1 of 60

Much Ado About �“No Thanks”?

Adult Age Differences in Avoidance Tendencies

Dr. Julia Nolte | Tilburg Dept. Of Social Psychology

2 of 60

Bricker et al. (2017), Löckenhoff (2018), Papanicolas et al., (2020), UN (2019), US Census Bureau (2017)

population aging

resources

high stakes

aging process

For those of you who do not study older adults, which is probably the majority of the audience, let me quickly outline WHY I think this is such an important and fascinating area to study.

I am sure all of you are already aware that societies around the globe are growing older. The number of adults over the age of 60 is expected to double between 2010 and 2050, and that means addressing age-related questions will become more and more important in the coming decades.

Older adults as decision makers are so interesting to study because they tend to command more wealth, have access to more leisure time, and hold more voting power than other age groups do. That makes older adults very important and influential opinion leaders that have the potential to shape policies and consumer trends for all age groups, not just their own.

Of course, older adults also face a lot of high stakes choices, from leaving the work force to creating a will and making decisions about risky medical procedures as well as their willingness to accept life-prolonging treatments.

Finally, we all know that aging changes us – it changes our cognitive capacities, our preferences for how to spend our time and money and who to spend that with, and what we put value on in decision contexts. So in many small and big ways, older decision makers are unlike decision makers of other age groups.

3 of 60

Age differences in decision making

Löckenhoff (2018)

Decision Identification/

Engagement

Information Search

Strategy Selection & Evaluation

Implementation

Post-Decision Evaluation

Although all of you study decision making, I think it might be helpful to briefly visualize decision making as a process with different stages that can also inform how we approach making decisions in the future. After identifying that there is a decision to be made, decision makers may engage in information search to see what information and options are available to them. Decision makers also will either consciously or unconsciously settle on a certain way of examining or comparing their options – some in more thorough way than others. A choice is then made and decision makers can take what they learned, for example whether they regret their decision, into the next decision process, if relevant.

As you can image, decision makers such as the colleagues I am recruiting for my special interest group have already conducted research at all of these stages.

4 of 60

Age differences in decision making

Löckenhoff (2018)

Decision Identification/

Engagement

Information Search

Strategy Selection & Evaluation

Implementation

Post-Decision Evaluation

Nolte & Löckenhoff (2022)

Deng et al. (2022)

Nolte et al. (2022)

Mei et al. (in prep.)

Nolte & Löckenhoff (2021)

Nolte et al. (2022)

Nolte, Hanoch et al. (2021a)

Nolte, Hanoch et al. (2021b)

Nolte, Hanoch et al. (under review)

Nolte et al. (2022)

5 of 60

Age differences in decision avoidance?

Chen et al. (2011), Fraenkel et al. (2015), Hudak et al. (2002), Löckenhoff (2018), Pethtel & Chen (2013), Wong et al. (2020)

Decision Identification/

Engagement

Information Search

Strategy Selection & Evaluation

Implementation

Post-Decision Evaluation

Decision Avoidance

From a lot of applied work, particularly research from the medical field but also from insurance uptake, we know that older adults are more likely to bypass the decision process, or to exit it prematurely by saying “no thank you” to making decisions. They do this in various ways, by delaying choice, rejecting options, or outsourcing decisions to other people, such as experts or family members. But surprisingly, there is almost no existing psychological or experimental research on this phenomenon. The little we know so far is that older adults avoid choice both in health and consumer contexts, and they do so regardless of how objectively challenging choices are, although for younger decision makers, the likelihood of avoiding choice increases as the choice becomes more challenging.

Because decision avoidance can put older adults at risk of foregoing timely treatment or financial benefits, I have spent some time in recent years examining two questions: First, in which types of decision contexts are older adults more likely to avoid choice. And second, since the underlying mechanisms are not at all clear at this point, WHY do older adults avoid choices more?

