Simonton (2007) - Creative Life Cycles

Psychology of Aesthetics, Creativity, and the Arts 2007, Vol. 1, No. 3, 133–139

Copyright 2007 by the American Psychological Association 1931-3896/07/$12.00 DOI: 10.1037/1931-3896.1.3.133

Creative Life Cycles in Literature: Poets Versus Novelists or Conceptualists Versus Experimentalists? Dean Keith Simonton University of California, Davis The economist Galenson (2005) proposed a theory of creative life cycles that distinguishes between early-peaking conceptual creators (finders) and late-peaking experimental creators (seekers). This contrast is claimed to invalidate previous research findings that poets tend to peak earlier than novelists. However, a multiple regression analysis of his published data on 23 creative writers shows that the poetry-novel genre contrast makes a contribution to the prediction of the career trajectory that is orthogonal to the conceptual-experimental contrast. The result is a fourfold typology of creative life cycles: conceptual poets, conceptual novelists, experimental poets, and experimental novelists who do their best work at ages 28, 34, 38, and 44, respectively. The article closes with a discussion of additional empirical and theoretical issues. Keywords: creativity, age, life cycles, literary genres, conceptual versus experimental creators

1975). Indeed, Simonton (1975) found that the age gap between poets and novelists was invariant across history (from ancient to modern times) and diverse literary traditions (Japanese, Chinese, Islamic, European, etc.). This effect is large enough to help explain why eminent poets tend to have a shorter life span than do novelists (Kaufman, 2003; Simonton, 1974; cf. Kaun, 1991). Because poets produce their best work at a younger age, they can die younger without that event imposing a severe cost on their reputation (cf. McCann, 2001). A similar effect holds for mathematicians who display both accelerated career trajectories and shorter life expectancies (Simonton, 1991a). The age-output results have been the subject of several theoretical interpretations (e.g., Beard, 1874; Diamond, 1984; Simonton, 1997; Stephan & Levin, 1992). Some of these explanations are sociological or economic, whereas others are clearly psychological in nature. In the latter category is a two-step combinatorial model of the creative process that has been developed in a series of articles (Simonton, 1984, 1989, 1991a, 1997) and books (Simonton, 1988b, 2004a). In simple terms, this mathematical model assumes that creators launch their careers with a certain amount of creative potential defined as a set of ideas or mental elements available for free combination. This potential then undergoes the two-step process of ideation, by which useful combinations are generated, and elaboration, by which those combinations are converted into overt products. This two-step cognitive procedure then yields a double-exponential age function that accurately predicts the career trajectories indicated in previously published data sets (Simonton, 1984, 1989, 1997). Furthermore, because the ideation and elaboration rates are assumed to be domain-specific, the model provides a theoretical explanation for contrasts in the career trajectory across different disciplines (Simonton, 1997). For instance, because the ideation and elaboration rates are faster for poetry than for novels, the age curve for poets peaks earlier and exhibits a more accelerated post-peak decline (Simonton, 1989). The combinatorial model can even account for cross-sectional variation in career trajectories. This provision ensues from two individual-

Beyond doubt, the oldest topic in the empirical study of creativity is the relation between age and creative output (Simonton, 1999a). The first scientific analysis regarding this question was conducted by Que´telet’s (1835/1968) concerning longitudinal changes in the dramatic production of eminent British and French playwrights. About a century later the creative life cycle was taken up by Lehman in a series of articles that culminated in the 1953 classic Age and Achievement. More recent research on this subject has been conducted by numerous other social and behavioral scientists (e.g., Cole, 1979; Dennis, 1966; Over, 1989; Stern, 1978; Simonton, 1991a; Zickar & Slaughter, 1999). In addition, it has been the subject of comprehensive literature reviews (e.g., Lindauer, 2003; Simonton, 1988a, 1996). Not surprisingly, given the sheer mass of research, many key results have been replicated so many times as to constitute among the most secure articles of knowledge in the psychological sciences. Two findings are especially robust (Lehman, 1953; Dennis, 1966; Simonton, 1988a, 1997). First, the output of creative products tends to change over time, rising relatively quickly to a career maximum and then declining somewhat gradually thereafter. Typically, the peak occurs sometime in the late 30s or early 40s, and the productivity toward the end of the career is about half that at the career maximum. This longitudinal trend is approximated by an “inverted backward-J” function (Simonton, 1988a). This function is specified by a second-order polynomial in age where the linear term is positive and the quadratic term is negative. Second, the specific shape of this single-peak function varies according to the domain of creative achievement. For instance, the optimal age for writing poetry tends to be somewhat younger than that for writing novels (Dennis, 1966; Lehman, 1953; Simonton,

