Alternate Uses (ALTUS)

1. Abstract

The Alternate Uses (ALTUS) test, historically rooted in the pioneering research of J. P. Guilford and his associates at the University of Southern California Aptitudes Research Project (Wilson, Guilford, Christensen, & Lewis, 1954; Guilford, Christensen, Merrifield, & Wilson, 1960), stands as one of the quintessential psychometric instruments designed to operationalize and quantify human divergent thinking. Specifically engineered as an evolution of the earlier Unusual Uses test, the ALTUS measures an individual’s capacity for creative ideation, with a dedicated diagnostic focus on cognitive flexibility—traditionally termed “spontaneous flexibility”—as well as ideational fluency. The standard paper-and-pencil instrument comprises two distinct, timed parts, each presenting three common target objects paired with their conventional, primary utility (e.g., a newspaper used for reading, an automobile tire used on the wheel of an automobile). Examinees are tasked with producing up to six alternative, plausible, and uncommon uses for each stimulus within an allotted timeframe (typically four minutes per section, totaling eight minutes of active production time).

Psychometrically, the ALTUS occupies a foundational position within Guilford’s Structure of Intellect (SOI) model, mapping predominantly to the divergent production of semantic classes (DMC) and semantic units (DMU). Extensive empirical evaluations conducted across diverse age cohorts have revealed robust internal consistency and alternate-form reliability estimates, with coefficients ranging from .75 to .91 across sixth-grade, ninth-grade, and adult samples when evaluating composite battery lengths. Test-retest reliability across developmental samples demonstrates moderate temporal stability (coefficients ranging between .53 and .67), reflecting both the trait-like disposition of creative potential and state-dependent fluctuations inherent to open-ended ideational tasks. Factor-analytic investigations consistently isolate spontaneous flexibility as an independent cognitive aptitude distinct from verbal intelligence, speeded retrieval, and general cognitive ability (g). Consequently, the ALTUS remains a cornerstone instrument in contemporary cognitive psychology, neuropsychological assessment, educational giftedness identification, and organizational creativity research.

2. Keywords

Alternate Uses, ALTUS, J. P. Guilford, Divergent Thinking, Creative Thinking, Spontaneous Flexibility, Ideational Fluency, Originality, Cognitive Flexibility, Structure of Intellect

3. Authors

The Alternate Uses test was conceptualized, empirically validated, and standardized through the collaborative efforts of principal psychometricians at the University of Southern California (USC) Psychological Laboratory:

• Joy Paul Guilford, Ph.D. (1897–1987) — Professor of Psychology at the University of Southern California, former President of the American Psychological Association (1950), and director of the Aptitudes Research Project. Guilford was a seminal figure in psychometrics, intelligence taxonomy, and creativity assessment.
• Paul R. Christensen, Ph.D. — Research Associate and psychometrician in the Aptitudes Research Project at the University of Southern California, co-investigator on studies investigating structural dimensions of creative and cognitive aptitudes.
• Philip R. Merrifield, Ph.D. — Psychometrician and research scientist affiliated with the Aptitudes Research Project at USC, later Professor of Educational Psychology at New York University, specializing in factor analysis and measurement theory.
• Robert C. Wilson, Ph.D. — Co-investigator on early factorial investigations into creative thinking aptitudes, contributing fundamentally to the precursor “Unusual Uses” paradigm.

The instrument is commercially cataloged, published, and distributed by Mind Garden, Inc. (https://www.mindgarden.com).

4. Purpose

The primary clinical, educational, and experimental objective of the Alternate Uses (ALTUS) test is to quantify individual differences in creative divergent thinking capacity, with an explicit emphasis on spontaneous cognitive flexibility. Historically, conventional intelligence batteries—such as the Stanford-Binet and Wechsler intelligence scales—focused almost exclusively on convergent thinking, wherein a test taker must synthesize or deduce a single, pre-determined, correct solution to an analytical or verbal problem. In his historic 1950 APA Presidential Address, J. P. Guilford posited that these traditional testing paradigms failed to evaluate the generative, exploratory cognitive operations fundamental to creative discovery, problem restructuring, and scientific or artistic innovation.

The ALTUS was designed to address this psychometric blind spot. Rather than asking examinees to identify what an object is, the instrument challenges them to transcend fixed mental representations—a phenomenon closely aligned with overcoming functional fixedness as formulated in Gestalt psychology (Duncker, 1945). By presenting a familiar object alongside its standard, culturally entrenched function, the test induces an initial semantic anchor. The examinee must actively suppress this prepotent semantic association, scan their mental lexicon and episodic/semantic knowledge bases, identify novel physical or conceptual attributes of the stimulus (e.g., shape, weight, chemical composition, absorbency), and map those attributes onto disparate contextual goals.

