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Detecting covariation in sequential events provides us with a powerful means of inferring the causal structure of our world. However, people often overestimate the causal relationship between unrelated events, a phenomenon referred to as illusory causation. This tendency is greatest when the putative effect occurs frequently; the widely replicated outcome density (OD) effect. Most laboratory research on illusory causation and the OD effect has focused on possible causes of a positive outcome, such as a drug that causes patient recovery. Despite its relevance, relatively few studies have examined illusory causation in cases where a cue is hypothesized to generate an unfavorable (negative) outcome, such as a drug that produces unwanted side effects. Here, we directly compared how people develop illusory beliefs about the generation of positive versus negative outcomes. We presented all participants with a drug treatment that was hypothesized to cause high readings of a fictitious cell count (but had no effect on cell count across a series of learning trials). We manipulated whether a high cell count occurred frequently or infrequently and whether a high cell count should be considered a beneficial medical outcome or an undesirable side effect. We found consistent evidence of an OD effect but no effect of the valence of the high cell count outcome. This suggests that illusory beliefs are not controlled by the desirability of the cause-effect relationship. We discuss implications for theories of applied causal reasoning.

A causal illusion occurs when people perceive a causal relationship between two events that are not contingent on each other. This experiment explored how this illusion varies when people reason diagnostically (i.e., in an effect-to-cause direction). Participants learnt about an illusory cause-effect relationship in which the probability of the cause and the probability of the effect were orthogonally manipulated to be either high or low. Participants learnt either predictively (i.e., cause-to-effect) or diagnostically, and at test had to make two causal judgements that encouraged either predictive (cause-to-effect) or diagnostic (effect-to-cause) reasoning. Diagnostic reasoning at test increased the strength of the cause density bias and decreased the strength of the effect density bias. It also decreased causal ratings, but only after predictive learning. Explaining these results requires an understanding of how the process of causal learning can impact later reasoning; something the current literature is yet to provide.

Illusions of causality arise when people observe statistically unrelated events and yet form a belief that the events are causally linked. When participants observe a sequence of discrete binary events (e.g., a patient was either administered a treatment or no treatment, and subsequently recovers or does not recover from their illness), the frequency of the putative cause and outcome occurring inflates the illusion of causality.Recently, similar effects have been observed using outcomes of continuous magnitude. Participants are more likely to endorse the causal status of a (completely ineffective) cue if the target outcome (e.g., high magnitude outcomes) occur frequently. Here, we extended these findings by investigating how predictions and causal judgments for a cue of continuous magnitude were affected by the distribution of cue values presented. Participants observed cue values (dose of a fictitious medicine) sourced from either a continuous distribution or from two discrete values, and were followed by outcomes that were either continuous (Experiment 1) or binary in nature (Experiment 2). Our results show that participants were more likely to assume a linear relationship between drug dose and magnitude of recovery when cue dosage were predominantly high than when they were predominantly low.

Response inhibition—our ability to suppress actions—is achieved through coordinated reactive and proactive processes that may recruit different neurophysiological mechanisms. Here we adapted a two-step continuous performance task in which the decision to respond depends on a combination of an initial context cue and a subsequent target probe. Using transcranial magnetic stimulation (TMS), we mapped changes in corticospinal excitability and inhibition, providing indices of reactive and proactive processes that influence the state of the motor system in the lead-up to initiating or suppressing a response. We found distinct changes in corticospinal excitability at critical timepoints when participants were preparing in advance to inhibit a response (during the cue) and while inhibiting a response (during the probe). Motor system activity during early timepoints correlated with behavioural indices of proactive capacity and predicted whether participants would later successfully inhibit their response. Implications for theories of inhibitory control will be discussed.

Evidence from a variety of learning tasks suggests that cuesthat are more predictive of an outcome attract greater attentionand are learned about more effectively in subsequent tasks. Wetested whether this learned predictiveness effect is due to theobjective strength of the cue-outcome association (cue-outcome correlation), or the degree to which the cue isinformative for making the correct choice on each trial (choicerelevance), by manipulating the possible outcome choicesavailable on each trial. Experiment 1 compared two sets of cuesthat were equally (and imperfectly) correlated with outcomesand showed learning biases in favor of the set of cues that hadinitially been more relevant for choices made on each trial.Experiment 2 used a more conventional learned predictivenessdesign in which the cue-outcome correlation was stronger forone set of cues (perfect predictors) than the other set (imperfectpredictors). However, here we manipulated whether or not theimperfect predictors could be used to make a correct choice,and thus whether the imperfect predictors possessed choicerelevance that was equal to or less than the perfect predictors.In this case, we found no evidence that the relevancemanipulation made any difference; learning biases towards theperfect predictor were evident regardless. The results suggestthat both cue-outcome correlation and choice relevance canlead to changes in associability and learning biases; both wereindividually sufficient but neither were necessary.