Correspondence concerning this article should be addressed to Dean Keith Simonton, Department of Psychology, One Shields Avenue, University of California, Davis, CA 95616-8686. E-mail: dksimonton@ ucdavis.edu 133

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difference parameters, namely, the person’s initial creative potential and the age at career onset (i.e., when the combinatorial process begins). As a consequence, the model successfully predicts the key features of individual differences and longitudinal changes in creative output (Simonton, 1997). Many of these predictions are so distinctive to the model that their empirical confirmation rules out several alternative explanations (e.g., any interpretation that defines the life cycle in terms of chronological rather than career age). Nonetheless, both this specific theoretical model and the general empirical findings face a direct challenge from an unlikely quarter—from economics rather than psychology. This challenge is presented in the 2005 Old Masters and Young Geniuses: The Two Life Cycles of Artistic Creativity by David Galenson, an economist at the University of Chicago.1 The author put forward an alternative theory of output trajectories in the arts and other creative disciplines. His theory is predicated on the distinction between two approaches to creativity, the conceptual and the experimental. In the former category are the young geniuses or finders who conceive their best work as sudden intellectual breakthroughs that appear very early in their careers. In the latter category are the old masters or seekers who work painstakingly via trial and error, and thus whose best work does not emerge until late in life when they can finally savor the fruits of their labors. According to Galenson, Picasso represents the typical conceptual creator, whereas Cezanne exemplifies the experimental creator. Although the theory was originally formulated with respect to painting (Galenson, 2001, Galenson (2005) extended it to encompass all major forms of artistic creativity, including sculpture, poetry, novels, and film. Indeed, he has even extended the distinction to cover scientific creativity (Weisberg & Galenson, 2005). Whatever the specific domain of creative behavior, the conceptual finders have identifiably different work habits than the experimental seekers. Galenson (2005) affirms that his theory is not just complementary to earlier research results and theories. On the contrary, he claims that his conception of the creative process supersedes the earlier psychological findings and interpretations (see especially pp. 171–177). Rather than the age curves being a function of the domain of creative activity, the trajectories are the upshot of a particular approach to the creative process. Conceptualists peak early and experimentalists peak late. Any apparent tendency for poets to peak earlier than novelists may merely reflect differential proportions of finders and seekers across the two literary genres (Galenson, 2006). If so, then controlling for the conceptualist versus experimentalist distinction would make the poet-novelist difference disappear. However, his argument is not immune from criticism. First, Galenson’s judgments of the psychological literature exhibit some misunderstandings of the research. For instance, he said that “the generalizations of the psychologists quoted here [regarding the poetry-novel age contrast] may stem from an uncritical acceptance of the findings of Lehman, buttressed by the dramatic examples of Byron, Keats, Shelley, and the other famous young geniuses who died prematurely” (p. 176). This statement overlooks the fact that the age gap has been established for hundreds of literary creators representing all of the world’s major literatures, and in analyses that implemented statistical controls for such contaminants as life span (Simonton, 1975). Moreover, psychologists do not uncritically claim that all creators have identical career trajectories but