In applied settings, the ALTUS serves several critical functions:

• Gifted and Talented Identification: Standard intellectual assessments often under-identify exceptionally creative students whose divergent faculties surpass their rote academic performance. The ALTUS provides an objective, standardized index of ideational flexibility to complement convergent IQ scores.
• Neuropsychological and Cognitive Aging Research: The test acts as a sensitive probe for executive functioning, specifically the integrity of prefrontal cortex-mediated cognitive switching, mental set-shifting, and inhibitory control. Reductions in spontaneous flexibility on the ALTUS have been investigated in populations presenting with frontal lobe lesions, Parkinson’s disease, and normal cognitive senescence.
• Organizational and Workplace Assessment: In industrial and organizational psychology, the ALTUS is deployed to evaluate problem-solving adaptability, lateral thinking, and innovative potential among personnel entering dynamic engineering, design, and executive leadership roles.
• Cognitive Neuroscience of Creativity: Contemporary neuroimaging protocols (e.g., functional Magnetic Resonance Imaging [fMRI] and electroencephalography [EEG]) regularly utilize adapted iterations of the Alternate Uses task to investigate the dynamic interplay between the default mode network (associated with spontaneous semantic generation) and the executive control network (responsible for evaluating and selecting plausible, novel candidates).

5. Psychological Construct

The Alternate Uses test evaluates multidimensional facets of divergent thinking, centered primarily around the core construct of spontaneous flexibility, alongside secondary operationalizations of ideational fluency and originality.

Spontaneous Flexibility (Divergent Production of Semantic Classes – DMC)

Spontaneous flexibility represents the ability and disposition to shift freely across diverse conceptual categories when generating ideas in an unconstrained setting. Unlike “adaptive flexibility,” wherein the task demands that the participant change their cognitive strategy in response to changing external constraints or negative feedback (as seen in the Wisconsin Card Sorting Test), spontaneous flexibility occurs in the absence of explicit task instructions mandating categorical shifts. The individual is not instructed to produce uses from different conceptual categories; rather, their intrinsic cognitive apparatus spontaneously avoids semantic perseveration.

For example, when presented with the stimulus item WOODEN PENCIL (used for writing):

• Low Flexibility Response Pattern: “Drawing a picture,” “drafting a blueprint,” “taking notes,” “marking a measurement,” “sketching a portrait.” Although the examinee displays ideational fluency (5 total uses), all responses remain rigidly confined to a single semantic category: making a graphic mark via carbon transfer. The spontaneous flexibility score for this cluster is 1.
• High Flexibility Response Pattern: “A hair bun fastener” (category: grooming/apparel accessory), “a drumstick for tapping a rhythm” (category: musical instrument), “a stake to support a small potted seedling” (category: horticultural tool), “a makeshift splint for a broken finger” (category: medical first aid), “a poker to clear a jammed hole” (category: mechanical implement). In this protocol, the examinee shifts conceptual categories across every single response, demonstrating high spontaneous flexibility (spontaneous flexibility score = 5).

Ideational Fluency (Divergent Production of Semantic Units – DMU)

Ideational fluency reflects the quantitative rate of generating acceptable, meaningful conceptual units within a delimited time boundary. In the ALTUS, fluency is scored as the total raw count of valid, non-redundant, and logical alternate uses produced across the items, regardless of the categorical diversity. A valid use must represent a viable alternate function; completely nonsensical responses or tautological restatements of the primary use (e.g., using a newspaper to “read the news”) are disqualified.

Originality and Semantic Distance

Although Guilford’s original standard manual for the ALTUS focused scoring rubrics primarily on spontaneous flexibility and fluency, contemporary psychometric applications frequently score ALTUS protocols for originality. Traditionally, originality was indexed via statistical infrequency (responses occurring in less than 5% or 1% of the standardization normative database). In modern computational psychometrics, originality is increasingly derived via automated natural language processing (NLP) algorithms using distributional semantic models (e.g., GloVe, Word2Vec, or latent semantic analysis [LSA]) to calculate the objective cosine distance between the target noun (e.g., “pencil”) and the produced verb-noun phrase (e.g., “splint finger”).

