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  • Saransh Sharma

Affect Heuristic - Definitions, Causes, Risks, Advantages & Debiasing

Updated: Feb 3, 2021



 

Affect is defined as the specific quality of "goodness" or "badness" that we

experience as a feeling. These feelings often work at an unconscious level and even

when we are aware of affect, many a times, we can’t even tell it’s source or trigger .

Affect is characterized by its valence i.e. positive or negative and arousal i.e. high

or low activation. Now, affect heuristic is defined as the tendency to make

decisions based on affective and emotional responses or gut feelings . Researchers

have found that when people have a pleasant feeling about something, they see the

benefits as high and the risks as low , and when they have a negative feeling, they

see the risks as high and the benefits as low (Slovic et al., 2005).


One of the early studies that threw light on the workings of affect heuristic

involved exposing participants to the image of either a smiling face, a frowning

face, or a neutral shape for about 1⁄250 of a second (.004s), followed by being

shown a Chinese character for two seconds and asked to rate the character on a

scale of liking. Researchers found that participants preferred the character preceded

with a smiling face as opposed to those preceded by a frowning face or neutral

shape despite the fact that the smiling face was shown for an amount of time which

wasn’t enough for the participants to even consciously aware of it (Winkelman et

al., 1997).



COGNITIVE CAUSES


Cognitive causes are the psychological mechanisms that explain the bias. It is likely that no one of the multiple explanations can explain every instance of the bias, and each explanation is valid in some cases and invalid in others.


A number of researchers subscribe to the dual process theory which proposes that

there are 2 complementary but fundamentally different modes of thinking and

decision making - experiential and analytical. While experiential thinking is

oriented towards immediate action and is driven by affect, analytical thinking is

oriented towards planning or evaluating and is driven by logic. Rather than one

system being superior to other, both systems are well-adapted to specific decision

contexts, giving us behavioral flexibility (Slovic et al., 2013).


Next, our brain is always predicting how things will impact us and preparing our

body for these impacts. So if a person gave us a cake last time we met them, our

brain predicts receiving a cake when we meet them again or even think of them,

and prepares our body for a rush of sugar. We feel these bodily changes based on

the brain’s predictions as affect - positive or negative feelings for predictions of

benefits and harms, and high or low level of arousal for predictions about bodily

resources required. So affect represents our predictions about objects rather than

properties of the object itself, but very commonly we are unable to tell the

difference between the two, and therefore believe that objects and people in the

world are inherently negative or positive. This phenomenon is known as affective

realism, and is closely related to Damasio’s somatic marker hypothesis (Barrett,

2017; Bechara & Damasio, 2005).


And finally, the affect-as-spotlight hypothesis proposes that affect guides our

information search while making decisions. Individuals who have positive feelings

about an object will spend more time looking at its benefits and will remember

them better, while they spend less time looking at its risks and will remember them

less well. This biased information search is something that contributes to the

confirmation bias as well (Peters et al., 2006).



BIOLOGICAL CAUSES


Affective system in our brains is quite sophisticated and involves multiple regions

of the brain and multiple levels of processing. Firstly, amygdala stores emotional

memories and helps in prediction of outcomes based on what we perceive. Now,

nucleus accumbens is involved in creating motivational states based on these

predictions. The internal states of the body are represented in terms of affective

information in the insula, also referred to as the interoceptive cortex. The

emotional, motivational and affective information from these brain regions is

integrated in the orbitofrontal cortex, where it informs decision making. All these

brain regions are densely interconnected and influence each other in both

directions (Kirkland & Cunningham, 2011).



DECISION ADVANTAGES


For the bias to be passed down genetically or culturally to us from our ancestors, it must be beneficial in certain conditions.


Well firstly, although analysis is certainly important in some decision-making

circumstances, reliance on affect and emotion is a quicker, easier, and more

efficient way to navigate in a complex, uncertain, and sometimes dangerous world.

Afterall, you don’t want to be calculating the speed of an approaching predator,

you want to be running away at the very hint of one (Slovic et al., 2005).