only that the most typical or average curve is roughly described by the inverted backward-J curve (see, e.g., Zickar & Slaughter, 1999). A minority of creators can depart appreciably from this overall trend without threatening the generalization. Accordingly, there is no logical reason for adopting an either-or stance. Because psychologists recognize individual differences in longitudinal trends, Galenson’s theory can be used to explain those instances in which some creators peak earlier or later than the norm. The early bloomers may be conceptualists and the late-bloomers experimentalists. Yet given that the overall curve remains single-peaked rather than bimodal, these departures would have to represent the exceptions rather than the rule (Cole, 1979; Dennis, 1966; Lehman, 1953; Que´telet, 1835/1968). The repeatedly replicated unimodal distribution could then indicate that most creators adopt a combination of conceptual and experimental styles. A more fundamental problem concerns the methodology underlying Galenson’s (2005) substantive conclusions. Few of his studies on this specific issue are published in refereed journals. Rather, the vast majority appear as “working papers” deposited at the National Bureau for Economic Research (http://papers.nber.org/ papers/). As a result, his research sometimes lacks the methodological rigor expected in peer-reviewed publications. For instance, Winner (2004), commenting on an earlier presentation of the theory (Galenson, 2001), observed that his operational definition of the core seeker-finder distinction appears too vague and even contradictory. Thus, although Galenson classified Picasso as a conceptualist painter, the manner in which that artist conceived Guernica is far more indicative of an experimental painter (see also Simonton, in press). At times Galenson appears to bifurcate a group of creators into early- and late-bloomers and then try to identify possible differences between the two groups—a procedure that confounds the operational definition with the empirical test of the theory. Furthermore, in contrast with the psychological research that he criticizes, Galenson (2005) does not usually apply statistical analyses that would enable him to (a) control for potential artifacts and (b) obtain unbiased estimates of effect sizes. Most often he simply provides tables and graphs depicting group differences in which the conclusions must be based on visual inspection. In addition, the samples on which he founds his inferences are frequently smaller than those seen in most psychological research (i.e., dozens rather than hundreds). Finally, the sampling procedures are often vaguely defined for any given investigation and inconsistent across separate investigations. Consequently, samples recurrently appear not just small but also arbitrary. All in all, Gardner (2006) may have been justified when he held that “Galenson. . .is not. . .a genuine empiricist, but rather a theorist who is posing as an empiricist” (p. 278). But for our current purposes the most substantial objection may be that Galenson’s (2005) research is fragmented rather than integrated. Rather than introduce a single method—sampling pro1

I should probably point out that I served as a referee when the book manuscript was submitted to Princeton University Press. Besides recommending publication, I consented to provide a quote for the book’s dust jacket. The blurb read “A very well written and intellectually stimulating piece of scholarship that deserves to be widely read and debated.” It is in that spirit that I contribute the current piece.

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cedures, variable definitions, and statistical analyses—to inclusive samples of creators, he favors methodologically variable inquiries into more narrow samples. For example, despite his strong objection to the generalizations of the psychological research on the poetry-novel difference in peak ages, he has never conducted an investigation looking at both poets and novelists together. Instead, he has put out one working paper on modern American poets (Galenson, 2003) and another on modern novelists (Galenson, 2004). In neither paper does he make direct numerical comparisons with the findings of the other paper. That means that he ignores whether the effect of literary genre operates as a determinant of creative life cycle independent of the impact of the contrast between finders and seekers. Yet, as already noted, these two factors need not be mutually exclusive. A more inclusive alternative is that four distinct career trajectories operate, namely, finder poets, seeker poets, finder novelists, and seeker novelists. To detect this second outcome requires that the two samples be incorporated into a single quantitative investigation. Fortunately, because Galenson (2003, 2004) published the raw data, this integrative analysis can be performed here. Are there four career trajectories, or just two?