6. Theoretical Framework

The architectural foundation of the Alternate Uses test is J. P. Guilford’s Structure of Intellect (SOI) model, a comprehensive taxonomic theory of human cognitive abilities designed to replace unifactorial models of general intelligence (Spearman’s g). Guilford classified intellectual functioning along three orthogonal, intersecting morphological dimensions: Operations, Contents, and Products.

The Structure of Intellect Taxonomy

The three dimensions within the classical SOI model interact systematically to define distinct intellectual aptitudes:

• Operations: The fundamental psychological processes performed on information, categorized into Cognition (discovering or recognizing information), Memory Recording, Memory Retention, Divergent Production, Convergent Production, and Evaluation.
• Contents: The broad substantive nature of the information involved, comprising Visual, Auditory, Symbolic (letters, digits), Semantic (verbal meanings and abstract concepts), and Behavioral (social interactions).
• Products: The structural form in which the information is processed or organized, organized hierarchically into Units, Classes, Relations, Systems, Transformations, and Implications.

Within this structural matrix, the Alternate Uses test was intentionally engineered to isolate and operationalize the intersection of Divergent Production with Semantic Content. Specifically, spontaneous flexibility on the ALTUS represents Divergent Production of Semantic Classes (DMC): the capacity to rapidly generate multiple conceptual classifications from a single semantic stimulus without external prompts. In contrast, simple ideational fluency corresponds to Divergent Production of Semantic Units (DMU), representing the unconstrained retrieval of singular semantic entities.

Overcoming Mental Set and Functional Fixedness

From a cognitive-mechanistic standpoint, the ALTUS is rooted in the information-processing dynamics of associative search and cognitive control. In semantic network theory (Collins & Loftus, 1975), concepts are organized as nodes within an interconnected web of associative links. High-frequency, conventional attributes occupy central, highly weighted positions (e.g., tire → car, rubber, drive, round). To generate an alternate use, an individual must engage active cognitive suppression to inhibit high-frequency prepotent links, navigate along distal, weakly activated associative pathways, and identify latent physical properties (e.g., tire → hollow torus, shock-absorbent, buoyant → boat fender, playground swing, garden planter). Thus, the ALTUS tests the theoretical boundary between automatic semantic spread and controlled executive retrieval.

7. Validity

The construct, factorial, convergent, and discriminant validity of the Alternate Uses test has been scrutinized through decades of empirical testing in educational, industrial, and experimental psychology.

Construct and Factorial Validity

Guilford, Christensen, Merrifield, and Wilson (1960) established the factorial validity of the ALTUS through extensive factor analyses conducted across military and academic cohorts. When administered alongside broad cognitive batteries measuring general intelligence, verbal comprehension, perceptual speed, and spatial visualization, the Alternate Uses items consistently loaded on a dedicated divergent production factor (spontaneous flexibility / DMC), demonstrating distinct factorial independence from general verbal intelligence (r with verbal IQ tests typically ranges between .20 and .35). This demonstrates that while a baseline threshold of verbal competence is necessary to comprehend the prompts and transcribe responses, spontaneous flexibility constitutes a distinct psychological trait rather than a mere artifact of verbal fluency.

Convergent Validity

Convergent validity is evidenced by significant positive correlations with other validated divergent thinking and creativity instruments:

• Torrance Tests of Creative Thinking (TTCT): ALTUS flexibility and fluency scores correlate moderately to strongly with verbal flexibility and verbal fluency indices of the TTCT (r values typically ranging from .45 to .62; Runco, 1991).
• Remote Associates Test (RAT): ALTUS scores exhibit low-to-moderate positive correlations with Mednick’s RAT (r ≈ .25 to .38), supporting the theoretical distinction between convergent creative synthesis (RAT) and divergent exploratory ideation (ALTUS).
• Real-World Creative Achievement: ALTUS flexibility metrics demonstrate significant predictive validity when evaluating self-reported creative achievements, extracurricular creative pursuits, and patent production, yielding typical predictive validity coefficients ranging from r = .28 to r = .42 (Plucker, 1999; Jauk et al., 2013).

Discriminant Validity

Discriminant validity is robustly documented against measures of standardized academic achievement, working memory span, and non-divergent cognitive speed. Correlations with standardized math achievement metrics (e.g., SAT-Math) are typically negligible (r < .15). Furthermore, Silvia et al. (2008) demonstrated that when latent variable modeling is applied to control for subjective scoring variances and general intelligence, divergent thinking factors derived from tasks like the Alternate Uses test maintain clear discriminant separation from the Big Five personality domains, showing only a specific, theoretically coherent positive association with Openness to Experience (r ≈ .30 to .45).

