntekin 2012; Fink and Benedek 2012). When participants were guessing the target logogriphs, the cognitive demand clearly increased from baseline interval to task and alpha ERD was found during guessing the target logogriphs. Thus, we used Klimesch’s inhibition theory to interpret the results. However, the increasing understanding of physiological meaning of alpha oscillation will extend our knowledge about insight problems solving.

Conclusion Our results suggested that in the time window of 800– 1400 ms after the presentation of target logogriphs, alpha power over parieto-central electrodes decreased in both the successful and unsuccessful logogriphs solving conditions

Exp Brain Res

relative to the reference interval. Alpha desynchronization observed in both conditions reflects the cognitive demands in solving the target logogriphs compared with the reference interval. Furthermore, difference in alpha power between the successful and unsuccessful conditions at parieto-occipital electrode sites is associated with the process of heuristic information. Alpha synchronization observed in the successful condition compared to the unsuccessful condition might reflect a state of defocused attention that facilitates insight problem solving. Acknowledgments  We are grateful to the participants in our studies. This research was supported by the National Natural Science Foundation of China (31470981) and the Fundamental Research Funds for the Central Universities (SWU1509338). Conflict of interest  The authors declare no conflict of interest.

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