Understanding Exploratory Creativity in a Visual Domain Kyle E. Jennings,1,2 Dean Keith Simonton,2 and Stephen E. Palmer1 Departments of Psychology University of California, Berkeley (1) University of California, Davis (2)
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ABSTRACT
This paper describes a computerized aesthetic composition task that is based on a “creativity as search” metaphor. The technique collects detailed, moment-to-moment data about people’s search behavior, which can help open the “black box” that separates independent variables that influence creativity from their outcomes. We first describe the technique and provide a detailed theoretical framework. Then, we discuss how the technique is typically applied, describe several in-progress studies, and present some preliminary results. Finally, we discuss relations to other work, limitations, and future directions. We argue that this technique and the research that it enables will facilitate a deeper understanding of the creative process, become a valued tool for creativity researchers, and contribute to methodological and theoretical advances in how creativity is studied and understood. Author Keywords
creativity, search, optimization, experimental methods ACM Classification Keywords
J.4 Computer Applications: Social and Behavioral Sciences—Psychology General Terms
Experimentation, Measurement, Theory BACKGROUND
The creative process can be thought of as the search for an ideal solution to a problem. One way to understand creativity is to understand this search process. This paper describes an under-development research technique that is based on the “creativity as search” metaphor. By capturing detailed information about the moment-to-moment progress of a person’s creative search, this technique can both test models of creativity as search, and enable more detailed studies of common phenomena in the creativity literature.
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Search can either be seen as finding a path from a starting state to a valid end state, or as finding the best (or nearly best) solution from among many others. The former case, which we call path search, is relevant when the desired outcome is known but the way to reach it (the path) is not (e.g., proving a mathematical theorem), and has been studied extensively [19, 20, 22]. The latter case, which we call place search, applies when the desired outcome (the place) is unclear but the means for achieving it are relatively straightforward, such as when an artist arranges a still life or composes a photograph. Particularly given that the creativity of artists depends more on these early decisions than on the execution of these decisions [10], this research focuses on how people select the desired outcome from among competing alternatives. In open-ended domains like the arts, choosing what outcome (or solution) to pursue is seldom a simple matter of deciding among a few choices. Instead, the set of possibilities is too vast to be considered simultaneously, meaning that searchers must iteratively consider subsets of the space. How people control this iterative process can be called a search strategy, and includes considerations such as how people move from one subset to another, and how people evaluate each solution. Though search strategies might be an important determinant of how creative the final solution is, they are not directly observable. However, if the options under consideration at each stage can at least partially be observed, it becomes possible to trace how people move through the space of possibilities over time. This search trajectory offers clues as to what kind of search strategy people use. We have developed an experimental paradigm that can efficiently capture detailed information about people’s search trajectories in a visual domain. Using this technique, we aim to determine: • what can be learned about people’s search strategies from their search trajectories; and, • whether differences in solution creativity can be traced to differences in search strategies. We also aim to apply our technique to better understand broader questions, such as how personality and situations affect people’s creative processes. Our technique should help open the creative process “black box” that sits between the independent and dependent variables in much of this research.
Satisfying the above aims requires a research method that can track people’s search trajectories. Because studies of personality and situational influences require large samples, this method must be economical to apply. Existing methods cannot satisfy both of these requirements [15], leading to the development of the present technique.
analyzing the final solution. Our tool expands this chain to encompass problem → creative process → search trajectory → solution. However, the process that produces the trajectory is still unknown. In order to make inferences about the process, we will first expand our model of the problem, and then consider the nature of the process more thoroughly. (See Figure 4 for the complete model.)
Structure of Present Technique
Search Landscapes and Landscape Topology
Our technique centers around a computerized aesthetic composition task. In this task, participants see a picture with several three-dimensional objects. They can manipulate the picture by changing the placement of the camera or the light source, both of which are on fixed orbits around the objects (see Figure 1). The moment-to-moment positions of the camera and light constitute the search trajectory. Because the objects reflect and refract the light, the task is more complex than it first appears (see Figure 2 for examples). Though we will eventually add more control dimensions, the current two-dimensional trajectories are easily plotted, which is helpful as we check our quantiative metrics against our visual intuitions.
