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Page 1 of 3

  • Technology and democracy: a paradox wrapped in a contradiction inside an irony

  • Part of
  • Stephan Lewandowsky, Peter Pomerantsev
  • Journal:
    Memory, Mind & Media / Volume 1 / 2022
    Published online by Cambridge University Press:
    09 December 2021, e5
    • Article
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  • Democracy is in retreat around the globe. Many commentators have blamed the Internet for this development, whereas others have celebrated the Internet as a tool for liberation, with each opinion being buttressed by supporting evidence. We try to resolve this paradox by reviewing some of the pressure points that arise between human cognition and the online information architecture, and their fallout for the well-being of democracy. We focus on the role of the attention economy, which has monetised dwell time on platforms, and the role of algorithms that satisfy users’ presumed preferences. We further note the inherent asymmetry in power between platforms and users that arises from these pressure points, and we conclude by sketching out the principles of a new Internet with democratic credentials.

  • Injection fears and COVID-19 vaccine hesitancy

  • Daniel Freeman, Sinéad Lambe, Ly-Mee Yu, Jason Freeman, Andrew Chadwick, Cristian Vaccari, Felicity Waite, Laina Rosebrock, Ariane Petit, Samantha Vanderslott, Stephan Lewandowsky, Michael Larkin, Stefania Innocenti, Helen McShane, Andrew J. Pollard, Bao Sheng Loe
  • Journal:
    Psychological Medicine / Volume 53 / Issue 4 / March 2023
    Published online by Cambridge University Press:
    11 June 2021, pp. 1185-1195
    • Article
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  • Background

    When vaccination depends on injection, it is plausible that the blood-injection-injury cluster of fears may contribute to hesitancy. Our primary aim was to estimate in the UK adult population the proportion of COVID-19 vaccine hesitancy explained by blood-injection-injury fears.

    Methods

    In total, 15 014 UK adults, quota sampled to match the population for age, gender, ethnicity, income and region, took part (19 January–5 February 2021) in a non-probability online survey. The Oxford COVID-19 Vaccine Hesitancy Scale assessed intent to be vaccinated. Two scales (Specific Phobia Scale-blood-injection-injury phobia and Medical Fear Survey–injections and blood subscale) assessed blood-injection-injury fears. Four items from these scales were used to create a factor score specifically for injection fears.

    Results

    In total, 3927 (26.2%) screened positive for blood-injection-injury phobia. Individuals screening positive (22.0%) were more likely to report COVID-19 vaccine hesitancy compared to individuals screening negative (11.5%), odds ratio = 2.18, 95% confidence interval (CI) 1.97–2.40, p < 0.001. The population attributable fraction (PAF) indicated that if blood-injection-injury phobia were absent then this may prevent 11.5% of all instances of vaccine hesitancy, AF = 0.11; 95% CI 0.09–0.14, p < 0.001. COVID-19 vaccine hesitancy was associated with higher scores on the Specific Phobia Scale, r = 0.22, p < 0.001, Medical Fear Survey, r = 0.23, p = <0.001 and injection fears, r = 0.25, p < 0.001. Injection fears were higher in youth and in Black and Asian ethnic groups, and explained a small degree of why vaccine hesitancy is higher in these groups.

    Conclusions

    Across the adult population, blood-injection-injury fears may explain approximately 10% of cases of COVID-19 vaccine hesitancy. Addressing such fears will likely improve the effectiveness of vaccination programmes.

  • COVID-19 vaccine hesitancy in the UK: the Oxford coronavirus explanations, attitudes, and narratives survey (Oceans) II

  • Daniel Freeman, Bao S. Loe, Andrew Chadwick, Cristian Vaccari, Felicity Waite, Laina Rosebrock, Lucy Jenner, Ariane Petit, Stephan Lewandowsky, Samantha Vanderslott, Stefania Innocenti, Michael Larkin, Alberto Giubilini, Ly-Mee Yu, Helen McShane, Andrew J. Pollard, Sinéad Lambe
  • Journal:
    Psychological Medicine / Volume 52 / Issue 14 / October 2022
    Published online by Cambridge University Press:
    11 December 2020, pp. 3127-3141
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  • Background

    Our aim was to estimate provisional willingness to receive a coronavirus 2019 (COVID-19) vaccine, identify predictive socio-demographic factors, and, principally, determine potential causes in order to guide information provision.

