Negativity Bias - Definitions, Causes, Risks, Advantages & Debiasing
Negativity bias is defined as the tendency for negative information, events, or stimuli to have a greater impact on human cognition, affect, and behavior than comparably positive instances (Hilbig, 2009). This tendency is pervasive, found in both our psychological processes and in interpersonal interactions; in connection with decisions about the future and with memories of the past (Baumeister, 2001). We pay more attention to negative information, we recall negative events more than positive ones, we experience negative emotions more intensely than positive emotions - and so on.
One of the most well-known and well-studied manifestations of negativity bias is loss aversion. Consider a bet being offered to you - think of the amount of money you need to have a sumptuous meal at your favorite neighborhood restaurant. You have to bet this amount of money - let's say it's $20 dollars. Once you bet $20, one of two digits, either '1' or '0' is randomly generated, with equal chances. If zero appears, you lose your $ 20 and if one appears, you win $20 in addition to your own $20. Of course, you can reject the bet if you like. Now, take a second to think about it - would you like to take this bet with equal chances of losing $20 and winning $20.
If you decided not to take the bet, would you take it if you were offered equal chances of losing $20 and winning $30. Those who still don't want to take the bet, what amount should be offered to you in this game for you to accept a 50% chance of losing your $20. Researchers have found that when the stakes are real i.e. people actually win or lose money in the bet, an average individual accepts to take the bet only when $30-$50 are offered against their $20. These results show that people avoid losses more than they seek gains - a tendency known as loss aversion (Kahneman & Tversky, 1992).
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.
Negativity bias manifests in four different ways: first, negative potency means that negative experiences, memories and emotions are stronger and more intense than positive ones. This implies that sadness is more negative than happiness is positive.
Second, steeper negative gradients mean that we are more sensitive to increase in negativity than increase in positivity. So, increase in pain is felt more intensely than an equivalent increase in pleasure.
Third, negativity dominance means that negativity of a negative entity is greater than positivity of an equivalent positive entity. We saw that we fear losing $20 dollars more than we are attracted to gaining $20.
And fourth, negative differentiation means we perceive negative entities more vividly, have more complex and varied representations of them, and respond to them in more complex manner compared to positive entities. For example, in most languages negative feeling words far outnumber positive feeling words (Rozin, & Royzman, 2001).
Next, experienced and anticipated negative events orient us to the risks and rewards in the environment, such that we focus more attention on navigating the risks and rewards. As a result, we regulate and structure our behavior more deliberately in relation to negative events (Yechiam & Hochman, 2013).
A number of brain regions have been observed to be involved in producing the negativity bias. Firstly, amygdala detects negative stimuli and inputs the information to other brain regions (Canessa et al, 2013; De Martino, 2010). Next, insula integrates information about negative value of stimuli from amygdala and other sources, and predicts how the negative stimuli is likely to affect us. Anterior cingulate cortex regulates our attention and monitors for errors in our predictions and expectation. Insular and cingulate cortex together form brain's defensive system signaling and responding to potential threats. Next, striatum adds motivational component of avoidance to the response to negative stimuli and contributes to learning by updating the negative or positive value of stimuli based on experiences and prediction errors. The net result of the activity of these brain regions is a prediction of a negative future state, leading to expression of fear an anxiety, which drive avoidance tendencies towards the negative stimuli (Canessa et al, 2013; Canessa et al., 2017). Also, striatum displays what is known as neural loss aversion - the activity in striatum decreases as losses increase, but the activity decreases faster for losses than the rate at which it increases for gains (Fox et al., 2008).
For the bias to be passed down genetically or culturally to us from our ancestors, it must be beneficial in certain conditions.
There is a natural inequality between negative and positive events. Extreme negative events are more threatening than extreme positive events are beneficial. The clear example here is death, a final, irreversible event. The positive value of good meal or a fit mate can never be equivalent to the negative value of death. So avoiding risks of death must be a matter of the highest priority in the evolutionary scheme. And the same goes for injury, sickness and loss of resources - each of which can have irreversible negative consequences (Rozin, & Royzman, 2001).
But there's a problem here - if we are driven only by negative instincts, we would miss a lot of beneficial opportunities to seek rewarding experiences. Therefore, when there is insufficient information available about a new stimulus, we exhibit a weak drive to approach that stimulus. This drive is known as positivity offset and when combined with negativity bias, we have the benefit of exploring the environment in the absence of negative input, and of rapid self-preservative behavior at even a slight hint of negative input (Vaish et al., 2008).
Also, in many situations, losses and threats may signal greater resources and potential opportunities, and not only substantial dangers. For example, a plant with more defensive mechanisms such as thorns and toxins often bears the sweetest and most nutritious fruit. In these contexts, it might be evolutionarily adaptive to keep alert and focused in situations involving losses in order to identify cases where such opportunities could be exploited (Yechiam & Hochman, 2013).