Method Sample The 23 writers in this study were divided into 11 poets from Galenson (2003) and 12 novelists from Galenson (2004). There were three female poets (Elizabeth Bishop, Marianne Moore, and Sylvia Plath) and one female novelist (Virginia Woolf). Although the poets are all from the United States, the novelists came from both the United States and Great Britain (counting Joseph Conrad as British rather than Polish). The birth years are even more heterogeneous. Despite the fact that both sets of writers were identified as “modern,” the poets were born between 1874 (Robert Frost) and 1932 (Sylvia Plath), and the novelists between 1812 (Charles Dickens) and 1899 (Ernest Hemmingway), making the poets more recent than the novelists. The difference between the mean birth years was about 35 years. However, this difference actually biases the data against finding the expected poet-novelist age gap. Because the lifespans of eminent creators have been gradually increasing over historical time (e.g., Simonton, 1977) and because longer lifespans are positively correlated with later career peaks (Lindauer, 1993; Simonton, 1975, 1991a, 1991b), the novelists should have their optima shifted toward earlier ages. In the present sample, the average life span of the poets was 72.1 year, that of the novelists 63.9 years, a gap of 8.2 years that should reduce the difference in career peaks.

Measures The dependent variable is the age at which the writer wrote his or her single best work. For the poets the best work was defined as the most frequently anthologized poem (according to Tables 5 and 7 of Galenson, 2003). For the novelists the best work was determined by the amount of space devoted to each work in 10 critical monographs devoted to each author (according to Tables 4 and 5 of Galenson, 2004). Although these two operational definitions are not equivalent, prior research shows that alternative archival indi-

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cators of esthetic merit exhibit high intercorrelations, a finding that holds for drama, poetry, opera, and film (Simonton, 1986, 1990, 1998, 2004b). So let us call this criterion Age best work. There are two substantive predictors, one capturing the claims of past psychological research and the other Galenson’s contrary position. The first is a dummy variable recording the domain of literary creativity. In particular, the variable Novelist equaled 1 if the writer was in the Galenson (2004) sample, but equaled 0 if in the Galenson (2003) sample. Although poets can be novelists, these two samples did not overlap, so that there was no ambiguity in the definition of this variable. For example, although Thomas Hardy was a notable English poet, he was not an American, and so he could not have been included in Galenson (2003) study. Accordingly, Hardy could be categorized exclusively as a novelist. Most likely, this is how he would have been identified anyway if the assignment was based on Hardy’s single greatest work, namely the novel Tess of the d’Urbervilles. The second substantive predictor was the dummy variable Experimentalist that equaled 1 if the writer was labeled such by Galenson (2003, 2004) and equaled 0 if identified as a conceptualist. Almost 57% of the writers across both literary genres were classified as experimental creators, the rest being conceptual creators. It should be noted that the manner in which Galenson derived these classifications was not identical for the novelists and poets. In the former case some resemblance of an operational definition played a greater role in the categorization, whereas in the latter case he seems to have worked backward from the ages at which the poets produced their best work. The last independent variable is a statistical control variable, namely, the writer’s life span. Because one poet was still living (Richard Wilbur), he was assigned an artificial life span by having him die in 2006, at the time of this investigation (viz., age 85). This appears reasonable given that many years have lapsed since the poet has made a major contribution to the genre (i.e., his last major book of poems appeared in 1988). With this inserted value the lifespans ranged from 31 (Sylvia Plath) to 89 (Robert Frost) with a mean of 68.4. To render the regression results more interpretable, this variable was put in mean-deviation form.

Results The analysis consisted of two consecutive multiple regressions. In Model 1 the dependent variable (age best work) was regressed on both substantive variables (Novelist and Experimentalist dummies) and in Model 2 the control variable (life span) was added to the equation. Table 1 shows the outcome for both models.