6 of 60

Option refusal/decision deferral

	PRICE	ROUTINE HANDLING	INTERIOR ROOMINESS	MAINTENANCE COSTS
A	GOOD	POOR	VERY POOR	VERY GOOD
B	VERY POOR	VERY GOOD	GOOD	POOR
C	POOR	GOOD	VERY GOOD	VERY POOR
D	VERY GOOD	VERY POOR	POOR	GOOD

N = 164 & 485 Nolte & Löckenhoff (in prep.)

7 of 60

Option refusal/decision deferral

	PRICE	ROUTINE HANDLING	INTERIOR ROOMINESS	MAINTENANCE COSTS
A	GOOD	POOR	VERY POOR	VERY GOOD
B	VERY POOR	VERY GOOD	GOOD	POOR
C	POOR	GOOD	VERY GOOD	VERY POOR
D	VERY GOOD	VERY POOR	POOR	GOOD

N = 164 & 485 Nolte & Löckenhoff (in prep.)

8 of 60

Option refusal/decision deferral

	PRICE	ROUTINE HANDLING	INTERIOR ROOMINESS	MAINTENANCE COSTS
A	GOOD	POOR	VERY POOR	VERY GOOD
B	VERY POOR	VERY GOOD	GOOD	POOR
C	POOR	GOOD	VERY GOOD	VERY POOR
D	VERY GOOD	VERY POOR	POOR	GOOD

N = 164 & 485 Nolte & Löckenhoff (in prep.)

9 of 60

Option refusal/decision deferral

N = 164 & 485 Nolte & Löckenhoff (in prep.)

Study 2 (N = 485)

M = 1.77 (SD = 1.63)

avoided choices

age (r_s= .35, p < .001***)

10 of 60

Option refusal/decision deferral

N = 164 & 485 Nolte & Löckenhoff (in prep.)

Study 2 (N = 485)

M = 1.77 (SD = 1.63)

avoided choices

age (r_s= .35, p < .001***)

11 of 60

Default effects

1		2		3		4		5		6		7		8		9

10	11	12	13	14	15

1		2		3		4		5		6		7		8		9

10	11	12	13	14	15

N = 500 Nolte & Löckenhoff (2023). Experimental Psychology: General.

Encouraged by this finding, and knowing that older adults like to outsource choices, I was curious as to whether older adults are also more likely to avoid choice by accepting decisions that others have made for them.

Therefore, we expanded the study of contexts in which older adults are more decision avoidant to the well-known phenomenon of default effects. Default effects describe the difference in choice between conditions in which options are pre-selected for the decision maker and conditions in which the options are not pre-selected. Decision makers are more likely to passively accept options that were pre-chosen for them to actively choose these options themselves.

To create an index of how strong a decision makers’ susceptibility to this bias is, we presented each decision maker with two decision scenarios relating to rental housing. In the opt-out condition,15 premium amenities that can be added for additional costs were pre-selected for the decision maker. By contrast, in the opt-in condition, 0 of 15 premium amenities were pre-chosen but could be added by the decision maker for additional costs.

12 of 60

Default effects

1		2		3		4		5		6		7		8		9

10	11	12	13	14	15

1		2		3		4		5		6		7		8		9

10	11	12	13	14	15

N = 500 Nolte & Löckenhoff (2023). Experimental Psychology: General.

13 of 60

Default effects

1		2		3		4		5		6		7		8		9

10	11	12	13	14	15

1		2		3		4		5		6		7		8		9

10	11	12	13	14	15

N = 500 Nolte & Löckenhoff (2023). Experimental Psychology: General.

14 of 60

Default effects

# amenities chosen & age

(β = -.22, p < .001***)

opt-in vs. opt-out condition & age

(β = .01, p = .807)

N = 500 Nolte & Löckenhoff (2023). Experimental Psychology: General.

When using this approach and examining results by age groups, you can see that decision makers of all ages choose more options in the opt-out condition, here shown in gray bars, than in the opt-in condition, here shown in white bars.