And lastly, it may be that humans show heightened awareness of and respond more quickly to negative information because it signals a need for change and prompts self-regulation. Through self-regulation, an organism can adapt and change itself to fit its environment. As a result, organisms that possess mechanisms for enhanced perception and processing of negative cues will achieve greater fitness in their environment and, consequently, will have a greater chance of surviving threats (Baumeister et al., 2001).
Negativity bias is pervasive and impacts many different kinds of behaviors and decisions. Indeed it has even been shown to affect the performance of professional sportsmen. A study found golfers hit birdie putts, which is a highly positive score, less accurately and less hard than they hit par putts, which is seen as a neutral or negative score (Pope & Schweitzer, 2011).
In a completely different domain, a study of court decisions observed the negativity bias in the treatment of losses and forgone gains; in cases of negligence, for example, compensation is more likely to be awarded for expenses already incurred than for unrealized profits (Tversky & Kahneman, 1991).
Another domain where we see a strong negativity bias is news media. A study which measured viewers' reactions, such as their heart rate and skin conductance, to actual news content showed that negative news content elicits stronger and longer lasting reactions than does positive news (Soroka & McAdams, 2015). Another study conducted on Twitter found that negative sentiment enhances virality for news content (Hansen et al., 2011).
Next, studies have found that positively framed incentives are less motivating than negatively framed incentives e.g. bonus given for good performance at the end of the year vs. bonus given at the beginning of the year and then deductions made for poor performance. Nonetheless, individuals preferred positively framed incentives and incorrectly predicted that they'd perform better with these (Goldsmith & Dhar, 2013; Imas et al., 2015).
Next, people are more likely to believe negatively framed information to be true than positively framed information. A study found that divorce rate was presented negatively, in terms of proportion of marriages divorced within 10 years, more individuals judged it to be true compared to framing positively i.e. proportion of marriages lasting 10 years or longer (Hilbig, 2009).
Another study found that in a blind game, participants were more likely to believe that they were playing against real humans rather than computer when they lost or experienced unfavorable outcomes (Morewedge, 2009).
And finally, let’s look at some strategies to manage the undesirable aspects of negativity and to use it to our advantage.
Negativity bias is weaker when the magnitude of losses or negativity is small (Mukherjee et al., 2017). Indeed, when an alternative is advantageous overall, but involves minor losses or negativity, it's attractiveness may actually increase compared to a completely positive alternative. This is known as the blemish effect and the reason for this pattern may be that the minor negativity leads to more attention on the alternative, so it's overall positivity becomes more salient (Yechiam et al., 2019).
Next, negativity bias has been observed mostly in situations where positive or negative alternatives are compared directly against each other. The bias is weaker when the alternatives are judged separately i.e. by the same person and on the same scale, but not directly against each other (McGraw et al., 2010).
Another situation where negativity bias fails to appear is online consumer reviews, where quality of information in the review is more important and impactful on consumer choices than whether the review is positive or negative (Wu, 2013). Similarly on social media platforms, negativity bias has not been observed for content other than news media (Stieglitz & Dang-Xuan, 2013).
Next, the degree of negativity bias, particularly loss aversion, has been found to be higher among women and individuals with low education attainment (Schmidt & Traub, 2002; Johnson et al., 2006). Older individuals exhibit higher loss aversion but lower degree of negativity bias in attention and memory (Gächter et al., 2010; Wood & Kisley, 2006). Also, conservatives tend to display higher negativity bias than liberals (Hibbing et al., 2014). And finally, individual from cultures with higher degree of individualism, hierarchy, inequality and masculinity exhibit higher levels of loss aversion (Wang et al., 2017).
Benjamin E. Hilbig. Sad, thus true. Negativity bias in judgments of truth. Journal of Experimental Social Psychology, Elsevier, 2009, 45 (4), pp.983.
Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad is stronger than good. Review of general psychology, 5(4), 323-370.
Kahneman, D., & Tversky, A. (2000). Prospect Theory: An Analysis of Decision under Risk. Choices, Values, and Frames, 17–43.
Kahneman, D. & Tversky, A. (1992). "Advances in prospect theory: Cumulative representation of uncertainty". Journal of Risk and Uncertainty. 5 (4): 297–323.
Rozin, P., & Royzman, E. B. (2001). Negativity bias, negativity dominance, and contagion. Personality and social psychology review, 5(4), 296-320.
Yechiam, E., & Hochman, G. (2013). Losses as modulators of attention: Review and analysis of the unique effects of losses over gains. Psychological Bulletin, 139(2), 497–518.