Model 1 In the first regression equation the intercept indicates the expected age at best work for those creative writers who were neither novelists nor experimentalists—namely, the conceptualist poets. This group writes their best poem around age 28. The unstandardized partial regression coefficient for the first dummy variable (Novelist) then indicates whether being a conceptual novelist changes this expectation. According to the results, these novelists are about 5 years older when they produce their best work. More precisely, they tend to be 33.53 (⫽ 28.27 ⫹ 5.26) when their best novel appears. The unstandarized partial regression coefficient for

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Table 1 Multiple Regression Analysis: Predictors of Age Best Work for 23 Poets and Novelists Model 1

Model 2

Style

b

␤

p

b

␤

p

Intercept (Poet/Conceptualist) Novelist Experimentalist Lifespan R2

28.27 5.26 10.67

.00 .34 .68

.0000 .0274 .0001

28.32 6.06 9.85 0.08

.60

.0001

.00 .39 .63 .16 .62

.0000 .0187 .0006 .3379 .0003

Note. The intercept is the predicted age when both Novelist ⫽ 0 and Experimentalist ⫽ 0. The statistics provided are the unstandardized partial regression coefficient (b), the standardized partial regression coefficient (␤), the probability level of the t test ( p), and the multiple correlation squared (R2), which gives the total proportion of variance explained.

the second dummy variable (Experimentalist) similarly indicates whether being an experimental poet alters the expected age relative to being a conceptual poet. In this case the increment is almost 11 years. More specifically, experimental poets produce their best work at age 38.94 (⫽ 28.27 ⫹ 10.67), or about 5 years older than conceptual novelists. The final contingency is when a writer is an experimental novelist, in which case the predicted age at best work is obtained by adding all three unstandardized coefficients. In particular, experimental novelists are expected to do their best work at age 44.20 (⫽ 28.27 ⫹ 5.26 ⫹ 10.67). Taken together these two dummy variables account for 60% of the variance in the age that creators do their best work. This fit to the model is exceptionally good (cf. Simonton, 1975). It is thus clear that both factors make substantively and statistically significant contributions to the prediction of the career peak. The effect size for genre is comparable to that found in earlier investigations. For instance, Simonton’s (1975) study of creative writers obtained an age difference of about 4 years. At the same time, the effect of the seeker-finder distinction is about double that of genre (i.e., 2.03 ⫽ 10.67/5.26). This greater impact is also indicated by comparing the two standardized partial regression coefficients; the Experimentalist coefficient is exactly double that for Novelist (i.e., 2 ⫽ .68/.34). Nevertheless, it must be emphasized that the two factors make practically orthogonal contributions to predicting the creative life cycle. That independence is shown by the fact that the correlation between the two dummy variables is practically zero (i.e., r ⫽ .04, p ⫽ .8627). More specifically, while there is a very slight tendency for poets to be conceptualists and novelists to be experimentalists, this association is neither substantively nor statistically significant. For the most part, conceptual and experimental writers are evenly distributed among the two literary genre, at least according to Galenson’s (2003, 2004) own data.

Model 2 Nonetheless, the results for Model 1 may be contaminated with the influence of life span. This possibility emerges from the fact that life span correlates with the dependent variable and both independent variables. The specific correlations are: Age best work .24, Novelist .32, and Experimentalist ⫺.32. Admittedly, because we are dealing with a very small sample, none of these correlation coefficients are statistically significant (viz., ps of .2795, .1358, and .1406, respectively). Even so, because they

