• Saransh Sharma

Impact Bias - Definitions, Causes, Risks, Advantages & Debiasing

Affective forecasts are people's predictions about their own emotional reactions to future events. Researchers have observed that people routinely mispredict how much pleasure or displeasure future events will bring and, as a result, sometimes work to bring about events that are not the best outcomes for themselves (Wilson & Gilbert, 2005).

Affective forecasting consists 4 types of predictions: whether we'll feel good or bad, what exact emotion will we feel, how intensely will we feel it and how long will it last (Loewenstein, 2007)? Turns out we're quite accurate at predicting the valence and specific emotions we'll feel in the future, but we're less accurate in predicting the intensity of these emotions, and least accurate in predicting how long the emotional reactions will last (Whan Park, 2006).

The most common form of affective misforecasting is the impact bias, whereby we overestimate the intensity and duration of our emotional reactions to future events (Wilson & Gilbert, 2005). E.g. expecting that a positive event will enable us to 'live happily ever after', or a negative event will 'ruin everything'. The tendency to overestimate impact is higher for negative events than positive ones (Finkenauer et al., 2007). But regardless of the event, in most cases, life returns to normal very soon - something that we've all experienced but keep forgetting.


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.

Affective forecasting can be broken down into 3 underlying cognitive mechanisms: firstly, we either mentally construct the future scenario or recall past events that the future scenario might resemble. This is known as prospecting. Next, we feel certain emotions in response to the imagined future, and these are referred to as anticipatory affect or premotions. And finally, we apply corrections and adjustments to the simulated emotional responses to predict how we'll feel when the event actually occurs in the future (Zhang et al. 2020). Each of these cognitive processes have their own set of limitations which contribute to affective misforecasting.

Let’s start with prospecting. Firstly, while mentally constructing or imagining the future scenario, we may focus only on the event and not other things that may also happen at that time, and impact our emotions (Wilson & Gilbert, 2003). Or we may imagine only a single way that the event might happen and not account for many other ways it may unfold which could lead to different emotions (Buehler & McFarland, 2001).

Next, when we use our memory of similar experiences to predict our reactions to future events, we may focus too much on the high and low points of the past experience and the feeling it ended with, and ignore large parts of the experience, including it's duration. Memories also fade with time and then we make up some of the details based on our expectations and beliefs (Hsee et al., 2008; Hsee & Hastie, 2006). Also, unusual past events stand out in memory, producing a bias in the direction of unusual or atypical experiences (Morewedge et al., 2005).

Now let's move on to next process i.e. premotions or the in-the-moment feelings during prospecting. The affect generated by imagining the future scenario may get contaminated with unrelated affect felt at the time of forecasting. If we're in an intensely emotional or 'hot' state at the time of forecasting —for example, angry, hungry, in pain, or sexually aroused, we experience difficulty imagining that we will eventually cool off, and end up overestimating intensity and duration of future emotional reactions. Conversely, when we're in a 'cold' or unemotional state and forecast for a 'hot' state, we often underestimate the emotional intensity and overestimate our self control in the future scenario (Whan Park, 2006; Wilson & Gilbert, 2003). Next, we may use affective forecasting for functional purposes. For example, if we're feeling down at the time of forecasting, we may inadvertently imagine a positive future outcome to feel better (Gilbert et al, 1998). Furthermore, as the future events draws closer, we may be motivated to prepare for the worst case scenario by underestimating the impact of positive outcomes and overestimating the impact of negative ones (Finkenauer et al., 2007).

And finally, let's look at causes of errors in our adjustments or corrections while forecasting. The failure to appreciate the differences between prediction and experience underlies most cases of insufficient correction (Hsee & Hastie, 2006). Firstly, we tend to overestimate the extent to which our future experience of an event will resemble our current or past experience of the same event (Gilbert et al., 2002). Secondly, we often focus on insignificant details of future scenario, or focus only on differences between alternatives and not their commonalities. This means that we often fail to consider criteria that will have a real impact on our experience (Buechel et al., 2017; Hsee et al., 2008). We may also have inaccurate theories or beliefs about which outcomes contribute to our happiness and satisfaction. The theories are usually learned in situations where they are valid, but are then over-generalized to situations where they aren't (Hsee & Hastie, 2006; Buehler & McFarland, 2001).

Next, we have a tendency to rationalize and explain unexpected events, thus reducing their emotional power. This is especially true for negative events (Wilson et al., 2003). One reason we may mispredict the intensity and duration of negative feelings is that we do not take into account the fact that our psychology works to minimize the discomfort caused by negative events (Wilson & Gilbert, 2003).

And lastly, as memory is often unreliable, we may not notice that we have made a forecasting error as we may misremember our forecast as consistent with our experience. Even when we do recognize forecasting errors, the lessons we take away may be specific rather than general (Meyvis et al., 2010; Gilbert et al., 1998). Moreover we may reinterpret actual experiences in line with our initial forecast to minimize the psychological discomfort of misprediction (Ayton et al., 2007).


