Crea.Blender: A Neural Network-Based Image Generation Game to Assess Creativity

We present a pilot study on crea.blender, a novel co-creative game designed for large-scale, systematic assessment of distinct constructs of human creativity. Co-creative systems are systems in which humans and computers (often with Machine Learning) collaborate on a creative task. This human-computer collaboration raises questions about the relevance and level of human creativity and involvement in the process. We expand on, and explore aspects of these questions in this pilot study. We observe participants play through three different play modes in crea.blender, each aligned with established creativity assessment methods. In these modes, players "blend" existing images into new images under varying constraints. Our study indicates that crea.blender provides a playful experience, affords players a sense of control over the interface, and elicits different types of player behavior, supporting further study of the tool for use in a scalable, playful, creativity assessment.Comment: 4 page, 6 figures, CHI Pla

The Body Speaks: Using the Mirror Game to Link Attachment and Non-verbal Behavior

The Mirror Game (MG) is a common exercise in dance/movement therapy and drama therapy. It is used to promote participants’ ability to enter and remain in a state of togetherness. In spite of the wide use of the MG by practitioners, it is only recently that scientists begun to use the MG in research, examining its correlates, validity, and reliability. This study joins this effort by reporting on the identification of scale items to describe the non-verbal behavior expressed during the MG and its correlation to measures of attachment. Thus, we explored the application of the MG as a tool for assessing the embodiment of attachment in adulthood. Forty-eight participants (22 females, mean age = 33.2) played the MG with the same gender-matched expert players. All MG were videotaped. In addition, participants were evaluated on two central measurements of attachment in adulthood: The Adult Attachment Interview (AAI) and the Experience in Close Relationship questionnaire (ECR). To analyze the data, we developed the “MG scale” that coded the non-verbal behavior during the movement interaction, using 19 parameters. The sub-scales were reduced using factor analysis into two dimensions referred to as “together” and “free.” The free factor was significantly correlated to both measurements of attachment: Participants classified as having secure attachment on the AAI, received higher scores on the MG free factor than participants classified as insecure [t(46) = 7.858, p = 0.000]. Participants, who were high on the avoidance dimension on the ECR, were low on the MG free factor [r(48) = −0.285, p = 0.007]. This is the first study to examine the MG as it is used by practitioners and its correlation to highly standardized measures. This exploratory study may be considered as part of the first steps of exploring the MG as a standardized assessment tool. The advantages of the MG as a simple, non-verbal movement interaction demonstrate some of the strengths of dance/movement and drama therapy practice

leaps_online_data_0.zip

Raw-data and data description for the following paper:<div><br></div><div> p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 11.0px Helvetica} p.p2 {margin: 0.0px 0.0px 0.0px 0.0px; font: 10.0px Helvetica} span.s1 {font: 6.5px Helvetica} <p><b>Creative Foraging: an Experimental Paradigm for Studying Exploration and</b></p> <p><b>Discovery</b></p> <p><br></p><p>Yuval Hart, Avraham E Mayo, Ruth Mayo, Liron Rozenkrantz, Avichai Tendler, Uri Alon*, and Lior Noy*</p><p><br></p><p>PLOS ONE, 2017 (Under Review)</p></div><div> </div

Mirror Game - CC Measure

Code used in the Mirror Game project to automatically detect Co-Confidence (CC) motion. See details in (Noy, Binun &Golland, 2015).<div><br></div><div>The code is published here in order to describe the exact details of the alogrithm. Some of the functions might need other toolboxes to run. I tried to put all needed functions here, contact me if something is missing.</div><div><br></div><div>The two main function that compute the CC detection are:</div><div>MG_Tools_CC_AZC</div><div>MG_Tools_Segments_Acc_Zero_Cross</div><div><br></div><div>See inside the code for detailed information</div><div><br><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div>Ref:</div><div><br></div><div><h3>L. Noy, N. Levit-Binun, and Y. Golland, “Being in the zone: physiological markers of togetherness in joint improvisation”, <b>Frontiers in Human Neuroscience</b>, 9:187, <b>2015</b> <br></h3></div></div

Mirror Game Wavelet-Complexity Measure

This is part of Matlab code used ot analyze 1D Mirror-Game data. These functions performed the 'wavelet-complexity-raio' compuation. See details in Noy, Dekel, Alon, PNAS 2011.<div><br></div><div>This code use Matlab-Wavelet-Toolbox</div><div><br></div><div>Copyright (c) 2008 Gabriel Peyre<br></div><div><br></div><div>https://www.mathworks.com/matlabcentral/fileexchange/5104-toolbox-wavelets?focused=5134594&tab=example<br></div

round table modelling Joint Action and Creativity: Round table, Invited

International audienc

Social Interaction Dynamics Modulates Collective Creativity

In an experimental study, we investigated how social interaction dynamics affect collective creativity. Pairs of participants collaborated in a computer game, creating “beautiful and interesting” shapes by moving tiles on a large touchscreen. We identified naturally emerging interaction styles by applying k-means clustering on participants’ tile moves. The game allowed us to quantify the unfolding creative process in a well-defined search space. Pairs characterized by a single dominating member tended to visit fewer areas of the solution space, stay there longer and created on average more (but less original) shapes. In contrast, pairs that took turns with every tile move tended to explore more, stayed in each area of the solution space for less time and created fewer (but more original) shapes. While previous literature found conflicting effects of ‘creating with another’, the current paper suggests naturally emerging interaction styles as a differentiating factor underlying how collective creativity unfolds