As is hopefully evident from the bars, older adults in general chose fewer amenities than their younger peers did. However, age did not interact with condition, that is, the discrepancy between conditions was not higher among older adults.

And although it looks as though the discrepancy is higher in midlife than at the end of life, we also did not find suitable evidence that there is a curvilinear age effect on the susceptibility to default effects.

Therefore, we find no evidence indicating that older adults are more susceptible to elements of the choice environment that encourage decision avoidance, or, at least, passive decision making. That being said, the recommendations in the case of our scenarios came from fictional landlords and rental agencies. It would be interesting to see if results were to change if recommendations came from more personal or trusted sources such as family members or one’s general physician, especially since older adults put so much emphasis on close social relationships.

15 of 60

Covariates considered

Demographics

Socioemotional Experience

& Health

Personality

Cognition

(Motives)

Information search

Bruine de Bruin et al. (2016), Löckenhoff (2018), Mata & Nunes (2010), Meyer et al. (2007), Reed et al. (2008), Strough et al. (2015), van Helversen & Mata (2012)

desire & consider fewer options

less likely to seek second opinion

settle for “good enough”

23 of 60

Age differences in information avoidance?

Löckenhoff (2018)

Decision Identification/

Engagement

Information Search

Strategy Selection & Evaluation

Implementation

Post-Decision Evaluation

24 of 60

Age differences in information avoidance?

Stephanie Deng ‘21

N = 195 & 500 Deng et al. (2022). Experimental Aging Research.

25 of 60

Age differences in information avoidance?

Imagine that you are thirsty and decide to buy a water bottle. In the supermarket, there are two plastic water bottles from the same company that you can choose between. One of the bottles, Mist, has a label that provides information about the global drinking water crisis. It is quite visible and covers almost half of the bottle. It includes a graph showing the percentage of people worldwide who are at risk

because they lack access to clean water.

The other bottle, Splash, does not provide information about the drinking water crisis.

Which bottle would you prefer to buy?

Stephanie Deng ‘21

N = 195 & 500 Deng et al. (2022). Experimental Aging Research.

26 of 60

N = 500 Deng et al. (2022). Experimental Aging Research.

M = 2.44

(SD = 1.89)

of info.

avoided

age

(β = .23

p < .001***)

27 of 60

Deng et al. (2022); Hertwig et al. (2021)

Older adults are more avoidant!

28 of 60

BUT…

… it depends on how you measure it!

29 of 60

Info

No info

OR

N = 195 & 500 Deng et al. (2022). Experimental Aging Research.

30 of 60

Info

No info

Info

No info

OR

N = 195 & 500 Deng et al. (2022). Experimental Aging Research.

age (β = .02, p = .827)

(50)

31 of 60

Self-report

Indirect

Behavior

N = 500 Nolte et al. (2022). Journals of Gerontology: Series B.

32 of 60

Self-report

Indirect

Behavior

N = 500 Nolte et al. (2022). Journals of Gerontology: Series B.

p < .05*

33 of 60

Self-report

Indirect

Behavior

N = 500 Nolte et al. (2022). Journals of Gerontology: Series B.

p < .05*

p < .001***

34 of 60

Self-report

Indirect

Behavior

N = 500 Nolte et al. (2022). Journals of Gerontology: Series B.

p < .05*

p < .001***

p < .01**

35 of 60

Covariates considered

Demographics

Socioemotional Experience

& Health

Personality

Cognition

Motives

Once again, of course, we did our best to understand why older adults might be more avoidant of receiving certain types of information. Once again, it does not seem to be due to age-related cognitive decrements, but to our surprise, it also does not seem to be the case that older adults avoid information because they want to avoid experiencing negative affect, which is a core characteristic of age-related preference shift. If you are familiar with Socioemotional Selectivity Theory, it is often the case that older adults favor positively valenced information or stimuli over those inducing negative effect. However, based on self-reported avoidance motives, this does not seem to be the case for our studies on avoiding information that may cue negative affect.