Default mode network, a system involved in imagination, mind-wandering, episodic memory and future thinking, has been found to play a key role in prospecting and premotions (Gilbert & Wilson, 2007). Key nodes of DMN involved in forecasting include: firstly, hippocampus, which is linked to long term episodic or autobiographical memories (Gaesser et al., 2013), secondly, posterior cingulate cortex which mediates interactions between episodic memory, emotions and motivations and relays this information to medial prefrontal cortex which is linked to decision making and emotional regulation (Sauciuc & Persson, 2020). In essence, system 2 or the prefrontal cortex determines how system 1, consisting of the regions linked to emotions and memory, will react in the future by tricking it into reacting in the present (Gilbert & Wilson, 2009).


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

Firstly, overestimating emotional impact can amplify and reinforce motivation for behaviors important for our well-being, survival, and success (Hoerger et al., 2010). For instance, overestimating the intensity of future sadness about an undesirable outcome may increase our motivation to pursue other options to avoid the expected negative feelings (Gautam et al., 2017).

Secondly, impact bias may also help in delaying gratification, that is foregoing rewards in the present for better rewards in the future, by exaggerating the anticipated positive emotions associated with the reward in future (Whan Park, 2006).

Next, being unaware of our ability to emotionally recover from negative experiences may function to keep us motivated to avoid those experiences rather than being complacent about them (Gilbert et al, 1998).

And lastly, exaggerating impact of future positive events can help us cope with and recover from unpleasant experiences in the present (Wilson & Gilbert, 2003).


Firstly, we wish for and work toward events that we believe will cause lasting happiness, not just a moment's pleasure. But if we overestimate how long our pleasure will last, we might be working toward the wrong things (Wilson et al., 2000). Similarly we tend believe that major traumas will have a more enduring emotional impact than minor problems will. But counterintuitively, minor problems that we ignore to fix like loose hinges, leaky faucets, and broken light switches can annoy us for months or years (Wilson & Gilbert, 2003), while we may adapt quicker and better to major problems than we expect e.g. people experiencing chronic illness and disability are happier than what healthy people predict they would be under similar circumstances (Ubel, 2005). Even loss aversion, wherein we try to avoid losses more than seek gains, may be a result of misforecasting the negative emotional impact of losses. Studies show that people recover very quickly from the displeasure of losses (Kermer et al., 2006).

Next, when we make a decision that is difficult to reverse, such as buying something that can't be returned, or marrying, we are strongly motivated to rationalize the decision and make the best of it. But when we can more easily undo a decision, we are less motivated to rationalize the choice, because we can always change our minds. Therefore, we often end up being happier with irreversible choices because our psychology does the work necessary to rationalize what we can’t undo. But because we're unaware of this psychological mechanism, we often avoid the binding commitments that could actually increase our satisfaction (Wilson & Gilbert, 2005).

Indeed, we often make efforts and bear costs to seek variety in future choices, but end up picking our preferred alternative every time we actually make the choice, thus rendering all the effort and cost of seeking variety redundant (Read & Loewenstein, 1995).

Managing Bias

And finally, let’s look at some strategies to manage the undesirable aspects of impact and to use it to our advantage.

First we can improve the mental construction of future scenarios, firstly, by thinking about peripheral or non-focal events that might occur simultaneously and distract us from the anticipated event (Hoerger et al., 2009), secondly, by referring more thoroughly to past experiences similar to the focal event (Buehler & McFarland, 2001), third, by considering concrete and minor details of the anticipated event, process of achieving it and different configurations of these details that are possible (Wesp et al., 2009; Whan Park, 2006), and lastly, by evaluating one option at a time and not multiple scenarios at once (Hsee et al., 2008).

Also, it is important to be aware that we overestimate the intensity of emotional reactions to events that we perceive to be important to our goals and needs (Lench et al., 2019).

Next, certain aspects of personality can influence the accuracy of affective forecasts.

Firstly, people high in emotional management ability, a component of emotional intelligence, recognize their ability to regulate emotions and adapt to affective events, leading them to make more accurate forecasts (Dunn et al., 2007).

Also, affective accuracy improves with age and this could be attributed to lower levels of immune neglect and higher emotional intelligence (Nielsen et al., 2008).

People who score high on neuroticism tend to overestimate the impact of negative experiences, while people high on extraversion overestimate the impact of positive ones (Hoerger et al., 2010).

Next, impact bias was found to be stronger in Western cultures compared to Eastern cultures, possibly because of higher level of focal thinking in the West (Lam et al., 2005).

And finally, practicing mindfulnes to develop an appreciation of how fleeting and dynamic our emotions are, can help improve our forecasting abilities (Emanuel et al., 2010).



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