Our software allows transitions to adjacent camera and light positions, but not large jumps. Therefore, we can imagine the search process using the metaphor of a hiker traversing mountainous terrain, which we will call a (search) landscape. The hiker’s longitude and latitude correspond to the camera and light angle of the current view, and the hiker’s elevation corresponds to how well the resulting image satisfies the search goal. Finding the best camera and light angles becomes analagous to finding the highest point on the landscape. However, dense fog limits visiblity and the hiker has no map, making nearby elevation changes the only way to navigate.
NEED FOR A NEW INSTRUMENT
Landscape Fogginess and Blind Variation
OBJECT
Figure 1. Illustration of camera and light configuration.
Relation to Creativity
Our task is similar to what an artist or photographer must do when composing a still life painting or photograph. Just as paintings and photographs will vary in their creativity, our participants’ final images will vary in their creativity. While our task is not as open-ended as its real-world analog, participants still have latitude in how they interpret the goal (e.g., what “harmonious” or “artistic” means in the context of the scene), and in how diligently they search for an ideal configuration. We believe that our task encompasses more of the creative process than insight or divergent thinking tasks, and that the loss in ecological validity relative to tasks used with holistic assessment or protocol analysis is outweighed by the gains in precision, consistency, and accuracy. THEORETICAL MODEL: OPENING THE BLACK BOX
A great deal of creativity research can be thought of as inputting a problem into a creative process “black box” and
Using the landscape metaphor, consider how problem structure will influence strategy choice. Suppose that the landscape is covered in such thick fog that there is zero visibility. In this case, the hiker must actually move to a new position in order to determine the landscape’s shape. Which direction to travel would largely be a matter of guesswork, leading to many false starts and a meandering trajectory. If instead the fog were very light, the hiker could move deliberately toward higher terrain, resulting in a more direct trajectory. The above two strategies vary in how “blind” or “sighted” their transitions are, a distinction introduced by Campbell [6] and further elaborated by Simonton [26, 27, 29] and others [21, 23, 31]. Countering the idea that creators always move deliberately toward their final solution, this Blind Variation and Selective Retention (BVSR) theory suggests that human creators often make progress by blind trial and error.1 Domains differ in how much “blindness” there is to the creative process, which may be inversely related to how constrained the domain is [28]. Artistic domains, which are much less constrained than scientific or engineering domains, are likely to involve a lot of blindness. For example, studies of Picasso’s sketches for his painting Guernica show that they do not move monotonically toward the final painting [8], suggesting a trial and error component to his method. Several things affect the “fog”, and hence expected variational blindness, in our experimental task. Scenes may vary in how much images at nearby perspectives or nearby light angles resemble each other, or in how easy it is for participants to imagine how the image would look from a different perspective without actually seeing it. Even if it is 1 Blindness is different than randomness. A variation process is blind if a desirable variation has as much chance of being produced as an undesirable variation. Thus, both “brute force” processes (trying each variation in order, as in a radar sweep), and purely random processes are blind, but not all blind processes are random. [30]
Figure 2. A variety of views from our most commonly-used scene.
easy to imagine the image itself, people may have a harder time evaluating an imagined image when given one goal (e.g., “harmonious”) than another (e.g., “aesthetically composed”). We can also reduce fogginess via the software interface by providing previews of images at adjacent camera and light angles. Landscape Ruggedness and Blind Retention
BVSR theory assumes that creators make variations blindly, but that desirable variations are selected deliberately. This may not be an optimal strategy, as illustrated by the landscapes in Figure 3. In the landscape on the left, the hiker can simply move uphill and be confident that the point where every adjacent step goes downhill is the best. In the landscape on the right, a hiker using this strategy might stop at the top of a molehill (called a local maximum) rather than the tallest mountain (the global maximum). We will use the term rugged to describe a landscape with many local maxima. How rugged a landscape is depends on the structure of the problem itself. A simple problem easily reveals the path to the best outcome and plays no tricks, whereas a hard problem tempts people toward decent outcomes, but hides the best outcomes.2
Figure 3. Smooth (left) and rugged (right) landscapes.