Yechiam, E., Ashby, N. J. S., & Hochman, G. (2019). Are we attracted by losses? Boundary conditions for the approach and avoidance effects of losses. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45(4), 591–605.
Canessa, N., Crespi, C., Motterlini, M., Baud-Bovy, G., Chierchia, G., Pantaleo, G., Tettamanti, M., & Cappa, S. F. (2013). The functional and structural neural basis of individual differences in loss aversion. The Journal of neuroscience : the official journal of the Society for Neuroscience, 33(36), 14307–14317.
Canessa, N., Crespi, C., Baud-Bovy, G., Dodich, A., Falini, A., Antonellis, G., & Cappa, S. F. (2017). Neural markers of loss aversion in resting-state brain activity. NeuroImage, 146, 257–265.
De Martino, B., Camerer, C. F., & Adolphs, R. (2010). Amygdala damage eliminates monetary loss aversion. Proceedings of the National Academy of Sciences, 107(8), 3788-3792.
Craig Fox, Sabrina Tom, Christopher Trepel, and Russel Poldrack (2008) ,"The Neural Basis of Loss Aversion in Decision- Making Under Risk", in NA - Advances in Consumer Research Volume 35, eds. Angela Y. Lee and Dilip Soman, Duluth, MN : Association for Consumer Research, Pages: 129-132.
Vaish, A., Grossmann, T., & Woodward, A. (2008). Not all emotions are created equal: the negativity bias in social-emotional development. Psychological bulletin, 134(3), 383–403.
Pope, D. G., & Schweitzer, M. E. (2011). Is Tiger Woods loss averse? Persistent bias in the face of experience, competition, and high stakes. American Economic Review, 101(1), 129-57.
Tversky, A., & Kahneman, D. (1991). Loss aversion in riskless choice: A reference-dependent model. The quarterly journal of economics, 106(4), 1039-1061.
Soroka, S., & McAdams, S. (2015). News, politics, and negativity. Political Communication, 32(1), 1-22.
Hansen, L. K., Arvidsson, A., Nielsen, F. Å., Colleoni, E., & Etter, M. (2011). Good friends, bad news-affect and virality in twitter. In Future information technology (pp. 34-43). Springer, Berlin, Heidelberg.
Goldsmith, K., & Dhar, R. (2013). Negativity bias and task motivation: Testing the effectiveness of positively versus negatively framed incentives. Journal of Experimental Psychology: Applied, 19(4), 358–366.
Imas, Alex; Sadoff, Sally; Samek, Anya (2015) : Do People Anticipate Loss Aversion?, CESifo Working Paper, No. 5277, Center for Economic Studies and ifo Institute (CESifo), Munich
Morewedge, C. K. (2009). Negativity bias in attribution of external agency. Journal of Experimental Psychology: General, 138(4), 535.
Mukherjee, S., Sahay, A., Pammi, V. C., & Srinivasan, N. (2017). Is loss-aversion magnitude-dependent? Measuring prospective affective judgments regarding gains and losses. Judgment and Decision making, 12(1), 81.
McGraw, A. P., Larsen, J. T., Kahneman, D., & Schkade, D. (2010). Comparing Gains and Losses. Psychological Science, 21(10), 1438–1445.
Wu, P. F. (2013). In search of negativity bias: An empirical study of perceived helpfulness of online reviews. Psychology & Marketing, 30(11), 971-984.
Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior. Journal of Management Information Systems, 29(4), 217–248.
Wang, M., Rieger, M. O., & Hens, T. (2017). The impact of culture on loss aversion. Journal of Behavioral Decision Making, 30(2), 270-281.
Hibbing, John R.; Smith, Kevin B.; and Alford, John R., "Differences in negativity bias underlie variations in political ideology" (2014). Faculty Publications: Political Science. 67.
Schmidt, U., & Traub, S. (2002). An experimental test of loss aversion. Journal of risk and Uncertainty, 25(3), 233-249.
Gächter, Simon; Johnson, Eric J.; Herrmann, Andreas (2010) : Individual- level loss aversion in riskless and risky choices, CeDEx Discussion Paper Series, No. 2010-20, The University of Nottingham, Centre for Decision Research and Experimental Economics (CeDEx), Nottingham
Johnson, Eric J.; Gächter, Simon; Herrmann, Andreas (2006) : Exploring the nature of loss aversion, IZA Discussion Papers, No. 2015, Institute for the Study of Labor (IZA), Bonn
Wood, S., & Kisley, M. A. (2006). The negativity bias is eliminated in older adults: Age-related reduction in event-related brain potentials associated with evaluative categorization. Psychology and aging, 21(4), 815.