Nocebo hyperalgesia is a pervasive problem in which the treatment context triggers negative expectations that exacerbate pain. Thus, developing ethical strategies to mitigate nocebo hyperalgesia is crucial. Emerging research suggests that choice has the capacity to reduce nocebo side effects, but choice effects on nocebo hyperalgesia have not been explored. This study investigated the impact of choice on conditioned nocebo hyperalgesia using a well-established electrocutaneous pain paradigm where increases in noxious stimulation were surreptitiously paired with the activation of a sham device. In study 1, healthy volunteers (N = 104) were randomised to choice over (nocebo) treatment administration, nocebo administration without choice, or a natural history control group. Nocebo hyperalgesia was greater for those with choice than no choice, suggesting that choice increased rather than diminished nocebo hyperalgesia. Study 2 tested whether providing positive information about the benefits of choice in coping with pain could counteract heightened nocebo hyperalgesia caused by choice. A different sample of healthy adults (N = 137) were randomised to receive nocebo treatment with choice and positive choice information, choice only, or no choice. The positive choice information failed to attenuate the effect of choice on nocebo hyperalgesia. The current results suggest that, rather than decreasing nocebo hyperalgesia, treatment choice may exacerbate pain outcomes when a painful procedure is repeatedly administered. As such, using choice as a strategy to mitigate nocebo outcomes should be treated with caution. [ABSTRACT FROM AUTHOR]

Failure to learn and generalize abstract relational rules has critical implications for education. In this study, we aimed to determine which training conditions facilitate relational transfer in a relatively simple (patterning) discrimination versus a relatively complex (biconditional) discrimination. The amount of training participants received had little influence on rates of relational transfer. Instead, trial-sequencing of the training contingencies influenced relational transfer in different ways depending on the complexity of the discrimination. Clustering instances of relational rules together during training improved transfer of both simpler patterning and more difficult biconditional rules, regardless of individual differences in cognitive reflection. However, blocking all trials of the same type together improved rule transfer only for biconditional discriminations. Individual differences in cognitive reflection were also more predictive of relational rule use under suboptimal training conditions. The results highlight the need for comprehensive accounts of relational learning to consider how learning conditions and individual differences affect the likelihood of engaging in learning relational structures.

Illusory causation is a consistent error in human learning inwhich people perceive two unrelated events as being causallyrelated. Causal illusions are greatly increased when the targetoutcome occurs frequently rather than rarely, a characteristicknown as the outcome density bias. Unlike most experimentaldesigns using binary outcomes, real-world problems to whichillusory causation is most applicable (e.g. beliefs aboutineffective health therapies) involve continuous and variableconsequences that are not readily classifiable as the presenceor absence of a salient event. This study used a causallearning task framed as a medical trial to investigate whetheroutcome density effects emerged when using a continuousand variable outcome that appeared on every trial.Experiment 1 compared the effects of using fixed outcomevalues (i.e. consistent low and high magnitudes) versusvariable outcome values (i.e. low and high magnitudesvarying around two means in a bimodal distribution).Experiment 2 compared positively skewed (low density) andnegatively skewed (high density) continuous distributions.These conditions yielded comparable outcome density effects,providing empirical support for the relevance of the outcomedensity bias to real-world situations in which outcomes arenot binary but occur to differing degrees.

Cue competition effects in human contingency learningappear to be sensitive to the causal nature of cue-outcomerelationships. While blocking effects are reliablydemonstrated in scenarios where cues are presented as causesof outcomes, several studies have failed to find blocking inscenarios where cues are presented as effects of outcomes, afinding that is typically taken as evidence for the involvementof controlled reasoning processes in cue competition. Thesestudies typically measure blocking with continuous causalratings about individual cues. Previous studies have foundthat sensitivity to causal model may depend on how the testquestion is phrased. In contrast, the current study tested thesensitivity of blocking to causal scenarios across differentformats of the same test question. Participants completed acausal learning task with instructions suggesting either apredictive (i.e. cue causes outcome) or diagnostic (cue iscaused by outcome) cue-outcome relationship. Participantswere then asked about the likelihood of outcomes occurringby either giving a continuous rating of each outcome or adiscrete choice about the most likely outcome. Whenmeasured by continuous ratings of individual cues, blockingwas evident in predictive, but not diagnostic scenarios.However, when measured by discrete choice or using acompound negation test, blocking was robust and insensitiveto causal scenario. The results suggest that contributions ofpredictive memory and causal reasoning to cue competitioneffects may depend substantially on the type of measure used.

