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The Divided Self: How a Better Understanding of the Human Mind Could Transform Society Professor Geoff Beattie Department of Psychology, Edge Hill University iafor ‘European Conference on Psychology and the Behavioral Sciences’ 7th July 2015 We are currently faced with a number of major challenges: 1. Relationship with the planet (climate change, sustainability, increasing demand on finite resource). 2. Relationship with each other (prejudice, inequality and conflict). I want to suggest that some new ideas in psychology may help us reframe both sets of challenges and thereby act more effectively. Defining the issues, imagining possible solutions I think that there is genuine frustration that these issues have proven to be so intractable. World leaders, economists, social scientists, the commercial sector have all offered solutions….that failed. Why is this? I’m going to suggest that it’s us that’s the problem and how we think and act. Firstly, climate change. Climate change The scientific evidence is overwhelming. Stern Review (2006): ‘Climate change threatens the basic elements of life for people around the world.’ IPCC report (2014): A rise in global temperature will have: severe and widespread impacts major risks to global food production more extreme fluctuations in weather, including droughts, flooding and storms.’ But there is something else: Stern (2006): ‘Human activities are a major driver of this rapid change in our climate… particularly patterns of consumption and energy use, driven by consumer demand for higher standards of living.’ Some governments, NGO’s and leading multinationals have got the message Unilever (turnover £40 billion; products sold in 190 countries; 2 billion times a day somebody uses one of their products). KPIs: primarily financial: 5% growth But they see the problem (‘there’s no profit from a dead planet’) ‘Business must be part of the solution.’ ‘in order to live within the natural limits of the planet we will have to decouple growth from environmental impact’. KPI: ‘Our Commitment’: ‘Halve the greenhouse gas impact of our products across the lifecycle by 2020.’ What Unilever did: 1. Reduced greenhouse gas emissions from manufacturing chain, reduced deforestation. 2. More environmentally friendly sourcing of raw materials. 3. Doubled their use of renewable energy. 4. Produced concentrated liquids and powders. 5. Reduced greenhouse gas emissions from transport and refrigeration. 6. Reduced employee travel. The end result: ‘Our greenhouse gas footprint impact per consumer has ……….. increased by around 5% since 2010.’ The problem ‘We have made good progress in those areas under our control but… the big challenges are those areas not under our direct control like…..consumer behaviour.’ Why don’t people get the message? The effects are less personal. It’s primarily about future generations. Easy to shift responsibility; it’s China’s fault. Uncertainty about time course (powerful lobbies behind this uncertainty, cf. the tobacco industry in the past). It’s complicated. Emotional valence of ‘global warming’. Too catastrophic to contemplate………. Hard but not impossible/we just need to ‘read’ people better and design better initiatives. So how do we promote sustainable consumption/living? Step 1: we need to be able to read the mind of the consumer. To ascertain what’s possible and what’s not. We need: 1. To understand consumers’ motives and intentions. 2. Assess how likely they are to change their patterns of consumption. 3. Decide whether they can break long established habits and routines. This mind reading might seem to be very difficult, but an extraordinary number of influential people/organizations think it is indeed possible. I will argue… It is more difficult than it might appear because consumers don’t have a mind they have two. They have two distinct cognitive systems each with its own properties and mode of operation. One of these systems is not open to introspection. But some great mind readers have tried ‘Customers want to do more in the fight against climate change if only we can make it easier and more affordable’ (Leahy, 2007). Numerous market research surveys supported his conclusion. Surveys of pro-environmental attitudes revealed: ‘70% of people agree that if there is no change in the world, we will soon experience a major environmental crisis’ ‘78% of people say that they are prepared to change their behaviour to help limit climate change.’ Downing and Ballantyne (2007) This view and philosophy shared by others, including government agencies (Defra) They also engaged in a spot of mind-reading- ‘Many people are willing to do more to limit their environmental impact, they have a much lower level of understanding about what they can do and what would make a difference.’ One major initiative by Tesco: carbon labelling Huge undertaking 1. The start of Leahy’s ‘Green Revolution’, to be led by consumer demand to drive the market. 2. What consumers said they wanted – info. 3. It had worked with health info on food. 4. Tesco planned to label all 70,000 own brand products. 5. Several months to calculate the carbon footprint of each individual product. 6. Validated by the independent Carbon Trust. 7. Consumers should now choose the low carbon footprint alternatives. 