indicate that the amount of shared variance is around 10%, the correlations are large enough to have substantive consequences, including suppression effects. This outcome is evident in the Model 2 statistics presented in Table 1. Despite the fact that life span is not a significant predictor of age at best work, life span does affect appreciably the magnitude of the poet-novelist and finder-seeker contrasts. Specifically, the effect of Novelist is increased by almost a year to a bit over 6 years while the effect of Experimentalist is decreased by almost exactly the same amount to a bit less than 10 years. As a consequence, the two effect sizes are more equal, albeit the conceptual-experimental contrast still surpasses that for poet-novelist. In any case, these revised coefficients can be combined with the slightly changed intercept to obtain new predicted scores for the four categories of literary creativity: conceptual poets 28.32, conceptual novelists 34.38 (⫽ 28.32 ⫹ 6.06), experimental poets 38.17 (⫽ 28.32 ⫹ 9.85), and experimental novelists 44.23 (⫽ 28.32 ⫹ 6.06 ⫹ 9.85). In more approximate terms, we obtain four distinct career peaks in the late 20s, the middle 30s, the late 30s, and the middle 40s. Almost 16 years separates the conceptual poets from the experimental novelists. It should now be apparent that there are not just two kinds of creative life cycles, as Galenson (2005) argued, but rather at least four. Furthermore, the near-zero correlation between the two dummies indicates that these four can be considered orthogonal types. The nature of this typology is indicated in Table 2 where the 23 writers are grouped according to placement along the two dimensions. Within each category the creators are ordered according to the age at which they produced their best work—their individual career peaks. Moreover, at the top of each quadrant are the predicted career peaks for each of the four types (using Model 2 from Table 1). It is manifest that the observed scores tend to be scattered around the corresponding predicted scores. About half of the writers have earlier peaks than expected and about half have later peaks than expected, with most having peaks fairly close to prediction. In addition, it is evident that the conceptualists tend to peak earlier than the experimentalists, with only modest overlap between the distributions. Yet it is also true that the poets tend to peak earlier than do the novelists. This is very apparent if you compare each poet with each novelist lined up in the same row: In almost every case the poet has an earlier peak than the corresponding novelist or novelists. This consistent ordinal difference shows that the genre distinction is not trivial.

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Table 2 Typology of Creative Life Cycles in Literature: Predicted and Observed Career Peaks Style

Poets

Novelists

Conceptualists (finders)

Predicted: 28 Eliot (1888 ⫺ 1965): 23 Cummings (1894 ⫺ 1962): 26 Plath (1932 ⫺ 1963): 30 Pound (1885 ⫺ 1972): 30 Wilbur (1921⫺): 34 Williams (1883 ⫺ 1963): 40

Predicted: 34 Fitzgerald (1896 ⫺ 1940): 29 Hemingway (1899 ⫺ 1961): 30 Melville (1819 ⫺ 1891): 32 Lawrence (1885 ⫺ 1930): 35 Joyce (1882 ⫺ 1941): 40

Experimentalists (seekers)

Predicted: 38 Bishop (1911 ⫺ 1979): 29 Moore (1887 ⫺ 1972): 32 Lowell (1917 ⫺ 1977): 41 Stevens (1879 ⫺ 1955): 42 Frost (1874 ⫺ 1963): 48

Predicted: 44 James (1843 ⫺ 1916): 38 Faulkner (1897 ⫺ 1962): 39 Dickens (1812 ⫺ 1870): Woolf (1882 ⫺ 1941): 45 Conrad (1857 ⫺ 1924): 47 Twain (1835 ⫺ 1910): 50 Hardy (1840 ⫺ 1928): 51

Note. The predicted career peak (age at single best work) was generated from Model 2 in Table 1 (rounded off to the nearest integer). Within each type are listed the writers in Galenson (2003, 2004) according to their observed career peaks (given after the colon).

Discussion According to Galenson’s (2003, 2004) own data, the distinction between poetry and novels remains a critical factor in understanding creative life cycles in literature. This is not to say that the domain of creativity is just as important as the style of creativity. After all, the seeker-finder distinction has a noticeably larger impact on age at best work. Still, the literary genre adds a significant increment to our predictive power. In the case of Model 1 (in Table 1), for example, the Experimental dummy variable alone would account for 48% of the variance. Adding the Novelist dummy variable to the equation increases that proportion to 60%. If science were a game of finding the single best predictor, the finder-seeker variable would be at present the predictor of choice. But science is not engaged in contests of this kind. The primary goal is to obtain a complete comprehension of a phenomenon using as many factors as necessary. As Table 2 shows, including both factors provides a richer understanding of creative life cycles in literature. To appreciate better the implications of these results I would like to address two sets of issues, one empirical and the other theoretical.

