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Intuition, Pattern Recognition, and Heuristics
Over the past 30 years, research has revealed that much information processing takes places implicitly—without intent, awareness, or conscious reasoning—and this implicit form of knowledge plays a crucial role in thinking, reasoning, and creativity (Kihlstrom, 1987; Polyani, 1966; Wagner & Sternberg, 1985). This has led to dual-process theories, which distinguish between two types or systems of thinking (Evans, 2008; Kahneman, 2011).
System 1 processes operate fast, automatically, and are not dependent on slower, conscious, and voluntary control systems. System 1 processes include affect, pattern recognition, intuition, heuristics, implicit learning, and latent inhibition. These processes are fast, unconscious, effortless, and involuntary. In contrast, system 2 processes require attention, are associated with g, and are voluntary, supervised, executive functioning. These processes are slow, linear, conscious, and effortful, and they come into play when and if we bother to question and then check the output of the system 1 processes. In this section, we discuss the relation of creativity to the following system 1 processes: intuition, pattern recognition, and heuristics.
Much well-practiced knowledge involves the automatic recognition of situations that resemble situations encountered previously. We sometimes call this kind of automatic recognition "intuition." For example, we have no trouble recognizing a table or a game when we see one, but how do we do this? Unlike circles, there is no one property that all games have in common (no necessary condition), and no one property that distinguishes a game from all other activities or a table from all other objects (no sufficient condition). Wittgenstein proposed that intuitive recognition is explained by "family resemblances" (1953/2009), but this explanation only substitutes one mystery for another. Seligman and Kahana (2008) suggested that the mind must perform three tasks to decide if a table is a table: First, identify all relevant dimensions to the decision (which, following Wittgenstein, are only relevant, but not necessary or sufficient); second, assign a value and a weight for each relevant dimension and each interaction; and third, create a decision rule for table-hood. This process results in a mathematical model that weights each dimension (and their interactions) in such a way as to reliably distinguish past instances of tables from non-tables. This model has been shown to work for recognizing faces, even when upside down or in different profiles (Lacroix, Murre, Postma, & van den Herik, 2006).
Thus, one of the core processes at play in intuition seems to be the recognition of patterns. Beyond recognizing tables, pattern recognition underpins analogical reasoning, which consists of mapping knowledge from a base domain to a target domain, for example, recognizing that the structure of an atom is understood by thinking about the structure of the solar system (Gentner, 1989). Isaac Newton's insight about gravity, for example, resulted from seeing an apple and the moon subtending the same visual angle. Armed with knowledge and expertise, Newton suddenly wondered if what draws the apple to the earth is the same force that holds the moon in orbit (Gleick, 2004). Many other creative moments illustrate the role of pattern recognition and analogical thinking in creativity: Benjamin Franklin's discovery that lightning contained the same stuff (electricity) that charged batteries is another example.
Pattern recognition is at the heart of Jeffrey Hawkins's theory of intelligence (and, in turn, of creativity). Hawkins and Blakeslee (2007) proposed that intelligence consists in the prediction of the future, not in the knowledge of the past. They used the visual cortex as their model (see also Clark, 2013). The visual cortex is layered, with activity of the neurons in the lowest layer (V1) reflecting the voluminous information that arrives at the retina. Each succeeding layer abstracts only some of the information from the layer below. Importantly, connections not only go up the layers, but also back down the layers, and in fact, there are 10 times as many downward connections as upward ones. In their theory, the downward connections tell the lower layers what pattern is predicted in the next moment's saccade (eye-movement): inhibiting unexpected connections and exciting expected connections.
Creativity, for Hawkins, resides in the patterns that are formed in the very top layers, the cross-modal layers that integrate all information available. How do creative insights emerge if unexpected information is inhibited and we only rely on memory to predict the future and act in the present? The answer, according to Hawkins's memory-prediction framework, lies in pattern recognition. When confronted with a novel problem, we conjure memories of similar situations, and find out how to solve it using analogical reasoning. Although Hawkins and Blakeslee (2007) deem all analogies creative, they explain that creativity is most obvious when "our memory- prediction system operates a higher level of abstraction, when it makes uncommon predictions using uncommon analogies" (p. 185). It must be remarked that Hawkins's theory is itself a theory that shows its sense. It argues that creativity proceeds by the discovery of very high order analogies, and it comes to this conclusion by using the visual system as a high order analogy of the creative process.
Heuristics. Closely related to intuition and pattern recognition are heuristics. Accumulated knowledge leads to the ability to use fast shortcuts, or "heuristics" to make decisions, rather than relying on effortful decision-making (Baron, 2000; Peters, Finucane, MacGregor, & Slovic, 2000). Importantly, heuristics are not algorithms (i.e., cookie-cutter methods for solving a problem), but involve a certain degree of flexibility that allows them to be useful for creative solutions (Amabile, 1996). Also, heuristics are often automatic (but still flexible) ways of processing information and making decisions. Thus, individuals may often not be able to verbalize what shortcut they are taking. Heuristics can be broadly categorized as "negative heuristics" (what to avoid) and "positive heuristics" (what to do) (Lakatos, 1970). Physicians' oath to do no harm, eight of the Ten Commandments (e.g., "Thou shalt not steal"), and of course, "If it ain't broke, don't fix it," are all examples of negative heuristics.