    Methods

    A non-probability online survey was conducted (24th September−17th October 2020) with 5,114 UK adults, quota sampled to match the population for age, gender, ethnicity, income, and region. The Oxford COVID-19 vaccine hesitancy scale assessed intent to take an approved vaccine. Structural equation modelling estimated explanatory factor relationships.

    Results

    71.7% (n=3,667) were willing to be vaccinated, 16.6% (n=849) were very unsure, and 11.7% (n=598) were strongly hesitant. An excellent model fit (RMSEA=0.05/CFI=0.97/TLI=0.97), explaining 86% of variance in hesitancy, was provided by beliefs about the collective importance, efficacy, side-effects, and speed of development of a COVID-19 vaccine. A second model, with reasonable fit (RMSEA=0.03/CFI=0.93/TLI=0.92), explaining 32% of variance, highlighted two higher-order explanatory factors: ‘excessive mistrust’ (r=0.51), including conspiracy beliefs, negative views of doctors, and need for chaos, and ‘positive healthcare experiences’ (r=−0.48), including supportive doctor interactions and good NHS care. Hesitancy was associated with younger age, female gender, lower income, and ethnicity, but socio-demographic information explained little variance (9.8%). Hesitancy was associated with lower adherence to social distancing guidelines.

    Conclusions

    COVID-19 vaccine hesitancy is relatively evenly spread across the population. Willingness to take a vaccine is closely bound to recognition of the collective importance. Vaccine public information that highlights prosocial benefits may be especially effective. Factors such as conspiracy beliefs that foster mistrust and erode social cohesion will lower vaccine up-take.

  • 4 - The ‘Post-Truth’ World, Misinformation, and Information Literacy: a Perspective From Cognitive Science

  • Edited by Stéphane Goldstein
  • Book:
    Informed Societies
    Published by:
    Facet
    Published online:
    22 February 2020
    Print publication:
    30 March 2019, pp 69-88

  • Summary

    The Party told you to reject the evidence of your eyes and ears. It was their final, most essential command.

    (George Orwell, 1984)

    Just remember, what you’re seeing and what you’re reading is not what's

    happening.

    (Donald Trump, 24 July 2018)

    Introduction

    There has been increasing concern with the growing infusion of disinformation, or ‘fake news’, into public discourse. Fact-checkers have reported that Donald Trump has been making more than six false or misleading statements every day during his term in office thus far. This chapter explores three questions that arise from this situation:

    • 1 Is disregard for the truth really an acceptable hallmark of contemporary politics? If so, how is this possible and what factors contributed to this state of affairs? I review research that shows how among partisans, endorsement of a politician has become decoupled from his or her perceived veracity. This may reflect disenchantment with the current political system and its ‘elite’.

    • 2 What are the political and psychological consequences of the ‘post-truth’ world? I survey the many findings that suggest that, once acquired, misinformation ‘sticks’ in memory and is difficult to dislodge. In addition to those direct consequences of misinformation, it has indirect fallouts that may undermine the democratic process.

    • 3 What is the appropriate response to this situation? I review the difficulties associated with debunking misinformation and suggest instead that ‘inoculating’ people against misinformation before it is presented is a more successful strategy. I contrast psychological approaches with the idea of ‘technocognition’, which blends findings from cognitive science with the design of information architectures that are more resilient to the spreading of misinformation.

    ‘Post-truth’ was nominated word of the year by Oxford dictionaries in 2016, to describe ‘circumstances in which objective facts are less influential in shaping public opinion than appeals to emotion and personal belief ‘. A year later, Collins dictionaries declared ‘fake news’ to be the next word of the year, to refer to ‘false, often sensational, information disseminated under the guise of news reporting’. The concern with truth – or indeed its absence – was largely triggered by two political events in 2016 that had global ramifications and that arguably involved an unprecedented extent of deception and misinformation: the Brexit referendum in the UK and the election of Donald Trump in the USA.

