Computational Paradigm to Elucidate the Effects of Arts-Based Approaches and Interventions: Individual and Collective Emerging Behaviors in Artwork Construction

Abstract

Art therapy, as well as other arts-based therapies and interventions, is used to reduce pain, stress, depression, breathlessness and other symptoms in a wide variety of serious and chronic diseases, such as cancer, Alzheimer and schizophrenia. Arts-based approaches are also known to contribute to one's well-being and quality of life. However, much research is required, since the mechanisms by which these non-pharmacological treatments exert their therapeutic and psychosocial effects are not adequately understood. A typical clinical setting utilizing the arts consists of the creation work itself, such as the artwork, as well as the therapist and the patient, all of which constitute a rich and dynamic environment of occurrences. The underlying complex, simultaneous and interwoven processes of this setting are often considered intractable to human observers, and as a consequence are usually interpreted subjectively and described verbally, which affect their subsequent analyses and understanding. We introduce a computational research method for elucidating and analyzing emergent expressive and social behaviors, aiming to understand how arts-based approaches operate. Our methodology, which centers on the visual language of Statecharts and tools for its execution, enables rigorous qualitative and quantitative tracking, analysis and documentation of the underlying creation and interaction processes. Also, it enables one to carry out exploratory, hypotheses-generating and knowledge discovery investigations, which are empirical-based. Furthermore, we illustrate our method's use in a proof-of-principle study, applying it to a real-world artwork investigation with human participants. We explore individual and collective emergent behaviors impacted by diverse drawing tasks, yielding significant gender and age hypotheses, which may account for variation factors in response to art use. We also discuss how to gear our research method to systematic and mechanistic investigations, as we wish to provide a broad empirical evidence for the uptake of arts-based approaches, also aiming to ameliorate their use in clinical settings.

Fig 1. The methodology’s architecture.

(A) The Computational Paradigm's and its comprising components. (B) The Computational Paradigm’s design of its processing modules and information flow.

Fig 2. The top-level model of the system.

The Statecharts visual formalism [46] exemplified in modeling the high-level state of the art room and three concurrent/orthogonal states (dashed lines) specifying the entities therein: the Artwork, Client (patient) and ArtTherapist. The figure also shows the events that trigger the beginning of the therapy session and its termination, specified as mutually exclusive states, ArtRoomSessionOn and ArtRoomSessionOff, respectively (with solid lines).

Fig 3. Hierarchical view of the system models.

The visual modeling of the system using the Statecharts formalism. (Top Panel) The Artwork model is a state entity in the art room, concurrent with other states there; i.e., the Client (patient) and ArtTherapist. (Middle Panel) The Artwork’s creator can be in one of three states: Painting, Materials_Selecting or Idle. (Bottom Panel) The Painting state is further decomposed into sub-states formulating the art creation process.

Fig 4. The paradigm applied to artwork.

An illustration of the technology used in artwork: Tracking, analyzing and documenting dynamic processes in a human subjects study. Marked in yellow are the methodology’s capabilities utilized in this work.

Fig 5. The table defining the parameters/metrics of the Artwork's construction process.

Fig 6. Visualization of the artwork construction dynamics.

(A) The visual report of the dynamics of an artwork progression, displaying rigorous tracking of events and states of the artwork over time. For example, stroke start and stroke end, the color and tool used for the stroke, its location in the page (in which quarter and whether in the boundary or center), the drawing pressure and direction. Idle periods are clearly seen, as well as erasing epochs of drawn objects. Snapshots of the artwork creation appear along the parameters temporal plotting. (B) Illustration of the interpretation of the visual report, which enables the reconstruction and formation sequences of objects in the artwork.

Fig 7. Quantification of the artwork construction dynamics.

The textual report of an art creation process dynamics. It shows the quantitative tracking and analysis of an artwork’s creation specified by the parameters' table of Fig 5 and their cross-section calculations (the respective visual report of the creation dynamics is shown in Fig 6).

Fig 8. The participants’ artworks in the study.

These are categorized according to the demographic attributes of gender and age. The ordinate is partitioned into males and females and the abscissa into age groups of participants in their 20s, in their 30-40s and in their 50s. The rows corresponding to (C) are the artworks of the warm-up calibration drawing task, (P) are the artworks of the positive feeling drawing task, (N) are those of the negative feeling drawing task, and (HTP) are those of the house-tree-person task. Each of the participant’s artwork are in his/her respective column.

Fig 9. Visualization of an art making process (a positive feeling).

A visual report of the creation process dynamics of an artwork imaging a positive feeling.

Fig 10. Visualization of an art making process (a negative feeling).

A visual report of the creation process dynamics of an artwork imaging a negative feeling.

Fig 11. An example of parameters/metrics comparison of an individual's artworks construction processes.

The processes compared are of the creation of a positive feeling image and that of a negative feeling.

Fig 12. Depiction of gender difference.

(A) Depiction of statistically significant mean differences in the gender study of the artworks. These have emerged for the mean drawing velocity, averaged over all artworks of males and females (n = 48), for mean percentage of time use of the blue color and of the pencil (n = 36), and for the mean number of tools used by both genders, depicted here as the percentage of total tools (n = 36). (B) The color and tool use of females, displayed as the percentage of drawing time. (C) The color and tool use of males. (D) Gender significance examples within specific drawing tasks. Mean difference in the number of tool switches has emerged for males and females imaging negative feeling. The artworks in the house-tree-person drawing task yielded mean differences in the average stroke size (n = 12). Data are reported as the mean ± SEM. *p < 0.05, **p < 0.01.

Fig 13. An example of artworks’ parameters/metrics construction processes comparison of female and male collectives.

Fig 14. Depiction of age-significant differences.

Differences in the means of parameters have emerged for participants in their 20s and in 50s. Mean differences are shown in the percentage of use time of the eraser (n = 18), and in the number of colors used, computed here as the percentage of colors used (n = 18). Data are reported as the mean ± SEM. *p < 0.05.