Empirical Issues As an empirical investigation this study leaves much to be desired. First and foremost, the sample size was much smaller than would be normally recommended. There were only 23 cases about evenly divided between poets and novelists. This contrasts greatly with the number of cases in previous psychological research on the age-output curve. For instance, Simonton (1975) examined 420 literary creators, and Simonton (1991a) has tested his model of interdisciplinary differences in career trajectories on over 2,000 scientists. Even Dennis (1966), who gathered somewhat smaller samples than usual, included 46 poets and 32 novelists, over three times the size of Galenson’s (2003, 2004) combined samples. There are two main reasons why this issue should be addressed with larger Ns than Galenson tends to use. First, small samples

make it more difficult to reject the null hypothesis (i.e., they have lower statistical power). As a result, it is possible that some more subtle determinants of creative life cycles will be overlooked because of Type II errors. Second, a fuller appreciation of the phenomenon requires more complex models than those implemented in the current investigation. For example, Simonton (1975) included 23 independent variables in his study of the career peaks of 420 writers—as many variables as the number of cases in the current analysis! Because N should amply exceed the number of predictors, a small sample size restricts the sophistication of the model that can be tested. The composition of the sample is also problematic. In the present secondary analysis the novelists formed a more heterogeneous group than the poets both with respect to historical period and geographical origins. Hence, the two groups were not equivalent. Either the novelists should have been confined to modern American novelists or the American poets should have been expanded to include all modern poets. Of course, the latter course of action is probably best. By increasing the diversity of the sample we can determine whether any observed differences are invariant across time and place (Lehman, 1953; Simonton, 1975, 1991a, 1991b). In addition, it is imperative to adopt a more uniform sampling strategy. As pointed out earlier, sometimes Galenson (2005) confounds the selection strategy with his theoretical concepts. This problem is apparent in his sample of poets. An inspection of Table 2 reveals that not a single poet produced their best work between the ages of 35 and 39 inclusively. This hiatus is perplexing because it represents the half decade in which the best work has a high probability of appearing. For instance, according to one transhistorical and cross-cultural study, the average age that poets produced their single most important composition was 38.8 (Simonton, 1975). And no investigation prior to Galenson’s (2003) has identified a bimodal distribution with a zero-output trough situated in this age interval (see, e.g., Lehman, 1953). Therefore, insofar as the work of the “average poet” has been omitted from the analysis, it is likely that the effect size for the seeker-finder

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distinction is biased upward. The true effect may be closer to that of genre and could even be smaller.2 A final methodological issue has already been mentioned in the introduction section: The need for a precise operational definition of what counts as conceptual versus experimental creativity (Gardner, 2006; Winner, 2004). Yet it turns out that this is not just a methodological issue but a theoretical problem besides.

Theoretical Issues In Simonton’s (1984, 1997) two-step model the ideation and elaboration rates that determine the career course are domain specific. That is, the two rates are based on the particular nature of the domain’s concepts, techniques, standards, and modes of expression or communication (Simonton, 2004a). The consequence of these features is then the distinctive life cycle that characterizes each domain. This does not mean that creators working within the same domain will not vary in their trajectories. On the contrary, the two-step model actually includes two variables to deal with individual differences, and the model also allows for the operation of diverse “random shocks” that deflect a particular creator’s output from the expected trajectory (Simonton, 1997, 2004a). Even so, domain-specific conditions play a major role in determining the creator’s overall career path. Galenson (2005), in contrast, believes that the creative life cycle resides in the individual creator. Some happen to be conceptual finders, others experimental seekers—and this distinction so dominates the career course that domain differences become irrelevant. It is for this reason that Galenson can dismiss the psychological investigations that document domain-specific career peaks. Ironically, in a sense Galenson’s theory is more psychological than Simonton’s because the former places the causal locus within the individual creator rather than outside in the domain. Yet when we more closely scrutinize Galenson’s theoretical position, we encounter two problems. First, if seekers and finders are manifestations of underlying psychological factors, it is difficult if not impossible to consider this distinction as categorical or even bimodal. Virtually every psychological construct ever measured is unimodal. Most often the trait is distributed in a close approximation to the normal or bell-shaped curve, but sometimes the characteristic will display a highly skewed but still unimodal distribution (Simonton, 1999b). Although psychologists may loosely speak of, say, extroverts versus introverts, it is commonly understood that the true extrovert or introvert is found on the extreme tail of the distribution. Most individuals fall in the middle, sometimes being extroverted and other times introverted. Given this fundamental reality of human individual-difference variables, pure conceptualists and pure experimentalists must be extremely rare should they have a psychological foundation. The overwhelming majority of creators would have to represent some mix of the two strategies. Notice that this problem is not found in any model that attributes the life cycles to domain characteristics. The properties of domains are discrete rather than continuous, multimodal rather than unimodal. Certainly the attributes of poetry are distinct from the attributes of novels. Creative products in the two genres aspire to accomplish very different literary ends with rather distinctive means. Indeed, the extreme lack of commonalities between the domains may account