8. Reliability

Reliability estimates for the Alternate Uses test encompass internal consistency, split-half metrics, alternate-form equivalence, and test-retest stability across multiple developmental strata.

Internal Consistency and Split-Half Reliability

Because the ALTUS consists of two structurally parallel timed parts, internal consistency is primarily evaluated via split-half correlations corrected by the Spearman-Brown prophecy formula, or via alternate-form correlations between Part I and Part II:

• Sixth-Grade Cohorts: Published normative studies report composite reliability coefficients ranging from .85 to .91 across test lengths varying between 6, 9, and 12 items.
• Ninth-Grade Cohorts: Reliability coefficients for total flexibility across standard administrations range from .75 to .86.
• Adult Samples: Composite reliability estimates in collegiate and adult professional samples consistently register between .75 and .86.

Test-Retest Stability

Evaluating the temporal stability of divergent thinking tasks is subject to unique psychometric challenges, including testing fatigue, memory carryover, and the tendency of participants to intentionally recall previously generated uses rather than engaging in spontaneous ideation. Nevertheless, test-retest reliability evaluations on a longitudinal sample of 489 students across grades 5 through 7 yielded the following stability coefficients:

• Form A: Test-retest reliability ranged from .54 to .67 across an inter-test interval.
• Form B: Test-retest reliability ranged from .53 to .63.

These values are consistent with the psychometric literature on timed divergent production tasks, indicating a stable trait core influenced by task-specific situational engagement.

Inter-Rater Reliability

Given that spontaneous flexibility requires human coders to determine whether a response constitutes an acceptable non-conventional use and to categorize categorical shifts, inter-rater reliability is a vital psychometric metric. When coders utilize standardized scoring guides with predetermined semantic category rubrics, inter-rater concordance—measured via two-way random intraclass correlation coefficients (ICC) or Cohen’s kappa (κ)—routinely exceeds .90 (Silvia et al., 2008; Runco et al., 2005).

9. Factor Analysis

Factor analytic work on the Alternate Uses test has been instrumental in shaping both historical structural theories and contemporary latent variable psychometric models of human intelligence.

Exploratory Factor Analysis (EFA) & Historical USC Aptitudes Studies

In the foundational EFA studies conducted by Guilford and colleagues (Wilson et al., 1954; Guilford et al., 1960), principal components and orthogonal (Varimax) rotations were performed on large batteries of cognitive, perceptual, and verbal tests administered to military cadets and university undergraduates. When ALTUS items were factor analyzed alongside convergent tasks (e.g., Vocabulary, Syllogisms, Arithmetic Reasoning) and other divergent tasks (e.g., Word Association, Plot Titles), the ALTUS loaded heavily on an independent factor designated as Spontaneous Flexibility (DMC) with factor loadings typically ranging between .55 and .74.

Critically, the factor analysis distinguished spontaneous flexibility from Adaptive Flexibility (the ability to solve problems where the rules or constraints change, exemplified by Matchstick Problems) and from simple Verbal Comprehension (V), where ALTUS cross-loadings remained consistently low (typically < .20).

Confirmatory Factor Analysis (CFA) and Modern Latent Modeling

Modern psychometric investigations employing Confirmatory Factor Analysis (CFA) have reassessed the dimensional structure of Alternate Uses data. A critical methodological issue identified in classical scoring was the presence of a strong “fluency confound”: individuals who write more responses (high fluency) automatically have more mathematical opportunities to exhibit categorical shifts (high flexibility), yielding raw correlations between fluency and flexibility that often exceed r = .80.

To resolve this, modern CFA investigations (e.g., Silvia et al., 2008; Nusbaum & Silvia, 2011) have modeled divergent thinking as a higher-order latent construct using “snapshot” scoring or ratio-based flexibility metrics. In structural equation models specifying a general Divergent Thinking factor with subordinate latent factors for Fluency, Flexibility, and Originality, the measurement models demonstrate excellent goodness-of-fit indices:

• Comparative Fit Index (CFI) ≥ .95
• Tucker-Lewis Index (TLI) ≥ .94
• Root Mean Square Error of Approximation (RMSEA) ≤ .05 (90% CI [.03, .07])
• Standardized Root Mean Square Residual (SRMR) ≤ .04

These structural findings confirm that while fluency and flexibility share significant common variance within the broader divergent production domain, spontaneous flexibility maintains significant independent residual variance, reflecting genuine executive set-shifting operations.

