The origins of insight in resting-state brain activity
Introduction
Systematic, relatively stable, patterns of resting-state brain activity are associated with aspects of personality, intelligence, psychopathology, and neurological disorder (Davidson, 2003; John, Prichep, Fridman, & Easton, 1988; Kumari, ffytche, Williams, & Gray, 2004; Thatcher, Northa, & Bivera, 2005), perhaps reflecting subtle differences in neuroanatomy or neurotransmitter levels (John et al., 1988). The existence of such associations suggests the possibility that resting-state neural activity may also be correlated with individual differences in the event-related, goal-oriented, cognitive processes that people use to negotiate the world around them, such as those used in problem solving.
The present study examined the hypothesis that resting-state neural activity influences the cognitive strategies people use to solve problems, in particular, the general strategies which result in problem solutions derived either by methodical search or by sudden insight. Determining whether the tendency to solve problems by search versus insight is influenced by resting-state activity would clarify whether the relevant neural computations are selected and engaged only once processing of a problem has begun, or whether preexisting biases in more fundamental neural processes influence the likelihood of using one strategy or the other. More generally, this study examined the hypothesis that event-related, goal-directed, neural computations are influenced by characteristics of the preceding resting state.
Most research on human cognition has focused on directed, goal-oriented, thought. In contrast, a relatively small body of research has focused on the spontaneous, undirected, thought that occurs during a resting state when a person is given no particular task to perform (Christoff, Ream, & Gabrieli, 2004). Results from functional neuroimaging studies have shown that resting-state activity is decomposable into a number of separate networks (Damoiseaux et al., 2006) and that some of these networks include brain areas also recruited during the performance of tasks involving higher cognitive functions (Andreasen et al., 1995, Christoff et al., 2004). This suggests that spontaneous thought during rest may involve some of the same thought processes engaged during problem solving.
The neural correlates of the resting state are not identical to the default state identified by Raichle et al. (2001). The default state consists of the network of brain regions that are more active during the resting state than during the performance of a task. Activity in this network is attenuated during active engagement in a task. Spontaneous thought during rest engages additional brain areas that are also active during task performance but are not part of the default-state network and are therefore not attenuated during task engagement (Christoff et al., 2004).
There are two general cognitive strategies which people use to solve problems. Search involves systematic evaluation of possible problem states intervening between the current state and the goal state, and the use of available operators to transform one state into another. The use of a search (or “analytic”) strategy involves systematic evaluation of problem states which lie on different possible paths linking the starting state and the goal state. These intermediate states and paths are computed by deliberate, predominantly conscious, manipulation of problem elements (Ericsson & Simon, 1993; Newell & Simon, 1972).
Another general strategy for problem solving involves insight (Bowden, Jung-Beeman, Fleck, & Kounios, 2005; Maier, 1931; Sternberg & Davidson, 1995; Wagner, Gais, Haider, Verleger, & Born, 2004). Insight is the sudden awareness of the solution to a problem (i.e., the “Aha!” phenomenon) with little or no conscious access to the processing leading up to that solution (Metcalfe & Wiebe, 1987; Smith & Kounios, 1996). The notion of sudden insight is related to the distinction between discrete, all-or-none, information processing and continuous or incremental processing (Kounios, 1993; Kounios, Osman, & Meyer, 1987; Meyer, Irwin, Osman, & Kounios, 1988; Sergent & Dehaene, 2004; cf. Lang et al., 2006), and has been identified as an important characteristic of creative thought (Andreasen, 2005; Ansburg & Hill, 2003; Friedman & Förster, 2005).