for the comparative rarity of writers who have managed to produce first-rate works in both genres. Second, when Galenson (2005) endeavors to root the distinction in the individual creator, his description of the conceptualexperimental contrast contains numerous references to domainspecific traits. To illustrate, he specifies the difference between the two kinds of poetry as follows: whereas conceptual poetry often involves introspection, experimental poetry typically involves observation. Conceptual poetry often grows out of a study of earlier poetry, whereas experimental poetry more often comes from study of the external world; conceptual poets may find their raw material in libraries, but experimental poets are more likely to find it by traveling or working at other professions. Conceptual poetry is often concerned primarily with technique, whereas experimental poetry tends to emphasize subject matter. Conceptual poetry is more often abstract, and aimed at universality, while experimental poetry is generally concrete, and concerned with specifics. The language of conceptual poetry is more likely to be formal or artificial, while that of experimental poetry may be informal and vernacular. Conceptual poetry is more often based on imagination, experimental poetry on the author’s perception of reality. (Galenson, 2003, pp. 8 –9)

Many of these identified differences are among those that Simonton (1984, 1989, 1991a, 1997, 2004a) used to characterize the domain-specific contrasts that influence the ideation and elaboration rates. In particular, domains with fast ideation rates deal with a more limited and well-defined inventory of abstract concepts that are highly constrained by a system of rules, whereas those with slower ideation rates deal with more unlimited and ill-defined inventory of concrete concepts that are more heavily dependent upon accumulated experience. These distinctions are important because they moderate the speed of the combinatorial process. In addition, subtle changes in domain attributes can exert large shifts in the expected creative life cycles (Simonton, 1989). These effects are even apparent within a specific domain like poetry. Despite the common tendency to treat all poetry as representing the same domain, this form of creativity should be differentiated into several subdomains, each with their separate expected career trajectories (Lehman, 1953; Simonton, 1975). Examples include such categories as lyric, pastoral, narrative, dramatic, epic, satiric, humorous, political, religious, and didactic—not even counting such specific forms as sonnets, odes, songs, and even limericks. Some of these have early predicted peaks and others late predicted peaks, all depending on the distinctive mix of attributes for that particular poetic expression. Hence, aside from all of the methodological issues that should be addressed, Galenson should develop a more complete theoretical basis for his distinction. This development would require (a) an indication of the psychological variables that determine whether someone becomes a finder or a seeker and (b) a specification of 2 Because the same gap is not found for the novelists, this assertion implies that the finder-seeker effect might be stronger for the poets. This possibility was examined by introducing into both models a multiplicative term for the Experimentalist ⫻ Novelist interaction effect. In neither case was the interaction term statistically or substantively significant. However, statistical tests for interaction effects have low power in small samples. So the result may be considered inconclusive.

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why any finder-seeker differences cannot be attributed to domainspecific features of the creative process.

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Received August 31, 2006 Revision received November 13, 2006 Accepted November 21, 2006 䡲