Behavioural Science Explained

Choice-supportive bias is a cognitive bias that compels individuals to justify their decisions, often by retroactively attributing positive qualities to chosen options and downplaying rejected ones. This fascinating aspect of human psychology helps us avoid cognitive dissonance—the mental discomfort arising from conflicting beliefs—and maintain a positive self-image. The bias shapes perceptions through memory distortion, causing people to recall their chosen option more favourably than it was and rejected options less favourably. It also leads to selective information gathering that supports previous choices. The provided sources detail the bias's widespread impact across various domains, including:

•Consumer behaviour, where it drives post-purchase rationalisation and brand loyalty

•Relationships, fostering idealised views of partners

•Scientific inquiry, where it can lead researchers to irrationally defend choices in subject selection, data treatment, and theory determination, potentially affecting research integrity.

We comprehensively cover the definition, psychological roots, widespread implications, and potential mitigation strategies of choice-supportive bias.

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The cheerleader effect describes how an individual is perceived as more attractive when seen within a group than in isolation. Popularised by How I Met Your Mother, this robust effect shows an average attractiveness increase of 1.5%–2.0% and has been replicated across various cultures, including collectivist societies like China.Proposed mechanisms include:

•Automatic ensemble averaging, where the brain averages group attractiveness, making individuals seem more appealing

•A change in evaluation mode, where judgment shifts from internal standards to comparison with flanking faces, causing the effect even without direct attractiveness contrast, especially for less attractive individuals

•The effect is moderated by group composition, being more evident with highly attractive individuals present. It generally boosts female attractiveness and partner value.

•It's robust to spatial arrangement and presentation time

•However, evidence suggests hierarchical encoding is not the sole cause, as the effect occurs even with non-human stimuli or identical distractorsThe Entire Behavioural Science Reading List on Amazon

The bizarreness effect is a psychological phenomenon defined as the increased likelihood of people remembering information that is strange or unusual compared to information that is mundane or expected. This effect has a significant impact on memory recall. Memory training experts have long advocated associating information with bizarre imagery to improve recall. The effect typically occurs when bizarre and common information are intermixed. While complex stimuli are generally less conducive to the effect, it can emerge with sufficient processing time. Key theoretical explanations include the distinctiveness account, which suggests that the uniqueness of bizarre items leads to extra processing, and the elaborative processing view, which states that bizarre stimuli attract more attention and require more working memory resources. Crucially, the bizarreness effect also heavily relies on retrieval processes, often only appearing when common and bizarre items are recalled togetheThe Entire Behavioural Science Reading List on Amazon https://amzn.to/4juLQTM

The bias blind spot (BBS) is a phenomenon where individuals are less likely to detect bias in themselves than in others. People typically believe they are, on average, less biased than their peers. This "metabias" or "cognitive blind spot" is rooted in naïve realism – the belief that one's own perceptions are objective. Consequently, people tend to attribute differing views in others to bias, while failing to recognise similar biases in themselves. The BBS is a distinct construct, largely independent of intelligence, decision-making ability, or personality traitsThe Entire Behavioural Science Reading List on Amazon (https://amzn.to/4juLQTM)

The base rate fallacy is a cognitive bias where individuals overvalue specific information and ignore the general prevalence (base rate) of an event, leading to misjudgments about likelihoods. It suggests people overlook how common or rare something is, favouring new, seemingly relevant details.

This phenomenon has been extensively studied, notably by Kahneman and Tversky, who attributed it to heuristics like representativeness. Critics, such as Gigerenzer, argued that experimenters failed to present uncertainty in understandable forms, specifically natural frequencies. Koehler's work re-examined the fallacy, noting that base rates are almost always used, and their influence depends on task structure and representation. The discussion extends to areas like media credibility and scientific realism.

Automation bias is the human tendency to over-rely on automated system recommendations, even when incorrect, leading to errors of commission or omission.

Explored across systematic reviews and domain-specific studies in public sector, national security, healthcare, and aviation, this bias shows mixed prevalence, yet even low levels carry significant risks. Psychological roots like cognitive laziness and trusting technology contribute as well. Modulators include task complexity and user experience

Limited AI knowledge often increases overreliance in a nonlinear fashion.

Mitigating strategies emphasise promoting skepticism and error awarenessThe Entire Behavioural Science Reading List on Amazon (https://amzn.to/4juLQTM)

The bandwagon effect is defined as an individual's propensity to adopt the viewpoint of the majority, even if their own differs, reflecting a desire to "join the crowd". It also describes adoption processes in networks driven by pressure from prior adopters, where a majority's action signals something is good, influencing both individual and organizational decisions. We extensively review studies on this effect from 1970 to 2021. Key areas include consumer demand and digital networks, voting behaviour, diffusion of innovation, and diffusion of management practices, encompassing various industries like luxury, healthcare, and apparel

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The Bystander effect, a phenomenon in which individuals are less likely to help in emergencies when others are present. One text details a computer model simulating this effect in cells exposed to radiation, while others examine the bystander effect in human contexts, such as workplace ethics and responses to violence. Research discussed includes studies showing diffusion of responsibility, pluralistic ignorance, and the role of personality traits in influencing bystander behaviour. Finally, a study demonstrates the bystander effect in rats, highlighting similarities between human and animal responses.

We explore the concept of ego depletion, a theory positing that self-control is a limited resource that diminishes with use, impacting subsequent self-regulatory efforts. The articles examine the theory's supporting evidence and critiques, including the role of motivation and the impact of depletion on various behaviours such as eating, spending, and social interactions. Debate surrounds the robustness of the ego depletion effect, with some research suggesting its influence is smaller than initially believed, potentially due to methodological issues and publication bias. However, the texts also explore potential biological underpinnings, like glucose levels, and the moderating role of motivation in self-control success or failure. Ultimately, the sources highlight the ongoing discussion around the nature and limits of self-control.

We examine cognitive dissonance theory, exploring its origins, mechanisms, and impact on mass communication, particularly in advertising and political messaging. It also discusses strategies for managing and mitigating dissonance in media contexts and suggests avenues for future research. The second source investigates how customer disconfirmation bias—the discrepancy between expectations and post-purchase experiences—affects online rating systems. It analyses the asymptotic behaviour of ratings across different system designs (complete, aggregate, average ratings), considering factors like granularity and customer heterogeneity, to determine how accurately these systems reflect true product quality.