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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