To understand how creators might move past local maxima, we can look at numerical optimization algorithms in computer science. Many optimization problems have numerous local maxima. Whereas algorithms that are guaranteed to find the global maximum are often prohibitively slow (not just mildly—their execution time grows exponentially with the problem size), several strategies have been devised for 2 The distinction between a rugged and smooth landscape is similar to Perkins’ [24] distinction between reasonable problems and unreasonable problems, though Perkins’ work deals with path searches (as in Newell and Simon, et al.) instead of place searches, as we are discussing here.
moving past inferior local maxima and toward higher maxima. Most of these strategies, known as metaheuristics, work by adjusting the algorithm’s balance between diversification (exploring a broad range of solutions) and intensification (incrementally improving the current solution) [4]. Campbell’s theory of blind variation and selective retention assumed that only the variation process was blind. However, metaheuristics work by making blind retentions as well. That is, they sometimes retain changes that make the solution worse, the aim being to facilitate movement away from a local maximum and toward a higher maximum. (These decisions are blind since the downhill moves are not guaranteed to lead toward higher maxima.) We expect to find similar dynamics in human creative search, allowing us to broaden Campbell’s theory to encompass both blind variation and blind retention. Inclusion of Landscapes in the Model
Knowing the structure of the search landscape helps us make stronger inferences about people’s search strategies. We can measure landscape fogginess on a per-participant basis by having participants rate a target image, and then predict their ratings for (unseen) adjacent images. These predictions can be compared to actual ratings, collected later. We can also assess landscape ruggedness using a broad sampling of ratings from each participant. We can now expand our model to include landscapes (see Figure 4). We first note that problem structure depends on the search interface, the goal, and the scene, all of which affect the ruggedness and fogginess of the search landscape, and all of which we control. These features of the search landscape, as well as the interface itself, should influence the search strategy that people choose, which should be reflected both in the search trajectory and the solution. We have good visibility into the search trajectory and partial visibility into the search landscape, and control the three aspects of the problem itself. This knowledge expands our ability to understand search trajectories, and to reverse engineer the remaining black box of search strategy. Translating Goals to Criteria
Our model thus far assumes that people are able to directly and reliably evaluate the particular objective that we give
Problem Interface
Search Strategy Interpretation Strategy
Exploration Strategy
Goal Scene
Criteria
Creative Process Search Landscape
Search Trajectory
Solution
Figure 4. Illustration of theoretical model used in this research.
them. While this may be true with very concrete objectives (e.g., finding the brightest image), the objectives we more typically use are much more open-ended (e.g., finding the most artistic, harmonious, or captivating image), which means that the participant must decide how he or she wishes to interpret the objective. We call the high-level objectives that we give our participants goals, and refer to the concrete translation of goals into features of the scene as criteria. The criteria that people use are likely to vary across individuals, and even to evolve during the course of each individual’s search. To accommodate criteria, we expand our model in two ways. First, we note that people’s search strategies will have two components: an interpretation strategy, which is how people translate goals into criteria, and an exploration strategy, which is how people move through the search landscape. Second, we note that we can ask people to describe their criteria in order to better understand both kinds of strategies. This is reflected in Figure 4. Thus, people engaged in our task are actually completing a dual-space search, encompassing a criterion space (how the goal is interpreted) and a solution space (the specific views of the scene). This is analogous to models of scientific problem solving [18] and permits creativity both in how the goal is interpreted, and in the specific solution that is found. People’s criteria help determine the shape of the landscape, and hence the difficulty of their search over solution space. One way that difficulty can increase is via conflicts between criteria. For instance, people may wish to find bright, symmetric images, but the structure of the scene may be such that the brightest images have a glare that detracts from the perceived symmetry. Such conflicts may emerge because the dimensions that people can control (camera and light angle) are irregularly related to the dimensions that people use to evaluate the image (e.g., symmetry and brightness). The difference between control and evaluation dimensions is analogous to the difference in genetics between genotypes (the genetic code for an individual) and phenotypes (characteristics affecting fitness). In genetics, epistasis refers to interactions among genes that lead to, say, the suppression of one gene’s effect by the presence of the other. Just as epistasis complicates the course of evolution [17] we believe that our participants’ task will be made more difficult via interactions and conflicts between control and evaluation dimensions. These