8. But how did consumers actually behave? They didn’t do what Tesco thought they would. (Talk about frustration!). And here’s something else they didn’t do. Analyzing gaze fixations Small black mark denotes where participants are looking. Each gaze fixation scored every 40 ms. Calculate proportion of time spent fixating on different areas. Distribution of gaze fixations (first 5 seconds) Carbon footprint Carbon footprint information Other features But was this mind-reading ever really justified? Both Terry Leahy and Defra based their strategy on data on consumer attitudes. Understanding ‘attitudes’ is the key The definition of an attitude ‘a mental and neural state of readiness organised through experience, exerting a directive or dynamic influence upon the individual’s response to all objects and situations with which it is related.’ (Allport, 1935) How do we measure attitudes? The mainstream approach Likert Scale: You simply introspect and report preferences (for example, towards high or low carbon footprint products). Following Allport, who wanted to escape ‘psychoanalytic excess’ (following his visit to Freud when he was a student). But Allport also said: ‘Often an attitude seemed to have no representation in consciousness other than a vague sense of need, or some indefinite or unanalyzable feeling of doubt, assent, conviction, effort, or familiarity’ (Allport 1935). The question is…. Can we build a science (and effective policy) on these vague senses and unanalyzable feelings of doubt, which may be crucial for the prediction of behaviour? That therefore is the challenge… One advance – recognition that there are two systems of the human mind. Nobel Laureate Daniel Kahneman: System 1 (automatic, fast, impulsive, unintended, unconscious). System 2 (intentional, slow, controlled, reflective, conscious). System 1 (automatic and fast; little or no effort): 1. Detect emotion in the human face (from 6 basic emotions: happiness, sadness, anger, fear, disgust, surprise). System 1 2. Orient to the source of a sudden sound. 3. Complete the phrase ‘bread and …’ 4. Answer 2 + 2 = ? System 2 is very different. System 2 What is: 17 x 24= (My guess is that whatever you are now doing is conscious, controlled, reflective, intentional and slow). System 2 (conscious and controlled; requires attention) Look for the a friend in the audience. Tell someone your phone number. Check out the validity of a complex logical argument. Relationship between System 1 and Systems 2 is critical 1. System 1 continuously generates suggestions for System 2 (impressions, intuitions, feelings). 2. If endorsed by System 2, these turn into voluntary actions. 3. When System 1 runs into difficulties it calls on System 2 for more detailed processing. 4. when System 1 has no answer, System 2 is mobilised. So far so good. As quickly as possible, answer the following question: A bat and ball costs £1.10. The bat costs £1 more than the ball. How much does the ball cost? What does this tell us? People tend not to check (more than 80% of university students give the wrong answer). System 2 endorsed an intuitive answer that it could have rejected with a small investment of effort. System 2 can be very lazy (according to Kahneman this judgment is ‘harsh but not unfair’). How System 1 works: Please look at the following two words. BANANAS BANANAS VOMIT The Associative Machine What has just happened to you: You experienced unpleasant images/feelings. Your mind assumed a causal connection between the two words. You will have experienced a temporary aversion to bananas. The state of your memory has changed. Complete the following word: ‘s-ck’ You are more likely to recognise objects and concepts associated with ‘vomit’ (this is called ‘priming’). And recognise words associated with ‘bananas’ (‘fruit’, ‘yellow’ etc.). You want to survive. Actions (not just thoughts) can be primed by events of which you are not even aware. Holland et al. (2005) Participants given a crumbly two conditions Lemon Scented Room biscuit to eat in one of Room with no Scent The nature of the human mind 1. Comprised of two systems that work on different principles. 2. The interaction between the two is critical. 3. One system is conscious (and we think reflects our ‘true self’) but lazy. 4. The other is unconscious and hidden from us (and a bit of a workaholic). But what about the long-term legacy of this (especially System 1)? 1. How do these associations work across time? 2. How do these shape our attitudes and behaviour? 3. Do we have unconscious ‘implicit’ attitudes (bases on this associative activation over time)? But how can we measure attitudes that elude conscious introspection? (funding from Tesco) By measuring associations directly in a computerised classification task. For example, how quickly can you associate ‘low/high carbon footprint’ with the concept of ‘good’ or ‘bad’? Harder to associate certain categories rather than others. Low versus high carbon footprint. Low carbon footprint High carbon footprint Good versus bad. Good Bad Glorious Good or High Carbon Footprints Vs. Bad or Low Carbon Footprints. Good or High carbon footprint Bad or Low carbon footprint Good or Low Carbon Footprints Vs. Bad or High Carbon Footprints. Good or Low carbon footprint Bad or High carbon footprint This test yields a ‘D’ score’ The difference in time to react to one set of combinations rather than the other. Measures of implicit attitudes and the prediction of behaviour Both predict behaviour in different domains, and in different circumstances. IAT is a better predictor of spontaneous behaviours when behaviour is under cognitive, emotional or time pressure (meta-analysis of over 100 studies). IAT is a better predictor of behaviour in sensitive domains (including racial discrimination and environmental issues). 