Research contrasting problem solving by insight and by analytic “noninsight” search strategies has identified distinguishing cognitive and neural mechanisms (Bowden & Jung-Beeman, 2003; Bowden et al., 2005, Fleck, in press; Friedman & Förster, 2005; Gilhooly & Murphy, 2005; Jung-Beeman et al., 2004; Kounios et al., 2006; Metcalfe & Wiebe, 1987; Smith & Kounios, 1996; Sternberg & Davidson, 1995). For example, the sudden awareness of insight solutions to verbal problems corresponds to a burst of high-frequency (gamma-band) oscillatory electroencephalogram (EEG) activity associated with an increase in functional magnetic resonance (fMRI) signal in the right anterior superior-temporal gyrus (Jung-Beeman et al., 2004). This finding is consistent with a special role for the right hemisphere (RH) in problem solving by insight, a hypothesis further supported by behavioral studies examining response times to lateralized visual presentation of potential solution words (Bowden & Jung-Beeman, 2003). These studies have demonstrated that the representation of a correct solution is activated at a subconscious level in the RH prior to conscious retrieval, but only for solutions associated with the “Aha!” experience characteristic of insight when they do become conscious (Bowden et al., 2005). The subconscious nature of the RH activity leading up to an insight suggests that analytic and insight processing can occur in parallel (cf. Kounios, 1993).
Differences among individuals in the tendency to solve problems with an insight versus an analytic strategy may be associated with more fundamental characteristics of information processing. For instance, psychometric measures of creativity and measures of real-world creative achievement are associated with a habitual tendency toward diffuse rather than focused attention, which results in ineffective filtering of distracting or irrelevant environmental stimuli (Carson, Peterson, & Higgins, 2003; Mendelsohn & Griswold, 1966; Rowe, Hirsh, & Anderson, 2007). One view describes creativity as the ability to utilize nonprepotent remote associations of problem elements in order to discover nonobvious solutions to a problem (Mednick, 1962). Diffuse attention facilitates access to remote associations because it enhances awareness of peripheral environmental stimuli that could serve as cues that trigger retrieval of such associations (Seifert, Meyer, Davidson, Patalano, & Yaniv, 1995). Furthermore, diffuse attention in the perceptual domain is associated with diffuse attention in the conceptual domain (Friedman & Förster, 2005; Rowe et al., 2007). Such diffuse conceptual attention allows a concept in semantic memory to activate both remote and close associates to approximately the same degree rather than according to a steeper gradient of association in which a concept activates similar concepts more strongly than remotely related ones (Beeman et al., 1994; Faust & Lavidor, 2003; Folley & Park, 2005; Howard-Jones, Blakemore, Samuel, Summers, & Claxton, 2005; Jung-Beeman et al., 2004, Mednick, 1962, Stringaris et al., 2006).
The tendency to solve problems with insight may also be associated with both structural and functional hemispheric asymmetry. Behavioral (Bowden & Jung-Beeman, 2003; Friedman & Förster, 2005), electrophysiological (Jung-Beeman et al., 2004), and neuroimaging (Jung-Beeman et al., 2004) studies suggest a special role for the right hemisphere (RH) in solving problems with insight. These findings are consistent with a hemispheric model of semantic processing in which the RH primarily processes remote associations of concepts, while the left hemisphere (LH) primarily processes close associations (Beeman et al., 1994; Faust & Lavidor, 2003; Folley & Park, 2005; Howard-Jones et al., 2005, Jung-Beeman, 2005, Jung-Beeman et al., 2004, Stringaris et al., 2006). This processing asymmetry may be a byproduct of specific architectonic differences between LH language areas and their RH homologues (Jung-Beeman, 2005; Hutsler & Galuske, 2003).
Prior research suggests two hypotheses about resting-state brain activity and problem solving strategy. The first hypothesis involves electroencephalogram oscillations in the high-alpha (10–13 Hz) and low-beta (13–18 Hz) frequency bands. Occipital alpha, especially in the 10–13 Hz high-alpha band, has been shown to reflect an inhibitory gating mechanism regulating the intake of visual information (Ray & Cole, 1985; Worden, Foxe, Wang, & Simpson, 2000). In contrast, evidence suggests that occipital beta reflects an excitatory mechanism associated with selective attention (Bekisz & Wróbel, 2003; Wróbel, 2000). Selective visual attention on the cortical level has been proposed to function comparably to lateral inhibition, with a center-increase/surround-decrease in cortical activity linked to thalamic gating (Pfurtscheller, 2003; Pfurtscheller & Lopes da Silva, 1999). An increase in activity at the center (beta) could therefore be coupled with inhibition of activity in surrounding regions associated with alpha synchronization.