interactions should complicate the search problem by introducing local maxima into the landscape [17, 1]. Model Summary
To restate our model (see Figure 4): • We provide participants with a problem, which consists of a goal to be satisfied, the scene that they will use, and an interface that constrains what information is available and what kinds of changes can be made • We can directly measure people’s solution (their final image), as well as the detailed search trajectory that got them there • We can partially measure criteria and landscape—partial because we may not be able to measure these things in their entirety, and may not be able to measure all changes to these things throughout the search • From this information, we hope to make inferences about people’s search strategies, which includes their interpretation strategy and their exploration strategy Guiding our efforts to understand these strategies is the view that landscape fogginess and ruggedness affect the strategies that people use, which we believe will incorporate degrees of both blind variation and blind retention. Finally, an important determinant of landscape ruggedness is the degree of epistasis that exists due to the choice of criteria. TYPICAL EXPERIMENTAL SETUP
Our typical experiment involves an exploration phase, a criteria description phase, and a landscape rating phase. The exploration phase comes first so that participants are unfamiliar with the scene as they attempt to achieve the given goal. The criteria description and landscape rating phases usually occur in the order listed, though in principle they could be reversed. Exploration Phase
In a typical experiment, participants are introduced to the setup of the scene using a diagram like that shown in Figure 1. Then, participants become acquainted with the exploration interface using a trial scene. Following the trial, participants are given their goal and explore the target scene.
We have used various scenes. As each consists of a potentially infinite number of variations, we naturally must choose a subset. Our first experiment [15] used every combination of 90 evenly-spaced camera and light angles. We have used smaller subsets in other experiments, for two practical reasons. First, we have conducted some studies online, which limits how many pre-rendered images we can use (for instance, a low-resolution, 10 by 10 subset of our main scene requires seven megabytes). Second, in some experiments we later get ratings of all images in the scene, again placing an upper limit on the total number of images we can include. We have used two kinds of exploration interfaces. The single-image interface presents one view of the scene, which participants can change by adjusting the camera and light angle through some control mechanism. In our first experiment [15], the controls were virtual “dials” that were manipulated with the mouse. Because this interface made people reluctant to switch between varying camera and light angle, subsequent experiments have presented a compass rose, where the north-south axis controls camera or light, the east-west axis controls light or camera, and the diagonal axes control both. This interface encourages more switching between dimensions, but is only suitable for smaller subsets. Recently, we have created a hardware controller with two physical dials that we hope will encourage frequent switching between dimensions (Figure 5). The controller also has up and down buttons, which we will eventually use to control a “zoom” dimension.
tures of the scene. To assist them in this description process, a slide show of a representative subset of the scene plays on the side of the screen as they are writing. We typically require participants to spend at least two minutes on this process. We also plan to develop a standardized, Likert-type questionnaire containing the most common criteria that people have mentioned in other studies. Landscape Rating Phase
To measure the coutours of the search landscape (the elevation of each position, in our hiking analogy), we again have two techniques. Our first technique is to ask participants to rate a standard subset of images from the much larger set that is available (e.g., a 5 by 5 subset). In studies where participants are limited to a small subset of the space, we can get ratings of the entire space. Typically, participants are first shown a movie that cycles through all of the images in the subset for one second each, with smooth transitions in between. When practical, participants rate all of the images twice, in two different random orders. This allows us to estimate how reliably people can make the ratings, which we have found varies across goals. Sometimes we may wish to have more detailed ratings for specific regions of the space. For instance, in our first experiment, we tabulated the amount of time each participant spent in various regions of the space, and determined the ten most visited regions. Then, participants were asked to rank and then rate each of these ten images. This technique allows us to look for features such as local maxima in the most relevant regions of the space. Our software can analyze people’s search trajectories and select the appropriate images during the course of the experiment. Technical Details
Our software is either written in the Processing language (for laboratory applications), or for modern web browsers using the jQuery library and an XML and PHP backend. Our hardware interface is based on an Arduino microcontroller. Analysis, including automated analyses done during the experiment, are performed using the R statistical software. Our stimuli are rendered using POVRay. ANALYSIS TECHNIQUES Figure 5. Hardware controller.