30% of participants demonstrated a strong preference for products with low carbon footprints. • 40% of participants demonstrated a moderate preference for products with low carbon footprints. • 26% of participants demonstrated no preference. • 4% of participants demonstrated a preference for products with high carbon footprints. Percentage • Percentage Likert results in area of sustainability (Beattie, 2010). Likert Score Implicit attitudes to high and low carbon footprint products (‘D’ scores) How do explicit and implicit attitudes connect? • No significant correlation in this domain. • Explicitly stated attitudes and implicit associations appear to be statistically dissociated. • There were many ‘surface greens’ with a reported positive attitude to low carbon but actually a positive implicit attitude to high carbon. The psychology of the ‘surface greens’ Understanding these conflicted individuals may be critical. They may be very common. They have not been identified as a group thus far. After all they report that they prefer low carbon products, are prepared to adapt their behaviour etc. Can we identify ‘surface greens’? Study the detailed way in which people talk about relevant issues. But not just focussing on the language/speech itself (cf. questionnaires, interviews, focus groups). Rather by micro-analysis of co-verbal behaviour. A focus on iconic gestures, which are: Spontaneous Multi-dimensional Meaningful (without the benefit of a lexicon) Unconscious (and therefore less open to editorial control) They: Often back-up speech But sometimes contradict it These are called gesture-speech mismatches. ‘True Green’ Explicit/implicit attitudes: convergent Pro-low carbon explicit attitude; prolow carbon implicit attitude Speech and gesture: matching ‘Surface Green’ Explicit and implicit attitudes: divergent Pro-low carbon explicit attitude; pro-high carbon implicit attitude Speech and gesture: mismatching Is it surprising? We probably do care about the environment (when we think consciously and carefully) and yet for years we have been socialized to think and feel………. Implications? Immediately raises issues about methods to reveal attitudes. Some methods (questionnaires, interviews, focus groups) may provide misleading results. Suggests that many people are psychologically ‘dissociated’ when it comes to the environment/climate change Implications for modelling the human mind How do these two systems influence behaviour? How do these two systems interact (if at all)? How do you change these two systems? How do you appeal to them? Some preliminary investigations Implicit attitude to carbon footprint predicts choice of low carbon products under time pressure. Implicit attitude to carbon footprint predicts unconscious eye fixations on climate change images. So what about attitudes towards ethnicity and race? Could there be some sort of dissociation between explicit and implicit attitudes here? Explicit attitudes to race changed dramatically in a relatively short period (35% acceptable to have a Black neighbour in 1942, 64% in 1963). But what actually happened? Is there any actual evidence of ethnic/racial bias today? (but avoiding asking people to merely report it) Take the domain of employment. First stage of any process: shortlisting One technique - the (experimentally manipulated) C.V. test: 2 versions of identical CV: White/non-White. Possible discrimination in the U.K. Wood et al., (2009): matched CVs with White and nonWhite sounding names. Sent to a range of jobs (IT, Accounts, H.R.) in 7 major British cities. Results: BME had to send 74% more applications that non-Whites to secure an interview. Conclusions 1. There seems to be some sort of bias 2. Does not identify type of bias or processes involved Could there be ethnic bias in universities? 11% of all White academics are professors compared to just 5% of BME academics in the U.K. 50 Black professors out of a total of 14,385 in Britain 0.35% of Professors are Black, but 2.8% of the population. Not a single Black Vice Chancellor in the United Kingdom. Note: all statistics correct at the start point of the research in 2009-10. Journal of Blacks in Higher Education (2009): ‘Blacks in faculty ranks (in the U.S.) will not reach parity with the Black percentage of the overall American workforce for another 140 years.’ Attempts at solutions 1. Operational Performance Reviews with close monitoring of data on ethnicity. 2. Career development advice. 3. Mentoring. Still minimal change at senior level. Could there be an implicit bias related to race or ethnicity operating in society? There does seem to be significant evidence of an implicit racial bias (Greenwald) (see, for example, Project Implicit: Harvard University website). Where does this implicit bias derive from? Emotional response to out-group versus own-group members (more activity in amygdala). The psychological pull of the familiar. Significantly affected by early socialization experiences. Significantly affected by our culture as a whole. Implications of this implicit bias? But the effects on relevant behaviours are less clear. This was the starting point for my own research. Measuring implicit attitudes to race/ethnicity There are limitations with many previous versions of the IAT (including the Race IAT) used in the literature: Project Implicit: Race IAT Stimuli First step: creation of new Ethnic IAT Likert Scale Simple self-report measure of underlying attitude along a 5-point scale (1 to 5). IAT: Critical trial Good or White Bad or Non-White IAT: Critical trial Good or Non-White Bad or White Distribution of Likert scores for White participants (5: ‘prefer Whites’; 1: ‘prefer non-Whites’; 3: ‘no preference). Results: self-reported and implicit attitude scores Likert score Mean Attitudinal Preference 3.18 3.0 = Neutral Medium preference for White (N.B. 0.80 = Strong preference for D score 0.73 White) D score of White participants: 0.87 But how do implicit attitudes influence actual behaviour (shortlisting)? Two genuine job adverts. One an academic position, the other an administrative post. Four C.V.s created for the lectureship post and four for the admin post. Each C.V. contained good and bad information. Good information: 2.1 or above at university, publications in high-ranking journals, relevant experience. Bad information: 2.2 or lower, publications in lowranking journals, gaps in employment history. Publications on one of the C.V.s Participants read through CVs, and then given a further minute to make decision. This ‘critical minute’ formed the basis of our analysis as during this time period participants scan rather than read the C.V.s. After seeing all four applicants for a given post, participants were asked to shortlist two candidates. Eye movements were recorded for a sample. Afterwards, explicit and implicit attitudes were measured. Eye tracking clip Example of eye movements Red dot = fixation point (with time sampling) Shortlisting decisions: Our participants were not colour blind. 1. White Participants: 77.1% of shortlisted candidates (academic post) were White. 2. 60.4% of White participants shortlisted 2 White candidates. 3. White participants were ten times more likely to shortlist two White candidates than two non-White candidates. 1. Non-White Participants: 61.5% of shortlisted candidates were non-White. 2. 68.8% chose one White and one non-White candidate (only 4.2% chose 2 White candidates). Explicit attitudes do not predict which candidates were shortlisted (Likert) Academic Job 2 Non-White candidates shortlisted 2 White candidates shortlisted 3.00 3.00 Implicit attitudes (‘D’ scores) of White participants predict shortlisting decisions Shortlisted two White applicants Shortlisted one White, one nonWhite applicant Shortlisted two non-White applicants Mean D score: 1.17 Mean D score: 0.71 Mean D score: -0.22 (strongly pro-White) (moderately pro-White) (slightly pro-non-White) Implicit attitudes (‘D’ score) of non-White participants predict shortlisting decisions Shortlisted two White candidates Shortlisted one White, one nonWhite candidate Shortlisted two non-White candidates Mean D score: 1.01 Mean D score: 0.52 Mean D score: 0.10 (strongly pro-White) (moderately pro-White) (neutral) Effects on gaze fixations: White participants viewing candidates for the academic post Proportion of time participants spent looking at good/bad information of White/non-White candidates D Score of Good info. participants White candidates Bad info. White candidates Good info. non-White Bad info. Non-White candidates candidates High (1.32) 58.91 41.09 47.25 52.76 Low (0.30) 52.19 47.81 50.86 49.15 Conclusions Ethnicity of person making the judgment seems to influence who is shortlisted for academic posts but not non-academic posts. Self-reported attitude has no effect on who is shortlisted. Implicit attitude does seem to affect who is shortlisted. Conclusions Strong pro-White bias spent more time looking at the good information of White candidates. Our implicit (and unconscious) attitude direct our unconscious eye movements when we consider their C.V.s. Our ‘rational’ decisions about the suitability of candidates are based on this biased pattern of fixation. Encouraging the ‘Green Revolution’ We need to recognize that people (unfortunately) have two minds not one. We need to understand that many people have implicit and explicit attitudes that are dissociated. These different attitudes direct behaviour in different domains, as a function of different variables (time, cognitive load, emotion). We need to find new ways of identifying these individuals (moving away from self-reports). The ‘Green Revolution’ We need to understand how those with dissociated attitudes process incoming information. What do these individuals see? What do they attend to? We need to find new ways of changing implicit attitude in the area of sustainability. Emerging methods for measuring implicit attitudes will tell us if these methods are working or not. Tackling implicit racism We need to introduce System 2 thinking. We need to interfere with the ‘natural connection’ between implicit processes and behaviour (no ‘first thoughts’ in shortlisting). We need to interfere with the connection between unconscious and conscious behaviour (e.g. implementation intention, ‘if…then’ formulations). We need to change contextual factors (no time pressure in shortlisting, lower the emotional/cognitive load in shortlisting). But most importantly of all: We need to work on changing our implicit attitude in this domain by influencing our underlying associative networks. Why do I think it is possible to change associations in this way? (Melinda Gates says that we need to learn hard lessons from the commercial world) Smoking. The one positive legacy of the tobacco industry! Some of the best psychologists of a previous generation worked on this. If we can make people smoke, we can help them do anything. Finally: I called this talk ‘The Divided Self’. I hope that I’ve convinced you that this is an accurate description The subtitle of the talk was: ‘How a Better Understanding of the Human Mind Could Transform Society’. I believe that some positive transformation is possible (these are challenges that we could meet head on) but that depends upon all of us, both inside and outside the academy.