These findings lead to the prediction that subjects exhibiting a tendency to solve problems with insight will have a tendency toward diffuse deployment of visual attention manifested as a reduction in resting-state occipital alpha (i.e., more general activation of visual processing areas resulting in broader intake of visual information) relative to subjects exhibiting a tendency to solve problems with an analytic noninsight strategy. Furthermore, subjects tending to solve problems analytically should have greater focused visual attention associated with increased occipital beta (i.e., more neural activity in specific visual pathways processing focally attended information) and increased occipital alpha (i.e., more inhibition of brain areas processing non-attended visual information) than subjects tending to solve problems with insight.1
The second hypothesis was that there would be hemispheric asymmetry indicative of greater RH activity and/or less LH activity in frontal, temporal, and parietal association areas implicated in semantic processing (Jung-Beeman, 2005) for subjects tending to solve problems with insight compared to subjects tending to solve problems without insight. This would be manifested in low-alpha (8–10 Hz) band activity reflecting inhibition or idling of association cortex (Kounios et al., 2006) and higher-frequency (i.e., higher beta- and gamma-band) oscillations which have been linked to cognitive processes such as the transient feature-binding associated with the activation of perceptual or conceptual representations (Jung-Beeman et al., 2004; Pulvermüller, 2001; Tallon-Baudry & Bertrand, 1999) and which are proportional to hemodynamic measures of neural activity (Kounios et al., 2006, Laufs et al., 2003).
Comparing resting-state neural activity of subjects who tend to solve problems with insight to resting-state neural activity of subjects who tend to solve problems with a noninsight analytic strategy necessitates determining which strategy each subject uses for each problem. Historically, the most common approach to studying insight has been to compare subjects’ solving of problems traditionally classified as insight problems to that of noninsight problems (Sternberg & Davidson, 1995). Much has been learned from this approach. However, what is often not recognized is that most problems can be solved either by insight or by analytical processing (Bowden et al., 2005). Therefore, an examination of neural correlates of the solution of problems traditionally considered to be insight problems could include brain activity corresponding to some solutions achieved without insight. Our research has taken an alternative approach by using problems that can be solved with or without insight and sorting the solutions by subjects’ trial-by-trial binary judgments of the solving method used.
The classical distinction between insight and noninsight problems rests largely on the differential phenomenology associated with the solutions, with the use of subjective reports of the solving experience dating back to the beginnings of insight research (Maier, 1931). In the modern era, Kaplan and Simon (1990) argued that the subjective “Aha!” experience is the defining feature of insight. More generally, the use of verbal protocols has been widely adopted in problem-solving research. Ericsson and Simon (1993) presented data to support the validity of such self-reports (both concurrent and retrospective) when these reports do not require subjects to formulate detailed interpretations of their cognitive strategies. Much work has also generally validated the use of self-reports in brain-imaging research (e.g., Baars, 2003; Kirchhoff & Buckner, 2006; Lutz, Lachaux, Martinerie, & Varela, 2002).
For the present study, in order to examine individual differences in the tendency to solve with insight, it was necessary to assess whether individuals solve particular problems with or without insight. Real-time verbalization of subjects’ thoughts during problem solving is not compatible with artifact-free electroencephalogram (EEG) measurement. However, an appropriate modification of this approach has proven successful in a number of studies (Bowden & Jung-Beeman, 2003; Bowden et al., 2005, Jung-Beeman et al., 2004, Kounios et al., 2006, Maier, 1931). This approach requires subjects to solve each problem, and then, immediately after reporting the solution, report whether or not that solution had been derived with insight (defined in terms of the suddenness of the awareness of the solution). This procedure circumvents the problem of electroencephalogram contamination by verbalization-related muscle artifact.
