Bridging the divide between causal illusions in the laboratory and the real world: the effects of outcome density with a variable continuous outcome

Illusory causation refers to a consistent error in human learning in which the learner develops a false belief that two unrelated events are causally associated. Laboratory studies usually demonstrate illusory causation by presenting two events—a cue (e.g., drug treatment) and a discrete outcome (e.g., patient has recovered from illness)—probabilistically across many trials such that the presence of the cue does not alter the probability of the outcome. Illusory causation in these studies is further augmented when the base rate of the outcome is high, a characteristic known as the outcome density effect. Illusory causation and the outcome density effect provide laboratory models of false beliefs that emerge in everyday life. However, unlike laboratory research, the real-world beliefs to which illusory causation is most applicable (e.g., ineffective health therapies) often involve consequences that are not readily classified in a discrete or binary manner. This study used a causal learning task framed as a medical trial to investigate whether similar outcome density effects emerged when using continuous outcomes. Across two experiments, participants observed outcomes that were either likely to be relatively low (low outcome density) or likely to be relatively high (high outcome density) along a numerical scale from 0 (no health improvement) to 100 (full recovery). In Experiment 1, a bimodal distribution of outcome magnitudes, incorporating variance around a high and low modal value, produced illusory causation and outcome density effects equivalent to a condition with two fixed outcome values. In Experiment 2, the outcome density effect was evident when using unimodal skewed distributions of outcomes that contained more ambiguous values around the midpoint of the scale. Together, these findings provide empirical support for the relevance of the outcome density bias to real-world situations in which outcomes are not binary but occur to differing degrees. This has implications for the way in which we apply our understanding of causal illusions in the laboratory to the development of false beliefs in everyday life

The impact of side effect framing on COVID-19 booster vaccine intentions in an Australian sample

Objective: To evaluate the effect of presenting positively attribute-framed side effect information on COVID-19 booster vaccine intention relative to standard negatively-framed wording and a no-intervention control. Design and participants: A representative sample of Australian adults (N = 1204) were randomised to one of six conditions within a factorial design: Framing (Positive; Negative; Control) × Vaccine (Familiar (Pfizer); Unfamiliar (Moderna)). Intervention: Negative Framing involved presenting the likelihood of experiencing side effects (e.g., heart inflammation is very rare, 1 in every 80,000 will be affected), whereas Positive Framing involved presenting the same information but as the likelihood of not experiencing side effects (e.g., 79,999 in every 80,000 will not be affected). Primary outcome: Booster vaccine intention measured pre- and post-intervention. Results: Participants were more familiar with the Pfizer vaccine (t(1203) = 28.63, p <.001, Cohen's dz = 0.83). Positive Framing (M = 75.7, SE = 0.9, 95% CI = [73.9, 77.4]) increased vaccine intention relative to Negative Framing (M = 70.7, SE = 0.9, 95% CI = [68.9, 72.4]) overall (F(1, 1192) = 4.68, p =.031, ηp2 = 0.004). Framing interacted with Vaccine and Baseline Intention (F(2, 1192) = 6.18, p =.002, ηp2 = 0.01). Positive Framing was superior, or at least equal, to Negative Framing and Control at increasing Booster Intention, irrespective of participants’ pre-intervention level of intent and vaccine type. Side effect worry and perceived severity mediated the effect of Positive vs. Negative Framing across vaccines. Conclusion: Positive framing of side effect information appears superior for increasing vaccine intent relative to the standard negative wording currently used. Pre-registration: See: aspredicted.org/LDX_2ZL

Pseudoscientific health beliefs and the perceived frequency of causal relationships

