Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets

Today, people generate and store more data than ever before as they interact with both real and virtual environments. These digital traces of behavior and cognition offer cognitive scientists and psychologists an unprecedented opportunity to test theories outside the laboratory. Despite general excitement about big data and naturally occurring datasets among researchers, three “gaps” stand in the way of their wider adoption in theory-driven research: the imagination gap, the skills gap, and the culture gap. We outline an approach to bridging these three gaps while respecting our responsibilities to the public as participants in and consumers of the resulting research. To that end, we introduce Data on the Mind (http://www.dataonthemind.org), a community-focused initiative aimed at meeting the unprecedented challenges and opportunities of theory-driven research with big data and naturally occurring datasets. We argue that big data and naturally occurring datasets are most powerfully used to supplement—not supplant—traditional experimental paradigms in order to understand human behavior and cognition, and we highlight emerging ethical issues related to the collection, sharing, and use of these powerful datasets.     

A psychometric comparison of the Internet Addiction Test, the Internet-Related Problem Scale, and self-diagnosis

One of the more prominent issues in the field of Internet addiction is the validity of the instrument used to assess users' level of Internet involvement. Many of the instruments used to assess Internet addiction have high face validity but have yet to be tested psychometrically. The aim of this study is to compare two of the most used Internet addiction research measures, the Internet Addiction Test (IAT) and the Internet-Related Problem Scale (IRPS), along with a self-diagnostic question simply asking Internet users if they thought they were addicted to the Internet. A total of 225 Internet users participated in the study (69 males and 156 females). Participants who defined themselves as Internet addicts had higher scores on both the IAT and IRPS, and the three different Internet addiction measures were strongly correlated to each other. For the IAT, factor analysis generated three factors (emotional/psychological conflict; time management issues; mood modification) explaining 56.3% of the variance. For the IRPS, factor analysis generated four factors (negative effects of Internet use; mood modification; loss of control; increased Internet use) explaining 60.2% of the variance. The implications for these findings are discussed.     

A rational model of the effects of distributional information on feature learning

Most psychological theories treat the features of objects as being fixed and immediately available to observers. However, novel objects have an infinite array of properties that could potentially be encoded as features, raising the question of how people learn which features to use in representing those objects. We focus on the effects of distributional information on feature learning, considering how a rational agent should use statistical information about the properties of objects in identifying features. Inspired by previous behavioral results on human feature learning, we present an ideal observer model based on nonparametric Bayesian statistics. This model balances the idea that objects have potentially infinitely many features with the goal of using a relatively small number of features to represent any finite set of objects. We then explore the predictions of this ideal observer model. In particular, we investigate whether people are sensitive to how parts co-vary over objects they observe. In a series of four behavioral experiments (three using visual stimuli, one using conceptual stimuli), we demonstrate that people infer different features to represent the same four objects depending on the distribution of parts over the objects they observe. Additionally in all four experiments, the features people infer have consequences for how they generalize properties to novel objects. We also show that simple models that use the raw sensory data as inputs and standard dimensionality reduction techniques (principal component analysis and independent component analysis) are insufficient to explain our results.     

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.     

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.