Next, affect plays an important role in triggering action. Judgments shaped by

affective impressions have a motivational component that can be lacking from

judgments that are purely analytical. For example, you may cognitively understand

the problem of homelessness, but you’re unlikely to do something about it unless

you feel emotionally about it (Seabright, 2010).


And finally, affect acts as a “common currency” allowing decision makers to

compare apples to oranges, literally. By converting more complex thoughts into

simpler affective judgements of good and bad feelings, decision makers can

compare objects and ideas which are otherwise too dissimilar to compare (Peters et

al., 2006).



DECISION RISKS


Firstly, affect heuristic makes us believe that risks and benefits are inversely

related i.e. if we perceive the benefits of something to be high, we automatically

underestimate its risks , and vice versa. But very often, risks and benefits in our

environment are positively related i.e. one being high means the other must also be

high . For example, hunting a larger prey is riskier , but also more rewarding as it

feeds more individuals. Similarly, sugary foods are more rewarding but they also

carry more risks of addiction and overconsumption (Slovic et al., 2005).


Secondly, when expected outcomes carry sharp and strong affective meaning, as is

the case with a large reward like a lottery jackpot or a terminal disease like cancer,

we become blind to the actual likelihood of these outcomes. For example, our

attitude towards something that has 5% chance of leading to cancer doesn’t differ

very much from something that has a 50% chance, because the negative affect of

cancer itself is too overpowering for us to think about its probability. This tendency

is referred to as probability neglect (Slovic & Peters, 2006).


Next, the affective system evolved to respond to direct, first hand experiences,

which are mostly limited to smaller numbers and smaller changes in our

environment - we can only meet a limited number of people in a day and deal with

a limited number of events. So the affective system is not well adapted towards

larger changes and large quantities. As a result, we are unable to understand

exponential growth such as the rate of growth of pandemics like COVID-19. Also,

our insensitivity to large numbers means that we may fail to respond adequately to

problems of famine, poverty,and disease afflicting large numbers of people. This is

because the difference between 1 death and 100 deaths may seem big to us in terms

of it’s emotional impact, but the emotional impact of 100,000 deaths and 200,000

deaths may not differ very much, as both are very large numbers. This insensitivity

is referred to as "psychophysical numbing" (Slovic et al., 2005).


Next, affect is based on the averages and stereotypes derived from the sum of all

our past experiences regarding a particular object, person or event. These

stereotypes or symbolic meanings may override the specific evidence that may be

available to us in a given situation. For example, nature may be generally

perceived as something positive and pristine. So threats with natural causes, such

as a pandemic, may be underestimated and may be associated more to the

inevitability of the event or to acceptance of natural cycles. On the other hand,

threats with human causes, such as risks of nuclear energy, may be associated with

irresponsibility, apathy or malice, and therefore overestimated (Siegrist &

Sütterlin, 2014).


Next, the affective system is designed to make sense of events that are close to us

in time and space. It does not do as well with information that is temporally,

physically or socially distant. This leads to insensitivity towards slow change like

global warming or distant events like genocide in another country (Seabright,

2010).


And finally, affective decision making makes us susceptible to manipulation

through marketing tactics. Once preference for a brand is formed, we begin

overvaluing it’s products’ benefits and underestimate their risks. Similarly, benefits

of a product communicated with strong affect can compensate for it’s

shortcomings (King & Slovic, 2014).



Managing Bias


The evaluability principle states that the impact of affect on decision is stronger if

there is a clear evaluation criteria in the decision context. For example, the size of

the container of food becomes the evaluation criteria for quantity of food. So a half

filled container is less while a fully or over filled container is more. But what if we

compare an overfilled container of smaller size with an underfilled larger

container. Well, this sometimes leads to the less-is-more effect, where people

prefer the overfilled small container. Without a context to give affective

perspective to quantities of money, food, and even lives, these quantities may not

convey enough meaning (Slovic et al., 2007).


