How do people choose whether to seek or avoid information about their health, finances and personal traits?

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People choose whether to seek or avoid information about their health, finances and personal traits based on how they think it will make them feel, how useful it is, and if it relates to things they think about often, finds a new study by UCL researchers.

Most people fall into one of three ‘information-seeking types’: those that mostly consider the impact of information on their feelings when deciding whether to get informed, those that mostly consider how useful information will be for making decisions, and those that mostly seek information about issues they think about often, according to the findings published in Nature Communications.

Co-lead author Professor Tali Sharot (UCL Psychology & Language Sciences and Max Planck UCL Centre for Computational Psychiatry and Ageing Research) said: “Vast amounts of information are now available to individuals. This includes everything from information about your genetic make-up to information about social issues and the economy.

We wanted to find out: how do people decide what they want to know?

And why do some people actively seek out information, for example about COVID vaccines, financial inequality and climate change, and others don’t?

“The information people decide to expose themselves to has important consequences for their health, finance and relationships. By better understanding why people choose to get informed, we could develop ways to convince people to educate themselves.”

The researchers conducted five experiments with 543 research participants, to gauge what factors influence information-seeking.

In one of the experiments, participants were asked how much they would like to know about health information, such as whether they had an Alzheimer’s risk gene or a gene conferring a strong immune system.

In another experiment, they were asked whether they wanted to see financial information, such as exchange rates or what income percentile they fall into, and in another one, whether they would have liked to learn how their family and friends rated them on traits such as intelligence and laziness.

Later, participants were asked how useful they thought the information would be, how they expected it would make them feel, and how often they thought about each subject matter in question.

The researchers found that people choose to seek information based on these three factors: expected utility, emotional impact, and whether it was relevant to things they thought of often. This three-factor model best explained decisions to seek or avoid information compared to a range of other alternative models tested.

Some participants repeated the experiments a couple of times, months apart. The researchers found that most people prioritise one of the three motives (feelings, usefulness, frequency of thought) over the others, and their specific tendency remained relatively stable across time and domains, suggesting that what drives each person to seek information is ‘trait-like’.

In two experiments, participants also filled out a questionnaire to gauge their general mental health. The researchers found that when people sought information about their own traits, participants who mostly wanted to know about traits they thought about often, reported better mental health.

Co-lead author, Ph.D. student Christopher Kelly (UCL Psychology & Language Sciences and Max Planck UCL Centre for Computational Psychiatry and Ageing Research) said: “By understanding people’s motivations to seek information, policy makers may be able to increase the likelihood that people will engage with and benefit from vital information.

For example, if policy makers highlight the potential usefulness of their message and the positive feelings that it may elicit, they may improve the effectiveness of their message.

“The research can also help policy makers decide whether information, for instance on food labels, needs to be disclosed, by describing how to fully assess the impact of information on welfare. At the moment policy-makers overlook the impact of information on people’s emotions or ability to understand the world around them, and focus only on whether information can guide decisions.”


Decision Making Processes and Outcomes

A significant body of research has examined problem solving and decision making performance in adulthood (see [1, 2] for reviews). Both problem solving and decision making are concerned with the ways in which people interpret problems, form goals, search information, and combine information to arrive at solutions.

Researchers often employ think-aloud and other process-tracing techniques to investigate the processes governing information search and cessation [3, 4]. The extant literature demonstrates that relative to younger and middle-aged adults, older adults approach decision making with different goals, apply different heuristics, seek different amounts and types of information in the predecision phase, and offer different decisions (e.g., [4–6]).

Research has examined several possible mechanisms to explain this age difference, including the role of cognitive resources (e.g., [5, 7]), the social context and personal experience [8, 9], affective context [10], and the decision domain [11]. Sophisticated studies have examined these factors individually and in combination [12]. For many decision tasks, basic and intermediate cognitive skills such as working memory and speed of processing often are the strongest predictor of decision outcome [13].

Process-tracing techniques may allow a more thorough examination of task performance and strategic processing [3, 4, 14, 15]. In the standard decision making task, materials are structured to reflect those available in the real-world, similar to the ecologically-rich social vignettes used in the everyday problem solving approach.

Although in actual real-world information searches, people are able to view all of the available information simultaneously, an advantage to the process-tracing technique is that one is able to directly examine how an adult: (a) selects important features, (b) combines the information obtained, (c) evaluates specific alternatives, and (d) arrives at an adequate decision [14–16].

Specifically, multiple measures are used to index task performance, including the amount of information searched and the order in which the information is searched. Indicators of the amount of information searched include time on task and the number of information cells viewed (i.e., thoroughness). Research shows that older adults often use less information than younger adults [4, 17].

Strategic search is also examined in the decision making task. The two main types of search strategies have been termed compensatory and noncompensatory [4, 18]. Compensatory search is an exhaustive strategy that relies on mathematical averaging of the features for each alternative.

Thus, compensatory searches require significant effort as most or all of the available information is read, processed, evaluated, and combined. Noncompensatory strategies reduce the number of viable alternatives quickly when the information load exceeds a person’s ability to attend to the information volume. Noncompensatory strategies accomplish this reduction by focusing exclusively on a few alternatives which have high or acceptable values on a set of key attributes. Thus, noncompensatory strategies are more resource-efficient than compensatory search strategies [14, 18].

One area that is often overlooked in decision making research concerns the value of the information participants choose to view. However, information quality has been studied in examinations of expert decisions [19]. For example, some studies of experts have shown that they search very little of the available information before making a decision, in some cases viewing only one or two pieces of highly relevant information [15].

The experts are able to “zero in” on the most critical pieces of information to make a high quality decision. If the value of any given piece of information is known, researchers can measure the selectivity with which decision makers choose information. Using search selectivity, the quality of information used can be separated from the amount used and from the order of search. Thus, selectivity allows one to interpret differences in thoroughness and strategy.

reference link :https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3824331/


More information: Whether people inform themselves or remain ignorant is due to three factors, Nature Communications (2021). DOI: 10.1038/s41467-021-27046-5

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