  • Preface

  • Simon Farrell, University of Western Australia, Perth, Stephan Lewandowsky, University of Bristol
  • Book:
    Computational Modeling of Cognition and Behavior
    Published online:
    05 February 2018
    Print publication:
    22 February 2018, pp xxi-xxii

  • Summary

    This book presents an integrated approach to the application of computational and mathematical models in psychology. Computational models have been extensively applied to better understand many domains of human behavior, such as perception, memory, reasoning, decision-making, communicating, and deciding. Modeling is often applied in these areas to different purposes – measurement, prediction, and model testing. Our major goal here is to provide a unified view on the interface between theories, simulations, and data, with a view to answering the central question: how can we learn from models of behavior?

    We cover several topics. Part I of the book explains what a computational model is and gives a general overview of models that have been applied to understanding human behavior. We also examine the process of converting theoretical statements into simulation code and give an overview of the various concepts required to understand modeling. Part II examines one use of models: parameter estimation. By fitting models to data, inferences can be made from the resulting parameter estimates, and statements made about the psychological mechanism(s) or representations that generated those data. We cover maximum likelihood estimation and Bayesian estimation, including estimation across multiple participants and hierarchical estimation. Part III explores how inferences can be made from models by using model comparison. We consider under what conditions statements of sufficiency and necessity can be made from data, and how model complexity can be conceptualized and quantified. Part III examines several approaches to accounting for complexity in model comparison, including information criteria and Bayes Factors. Part IV considers the role of computational modeling in advancing psychological theory. We explore use of models as adjuncts to human reasoning, and the interaction between human and artificial intelligence to guide theorizing and generation of conceptual insights. We also consider the use of models as tools to arrive at shared understanding between researchers (i.e. the use of models as common terms of reference), and practices for communicating and sharing models. We finish by giving an overview of the application of models in several popular areas: neural network models, models of choice response time, and the application of models to understand neural data.

    To accomplish all this, we use a freely available computer language, called R, which was initially developed for statistical data analysis but has broad applicability and is now used by many modellers.

  • 6 - Bayesian Parameter Estimation

  • from Part II - Parameter Estimation
  • Simon Farrell, University of Western Australia, Perth, Stephan Lewandowsky, University of Bristol
  • Book:
    Computational Modeling of Cognition and Behavior
    Published online:
    05 February 2018
    Print publication:
    22 February 2018, pp 126-145

  • Summary

    The goal of this chapter is to give the reader a thorough understanding of the principles of Bayesian Parameter Estimation and its application using analytic and numerical methods. Readers interested in the broader background of Bayesian statistics may wish to consult the books by Kruschke (2011), Gelman et al. (2004), or Jaynes (2003), to mention but a few that we find particularly helpful.

    What Is Bayesian Inference?

    In Chapter 4 we introduced the likelihood function as a means of identifying the most likely value of a parameter in light of the observed data. We also cautioned against confusing the likelihood with a probability. Likelihoods permit relative comparisons between different parameter values – thus allowing us to maximize likelihoods in order to obtain parameter estimates – but they are not suited for estimating absolute probabilities.

    Some of the most intuitively obvious questions one might ask of parameter estimates therefore require an approach other than maximum likelihood estimation. Suppose we estimate a parameter M from a given data set that we believe represents the capacity of working memory; that is, how many items people can hold in mind at the same time in the face of distraction (e.g., Kane et al., 2005).We need not worry about how exactly this was done, but let's suppose the best estimate turns out to be 3.2. That punctate information by itself tells us relatively little because we know that however cleverly we designed our experiment, there would be some measurement error associated with our single estimate. What we really want to know is the likely range of the “true” parameter value that we can infer from our measurement. Ideally, we want to have information about the probability distribution of that parameter so we can draw more educated conclusions. This requires the use of Bayesian parameter estimation, and we devote the next four chapters to an exploration of Bayesian concepts. We begin by introducing some additional basic facts about conditional probabilities.

    Analytic Methods for Obtaining Posteriors Our first example of Bayesian inference involves multiple flips of a slightly biased coin. Our example actually involves a natural process but we will not reveal that process until later. Our goal is to identify exactly how biased the “coin” is, and we apply Bayes Theorem to estimate the parameter that characterizes the coin's behavior.