10. Instrument / Measurement Tool

• Test Type: Paper-and-pencil divergent thinking inventory / open-ended cognitive performance test.
• Format: Structured production test divided into two individually timed sections (Part I and Part II). Each part presents three familiar objects with their conventional uses. Examinees are provided six designated response slots per object.
• Item Count: 6 standard items total (3 items in Part I; 3 items in Part II).
• Response Format: Open-ended production (participants list up to 6 different alternative uses for each object within an allotted time limit, typically 4 minutes per section).
• Administration Time: Approximately 10 to 12 minutes total (including instructional delivery, sample item walk-through, and exactly 4 minutes of testing time per part).
• Scoring Metrics:
  • Spontaneous Flexibility: Scored primarily by counting the number of shifts in semantic categories across acceptable, non-conventional responses. Conventional uses or minor variations of the standard use receive 0 points. Repeated uses within the same semantic category receive no additional flexibility credit. Maximum flexibility score per item = 6 (Total maximum score across 6 items = 36).
  • Ideational Fluency: Total number of valid, plausible, non-conventional alternate uses produced across all items.
  • Originality (Optional/Modern Scoring): Evaluated either via normative infrequency rubrics or automated computational semantic distance algorithms.
• Available Alternate Forms: Form A, Form B, and Form C (allowing for pre-test/post-test experimental designs and enhanced reliability testing).

11. Permissions & Fee and Test Year

• Year of Original Publication: 1960 (with foundational developmental precursors published in 1954).
• Authors: J. P. Guilford, Paul R. Christensen, Philip R. Merrifield, and Robert C. Wilson.
• Copyright & Commercial Distribution: The Alternate Uses test is a proprietary, copyrighted psychometric instrument. Commercial distribution, licensing, and administration manuals are managed by Mind Garden, Inc. (www.mindgarden.com).
• Fee Structure: The instrument is commercial; fees are required to purchase physical test booklets, administrative manuals, and digital reproduction licenses for research or organizational deployment. Mind Garden offers academic discounts for validated educational and scientific research endeavors.

12. References

• Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82(6), 407–428. https://doi.org/10.1037/0033-295X.82.6.407
• Duncker, K. (1945). On problem-solving. Psychological Monographs, 58(5), i–113. https://doi.org/10.1037/h0093599
• Guilford, J. P. (1950). Creativity. American Psychologist, 5(9), 444–454. https://doi.org/10.1037/h0063487
• Guilford, J. P. (1967). The nature of human intelligence. McGraw-Hill.
• Guilford, J. P., Christensen, P. R., Merrifield, P. R., & Wilson, R. C. (1960). Guilford’s Alternate Uses Test. Mind Garden, Inc. https://www.mindgarden.com
• Jauk, E., Benedek, M., Dunst, B., & Neubauer, A. C. (2013). The relationship between intelligence and creativity: New support for the threshold hypothesis by means of empirical breakpoint detection. Intelligence, 41(4), 212–221. https://doi.org/10.1016/j.intell.2013.03.003
• Nusbaum, E. C., & Silvia, P. J. (2011). Are intelligence and creativity really so different? Fluid intelligence, executive processes, and strategy use in divergent thinking. Intelligence, 39(1), 36–45. https://doi.org/10.1016/j.intell.2010.11.002
• Plucker, J. A. (1999). Is the proof in the pudding? Reanalyses of Torrance’s (1958 to present) longitudinal data. Creativity Research Journal, 12(2), 103–114. https://doi.org/10.1207/s15326934crj1202_3
• Runco, M. A. (1991). Divergent thinking. Ablex Publishing Corporation.
• Runco, M. A., Dow, G., & Smith, W. R. (2005). Information, experience, and divergent thinking: An empirical test. Creativity Research Journal, 17(2-3), 167–177. https://doi.org/10.1080/10400419.2005.9651478
• Silvia, P. J., Winterstein, B. P., Willse, J. T., Barona, C. M., Cram, J. T., Hess, K. I., Martinez, J. L., & Richard, C. A. (2008). Assessing creativity with divergent thinking tasks: Exploring the reliability and validity of new subjective scoring methods. Psychology of Aesthetics, Creativity, and the Arts, 2(2), 68–85. https://doi.org/10.1037/1931-3896.2.2.68
• Wilson, R. C., Guilford, J. P., Christensen, P. R., & Lewis, D. J. (1954). A factor-analytic study of creative-thinking abilities. Psychometrika, 19(4), 297–311. https://doi.org/10.1007/BF02289230

13. Items of the Scale (Questionnaire)