Improving objects, ideas or situations—whether a designer seeks to advance technology, a writer seeks to strengthen an argument or a manager seeks to encourage desired behaviour—requires a mental search for possible changes1,2,3. We investigated whether people are as likely to consider changes that subtract components from an object, idea or situation as they are to consider changes that add new components. People typically consider a limited number of promising ideas in order to manage the cognitive burden of searching through all possible ideas, but this can lead them to accept adequate solutions without considering potentially superior alternatives4,5,6,7,8,9,10. Here we show that people systematically default to searching for additive transformations, and consequently overlook subtractive transformations. Across eight experiments, participants were less likely to identify advantageous subtractive changes when the task did not (versus did) cue them to consider subtraction, when they had only one opportunity (versus several) to recognize the shortcomings of an additive search strategy or when they were under a higher (versus lower) cognitive load. Defaulting to searches for additive changes may be one reason that people struggle to mitigate overburdened schedules11, institutional red tape12 and damaging effects on the planet13,14.
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All data and materials are available on the Open Science Framework at https://osf.io/7v6r2/. The coded responses from study S2 are posted per our agreement with the organization that supplied the data. Open-ended responses are available from the authors upon reasonable request and with permission of the organization.
All R code used to produce analyses for all studies is available on the Open Science Framework at https://osf.io/7v6r2/.
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We thank E. Caruso, J. Goldstein-Greenwood, D. Player, K. Stenger, S. Trawalter and T. Wilson for comments; Y. Muramoto, Z. Magraw-Mickelson and M. Gollwitzer for assisting with data collection for study S12; A. Myer, J. Holland and M. Wiwuga for research management; and B. Stein and H. Koizumi for research assistance. This research was supported by a 3 Cavaliers grant from the University of Virginia and US National Science Foundation grant no. 153104.
The authors declare no competing interests.
Peer review information Nature thanks Tom Meyvis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 People are more likely to subtract from recipes with atypical ingredients (study S9).
Each dot represents one recipe (k = 27). Placement on the x axis reflects the atypicality score for each recipe, determined by the mean rating from n = 80 workers from Amazon Mechanical Turk. Independent samples of n = 7–12 workers from Amazon Mechanical Turk (total, n = 284) then had an opportunity to transform one of the randomly assigned recipes. Placement on the y axis reflects the percentage of participants who produced a subtractive transformation of each recipe. The atypicality score of an ingredient positively predicted the percentage of participants who subtracted; r25 = 0.54, P = 0.003; two-sided test. Error band represents the 95% confidence interval of predicted percentage subtracting.
In the stimuli that participants saw, a toy action figure (image removed for reasons of copyright) stood at the height marked on the white paper. Participants could stabilize the top platform of the Lego structure so it could hold a masonry brick above the head of the action figure by adding new supports to reinforce the single corner block or by removing the corner block and letting the platform sit flush on the layer below. They earned $1 for successful completion, but adding Lego bricks cost money. The most profitable solution was to remove the single support. Participants were randomly assigned to instructions that explicitly stated ‘removing pieces is free’ (cue condition) or instructions that did not mention removing pieces (control condition).
Participants reported their ideas for how to change this miniature golf hole. We coded whether each idea was additive (for example, ‘add a windmill’), subtractive (for example, ‘remove the sand trap’) or neither (for example, ‘reverse the direction’). Participants were randomly assigned to a no-cue instruction that mentioned neither addition nor subtraction or to a cue condition that reminded participants they could ‘add or subtract’. In experiments 2 and 3, participants reported all of the ways that they might improve the original. In experiment 4, participants were randomly assigned to a condition that solicited their improvement ideas or a condition that solicited their ideas for making the hole worse.
Extended Data Fig. 4 Cumulative percentage of participants who included at least one type of idea by the ith idea in their list in experiments 2 to 4.
Participants were randomly assigned to one of two conditions in experiments 2 and 3 (no-cue versus cue) (n = 312); and one of four conditions in experiment 4 ((no-cue versus cue) × (improve versus make-it-worse)) (n = 369). The y axes show cumulative percentage. The x axes show idea order (i). Empty blue shapes represent subtractive ideas and filled orange shapes represent additive ideas. Dotted lines represent no-cue conditions and solid lines represent cue conditions. Circles represent responses to an improve prompt and triangles represent responses to a make-it-worse prompt. a, b, Across experiments 2 (a) and 3 (b), we did not find evidence that the cue affected the likelihood of participants listing at least one additive idea (odds ratio = 0.92, z = −0.24, P = 0.810), but we did find evidence that it increased the likelihood of participants listing at least one subtractive idea (odds ratio = 1.93, z = 2.73, P = 0.006). c, In experiment 4, the cue increased the likelihood of participants listing at least one subtractive idea within the improvement conditions (no-cue = 21%, cue = 48%, χ2 = 13.63, P < 0.001) and the make-it-worse conditions (no-cue = 28%, cue = 50%, χ2 = 9.71, P = 0.002). The error band represents s.e. of proportion.
This file contains information about participant exclusions, analyses, and results for experiments 1—8 (section 1), descriptions of methods and results for studies S1—S12 (section 2), and additional references (see page 1 for details).