The metacognition of vigilance: Using self-scheduled breaks to improve sustained attention.

Attention fluctuates over time and is prone to fatigue. Thus, maintaining sustained attention is difficult. The goal of this article is to evaluate the metacognitive penetrability of attention by examining whether dynamic control over the pacing of an ongoing attention-demanding task helps individuals maintain attention.

How experts and novices judge other people's knowledgeability from language use.

How accurate are people in judging someone else's knowledge based on their language use, and do more knowledgeable people use different cues to make these judgments? We address this by recruiting a group of participants ("informants") to answer general knowledge questions and describe various images belonging to different categories (e.g., cartoons, basketball).

Differentiating mental models of self and others: A hierarchical framework for knowledge assessment.

Developing an accurate model of another agent's knowledge is central to communication and cooperation between agents. In this article, we propose a hierarchical framework of knowledge assessment that explains how people construct mental models of their own knowledge and the knowledge of others. Our framework posits that people integrate information about their own and others' knowledge via Bayesian inference.

Three Challenges for AI-Assisted Decision-Making.

Artificial intelligence (AI) has the potential to improve human decision-making by providing decision recommendations and problem-relevant information to assist human decision-makers. However, the full realization of the potential of human-AI collaboration continues to face several challenges. First, the conditions that support complementarity (i.

Teaching categories via examples and explanations.

People often learn categories through interaction with knowledgeable others who may use verbal explanations, visual exemplars, or both, to share their knowledge. Verbal and nonverbal means of pedagogical communication are commonly used in conjunction, but their respective roles are not fully understood. In this work, we studied how well these modes of communication work with different category structures.

Jointly modeling behavioral and EEG measures of proactive control in task switching.

In this study, we implement joint modeling of behavioral and single-trial electroencephalography (EEG) data derived from a cued-trials task-switching paradigm to test the hypothesis that trial-by-trial adjustment of response criterion can be linked to changes in the event-related potentials (ERPs) elicited during the cue-target interval (CTI). Specifically, we assess whether ERP components associated with preparation to switch task and preparation of the relevant task are linked to a response criterion parameter derived from a simple diffusion decision model (DDM). Joint modeling frameworks characterize the brain-behavior link by simultaneously modeling behavioral and neural data and implementing a linking function to bind these two submodels.

Building and benchmarking the motivated deception corpus: Improving the quality of deceptive text through gaming.

When one studies fake news or false reviews, the first step to take is to find a corpus of text samples to work with. However, most deceptive corpora suffer from an intrinsic problem: there is little incentive for the providers of the deception to put their best effort, which risks lowering the quality and realism of the deception. The corpus described in this project, the Motivated Deception Corpus, aims to rectify this problem by gamifying the process of deceptive text collection.

Linking computational models of two core tasks of cognitive control.

Cognitive control refers to the ability to maintain goal-relevant information in the face of distraction, making it a core construct for understanding human thought and behavior. There is great theoretical and practical value in building theories that can be used to explain or to predict variations in cognitive control as a function of experimental manipulations or individual differences. A critical step toward building such theories is determining which latent constructs are shared between laboratory tasks that are designed to measure cognitive control.

Comparing models of learning and relearning in large-scale cognitive training data sets.

Practice in real-world settings exhibits many idiosyncracies of scheduling and duration that can only be roughly approximated by laboratory research. Here we investigate 39,157 individuals' performance on two cognitive games on the Lumosity platform over a span of 5 years. The large-scale nature of the data allows us to observe highly varied lengths of uncontrolled interruptions to practice and offers a unique view of learning in naturalistic settings.

Bayesian modeling of human-AI complementarity.

SignificanceWith the increase in artificial intelligence in real-world applications, there is interest in building hybrid systems that take both human and machine predictions into account. Previous work has shown the benefits of separately combining the predictions of diverse machine classifiers or groups of people. Using a Bayesian modeling framework, we extend these results by systematically investigating the factors that influence the performance of hybrid combinations of human and machine classifiers while taking into account the unique ways human and algorithmic confidence is expressed.

Perceptions of AI engaging in human expression.

Though humans should defer to the superior judgement of AI in an increasing number of domains, certain biases prevent us from doing so. Understanding when and why these biases occur is a central challenge for human-computer interaction. One proposed source of such bias is task subjectivity.

A Critical Review of Network-Based and Distributional Approaches to Semantic Memory Structure and Processes.