  • 4 - Maximum Likelihood Parameter Estimation

  • from Part II - Parameter Estimation
  • Simon Farrell, University of Western Australia, Perth, Stephan Lewandowsky, University of Bristol
  • Book:
    Computational Modeling of Cognition and Behavior
    Published online:
    05 February 2018
    Print publication:
    22 February 2018, pp 72-104

  • Summary

    In the previous chapters, we encountered one of the key issues in computational modeling: a full, quantitative specification of a model involves not just a description of the model (in the form of algorithms or equations), but also a specification of the parameters of the model and their values. Although in some cases we can use known parameter values (e.g., those determined from previous applications of the model; see Oberauer and Lewandowsky, 2008), in most cases we must estimate those parameters from the data. Chapter 3 described the basics of parameter estimation by minimizing the discrepancy between the data and the model's predictions. Chapter 4 deals with a popular and more principled alternative approach to parameter estimation called maximum likelihood estimation.

    Unlike the techniques discussed in the previous chapter, maximum likelihood estimation is deeply rooted in statistical theory. Maximum likelihood estimators have known properties that are not possessed by estimates obtained via minimizing RMSD (except under specific situations detailed later); for example, maximum likelihood estimates are guaranteed to become more accurate on average with increasing sample size. Additionally, likelihood can be used to make statements about the relative weight of evidence for a particular hypothesis, either about the value of a particular parameter or about a model as a whole. This lays the groundwork for the material in upcoming chapters: likelihood plays a key role in Bayesian parameter estimation, and we will later use the idea of likelihood as the strength of evidence to explore a principled and rigorous technique for evaluating scientific models.

    Basics of Probabilities

    The term “likelihood” in common parlance is used interchangeably with probability; we might consider the likelihood of it raining tomorrow (which varies considerably between the two authors, who at the time of writing live in Australia and the UK), or the likelihood that an individual randomly selected from the population will live past the age of 80. By contrast, when considering statistical or computational modeling, the term likelihood takes on a very strict meaning which is subtly – but fundamentally – different from that of probability.

    The best way to define likelihood, and to distinguish it from probability.

    is to start with the concept of probability itself. We all have some intuitive notion of what a probability is, and these intuitions probably make some connection with the formal definitions we will introduce here.

  • 13 - Neural Network Models

  • from Part IV - Models in Psychology
  • Simon Farrell, University of Western Australia, Perth, Stephan Lewandowsky, University of Bristol
  • Book:
    Computational Modeling of Cognition and Behavior
    Published online:
    05 February 2018
    Print publication:
    22 February 2018, pp 334-368

  • Summary

    The ability to learn is a fundamental one. Many models of cognition incorporate some form of learning, as it is rarely the case that performance on a task is unaffected by experience. This chapter presents a family of models that provide a simple and profound architecture for understanding memory and how we learn about regularities in our world.

    Neural network models, or connectionist models, model human behavior using structures and representations that loosely capture our understanding of how the brain functions. Although some models do attempt to capture fine-grained details of the operation of neurons, the framework we discuss here abstracts some general operating principles from the brain and uses these to account for performance on learning and memory tasks. Most connectionist models have the following features in common:

  • The modeling architecture is composed of units (“neurons”) connected by weights;

  • Each unit has its own firing rate;

  • The pattern of activation (i.e., firing rates) across an ensemble of units can be treated as a representation;

  • The activity of a unit is determined by the incoming weights, and the activity of the units that are passing activation through those weights;

  • Learning is accomplished by modifying the connection strengths – the weights – between units.

    • Don't worry if you found that description very abstract – it is! Like many things, neural networks are better understood by example than by description. We next go through a simple and popular example of a connectionist model to put some flesh on this abstract description.

    Hebbian Models

    The Hebbian Associator

    Associative memory refers to our ability to remember pairings between items or objects. One example of an associative memory task is the cued recall task, where a person is shown pairings of stimuli and responses, and must produce the appropriate response on seeing a stimulus. A real-world example of a cued recall task is remembering someone's name when you see their face at a conference. Here, we will assume that the stimuli and responses are both words; so, for example, if the word trench has been paired with the word scissors, then the goal is to say scissors when the word trench is later seen again.