Beliefs about cause and effect, including health beliefs, are thought to be related to the frequency of the target outcome (e.g., health recovery) occurring when the putative cause is present and when it is absent (treatment administered vs. no treatment); this is known as contingency learning. However, it is unclear whether unvalidated health beliefs, where there is no evidence of cause– effect contingency, are also influenced by the subjective perception of a meaningful contingency between events. In a survey, respondents were asked to judge a range of health beliefs and estimate the probability of the target outcome occurring with and without the putative cause present. Over-all, we found evidence that causal beliefs are related to perceived cause–effect contingency. Interestingly, beliefs that were not predicted by perceived contingency were meaningfully related to scores on the paranormal belief scale. These findings suggest heterogeneity in pseudoscientific health beliefs and the need to tailor intervention strategies according to underlying causes

The effect of threat on cognitive biases and pain outcomes: An eye-tracking study

Background: Theoretical accounts of attentional and interpretation biases in pain suggest that these biases are interrelated and are both influenced by perceived threat. A laboratory-based study was conducted to test whether these biases are influenced by threat and their interrelationship and whether attention or interpretation biases predict pain outcomes. Methods: Healthy participants (n = 87) received either threatening or reassuring pain information and then completed questionnaires, interpretation and attentional bias tasks (with eye-tracking) and a pain task (the cold pressor). Results: There was an interaction effect for threat group and stimuli type on mean dwell time for face stimuli, such that there was an attentional bias towards happy faces in the low- but not high-threat group. Further, high threat was also associated with shorter pain tolerance, increased pain and distress. In correlational analyses, avoidance of affective pain words was associated with increased pain. However, no relationship was found between attention and interpretation biases, and interpretation biases were not influenced by threat or associated with pain. Conclusions: These findings provide partial support for the threat interpretation model and the importance of threat and affective pain biases, yet no relationship between cognitive processing biases was found, which may only occur in clinical pain samples. What does this study add?: In healthy participants, no relationship between attention and interpretation biases was found. Eye tracking revealed an association between later attentional processes and pain. Threat influenced attentional biases and pain outcomes, partially supporting theoretical accounts. © 2016 European Pain Federation - EFIC

Follow-up of blood-pressure lowering and glucose control in type 2 diabetes.

BACKGROUND In the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) factorial trial, the combination of perindopril and indapamide reduced mortality among patients with type 2 diabetes, but intensive glucose control, targeting a glycated hemoglobin level of less than 6.5%, did not. We now report results of the 6-year post-trial follow-up. METHODS We invited surviving participants, who had previously been assigned to perindopril–indapamide or placebo and to intensive or standard glucose control (with the glucose-control comparison extending for an additional 6 months), to participate in a post-trial follow-up evaluation. The primary end points were death from any cause and major macrovascular events. RESULTS The baseline characteristics were similar among the 11,140 patients who originally underwent randomization and the 8494 patients who participated in the post-trial follow-up for a median of 5.9 years (blood-pressure–lowering comparison) or 5.4 years (glucose-control comparison). Between-group differences in blood pressure and glycated hemoglobin levels during the trial were no longer evident by the first post-trial visit. The reductions in the risk of death from any cause and of death from cardiovascular causes that had been observed in the group receiving active blood-pressure–lowering treatment during the trial were attenuated but significant at the end of the post-trial follow-up; the hazard ratios were 0.91 (95% confidence interval [CI], 0.84 to 0.99; P=0.03) and 0.88 (95% CI, 0.77 to 0.99; P=0.04), respectively. No differences were observed during follow-up in the risk of death from any cause or major macrovascular events between the intensive-glucose-control group and the standard-glucose-control group; the hazard ratios were 1.00 (95% CI, 0.92 to 1.08) and 1.00 (95% CI, 0.92 to 1.08), respectively. CONCLUSIONS The benefits with respect to mortality that had been observed among patients originally assigned to blood-pressure–lowering therapy were attenuated but still evident at the end of follow-up. There was no evidence that intensive glucose control during the trial led to long-term benefits with respect to mortality or macrovascular events

Haemoglobin glycation index and risk for diabetes-related complications in the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) trial

