Look outside the box, to think outside the box: insight, eye movements and solution accuracy

Many contributions have been made since Gestalt Psychology introduced the study of insight problem solving. Taking advantage of recent progression in neuroscientific techniques, scientists have supplemented traditional theories with data from newer paradigms. This has meant a leap forward in our knowledge of insight and it has also raised new questions about which cognitive processes are involved. Based on the most recent neurological model developed by Beeman, Bowden and Kounios (Beeman & Bowden, 2000; Bowden, Jung-Beeman, Fleck, & Kounios, 2005; Kounios, Frymiare, Bowden, Fleck, Subramaniam, Parrish, & Jung-Beeman, 2006; Kounios & Beeman, 2009), the work presented in this thesis investigated how problem solving involves attention and the visual system (Chapter 1). Previous studies demonstrated distinct patterns of neural activity for solving verbal problems via insight compared to analysis, both immediately prior the solutions and in rest periods before the presentation of each problem (Jung-Beeman et al., 2004; Kounios et al., 2006). We examined eye movement and blink patterns associated with the two different problem-solving styles, and in accordance with previous behavioral and neuroimaging research, the patterns have been found to be distinct. Specifically, more frequent and longer-lasting blinks were recorded during the two-second preparatory period before subjects saw problems that they subsequently solved with insight, compared to problems they went on to solve analytically. In the two-second period immediately prior to achieving insight solutions, participants also blinked longer and made more fixations outside the problem area compared to the same period preceding analytical solutions. A follow-up experiment further confirmed these findings, showing a modulation of the incoming bottom-up visual information during the preparatory period. These results reinforce and supplement previous studies which suggested that insight is related to the attention system, which modulates eye movements and blinking patterns, and also indexes a discrimination of internal vs. external directed visual attention between the two solving styles (Kounios & Beeman, 2009; Wegbreit, Suzuki, Grabowecky, Kounios, & Beeman, 2012). In Chapter 2, new data supplements Beeman, Bowden and Kounios’s model by detecting the influence of problem-solving styles on solution accuracy. The data obtained from four different types of problems demonstrated that solutions achieved via insight are more likely to be correct than answers achieved via analysis. Several potential explanations of the result are discussed, foremost the idea that insight depends on the integration of multiple weak associations; i.e., weak associations of problem related info to the solution, which summate boost the activation of the solution concept into consciousness. Thus, when the solution does emerge, it is necessarily correct. Taken together, these results indicated that insight problem solving is promoted by the gating of visual inputs. This diminishes external noise and directs attention inwardly, to facilitate answer accuracy. Furthermore, in order to allow the study of insight problem solving in Italy, the Italian language version of two of the most used tasks to study insight problem solving, has been tested and validated. In particular we benchmarked the Compound Remote Associates (CRA) word problems (Bowden & Jung-Beeman, 2003a) and the Rebus Problems (MacGregor & Cunningham, 2008).