Secondly, for quantitative information, frequency formats are more affect laden

than percentages. For example, to increase the affective meaning of 20% risk of

death it can be reframed as 1 out of 5 people are likely to die. Affective impact can

be increased further by using imagery or individualized narrative (Slovic et al.,

2005). For example, “800,000 killed in the last 100 days” can be broken down and

reframed as “1 life lost every 11 seconds.” Both the one life lost and the near-time

horizon of “every 11 seconds” are more affective. Another example - in a rally to

get the US Congress to do something about 38,000 deaths a year from handguns,

the organizers piled 38,000 pairs of shoes in a mound in front of the Capitol

(Slovic & Västfjäll, 2010).


Next, once an affective association is created, it exists separately from the

information or reasoning that led to it. So if further information is provided

invalidating that original information, the affective response is still going to persist.

The only way to nullify an existing affective association is to invoke a strong

opposite affective association (Sherman & Kim, 2002).


Next, our reliance on affect increases with time scarcity and other environmental

stressors that deplete our mental bandwidth. So, unless an important decision is

urgent, it is advisable to mull over it a little bit to balance affective and analytical

evaluations (Finucane et al., 2000).


And finally, we deliberately manipulate our affective states all the time and

researchers have found that our attempts are highly successful i.e. they actually do

lead to changes in affective states. We do this when we expect that an adjustment

in our affective state will lead to improvement in our performance at a certain task.

Indeed studies have shown that people sometimes induce or maintain negative

affective states in situations where they anticipate that negative mood would be

beneficial in decision making (Cohen & Andrade, 2004).


 

REFERENCES

Slovic, P., Peters, E., Finucane, M. L., & MacGregor, D. G. (2005). Affect, risk, and decision making. Health psychology, 24(4S), S35.

Van den Berg, H., Manstead, A. S., van der Pligt, J., & Wigboldus, D. H. (2006). The impact of affective and cognitive focus on attitude formation. Journal of Experimental Social Psychology, 42(3), 373-379.

Slovic, P., & Peters, E. (2006). Risk perception and affect. Current directions in psychological science, 15(6), 322-325

Slovic, P., & Västfjäll, D. (2010). Affect, Moral Intuition, and Risk. Psychological Inquiry, 21(4), 387–398.

Siegrist, M., & Sütterlin, B. (2014). Human and nature‐caused hazards: The affect heuristic causes biased decisions. Risk Analysis, 34(8), 1482-1494.

Mark A. Seabright (2010) The role of the affect heuristic in moral reactions to climate change, Journal of Global Ethics, 6:1, 5-15

King, J., & Slovic, P. (2014). The affect heuristic in early judgments of product innovations. Journal of Consumer Behaviour, 13(6), 411-428.

Winkielman, P; Zajonc, R.B.; Schwarz, N. (1997). "Subliminal affective priming effects resists attributional interventions". Cognition and Emotion. 11 (4): 433–465.

Slovic, P., Finucane, M. L., Peters, E., & MacGregor, D. G. (2013). Risk as analysis and risk as feelings: Some thoughts about affect, reason, risk and rationality. In The Feeling of Risk (pp. 49-64).

Barrett, L. F. (2017). How Your Emotions Are Made: The Secret Life of the Brain and the Future of Human Nature. Pan Macmillan.

Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior, 52(2), 336–372.

Peters, E., Västfjäll, D., Gärling, T., & Slovic, P. (2006). Affect and decision making: A “hot” topic. Journal of behavioral decision making, 19(2), 79-85.

Kirkland, T., & Cunningham, W. A. (2011). Neural basis of affect and emotion. Wiley Interdisciplinary Reviews: Cognitive Science, 2(6), 656-665.

Slovic, P., Finucane, M. L., Peters, E., & MacGregor, D. G. (2007). The affect heuristic. European journal of operational research, 177(3), 1333-1352.

Sherman, D. K., & Kim, H. S. (2002). Affective perseverance: The resistance of affect to cognitive invalidation. Personality and Social Psychology Bulletin, 28(2), 224-237.

Cohen, J. B., & Andrade, E. B. (2004). Affective intuition and task-contingent affect regulation. Journal of Consumer Research, 31(2), 358-367.

Finucane, M.L.; Alhakami, A.; Slovic, P.; Johnson, S.M. (January 2000). "The Affect Heuristic in Judgment of Risks and Benefits". Journal of Behavioral Decision Making. 13 (1): 1–17.

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