Several lines of evidence support the notion that such self-reports accurately reflect the sudden availability of the solution to a problem, rather than reflecting ancillary phenomena such as an affective response to the solution (Jung-Beeman et al., 2004). For example, the main neural correlate of insight self-reports is a burst of gamma-band EEG activity recorded over the right anterior superior-temporal lobe beginning at approximately the moment when the solution becomes available (i.e., approximately .3 s before a manual response is made to indicate that a solution is available for report) and not as a later affective or novelty response to the availability of this solution. [.3 s is approximately the time needed to access available response information and execute a button-press response (Smith & Kounios, 1996).] This gamma-band response corresponds to an increase in BOLD activity in the right anterior superior-temporal gyrus, an area of association cortex that has not been associated with affective or novelty processing (Jung-Beeman, 2005). Moreover, in addition to an increase in hemodynamic activity in this area at about the time that the solution becomes available, there is also earlier activity in this area beginning at about the time the problem is initially displayed, further supporting the notion that signal changes in this area reflect engagement of cognitive processes, not affective or novelty responses. Most importantly, below we present behavioral results consistent with the notion that self-reports of insight reflect the cognitive phenomenon of solutions becoming available in a sudden, discrete, fashion.2
High-density electroencephalograms (EEGs) were recorded from subjects during rest (first with eyes closed, then with eyes open) before they were told the nature of the subsequent task. They were then directed to solve a series of anagrams. For each anagram, subjects pressed a button immediately upon deriving the solution and then reported whether or not that solution had been derived with insight (defined in terms of the suddenness of the awareness of the solution) (Bowden & Jung-Beeman, 2003; Bowden et al., 2005, Jung-Beeman et al., 2004, Kounios et al., 2006). Using the ratio of insight solutions to noninsight solutions, subjects were classified as high insight (HI) or low insight (LI). Power spectra for subjects’ resting-state EEGs were computed and group comparisons of the EEG power values for each frequency band were computed in order to determine whether resting-state brain activity associated with individuals tending to solve problems with insight differed from resting-state brain activity associated with individuals tending to solve problems without insight.
Section snippets
Subjects
Twenty-six right-handed, native English-speaking, subjects participated. Subjects were divided into HI and LI groups by performing a median split on the ratio of insight to noninsight correct anagram solutions. The HI group (mean age: 21.3 years, S.D.: 4.8) had a mean ratio of 3.5 (S.D.: 2.3) and included six males and seven females. The low-insight (LI) group (mean age: 22.5, S.D.: 3.4) had a mean ratio of .8 (S.D.: .4) and included seven males and six females. All subjects signed an informed
Mean performance
On average, subjects solved 70.0% (S.D.: 8.1) of the anagrams correctly. Of these, 56.1% (S.D.: 19.2) were solved with insight. The HI and LI groups correctly solved approximately the same percentage of anagrams (HI: 68.9%; LI: 70.9%; t[24] = .60, p = .55). Mean reaction times for correctly solved anagrams were not significantly different for the HI (5.19 s, S.D.: .86) and LI groups (5.10 s, S.D.: .93) (t[24] = .28, p = .78). Mean error rates (i.e., proportion of responses that were incorrect) were low
Discussion
The present study demonstrates that goal-oriented, event-related, cognitive processing is not completely determined by goals or task demands. Individual differences in resting-state brain activity also influence such neural computations. Specifically, subjects’ preferred strategy for solving a series of anagrams (insight versus search), was influenced by characteristics of their prior resting state. This phenomenon is fundamentally different from the previous demonstration of a relationship
Acknowledgments
This research was supported by NIDCD grants DC-04818 (to JK) and DC-04052 (to MJ-B).
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