Although we found some covariates that seemed to matter, these were not consistent across avoidance contexts. For example, older adults’ behavioral avoidance of COVID-19 information could be linked to oder adults’ higher knowledge levels, perceived proximity to the end of their lives, and reduced focus on information- and growth-based goals. Put differently, various covariates indicated that the COVID-19 information may simply be less useful or less of interest to older adults.

36 of 60

How can we encourage informed decision making?

37 of 60

Age differences in information search

Mata & Nunes (2010)

14

10

38 of 60

Different studies, different stimuli

Verbatim

Mixed

Gist

“15 EUR“

“10 minutes”

“15 EUR“

“very good”

“poor“

“very good”

39 of 60

Systematic differences in stimuli…

Verbatim

Mixed

Gist

Cole & Balasubramanian (1993)

Johnson (1990)

Reed et al. (2013)

Riggle & Johnson (1996)

Stephens & Johnson (2000)

Johnson (1993)

Johnson & Drungle (2000)

English & Carstensen (2015)

Geiger (2017)

Löckenhoff & Carstensen (2007)

Löckenhoff & Carstensen (2009)

Queen et al. (2012)

40 of 60

… lead to systematic differences in findings!

Verbatim

Mixed

Gist

Cole & Balasubramanian (1993)

Johnson (1990)

Reed et al. (2013)

Riggle & Johnson (1996)

Stephens & Johnson (2000)

Johnson (1993)

41 of 60

Fuzzy-Trace Theory

Reyna (2012)

Verbatim

Gist

“15 EUR“

“10 minutes”

“poor“

“very good”

exact details

numbers

yes – no

good - bad

more – less

better - worse

To understand what is going on here, we drew on a framework called Fuzzy-Trace Theory, by a colleague also at Cornell University.

According to this framework, when we come across information such as something taking 10 minutes or costing 15 EUR, we encode not one but multiple representations of the same stimulus.

So we do retain a verbatim memory of the numer and specifics, but also increasingly fuzzy and subjective representations of what that information means to us. While 15 EUR is objectively the same number for all of us, whether or not that is a good or bad price will be different, and so people may extract different gist traces from the same objective verbatim piece of information.

Because verbatim details are harder to remember over time, and having lots of knowledge and experience helps with extracting gist meaning from information, it has often been assumed that older adults may favor gist-based decision making over relying on verbatim numbers.

42 of 60

On the advanced deductive reasoning task, 10% answer at least 9 out of 10

questions correctly. Michelle enjoys solving puzzles and brainteasers. What is the probability that Michelle will correctly answer at least 9 out of 10 questions on the advanced deductive reasoning task? _____________________ %

Verbatim

Gist

Behavioral Preference (p < .01**)

N = 134 Nolte et al. (2022). Psychology & Aging.

43 of 60

On the advanced deductive reasoning task, 10% answer at least 9 out of 10

questions correctly. Michelle enjoys solving puzzles and brainteasers. What is the probability that Michelle will correctly answer at least 9 out of 10 questions on the advanced deductive reasoning task? _____________________ %

Verbatim

Gist

Verbatim

Gist

Self-reported Preference (p < .001***)

Behavioral Preference (p < .01**)

N = 134 Nolte et al. (2022). Psychology & Aging.

44 of 60

“15 EUR”

“very good”

N = 134 Nolte et al. (2022). Psychology & Aging.

45 of 60

Total Views

N = 134 Nolte et al. (2022). Psychology & Aging.

46 of 60

Unique Views

Total Views

N = 134 Nolte et al. (2022). Psychology & Aging.

47 of 60

Unique Views

Total Views

Preferences account for these effects!

N = 134 Nolte et al. (2022). Psychology & Aging.

48 of 60

Does preference matching decrease avoidance?

Suppose you were recently diagnosed with an inoperable aneurysm in your brain (i.e., an enlarged blood vessel). The aneurysm is not causing any symptoms at the moment but if the aneurysm were ever to rupture, you would be at high risk of dying. Your healthcare provider has used a new algorithm to calculate your personal

risk of the aneurysm rupturing in the next ten years (e.g., … ).

