Professional success, financial security, a healthy life and a happy family: Who would not wish for these and hope for a bright future for themselves and their loved ones? Dreaming of overly positive and desirable life events to take place in our future is nothing unusual, but dreams don’t necessarily reflect what we realistically would expect to happen at any time to come, would they?
In a study with college students Weinstein (1980) showed that such overly positive and unrealistic expectations actually did exist. In comparison to their peers at college, students believed to experience more positive and less negative events to happen to them than on average. Having a starting salary greater than $15,000, getting a good job offer before graduating, living past 80 and having a mentally gifted child were only some life events that individuals believed to experience with a higher likelihood than their peers. In contrast, negative events, such as drinking problems, suicide attempts, suffering from a heart attack or lung cancer, being fired from a job, or getting divorced, were assumed to be less likely to happen to each participant than the expected average within the group of peers. These findings, which suggest that we incorporate a positive view of our own future, are not unique and they have been replicated in other studies: Everyday life events (Sharot, 2011), crime and violence in domestic life (Chapin & Coleman, 2009), being arrested for shop lifting or driving unsafely (Chapin, 1999), smoking in adolescent and adult smokers (Arnett, 2000), health care (e.g. Paling, 2003, Oster et al., 2013), risk taking and economics (van Neumann & Morgenstern, 1953; Helweg‑Larsen & Shepperd, 2001; Shepperd et al., 2002; Flynbjerg, 2006), or climate change (Paton, 2003) are some examples of what research has focused on in the past. Moreover, with this overly optimistic attitude humans are not alone: Animals similarly display an optimistic behaviour when trained to carry out a task in return for food as a reward (Matheson et al., 2008; Rygula et al., 2015; Harding et al., 2004; Salmeto et al., 2011; Douglas et al., 2012).
In general, this overly positive point of view that is characterised by a difference between an individual’s optimistic expectation about an event and the actual outcome that follows in reality is called the optimism bias. In general, the bias is optimistic if the expectations exceed the real outcomes and pessimistic if it turns out that reality holds a better outcome than anticipated beforehand (Sharot, 2011, cf. also Weinstein, 1980, 1989). Now, what are the gains and the dangers of such a bias?
Several studies pointed out what the bias holds for us: On the one hand we tend to underestimate risks to ourselves, such as cancer caused by smoking (Arnett, 2000) or we are subject to inaccurate estimations for planning major projects (e.g. Flynbjerg, 2006; Helweg-Larsen & Shepperd, 2001) and as a result, we are not precautious enough and disregard warning signs as studies with Huntington disease show (Oster, Shoulson & Dorsey, 2013). On the other hand, we can profit from increased motivation and performance (e.g. Solberg Nes et al., 2009; Carver & Scheier, 2014, Rygula et al., 2015), happiness, mental and physical well‑being, and a healthy lifestyle (e.g. Conversano et al., 2010; Scheier & Carver, 1992). These advantages that impact the quality of our life might trigger the question if it was possible to influence the bias consciously, so we can benefit from it and its positive effects. In order to answer these questions, it is important to understand how this bias is generated.
Even though the theory of reinforcement learning (e.g. Sutton & Barto, 1998) is prominently used to explain the process of embedding previous experiences into existing knowledge, it cannot be used exclusively to explain the optimism bias. The reason is that we seem to learn differently from information that we perceive as positive in comparison to information with a negative effect. For instance, Sharot and Garrett (2016) report a difference for the process of updating one’s expectations, depending on the valence of the information (cf. Niv et al., 2012; Christakou et al., 2013, Eil & Rao, 2011). Participants updated their expectations more willingly in direction of the evidence presented when their beliefs were less optimistic than the actual likelihood for that event and thus the information held a positive value for them. In contrast to receiving such good news, participants made significantly smaller adaptations to their expectations when their estimates were more optimistic than the information they were presented with, which in this case held a negative value for them in form of bad news. This difference between the initial expectation and the actual likelihood that describes reality appropriately is called estimation error. It can be experienced as a positive estimation error for good news or as a negative estimation error when receiving bad news.
Such asymmetric updating of one’s beliefs was also found in other studies focusing on risk perception of medical results (Krieger et al., 2016), college students’ awareness of the financial situation (Wiswall & Zafar, 2013) or evaluating social feedback about their personality (Korn et al., 2012). This differences in updating were also found on a neural level (e.g. Sharot et al., 2011; Moutsiana et al., 2015; Sharot et al., 2012), where positive and negative estimation errors are represented in different areas of the brain. In particular, some areas of the cortex enhanced the updating of positive news and inhibited updating of negative news (Sharot et al., 2012). When these areas were disrupted, participants used negative and positive information similarly well to update their beliefs. Even though it seems possible to ‘switch off’ the optimism bias, this method only provides an artificial circumstance that was only used for study purposes of neural activity.
In a more natural context with animals as described above, studies also found evidence for an environmental effect. In a study with pigs, Douglas et al. (2012) showed that an enriched environment supported the optimism bias, whereas the effect vanished in a less attractive environment.
On a larger scale, these findings are also in line with studies investigating age dependent learning from bad news (Moutsiana et al., 2013; Chowdhury et al. 2013). Results of a study with participants aged between 9 and 26 years indicate no age differences for updating of good news, but a diminished updating of bad news in younger participants (Moutsiana et al., 2013).
As these studies show, there are factors, such as environment and age, that diminish the optimism bias and make learning from bad news equally possible as learning from good news, but so far, there has been no support for possessing the ability of changing this bias consciously. Another open question that has not been answered yet is, in how far the seeming reluctance of taking in bad news, can serve self-protective purposes. In particular, psychological defence mechanisms, such as denial or reaction formation, can be displayed in situations with a negative, unwelcoming, sometimes even threatening value in order to distance ourselves from these situations. Bad news could function as such a threat to the self and hence be prone to such defence mechanisms. Investigating such connections, might reveal more about the nature of asymmetric updating and promote understanding of why we don’t integrate bad and good news equally.
In summary, the optimism bias can be understood as an overly positive expectation of one’s own future in comparison to the average likelihood of an event for a peer group. The way we update these expectation after receiving information about the likelihood is asymmetric and it depends strongly on the valence of the information. This asymmetry changes with our age and it can be altered through environmental circumstances. The optimism bias provides several advantages as it impacts motivation, emotions, and our physical and mental well‑being positively. The downsides are that we are likely to underestimate risks that could for instance affect our health care, or our financial situation.
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