But a major caveat about negative heuristics is in order: Not getting it wrong does not equal getting it right. Imagine making a speech with no grammatical errors. Or writing a biography in which nothing untrue is said. Or serving a meal in which nothing tastes bad. Or proving a theorem in which every statement was true. Or playing the Beethoven Opus 109 with no mistakes. Or chairing a meeting in which no one was discourteous. None of these would guarantee a good speech, a good book, a good meal, a good proof, a good performance, or a good meeting.
This is where positive heuristics come in, by providing us with shortcuts to figure out the right thing to do. To complicate matters, however, such heuristics often lead to biases. The study of how ordinarily useful heuristics can go wrong has been the meat and potatoes of the work of Kahneman and Tversky (Gilovich, Griffin, & Kahneman, 2002; Kahneman, 2011). Consider the "availability heuristic": For example, a woman is asked to estimate the frequency with which physical assaults take place in her city. She replies that such assaults are extremely common. Following the availability heuristic, this person based her judgment on "the ease with which instances come to mind" (Kahneman, 2011). In this case, the woman had just read about an assault. If she lived in an unsafe city, the heuristic led to an accurate answer and saved time. If her city is indeed safe compared to most other cities, the heuristic saved cognitive processing time but led to an inaccurate answer. Thus, the availability heuristic often leads to a bias.
There is no doubt that the study of problems associated with heuristics is important given their occasional negative consequences. However, researchers should not neglect to further investigate adaptive heuristics and how they often lead to enhanced outcomes. Heuristics may not usually lead to errors in thinking. We believe that system 1 and the shortcuts it relies on are at the heart of the adaptive prospecting of possible futures. At the margins, heuristics make errors, but by and large, they are our first and most robust way of navigating the future (Seligman et al., 2013). When they do not lead to biases, positive heuristics allow goodness, rightness, beauty, and truth to occur over and above the mere absence of badness, wrongness, ugliness, or falsehood.
George Polya's classic book How to Solve It (1945) provides shortcuts to help students learn to solve mathematical problems independently (e.g., "Do you know a related problem?"). Anne Lamott, the author of bestseller Bird by Bird (1994), begins all of her workshops by telling students that "good writing is about telling the truth." Gordon's (1961) Synectics encourages participants to "make the familiar strange and the strange familiar." The creative problem solving (CPS) approach suggests the virtue of "looking at something, and seeing something else" (Treffinger, Isaksen, & Dorval, 2000, p. 57). Although some creativity-training programs (e.g., CPS) have received empirical support (e.g., Puccio, Firestien, Coyle, & Masucci, 2006), the specific contribution of the use of positive heuristics for creative thinking has (to the best of our knowledge) not yet been dismantled.
In addition, the degree to which positive heuristics can be domain- general remains unclear. Many heuristics may be domain-specific and work because they succinctly convey domain-s pecific knowledge. Yet, some broad general principles may also apply across fields, and the creativity-training programs described earlier generally seek to offer such domain-general heuristics. Given that positive heuristics may constitute a major source of creativity and likely are a good part of what "wisdom" means, we commend science on positive heuristics to the future. Consider the following examples of additional potential heuristics for creativity in various domains:Above all be kind.A good tragedy "takes an ice-axe to the frozen sea inside of us."When in doubt, stand on principle.A good symphony comes to perfect resolution.In comedy the funniest line comes last. The next funniest comes first.A good meal brings out the best in its natural ingredients.
A good piece of science tells us that something we thought was false is true, or something we thought was true is false—or even just gets us to think about something we never thought about before. Consider the following:A good theory makes counterintuitive predictions.A good person shows us how to lead our lives.
Intuition, pattern recognition, heuristics, and aging. How do intuition, pattern recognition, and heuristics fare with age? As with all cognitive abilities, seeing patterns sometimes requires abstract integration and fluid reasoning (see Green, Kraemer, Fugelsang, Gray, & Dunbar, 2010, 2012) and so will show some deterioration with aging. But those aspects of pattern recognition that are automatic draw heavily on knowledge and domain-specific expertise, which are factors that likely improve with age.
Thus, analogies, pattern recognition, and intuitions may constitute paradigm cases of the "compensatory" mechanisms that Baltes and Baltes (1990) invoked in their theory of "optimization with compensation" as we age. The older we get, the more information and experiences are available to us, and the more examples of successful patterns, heuristics, and intuitions we have to draw on. As time passes, we may also be able to refine the dimensions along which we intuitively weigh information. Consider a lieutenant recognizing a likely ambush. The lieutenant intuitively and correctly decides that this bit of forest is a likely ambush site, based on weighing relevant dimensions. The more ambushes (or the more simulations of ambushes) the lieutenant has faced, (a) the more relevant dimensions of ambushes will be identified, (b) the more accurate is the mean value put on each dimension (insect quiet likely means ambush), and (c) the more accurate the weight of each dimension and their interactions are (insect quiet plus no adult men in the village almost surely means ambush). The accuracy of the decision rule in multidimensional space improves with experience, but with one major proviso. Useful experience must be at the knife edge of the decision process, close decisions that vary the relevant features of the dimensions. If the experience is only repeated clear-cut instances of ambush and non-ambush, experience adds nothing—20 years of experience versus 1 year of experience twenty times.
So we conclude our review of cognitive factors in creativity with three that likely improve with age and bode well for the aging authors:
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