Would you want to know or would you prefer not to know?

25%, 75%

low, high

Lauren Mei ‘22

N = 164 & 485 Mei et al. (in prep.)

49 of 60

Old age = “age of avoidance” ?!�� … IF measured a certain way�� … BUT underlying mechanisms and � potential remedies still unclear

50 of 60

Acknowledgments

Dr. Corinna

Löckenhoff

Cornell University

Stephanie Deng

(Cornell ’21)

Harvard Medical School

Lauren Mei

(Cornell ’22)

Cornell Medical School

Funding

LEEDR

HD Thesis Grant

HD Dissertation Grant

SRNDNA

Rawlings Award

HEAA Grant

Einhorn Center

HEAA Grant

APAGS Award

National Institute on Aging

CIDS Dissertation Award

julianolte.org

j.nolte@tilburguniversity.edu

@Dr_Julia_Nolte

Cornell Healthy Aging Laboratory

51 of 60

In-person events at annual GSA conferences 🡪 starting with the 2024 conference in Seattle!

An official group meeting as part of the conference program

Conference symposia

Online 🡪 starting in the coming months!

Zoom-events (e.g., networking opportunities, talks…)

Shared updates, resources, etc. (e.g., funding, jobs, summer schools…)

52 of 60

Does preference matching decrease avoidance?

Lauren Mei ‘22

N = 164 & 485 Mei et al. (in prep.)

53 of 60

Information avoidance

Study 2 (N = 485)

N = 164 & 485 Mei et al. (in prep.)

verbatim numbers avoided more

(β = .26, p = .039*)

older adults may avoid numbers less…

(β = -.23, p = .081)

54 of 60

Information avoidance

Study 2 (N = 485)

N = 164 & 485 Mei et al. (in prep.)

verbatim numbers avoided more

(β = .26, p = .039*)

older adults may avoid numbers less…

(β = -.23, p = .081)

55 of 60

Supplementary �Slides

56 of 60

Open questions

Avoiding decisions to…

wait for more/better information
wait for more/better options
take more time to think

seek advice
maintain high decision standards
status quo > choice options

MOTIVES?

ADAPTIVE?

57 of 60

Open questions

Avoid decision

Disregard information

Avoid information

Disregard decision

Common cause

Disregard decision

Disregard information

Decision Avoidance

Information Avoidance

58 of 60

Covariates considered

Demographics

Socioemotional Experience/Health

Personality

Age

Gender

Race/ethnicity

Income

Education

Cognition

Physical health (SR)

Emotional health (SR)

Affect/info goals

Growth/maintenance goals

Life position (SR)

Neuroticism

Extraversion

Openness

Agreeableness

Conscientiousness

Learning ability (SR)

Memory (SR)

Cognitive reflection ability

Numeracy

Information processing

59 of 60

AGING

WELL

BEING

CHOICE

60 of 60

AGING

WELL

BEING

CHOICE

Cornell

Healthy Aging

Lab

Harvard

Nock Lab for Suicide & Self-Harm

London

Alzheimer’s Society

Cambridge

Winton Centre for Risk &

Evidence Communication

Heidelberg

Funke Lab for Problem-Solving

Oxford

Cognition & Health Research Group

Cornell

Rational Decision Making Lab

Before coming to Tilburg, I was fortunate to work on different parts of this trifecta at different institutions. I spent most of my time at Heidelberg University in Germany, where I focused on decision research, and at Cornell University in the US, where I focused on aging research. I am very excited to have landed in Tilburg now, where I feel like the Communications Department, the ACC on Digital Health and Wellbeing, and the Ageing Network Tilburg will help me continue explore research in all three areas. Still, because my own department does not focus on questions of development, I would really like to find collaborators here in the Developmental Psychology department.

Although I have researched different combinations of these three themes, my main line of research examines age differences in how people make choices and engage with decision-relevant information.