Thought processes of seven artist-level jazz musicians, each of whom recorded an improvised solo, were investigated. Immediately after completing their improvisations, participants listened to recordings of their playing and looked at the notation of their solos as they described in a directed interview the thinking processes that led to the realization of their improvisations. In all of the interviews, artists described making sketch plans, which outlined one or more musical features of upcoming passages. The artists also described monitoring and evaluating their own output as they performed, making judgments that often were incorporated into future planning. Four strategies used by the artists for generating the note content of the improvisations emerged from the analysis: recalling well-learned ideas from memory and inserting them into the ongoing improvisation, choosing notes based on a harmonic priority, choosing notes based on a melodic priority, and repeating material played in earlier sections of the improvisation.

Musical improvisation is one of the most complex forms of creative behavior, which offers a realistic task paradigm for the investigation of real-time creativity where revision is not possible. Despite some previous studies on musical improvisation and brain activity, what and how brain areas are involved during musical improvisation are not clearly understood. In this article, we designed a new functional magnetic resonance imaging (fMRI) study, in which, while being in the MRI scanner, advanced jazz improvisers performed improvisatory vocalization and imagery as main tasks and performed a prelearned melody as a control task. We incorporated a musical imagery task to avoid possible confounds of mixed motor and perceptual variables in previous studies. We found that musical improvisation compared with prelearned melody is characterized by higher node activity in the Broca's area, dorsolateral prefrontal cortex, lateral premotor cortex, supplementary motor area and cerebellum, and lower functional connectivity in number and strength among these regions. We discuss various explanations for the divergent activation and connectivity results. These results point to the notion that a human creative behavior performed under real-time constraints is an internally directed behavior controlled primarily by a smaller brain network in the frontal cortex.

Hundreds of educators contributed to writing and reviewing the new National Core Music Standards over two years of development. Thousands more will find their professional lives shaped by these standards over decades to come. Three individuals involved in standards development explain.

Building on previous work, which suggests that jazz improvisers insert patterns stored in procedural memory, a probabiMstIc model based on patterns from a corpus of Charlie Parker solos was developed and implemented. In previous analysis, patterns were detected in the corpus in significant proportions; however, the results of a parallel control situation showed minimal patterns. The control improvisation was generated by software based on grammars and contours, coincident with the cognitive position that emphasizes learned rule-based procedures in improvisation, as opposed to stored patterns. The present pattern-based improvisations, using our model, have graphs that coincide significantly with the actual human improvisation. Though briefly described earlier (Norgaard, Montiel, & Spencer, 20t3), the current article expands the theoretical foundation and adds methods for evaluating our algorithm using interval distributions and alternate corpora. Specifically, we show that the algorithm is capable of generating improvisations in fiddle and classical styles, demonstrating that the pattern-based algorithm is style independent. Our model shows much promise both for future research in the cognitive underpinnings of musical improvisation as well as for the development of software based on a stylistically appropriate concatenation of actual patterns.Perfortnance of preexisting tnusic and mtjsical improvisation both involve learned movements. However, during mu.sical improvisation, the exact configuration of those movements is determined in the moment. How is this accomplished? What information is stored in the improviser's brain that enables this complex behavior? One theory posits memorized schémas form the basis for the improvised output (Pressing, 1988), while a competing theory emphasizes learned rules (Johnson-Laird, 2002). The current project further explores these questions through the implementation of a computer algorithm for improvisation based on the principle advocated by Pressing. We compare output from our model with the results of a jazz analysis study as well as with the output of a competing model that uses a rule-based algorithm to generate melodies in a jazz style. In addition, we show that our algorithm is capable of generating melodic output in other styles given a corpus in that style.Pressing's (1988) model of the cognitive processes underlying improvisation is still widely cited (

Musical improvisation offers an excellent experimental paradigm for the study of real-time human creativity. It involves moment-to-moment decision-making, monitoring of one's performance, and utilizing external feedback to spontaneously create new melodies or variations on a melody. Recent neuroimaging studies have begun to study the brain activity during musical improvisation, aiming to unlock the mystery of human creativity. What brain resources come together and how these are utilized during musical improvisation are not well understood. To help answer these questions, we recorded electroencephalography (EEG) signals from 19 experienced musicians while they played or imagined short isochronous learned melodies and improvised on those learned melodies. These four conditions (Play-Prelearned, Play-Improvised, Imagine-Prelearned, Imagine-Improvised) were randomly interspersed in a total of 300 trials per participant. From the sensor-level EEG, we found that there were power differences in the alpha (8-12 Hz) and beta (13-30 Hz) bands in separate clusters of frontal, parietal, temporal, and occipital electrodes. Using EEG source localization and dipole modeling methods for task-related signals, we identified the locations and network activities of five sources: the left superior frontal gyrus (L SFG), supplementary motor area (SMA), left inferior parietal lobule (L IPL), right dorsolateral prefrontal cortex, and right superior temporal gyrus. During improvisation, the network activity between L SFG, SMA, and L IPL was significantly less than during the prelearned conditions. Our results support the general idea that attenuated cognitive control facilitates the production of creative output.