  • 3 - Basic Parameter Estimation Techniques

  • from Part II - Parameter Estimation
  • Simon Farrell, University of Western Australia, Perth, Stephan Lewandowsky, University of Bristol
  • Book:
    Computational Modeling of Cognition and Behavior
    Published online:
    05 February 2018
    Print publication:
    22 February 2018, pp 47-71

  • Summary

    We know from the preceding chapter that the goal of parameter estimation is to find those parameter values that maximize the agreement between the model's predictions and the data. The extent of that agreement then tells us something (though not everything!) about the utility of the model. Moreover, interpretation of those parameter values can often shed a light on the underlying processes. In this chapter we provide the basic set of tools necessary to achieve these goals.

    Discrepancy Function

    Although we wish to maximise the similarity between the model predictions and the data, most parameter estimation procedures reframe this intention by instead minimizing the discrepancy between predictions and data.

    Minimization requires a continuous discrepancy function that condenses the discrepancy between predictions and data into a single number. That discrepancy function is minimized by gradual and iterative adjustment of the parameters. The discrepancy function is also variously known as objective function, cost function, or error function, and we will consider a few such functions along the way.

    To illustrate, consider Figure 3.1, which presents data from one condition in a forgetting experiment reported by Carpenter et al. (2008). In the experiment, participants studied a set of 60 obscure facts (e.g., “greyhounds have the best eyesight of any dog”), and their memory for those facts was tested after 5 minutes, and again 1, 2, 7, 14, 42 days later. A different subset of items was tested at each retention interval. The figure also shows the best-fitting predictions of a model of forgetting based on a power function. We first encountered the power function in Chapter 1 in connection with practice effects. As we foreshadowed there, the power function is also a good way to characterize forgetting. Carpenter et al. (2008) used this form of the power function:

    Fitting Models to Data: Parameter Estimation Techniques

    How do we minimize the discrepancy function? A number of competing approaches exist, and we will discuss them throughout the remainder of the book. The first two approaches are known as least-squares and maximum likelihood estimation, respectively, and this chapter and the next one is devoted to presenting them. The third approach, which involves application of Bayesian statistics, will be discussed later in Chapters 6 through 9. Although the mechanics of least-squares and maximum likelihood estimation are quite similar, their underlying motivation and properties differ considerably.

  • 11 - Bayesian Model Comparison Using Bayes Factors

  • from Part III - Model Comparison
  • Simon Farrell, University of Western Australia, Perth, Stephan Lewandowsky, University of Bristol
  • Book:
    Computational Modeling of Cognition and Behavior
    Published online:
    05 February 2018
    Print publication:
    22 February 2018, pp 273-308

  • Summary

    The previous chapter explored the issue of model complexity, and focused on the fact that a model may give a good fit to a set of data simply by virtue of being more flexible. We also discussed several methods by which we can correct for complexity to obtain an unbiased measure of the fit of a model, in particular the AIC as a corrected estimator of the distance between the data and the “true” model. We pick up on both of these themes in this chapter, in which we discuss the Bayesian approach to model comparison, and how the Bayesian approach naturally accounts for model complexity.

    We begin by presenting the core component of Bayesian model comparison – the marginal likelihood – and discuss how the relative fit of two models can be expressed in terms of Bayes factors. We then survey several methods for calculating the marginal likelihood, before discussing the particular role of the prior distributions when performing Bayesian model comparison.

    Marginal Likelihoods and Bayes Factors

    To understand Bayes factors, it is useful to first remind ourselves of Bayes theorem, as it applies to Bayesian parameter estimation (Equation 6.6). For convenience, we restate the theorem here:

    For much of our discussion thus far, we focussed on the proportional relationship P(θ|y) ∝ P(y| θ)P(θ), and dropped P(y) as it can be treated as a normalising constant (Equation 7.1). It turns out that P(y) – the marginal likelihood – plays a critical role in Bayesian model comparison. Indeed, it is also called the evidence because it quantifies the evidence the data y provide for the model.

    To understand why, we need to remind ourselves that all of the components in Equation 11.1 are conditional on a particular model M; that is, the equation should really be presented as:

    Accordingly, the evidence P(y|M) tells us about the probability of obtaining data y under model M, and thus how consistent the data are with the model.

    It might not seem obvious how we can calculate P(y|M), but all the information we need is already used in Equation 11.2. The evidence is obtained by calculating the marginal likelihood of the data given the model:

    Effectively, what we are doing is considering how likely the data are for each point in the parameter space, and then averaging the resulting values.

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