Our grid interface presents a three by three array of images. The center image is the current image, and the surrounding images are a single step difference in camera and/or light angle. In some cases, the adjacent images are obscured until clicked on. Participants then select a new “current” image by double-clicking one of the variations. This allows us to explicitly measure how people explore the neighborhood of the current position in the landscape. Criteria Description Phase
At present, we measure criteria by asking participants to write open-ended descriptions of how they interpreted the goal. We encourage people to be concrete, referring to the camera and light angles, as well as the objects and visual fea-
Our technique offers the mixed blessing of incredibly detailed data. This section provides a sampling of our ongoing efforts to develop a standardized analysis approach. What is “Creative”?
Researchers generally define something as creative if it is both “novel” (meaning statistically rare) and “appropriate” (meaning that it adequately addresses the problem at hand). For each scene-goal combination that we use, we can measure novelty by keeping track of the relative frequency of different final points across studies. For appropriateness, creativity researchers generally use a permissive standard of only excluding outright inappropriate responses (e.g., answering “strawberries” when listing things that are blue). In our case, we can safely conclude that common solutions are unlikely to be inappropriate. For uncommon solutions,
we have raters who are blind to study conditions determine whether the solution satisfies the given goal, and ask them to be open to metaphorical or unusual interpretations of the goal. Only if a solution fails by this standard will we exclude it. This being done, we operationalize creativity as statistical unusualness relative to norms that we are accumulating. Characterizing Trajectories
At present, we characterize trajectories with aggregate metrics, such as the proportion of space explored, the average rate of exploration, the proportion of explored space that was revisited, the number of times that the trajectory doubles back (an indication of uncertainty), and so on. We also plan to use multivariate time series analysis in order to look at things such as changes in rate over time, which may indicate diversification and intensification. We are also examining how spatial trajectories are analyzed in zoology and other disciplines.
• Modeling Exploration Strategies — here the goal is to “reverse engineer” people’s exploration strategies using a combination of behavioral research and computer simulations • Characterizing Interpretation Strategies — this aspect involves characterizing the nature of criteria change through the course of exploration, which will suggest questions for future research into interpretation strategies • Application to Common Phenomena — here we apply the technique to common findings from the creativity literature (e.g., [11, 13, 2, 7]) Details of an earlier experiment can be found in [15]. In this section, we will discuss ongoing work and future plans in each of these areas. Connecting Goal and Scene to Criteria, Trajectory, and Solution
Coding Criteria Descriptions
We have developed a detailed coding language for translating open-ended criteria descriptions into standardized, embedded propositions. For instance “I liked images where the cone reflected on the sphere” would be translated as: PRESENCE of[REFLECTION of[Scene/Objects/Cone] on[Scene/Objects/Sphere]] is(+) In words, this translates to “the presence of a reflection of the cone on the sphere is good”. We are developing a software tool to facilitate detailed analyses of these propositional representations. Our aim is to use this language to build a comprehensive map of the criterion space that people are exploring, as well as techniques for characterizing movement through this space. This effort is related to separate work we have begun on characterizing idea generation as movement through a semantic space [14]. Analyzing Landscape Ratings
We analyze landscape ratings with three objectives. First, we aim to see how reliably people can rate different goals, which we currently operationalize as the correlation between the two sets of ratings we typically collect. Second, we aim to see how consistently different people rate the same goal, which we currently measure with the Intraclass Correlation Coefficient [25]. Third, we are interested in how different one person’s ratings are from other people’s ratings, which we operationalize as the correlation between that person’s ratings and the average ratings across all participants. We are also exploring more sophisticated measures that recognize differences in reliability across goals [3]. EARLY RESULTS
Our work on this project fits within four categories: • Connecting Goal and Scene to Criteria, Trajectory, and Solution — this work will test the predicted connections between the observable parts of our model