The ability to form relational categories for objects that share few features in common is a hallmark of humancognition. However until recently, neuroimaging research largely focused solely on how people acquire categories defined byfeatures. In the current electroencephalography (EEG) study, we examine how relational and feature-based category learningcompare in well-matched learning tasks. Building on a previous functional magnetic resonance imaging study by our labo-ratory, we capitalise on the rich temporal information offered by EEG. Focusing on the neural dynamics of how people learncategory memberships over individual trials in an experimental task, we investigate how these single trial dynamics modulatecomputational estimates from decision-making modelling frameworks. Specifically, by sorting participants’ individual trialsby their position in the experimental sequence we observe striking relationships between EEG dynamics (e.g., frontal thetaoscillations and P300 component) and feature-based and relational categorisation behaviour.

The "Perruchet effect" refers to a dissociation between the conscious expectancy of an outcome and the strength or speed of responding in anticipation of that outcome. This dissociation is considered by some to be the best evidence for multiple learning processes with expectancy governed by participants' explicit beliefs and responding driven by the associative history of the cues that partially predict the outcome. However, an alternative nonassociative explanation is that the trends in responding are the result of recent experience with the same outcome (i.e., repetition priming based on event recency). This explanation casts doubt on the theoretical import of the dissociation because it suggests that associative learning may not be involved in generating the observed trends in response strength. Associative accounts of the Perruchet effect predict a weakening of the response strength trends when the cues perfectly predict the occurrence of the outcome. In two experiments, we compared a condition with two cues that were each perfect predictors of an outcome with control conditions in which the cues did not perfectly predict the outcome. In both experiments, the typical downward trend in response time (RT) observed in Perruchet effect experiments was substantially weaker (and indeed absent) for the predictive group, suggesting an associative contribution to the effect.

Often failures of problem solving on educational assessments are failures of problem categorization. That is, whenreasoners do not properly classify a novel problem they do not know what solution to apply. For example, often physics studentsdo not recognize the underlying commonalities in the relationships among the variables in different problems concerningNewton’s laws of motion (Chi, Feltovich, & Glaser, 1981). Addressing this challenge there have been separate lines of researchexamining 1. how differences in students’ learning strategies or cognitive abilities affects their propensity to discover relationalcommonalities (e.g., Little & MacDaniel, 2015) and 2. how variations in task structure change the likelihood of successfulcategorization (e.g., Roher & Pashler, 2010). However, relatively little research has examined whether the optimal task structuredepends on the learner’s strategy or ability. Across several experiments, we demonstrate multiple dependencies between theeffectiveness of different task structures on differences in learning strategy.

Recent research has identified key differences in the wayindividuals make decisions in predictive learning tasks,including the use of feature- and rule-based strategies incausal learning and model-based versus model-free choices inreinforcement learning. These results suggest that people relyto varying degrees on separable psychological processes.However, the relationship between these types of learningstrategies has not been explored in any depth. This studyinvestigated the relationship between feature- vs rule-basedstrategies in a causal learning task and indices of model-freeand model-based choice in a two-step reinforcement learningprocedure. We found that rule-based transfer was associatedwith the use of model-based, but not model-free responding ina two-step task.

Two experiments examined biases in selective attention during contextual cuing of visual search. When participants were instructed to search for a target of a particular color, overt attention (as measured by the location of fixations) was biased strongly toward distractors presented in that same color. However, when participants searched for targets that could be presented in 1 of 2 possible colors, overt attention was not biased between the different distractors, regardless of whether these distractors predicted the location of the target (repeating) or did not (randomly arranged). These data suggest that selective attention in visual search is guided only by the demands of the target detection task (the attentional set) and not by the predictive validity of the distractor elements.

Many studies indicate that deceptively administered placebos can improve pain outcomes. However, the deception involved presents an ethical barrier to translation because it violates informed consent and patient autonomy. Open-label placebos (OLPs), inert treatments that are openly administered as placebos, have been proposed as an ethically acceptable alternative. Early studies have suggested that OLP can improve pain outcomes, but important questions remain as to how to maximise OLP hypoalgesia to improve treatment outcomes in pain patients. This study investigated whether providing choice over when to administer an OLP treatment has the capacity to enhance OLP hypoalgesia using an electrocutaneous pain paradigm. One hundred thirty-two healthy volunteers were randomised to 3 types of treatment: OLP with choice, OLP without choice, and no treatment (natural history). The OLP groups were further randomised such that half were tested with a consistent pain intensity and the other half were tested with variable pain intensity to mimic day-to-day variability in pain intensity in health settings. The results indicated that treatment provided with choice exhibited greater OLP hypoalgesia than that provided without choice and that greater expectancy mediated this effect. Of interest, there was no evidence for OLP hypoalgesia without choice relative to natural history. Furthermore, variability in pain intensity did not affect OLP hypoalgesia. The current findings present novel evidence that choice over treatment administration may be a cheap and effective strategy for boosting the efficacy of OLPs in the clinical care of pain. [ABSTRACT FROM AUTHOR]