Two Tracks of Thought: A Structural Model of the Test for Creative Thinking-Drawing Production (TCT-DP)

Nogueira, Sara Ibérico; Almeida, Lurdes; Lima, Tiago Jessé Souza de

doi:10.1080/10400419.2017.1303312

Cited by 13 publications

(9 citation statements)

References 16 publications

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“…TCT-DP total score was also similar between conditions, F(2, 53) = .09, p = .918. Despite no differences between conditions, normative values for this test showed that children were between the 50 and the 70 percentile, denoting a medium-high of graphic-figural creativity levels after intervention [57].…”

Section: H1mentioning

confidence: 76%

“…The scoring of the final drawing of children is made according to 14 categories for evaluating figural-graphic creativity, detailed in the test's manual [22]. The final score is divided into the dimensions of Adaptiveness (related with conventional ways of thinking), Innovativeness (related to unconventional ways of thinking, such as breaking of limits, perspectives, and humor), and a total score consisting of a sum of the previous ones [57].…”

Section: Test For Creative Thinking-drawing Production (Tct-dp)mentioning

confidence: 99%

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Creativity Encounters Between Children and Robots

Alves‐Oliveira

Arriaga

Cronin

et al. 2020

Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

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Creativity is an intrinsic human ability with multiple benefits across the lifespan. Despite its importance, societies not always are well equipped with contexts for creativity stimulation; as a consequence, a major decline in creative abilities occurs at the age of 7 years old. We investigated the effectiveness of using a robotic system named YOLO as an intervention tool to stimulate creativity in children. During the intervention, children used YOLO as a character for their stories and through the interaction with the robot, creative abilities were stimulated. Our study (n = 62) included 3 experimental conditions: i) YOLO displayed behaviors based on creativity techniques; ii) YOLO displayed behaviors based on creativity techniques plus social behaviors; iii) YOLO was turned off, not displaying any behaviors. We measured children's creative abilities at pre-and post-testing and their creative process through behavior analysis. Results showed that the interaction with YOLO contributed to higher creativity levels in children, specifically contributing to the generation of more original ideas during story creation. This study shows the potential of using social robots as tools to empower intrinsic human abilities, such as the ability to be creative.

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Section: H1mentioning

confidence: 76%

Section: Test For Creative Thinking-drawing Production (Tct-dp)mentioning

confidence: 99%

Creativity Encounters Between Children and Robots

Alves‐Oliveira

Arriaga

Cronin

et al. 2020

Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

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“…Results reveled no significant result in figural creativity levels for the robot condition (Mean rank = 14.19; M = 25.57, SD = 7.86) compared to the tablet condition (Mean rank = 17.00; M = 28.29, SD = 8.90), U = 91.000, p = 0.400, r = −1.60. Additionally, we explored two additional dimensions of the TCT-DP introduced by Nogueira (2017), namely conventional and the non-conventional thinkings styles in creativity [50]. The reliability of the items for the non-conventional thinking was poor, p = 0.475, and questionable for the conventional thinking p = 0.688.…”

Section: Resultsmentioning

confidence: 99%

Sparking Creativity with Robots: A Design Perspective

Alves‐Oliveira¹,

Tulli²,

Wilken³

et al. 2019

Preprint

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We present the design and evaluation process of a robot aimed at stimulating creativity in humans in a collaborative drawing-oriented task. Collaboration towards a common goal is known to be one of the most influential factors for creativity nourishing. The social robot acted as a collaborative peer in a drawing activity, taking turns with the human, to complete their drawing. In each turn, both "artists" (the robot and the human) draw something new until they achieved their final drawing. The goal was to study if a robot, in comparison with a tablet, can stimulate creativity. A total of 28 participants were distributed across robot and tablet conditions. Creativity scores for each drawing were calculated. Contrary to what was expected, the creativity score did not present a statistically significant main effect across conditions. We discuss our results under a design perspective, addressing the role of the robot’s embodiment and presence, as well as the purpose of use for robots in society and what implications there is for design.

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“…The TCT-DP also includes a score for the time taken to complete the test (i.e., speed). However, many of the sample images in the available in the test manual, and indeed, many empirical studies (e.g., Dollinger et al, 2004; Nogueira et al, 2017) have excluded speed, and this criterion was excluded from the development of the CNN. TCT-DP tests scores range from 0 to 72 (when the speed is included) or 0 to 66 (when speed is excluded).…”

Section: Methodsmentioning

confidence: 99%

Automated scoring of figural creativity using a convolutional neural network.

Cropley¹,

Marrone²

2022

Psychology of Aesthetics, Creativity, and the Arts

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One of the abiding challenges in creativity research is assessment. Objectively scored tests of creativity such as the Torrance Tests of Creativity and the test of Creative Thinking-Drawing Production (TCT-DP; Urban & Jellen, 1996) offer high levels of reliability and validity but are slow and expensive to administer and score. As a result, many creativity researchers default to simpler and faster self-report measures of creativity and related constructs (e.g., creative self-efficacy, openness). Recent research, however, has begun to explore the use of computational approaches to address these limitations. Examples include the Divergent Association Task (Olson et al., 2021) that uses computational methods to rapidly assess the semantic distance of words, as a proxy for divergent thinking. To date, however, no research appears to have emerged that uses methods drawn from the field of artificial intelligence to assess existing objective, figural (i.e., drawing) tests of creativity. This article describes the application of machine learning, in the form of a convolutional neural network, to the assessment of a figural creativity test-the TCT-DP. The approach shows excellent accuracy and speed, eliminating traditional barriers to the use of these objective, figural creativity tests and opening new avenues for automated creativity assessment.

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Two Tracks of Thought: A Structural Model of the Test for Creative Thinking-Drawing Production (TCT-DP)

Cited by 13 publications

References 16 publications

Creativity Encounters Between Children and Robots

Creativity Encounters Between Children and Robots

Sparking Creativity with Robots: A Design Perspective

Automated scoring of figural creativity using a convolutional neural network.

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