There are many questions that can be explored by focusing just on the observable parts of the model (that is, without reverse engineering search strategies). This approach includes mapping how different goals are translated into criteria, determining how criteria affect search landscape topology, looking for specific landscape features such as ruggedness and epistasis, looking for evidence of diversification and intensification in trajectories, and connecting differences in outcome creativity to trajectory features, landscapes, and criteria choice. Currently, we are analyzing data from a study that asked 40 participants to consider different goals and to (1) rate how well each of 25 images satisified that goal and (2) describe their criteria. We chose six different goals (including being “bright”, which is more accurately a criterion in our nomenclature, and serves as a control) that are meant to vary the subjectivity of the goal and the complexity of the resulting criteria (see Table 1). We also varied whether people looked at our standard, “complex” scene (Figure 2), or a “simple” scene where the objects were opaque and less reflective. Table 1 shows the correlations among the averaged effectiveness ratings of the image subset for the different goals, and Table 2 shows the averaged test-retest correlation, intraclass correlation coefficient, and averaged spatial auto-correlation for the six goals over the simple and complex landscape. 1. 2. 3. 4. 5. 6.
bright aesthetic artistic captivating convoluted harmonious
(1) .97 .61 .59 .95 -.36 .37
(2) .51 .90 .55 .76 -.76 .85
(3) -.73 -.01 -.10 .70 -.20 .19
(4) .54 .77 .05 .71 -.46 .49
(5) -.72 -.68 .47 -.57 .64 -.86
(6) .23 .77 .13 .55 -.70 .91
Table 1. Correlations of averaged goal ratings with respect to different goals. Entries above the diagonal are for the simple scene, entries below the diagonal are for the complex scene, and entries on the diagnoal are correlations across the simple and complex scenes for the same goal.
Perhaps unsurprisingly, bright is universally the most reliable and agreed-upon goal, and results in the smoothest land-
Test-Retest ICC Spatial Corr.
Simple Complex Simple Complex Simple Complex
Bright .91 .84 .86 .72 .78 .72
Aesthetic .64 .67 .08 .14 .28 .23
Artistic .36 .44 .06 .02 .07 -.02
Captivating .59 .63 .00 .19 .28 .49
Convoluted .49 .54 .08 .10 -.01 .14
Harmonious .64 .74 .14 .13 .18 .26
Table 2. Landscape statistics for different goals.
scapes. The other goals vary greatly in their reliability and smoothness, as well as in how distinct they are from each other. Interestingly, the simple landscape does not necessarily lead to more reliable ratings or smoother landscapes, which may in part be due to uncertainty about how to interpret some goals when the objects are all opaque. For instance, “convoluted” has a clear meaning in the complex scene, where the glass cone produces very distorted images, but may be harder to apply to the simple scene. We have coded the open-ended criteria descriptions from this study, and are currently analyzing them. After we have created and validated a closed-form questionnaire for eliciting people’s criteria, we plan to conduct a large-scale study to test the causal pathways implied by Figure 4. For instance, we expect to find that the originality of a person’s solution is related to how different that person’s search landscape is from the norm, which should itself be related to how unusual the person’s criteria are. Modeling Exploration Strategies
In our typical experiments, we expect both that criteria will differ across individuals, and that each person’s criteria will evolve over the course of the search. This makes it difficult to reverse engineer exploration strategies in great detail. However, relatively objective criteria (e.g., brightest, most symmetric) show less evidence of change and are judged more consistently across participants. This allows us to use standardized ratings over the entire landscape to better understand each step of a trajectory. Though using objective criteria may limit creativity, it enables us to make stronger inferences about exploration strategies, which will provide a baseline for understanding how people approach more openended goals. So far, our research has used the exploration interface that presents the current image and its eight neighbors. In such a setup, people’s behavior can be described by the following algorithm: x ← random starting point while stopping criteria not met do N ← neighborhood of x x ← select point from N ∪ {x} end while Our goal is to understand how people choose a neighborhood, select the next image, and decide to stop. We ran two experiments designed to test whether people use a “hill climbing” strategy, meaning the greedy strategy of always
