Widely employed by innovative organizations, a well-designed simple data visualization has been shown to enhance user experience and aid in decision making; while a more embellished visualization may cause overload, it has the potential to create deeper processing and learning. Furthermore, individual characteristics may impact on how users seek information on these different types of visualization. This study proposes that thinking styles (analytical vs holistic) and domain expertise moderate the effects of data visualization types on decision performances in terms of decision accuracy, decision confidence, memory recall, and cognitive load. To test our hypotheses, an experimental study involving visual manipulations in the context of personal finance was conducted on two types of visualizations (simple and clutter). Results suggest that simple visualizations enhance decision accuracy and reduce cognitive load. We also find that cognitive load is further reduced when analytical thinkers are presented with simple visualizations. These findings can help designers understand how user characteristics may be considered when designing and evaluating visualizations for decision makers.
MoacdiehNSarterN. Display clutter: a review of definitions and measurement techniques. Human Factors2015; 57(1): 61–100.
7.
BatemanSMandrykRLGutwinC, et al. Useful junk?: The effects of visual embellishment on comprehension and memorability of charts. In: Proceedings of the SIGCHI conference on human factors in computing systems, 2010, pp.2573–2582. Atlanta, GA: ACM.
8.
OgnjanovicSThüringMMurphyRO, et al. Display clutter and its effects on visual attention distribution and financial risk judgment. Appl Ergon2019; 80: 168–174.
9.
HullmanJAdarEShahP. Benefitting InfoVis with visual difficulties. IEEE Trans Vis Comput Graph2011; 17(12): 2213–2222.
10.
ZiemkiewiczCOttleyACrouserRJ, et al. Understanding visualization by understanding individual users. IEEE Comput Graph Appl2012; 32(6): 88–94
11.
PadillaLMCreem-RegehrSHHegartyM, et al. Correction to: Decision making with visualizations: a cognitive framework across disciplines. Cogn Res Princ Implic2018; 3(1): 34.
12.
WuKPetersenEAhmadT, et al. Understanding data accessibility for people with intellectual and Developmental Disabilities. In: Proceedings of the 2021 CHI conference on human factors in computing systems2021; 1–16. Yokohama, Japan: ACM.
13.
HallKWKouroupisABezerianosA, et al. Professional differences: a comparative study of visualization task performance and spatial ability across disciplines. IEEE Trans Vis Comput Graph2022; 28(1): 654–664.
14.
HammondKHammRGrassiaJ, et al. Direct comparison of the efficacy of intuitive and analytical cognition in expert judgment. IEEE Trans Syst Man Cybern1987; 17: 753–770.
15.
ReberRSchwarzNWinkielmanP. Processing fluency and aesthetic pleasure: is beauty in the perceiver's processing experience?Pers Soc Psychol Rev2004; 8(4): 364–382.
16.
PhillipsWJFletcherJMMarksAD, et al. Thinking styles and decision making: a meta-analysis. Psychol Bull2016; 142(3): 260–290.
17.
GreifenederRKellerJ. Habitual self-regulatory orientation and ease-of-retrieval: regulatory focus qualifies the impact of subjective experiences in judgment. Motiv Emot2012; 36(3): 338–348.
18.
ThomasMTsaiCI. Psychological distance and subjective experience: How distancing reduces the feeling of difficulty. J Consum Res2012; 39(2): 324–340.
19.
AlterALOppenheimerDM. Uniting the tribes of fluency to form a metacognitive nation. Person Soc Psychol Rev2009; 13(3): 219–235.
20.
EberhardK. The effects of visualization on judgment and decision-making: a systematic literature review. Management Review Quarterly2021; 73:1–48.
21.
PaasFRenklASwellerJ. 2003Cognitive load theory and instructional design: Recent developments. Educ Psychol2003; 38(1): 1–4.
22.
AtkinsonRCShiffrinRM. Chapter: human memory: a proposed system and its control processes. In: SpenceKWSpenceJT (eds) The psychology of learning and motivation, Vol. 2. New York, NY: Academic Press, 1968, pp.89–195.
23.
SwellerJ. Cognitive load during Problem-Solving: Effects on learning. Cogn Sci1988; 12(2): 257–285.
24.
Diemand-YaumanCOppenheimerDMVaughanEB. Fortune favors the bold (and the italicized): Effects of disfluency on educational outcomes. Cognition2011; 118(1): 114–118.
25.
ConatiCMaclarenH. 2008. Exploring the role of individual differences in information visualization. In: Proceedings of the workshop on advanced visual interfaces AVI, pp.199–206. Napoli, Italy: AVI.
26.
LawrenceGD. A synthesis of learning style research involving the MBTI. Journal Psychol Type1984; 8: 2–15.
27.
EvansJS. Dual-processing accounts of reasoning, judgment, and social cognition. Annu Rev Psychol2008; 59: 255–278.
28.
EpsteinS. Integration of the cognitive and the psychodynamic unconscious. Am Psychol1994; 49(8): 709–724.
29.
KaoGYLeiPSunC. Thinking style impacts on web search strategies. Comput Human Behav2008; 24: 1330–1341.
30.
SternbergRJ. The concept of intelligence and its role in lifelong learning and success. Am Psychol1997; 52(10): 1030–1037.
31.
SchwartzB. The paradox of choice: why more is less. New York, NY: Harper Collins Publishers, 2004.
HaiderHFrenschPA. Information reduction during skill acquisition: the influence of task instruction. J Exp Psychol Appl1999; 5(2): 129–151.
34.
GegenfurtnerALehtinenESäljöR. Expertise differences in the comprehension of visualizations: A meta-analysis of eye-tracking research in professional domains. Educ Psychol Rev2011; 23(4): 523–552.
35.
ArslanogluCDoğanEAcarK. Investigation of decision making and thinking styles of volleyball referees in terms of some variables. J Educ Train Stud2018; 6: 21.
36.
ChristophKMacEachrenAMSchieweJ. How to assess visual communication of uncertainty? A systematic review of geospatial uncertainty visualization user studies. Cartogr J2014; 51(4): 372–386.
37.
EpsteinSPaciniRDenes-RajV, et al. Individual differences in intuitive-experiential and analytical-rational thinking styles. J Pers Soc Psychol1996; 71(2): 390–405.
38.
TuretkenOShardaR. Clustering-based visual interfaces for presentation of web search results: an empirical investigation. Inf Syst Front2005; 7: 273–297.
39.
HongWThongJYLKar YanT. The effects of information format and shopping task on consumers' online shopping behavior: a cognitive fit perspective. J Manag Inf Syst2004; 21(3): 149–184.
40.
AikenLSWestSGRenoRR, et al. Multiple regression: testing and interpreting interactions. Thousand Oaks, CA: SAGE, 1991.
41.
CronbachLJ. Statistical tests for moderator variables: Flaws in analyses recently proposed, 1987
LalléSConatiC. 2019). The role of user differences in customization: a case study in personalization for infovis-based content. In: Proceedings of the 24th international conference on intelligent user interfaces, pp.329–339. Marina del Ray, CA: Association for Computing Machinery.
44.
DaneERockmannKWPrattMG. When should I trust my gut? Linking domain expertise to intuitive decision-making effectiveness. Organ Behav Hum Decis Process2012; 119(2): 187–194.
45.
TomasiSSchuffDTuretkenO. Understanding novelty: how task structure and tool familiarity moderate performance. Behav Inf Technol2018; 37(4): 406–418.
46.
NorrisPEpsteinS. An experiential thinking style: its facets and relations with objective and subjective criterion measures. J Pers2011; 79(5): 1043–1079.
