Bayes and blickets: effects of knowledge on causal induction in children and adults

People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults' judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children's judgments (Experiments 3 and 5) agreed qualitatively with this account.     

Maintaining close relationships: gratitude as a motivator and a detector of maintenance behavior

This research examined the dual function of gratitude for relationship maintenance in close relationships. In a longitudinal study among married couples, the authors tested the dyadic effects of gratitude over three time points for approximately 4 years following marriage. They found that feelings of gratitude toward a partner stem from the partner's relationship maintenance behaviors, partly because such behaviors create the perception of responsiveness to one's needs. In turn, gratitude motivates partners to engage in relationship maintenance. Hence, the present model emphasizes that gratitude between close partners (a) originates from partners' relationship maintenance behaviors and the perception of a partner's responsiveness and (b) promotes a partner's reciprocal maintenance behaviors. Thus, the authors' findings add credence to their model, in that gratitude contributes to a reciprocal process of relationship maintenance, whereby each partner's maintenance behaviors, perceptions of responsiveness, and feelings of gratitude feed back on and influence the other's behaviors, perceptions, and feelings.     

Overt attention and predictiveness in human contingency learning

Two experiments used eye-tracking procedures to investigate the relationship between attention and associative learning in human participants. These experiments found greater overt attention to cues experienced as predictive of the outcomes with which they were paired, than to cues experienced as nonpredictive. Moreover, this attentional bias persisted into a second training phase when all cues were equally predictive of the outcomes with which they were paired, and it was accompanied by a related bias in the rate of learning about these cues. These findings are consistent with the attentional model of associative learning proposed by Mackintosh (1975), but not with that proposed by Pearce and Hall (1980).     

Superstition predicts perception of illusory control

Superstitions are common, yet we have little understanding of the cognitive mechanisms that bring them about. This study used a laboratory‐based analogue for superstitious beliefs that involved people monitoring the relationship between undertaking an action (pressing a button) and an outcome occurring (a light illuminating). The task was arranged such that there was no objective contingency between pressing the button and the light illuminating – the light was just as likely to illuminate whether the button was pressed or not. Nevertheless, most people rated the causal relationship between the button press and the light illuminating to be moderately positive, demonstrating an illusion of causality. This study found that the magnitude of this illusion was predicted by people's level of endorsement of common superstitious beliefs (measured using a novel Superstitious Beliefs Questionnaire), but was not associated with mood variables or their self‐rated locus of control. This observation is consistent with a more general individual difference or bias to overweight conjunctive events over disjunctive events during causal reasoning in those with a propensity for superstitious beliefs.     

Disturbed memory and amnesia related to intensive care

Patients, when admitted to an intensive care unit (ICU), have one thing in common: their illness is life-threatening. Patients may remain on ICU in a critical condition, needing support with their breathing, circulation, and/or kidneys for varying lengths of time, from days to weeks. During that time the patients will receive sedative and analgesic drugs to ensure compliance with artificial ventilation. Patients recovering from critical illness frequently have little or no recall of their period in ICU, or remember nightmare, hallucinations, or paranoid delusions. The nature, extent and reason for these difficulties, have been under-reported and consequently our purpose was to conduct a review of memory problems experienced by ICU patients. A systematic literature review of computer databases (Medline, PsycLit, and CINAHL) identified 25 relevant papers. In addition, other relevant articles were obtained, citation lists and associated articles retrieved. Due to lack of research on processes underlying memory problems in ICU patients all articles that introduced an insight into possible mechanisms were included in the review. There seem to be two possible processes contributing to memory problems in ICU patients. First the illness and treatment may have a general dampening effect on memory. Delirium and sleep disturbance are both common in ICU patients. Delirium can result in a profound amnesia for the period of confusion. Sleep deprivation exacerbates the confusional state. Slow wave sleep is important for the consolidation of episodic memories. Treatment administered to patients in ICU can have effects on memory. Opiates, benzodiazepines, sedative drugs such as propofol, adrenaline, and corticosteroids can all influence memory. In addition, the withdrawal of drugs, such as benzodiazepines, can cause profound withdrawal reactions, which may contribute to delirium. Second, we hypothesise that there is a process that affects memory negatively for external events but enhances memory for internal events. The physical constraints and social isolation experienced by ICU patients and the life-threatening nature of the illness may increase the experience of hypnagogic hallucinations. Attentional shift during hypnagogic images from external stimuli to internally generated images would explain why ICU patients have such poor recall of external ICU events, but can clearly remember hallucinations and nightmares. Patients describe these memories as being very vivid and this is explored in terms of flashbulb memory formation. The absence of memories for real events on ICU can result in ICU patients remembering paranoid delusions of staff trying to kill them, with little information to reject these vivid memories as unreal. This has implications for patients' future psychological health.     

The Challenges of Large-Scale,Web-Based Language Datasets: Word Length and Predictability Revisited

Language research has come to rely heavily on large-scale, web-based datasets. These datasets can present significant methodological challenges, requiring researchers to make a number of decisions about how they are collected, represented, and analyzed. These decisions often concern long-standing challenges in corpus-based language research, including determining what counts as a word, deciding which words should be analyzed, and matching sets of words across languages. We illustrate these challenges by revisiting “Word lengths are optimized for efficient communication” (Piantadosi, Tily, & Gibson, 2011), which found that word lengths in 11 languages are more strongly correlated with their average predictability (or average information content) than their frequency. Using what we argue to be best practices for large-scale corpus analyses, we find significantly attenuated support for this result and demonstrate that a stronger relationship obtains between word frequency and length for a majority of the languages in the sample. We consider the implications of the results for language research more broadly and provide several recommendations to researchers regarding best practices.     

Sensitivity to Shared Information in Social Learning

Social learning has been shown to be an evolutionarily adaptive strategy, but it can be implemented via many different cognitive mechanisms. The adaptive advantage of social learning depends crucially on the ability of each learner to obtain relevant and accurate information from informants. The source of informants’ knowledge is a particularly important cue for evaluating advice from multiple informants; if the informants share the source of their information or have obtained their information from each other, then their testimony is statistically dependent and may be less reliable than testimony from informants who do not share information. In this study, we use a Bayesian model to determine how rational learners should incorporate the effects of shared information when learning from other people, conducting three experiments that examine whether human learners behave similarly. We find that people are sensitive to a number of different patterns of dependency, supporting the use of a sophisticated strategy for social learning that goes beyond copying the majority, and broadening the situations in which social learning is likely to be an adaptive strategy.     

Faster Teaching via POMDP Planning

Human and automated tutors attempt to choose pedagogical activities that will maximize student learning, informed by their estimates of the student's current knowledge. There has been substantial research on tracking and modeling student learning, but significantly less attention on how to plan teaching actions and how the assumed student model impacts the resulting plans. We frame the problem of optimally selecting teaching actions using a decision‐theoretic approach and show how to formulate teaching as a partially observable Markov decision process planning problem. This framework makes it possible to explore how different assumptions about student learning and behavior should affect the selection of teaching actions. We consider how to apply this framework to concept learning problems, and we present approximate methods for finding optimal teaching actions, given the large state and action spaces that arise in teaching. Through simulations and behavioral experiments, we explore the consequences of choosing teacher actions under different assumed student models. In two concept‐learning tasks, we show that this technique can accelerate learning relative to baseline performance.