selecting the best image in the neighborhood and stopping when the current image is better than all neighbors. In these experiments, the entire neighborhood was visible at all times. In our first experiment (N = 24), people looked for both the brightest and darkest images (order counterbalanced). Compared to simulations using a hill climbing algorithm, people’s path lengths were longer, though their solutions fit with a hill climbing approach. We also found a great deal of variability in path length, but reasonable within-subjects consistency across searches (r = .47). We hypothesized that people were using a hill climbing algorithm, but evaluated images with error and varied in the size of neighborhood they considered. In the second experiment (N = 24), we first simulated a search for the most symmetric and asymmetric images, using algorithms that considered various neighborhood sizes, and made evaluations with error proportional to the variability of pilot ratings of each image’s symmetry. The experiment’s results matched the simulations in most regards, but we found that while our simulations predicted longer paths for the asymmetric goal than the symmetric goal (because the former was harder to evaluate), our data showed the opposite. We are running a follow-up experiment that requires participants to click on images in the neighborhood to expose them, which will explicitly test our hypotheses about variable neighborhood size and allow us to model stopping criteria. Our plan is to continue modeling search strategies based on data from the grid interface, and then to begin modeling search strategies based on data from the single-image interface. We also plan to test searches with multiple criteria, and then to expand in the direction of searches using more open-ended criteria. Characterizing Interpretation Strategies
We are presently collecting data for a study where participants rate an image subset on four goals (bright, aesthetic, artistic, and captivating) on two consecutive days. On both days, participants rate the images in two random orders. The purpose of this study is to disentangle the unreliability of individual ratings from actual changes in how people evaluate the goals over time. By gauging the temporal stability of these goals, we can better understand how large a role interpretation strategies play in people’s exploration processes. Moving forward, we plan to use a variety of methods (e.g., think-aloud, surprise interruptions) to capture criteria change over the course of exploration. Our aim is to discover triggers and indicators of criteria change, and then to conduct studies that induce change.
Application to Common Phenomena
Replicating common phenemena from the creativity literature serves two purposes. First, it establishes that our technique does indeed allow room for the sort of creativity that has been studied in previous research. Second, our technique can be used to expand understanding of how various known influences on creativity have their effects.
Outside of the creativity literature, Hills, Todd, and Goldstone [12] have drawn connections between foraging behavior in animals and both spatial and conceptual foraging behavior in humans. We believe that their work has useful connections to ours, but see foraging, for which the goal is to accumulate resources, and optimization, for which the goal is to maximize an objective function, as entailing different normative considerations. Our work is also relevant to work in computational creativity, which encompasses both computer simulations of creative cognition and creative societies, and programming creative artificial intelligence. As we better integrate our work with theories from computer science, we hope to be able to build stronger connections between psychology and computational creativity research.
Goal
95.5
Artistic Aesthetic
95.0
Mean Originality
96.0
One early result in the divergent thinking literature is that simply telling people to “be creative” on a creativity test will increase their scores [11]. We ran a study (N = 189) where we manipulated the instructions (control and “be creative”) and goal (finding the most “aesthetically composed” or “artistic” image). We found that while instructions had no effect in the “aesthetically composed” condition, the “be creative” instructions significantly increased creativity in the “artistic” condition (see Figure 6). We are planning followup work to better understand this dissociation.
Finke, Ward, and Smith [9] conducted extensive research culminating in what they called the geneplore model (for generate and explore). Their work focused on how people’s creative processes involve generating so-called “preinventive forms”, and then exploring the possible desirable outcomes that can arise from combining these forms. Though their approach is both as comprehensive and less constrained than ours, their methodology is time-intensive to apply, and they do not offer as detailed a theory of how people coordinate their cognitive processes as we aim to produce.