AIMS/HYPOTHESIS: Previous studies have suggested that the haemoglobin glycation index (HGI) can be used as a predictor of diabetes-related complications in individuals with type 1 and type 2 diabetes. We investigated whether HGI was a predictor of adverse outcomes of intensive glucose lowering and of diabetes-related complications in general, using data from the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) trial. METHODS: We studied participants in the ADVANCE trial with data available for baseline HbA1c and fasting plasma glucose (FPG) (n = 11,083). HGI is the difference between observed HbA1c and HbA1c predicted from a simple linear regression of HbA1c on FPG. Using Cox regression, we investigated the association between HGI, both categorised and continuous, and adverse outcomes, considering treatment allocation (intensive or standard glucose control) and compared prediction of HGI and HbA1c. RESULTS: Intensive glucose control lowered mortality risk in individuals with high HGI only (HR 0.74 [95% CI 0.61, 0.91]; p = 0.003), while there was no difference in the effect of intensive treatment on mortality in those with high HbA1c. Irrespective of treatment allocation, every SD increase in HGI was associated with a significant risk increase of 14-17% for macrovascular and microvascular disease and mortality. However, when adjusted for identical covariates, HbA1c was a stronger predictor of these outcomes than HGI. CONCLUSIONS/INTERPRETATION: HGI predicts risk for complications in ADVANCE participants, irrespective of treatment allocation, but no better than HbA1c. Individuals with high HGI have a lower risk for mortality when on intensive treatment. Given the discordant results and uncertain relevance beyond HbA1c, clinical use of HGI in type 2 diabetes cannot currently be recommended

Relationship of Insulin Resistance and Related Metabolic Variables to Coronary Artery Disease: A Mathematical Analysis

OBJECTIVE—People with diabetes have an increased risk of coronary artery disease (CAD). An unanswered question is what portion of CAD can be attributed to insulin resistance, related metabolic variables, and other known CAD risk factors

Factors influencing participant enrolment in a diabetes prevention program in general practice: lessons from the Sydney diabetes prevention program

Background: The effectiveness of lifestyle interventions in reducing diabetes incidence has been well established. Little is known, however, about factors influencing the reach of diabetes prevention programs. This study examines the predictors of enrolment in the Sydney Diabetes Prevention Program (SDPP), a community-based diabetes prevention program conducted in general practice, New South Wales, Australia from 2008&ndash;2011.Methods: SDPP was an effectiveness trial. Participating general practitioners (GPs) from three Divisions of General Practice invited individuals aged 50&ndash;65 years without known diabetes to complete the Australian Type 2 Diabetes Risk Assessment tool. Individuals at high risk of diabetes were invited to participate in a lifestyle modification program. A multivariate model using generalized estimating equations to control for clustering of enrolment outcomes by GPs was used to examine independent predictors of enrolment in the program. Predictors included age, gender, indigenous status, region of birth, socio-economic status, family history of diabetes, history of high glucose, use of anti-hypertensive medication, smoking status, fruit and vegetable intake, physical activity level and waist measurement.Results: Of the 1821 eligible people identified as high risk, one third chose not to enrol in the lifestyle program. In multivariant analysis, physically inactive individuals (OR: 1.48, P = 0.004) and those with a family history of diabetes (OR: 1.67, P = 0.000) and history of high blood glucose levels (OR: 1.48, P = 0.001) were significantly more likely to enrol in the program. However, high risk individuals who smoked (OR: 0.52, P = 0.000), were born in a country with high diabetes risk (OR: 0.52, P = 0.000), were taking blood pressure lowering medications (OR: 0.80, P = 0.040) and consumed little fruit and vegetables (OR: 0.76, P = 0.047) were significantly less likely to take up the program.Conclusions: Targeted strategies are likely to be needed to engage groups such as smokers and high risk ethnic groups. Further research is required to better understand factors influencing enrolment in diabetes prevention programs in the primary health care setting, both at the GP and individual level.<br /