94.5
Limitations
Control
Be Creative Instructions
Figure 6. Results for “be creative” study.
Moving forward, we plan to explore other independent variables, such as positive affect [13] and intrinsic motivation [2]. We also expect to find more interesting connections between personality and search strategies, both with high-level predictors such as the Big Five [16], and with more specific individual differences like latent inhibition [7]. GENERAL DISCUSSION Relation to Other Work
We are not the first to view creativity as a search process. As already mentioned, Simon and colleagues have done extensive work on what we call path search, wherein the task is to reach a predetermined solution (or class of solutions) to a problem (e.g., [20]). However, this work has been criticized for neglecting problem identification or framing (see, e.g., [5, 24]), which we address in part by considering criteria change. Also, the earlier work focuses on formal domains such as math and science, whereas we focus on a visual, aesthetic domain.
We acknowledge that our technique taps into a restricted form of creativity. Though broader in scope than some techniques (e.g., insight tasks), it is certainly more constrained than others (e.g., holistic assessement), and vulnerable to the criticism that our participants—who, after all, are selecting one from among many pre-rendered images—are not in fact making anything new. We therefore acknowledge that this task only taps exploratory creativity, and not transformational creativity [5], though we are sympathetic to the argument that exploratory creativity isn’t necessarily easy [32]. We also acknowledge that while our task may be an externally valid proxy for some kinds of creativity (e.g., still-life photography) and may generalize to less constrained aesthetic tasks, it may not be an appropriate model for scientific or engineering creativity. Despite these limitations, we believe that our work moves in several useful directions. First, we note that divergent thinking tasks, perhaps the most popular index of creativity, only tap a small part of the creative process and often lack external validity. Despite this, they have contributed a great deal of useful information about the influences on creativity, and continue to be valued for their practicality. Second, we believe that our technique’s limitations are outweighed by the depth of real-time information that can be gathered about people’s search behavior. We have constrained our participants so that we can automatically collect tractible, quantitative data. Once we have refined our analyses, we can expand the task dimensions that participants can control. In a similar vein, we hope that this technique will help create a broader approach to studying the entire creative process, which can
then be extended to any sort of problem in which movement through solution space can be tracked in real-time, and the outcome is a single solution. CONCLUSIONS
We hope that our work will lead to a more comprehensive understanding of how problem structure and search strategies affect creative outcomes. In so doing, we hope to help connect the theoretical work in psychology (BVSR theory) and computer science (metaheuristic optimization) that underlies our approach. As our technique matures, we also look forward to sharing our tools with other researchers, who will no doubt apply them to questions that we cannot anticipate. ACKNOWLEDGEMENTS
10. J. W. Getzels and M. Csikszentmihalyi. The Creative Vision: A Longitudinal Study of Problem Finding in Art. John Wiley & Sons, New York, 1976. 11. D. M. Harrington. Effects of explicit instructions to “be creative” on the psychological meaning of divergent thinking test scores. Journal of Personality, 43:434–454, 1975. 12. T. T. Hills, P. M. Todd, and R. L. Goldstone. Search in external and internal spaces. Psychological Science, 19(8):802–808, 2008. 13. A. M. Isen, K. A. Daubman, and G. P. Nowicki. Positive affect facilitates creative problem solving. Journal of Personality and Social Psychology, 52:1122–1131, 1987.
The authors acknowledge Matthew Peters and Melissa Lim for contributions to data coding and software (respectively), to Rosa Pogessi, Rodica Damian, and Palmer and Simtonton Lab research assistants for logistical assistance, and the hundreds of research participants who provided the data.
14. K. E. Jennings. Early results with faceted classification of “alternate uses” test responses. In C&C ’09: Proceeding of the seventh ACM conference on Creativity and Cognition, pages 383–384, New York, NY, USA, 2009. ACM.
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