Some of the earliest work on understanding how concepts are organized in memory used a network-based approach, where words or concepts are represented as nodes, and relationships between words are represented by links between nodes. Over the past two decades, advances in network science and graph theoretical methods have led to the development of computational semantic networks. This review provides a modern perspective on how computational semantic networks have proven to be useful tools to investigate the structure of semantic memory as well as search and retrieval processes within semantic memory, to ultimately model performance in a wide variety of cognitive tasks.

Going, going, gone: competitive decision-making in Dutch auctions.

In a Dutch auction, an item is offered for sale at a set maximum price. The price is then gradually lowered over a fixed interval of time until a bid is made, securing the item for the bidder at the current price. Bidders must trade-off between certainty and price: bid early to secure the item and you pay a premium; bid later at a lower price but risk losing to another bidder.

Inferring latent learning factors in large-scale cognitive training data.

The flexibility to learn diverse tasks is a hallmark of human cognition. To improve our understanding of individual differences and dynamics of learning across tasks, we analyse the latent structure of learning trajectories from 36,297 individuals as they learned 51 different tasks on the Lumosity online cognitive training platform. Through a data-driven modelling approach using probabilistic dimensionality reduction, we investigate covariation across learning trajectories with few assumptions about learning curve form or relationships between tasks.

Distant connectivity and multiple-step priming in large-scale semantic networks.

We examined 3 different network models of representing semantic knowledge (5,018-word directed and undirected step distance networks, and an association-correlation network) to predict lexical priming effects. In Experiment 1, participants made semantic relatedness judgments for word pairs with varying path lengths. Response latencies for judgments followed a quadratic relationship with network path lengths, replicating and extending a recent pattern reported by Kenett, Levi, Anaki, and Faust (2017) for an 800-word association-correlation network in Hebrew.

A large-scale analysis of task switching practice effects across the lifespan.

An important feature of human cognition is the ability to flexibly and efficiently adapt behavior in response to continuously changing contextual demands. We leverage a large-scale dataset from Lumosity, an online cognitive-training platform, to investigate how cognitive processes involved in cued switching between tasks are affected by level of task practice across the adult lifespan. We develop a computational account of task switching that specifies the temporal dynamics of activating task-relevant representations and inhibiting task-irrelevant representations and how they vary with extended task practice across a number of age groups.

Experimental design modulates variance in BOLD activation: The variance design general linear model.

Typical fMRI studies have focused on either the mean trend in the blood-oxygen-level-dependent (BOLD) time course or functional connectivity (FC). However, other statistics of the neuroimaging data may contain important information. Despite studies showing links between the variance in the BOLD time series (BV) and age and cognitive performance, a formal framework for testing these effects has not yet been developed.

Predicting Task and Subject Differences with Functional Connectivity and Blood-Oxygen-Level-Dependent Variability.

Previous research has found that functional connectivity (FC) can accurately predict the identity of a subject performing a task and the type of task being performed. These results are replicated using a large data set collected at the Ohio State University Center for Cognitive and Behavioral Brain Imaging. This work introduces a novel perspective on task and subject identity prediction: blood-oxygen-level-dependent variability (BV).

Monitoring the ebb and flow of attention: Does controlling the onset of stimuli during encoding enhance memory?

Central to the operation of the Atkinson and Shiffrin's (Psychology of learning and motivation, 2, 89-195, 1968) model of human memory are a variety of control processes that manage information flow. Research on metacognition reveals that provision of control in laboratory learning tasks is generally beneficial to memory. In this paper, we investigate the novel domain of attentional fluctuations during study.

Large scale structure-function mappings of the human subcortex.

Currently little is known about structure-function mappings in the human subcortex. Here we present a large-scale automated meta-analysis on the literature to understand the structure-function mapping in the human subcortex. The results provide converging evidence into unique large scale structure-function mappings of the human subcortex based on their functional and anatomical similarity.

The joint contribution of participation and performance to learning functions: Exploring the effects of age in large-scale data sets.

Large-scale data sets from online training and game platforms offer the opportunity for more extensive and more precise investigations of human learning than is typically achievable in the laboratory. However, because people make their own choices about participation, any investigation into learning using these data sets must simultaneously model performance-that is, the learning function-and participation. Using a data set of 54 million gameplays from the online brain training site Lumosity, we show that learning functions of participants are systematically biased by participation policies that vary with age.

Modeling the Covariance Structure of Complex Datasets Using Cognitive Models: An Application to Individual Differences and the Heritability of Cognitive Ability.

Understanding individual differences in cognitive performance is an important part of understanding how variations in underlying cognitive processes can result in variations in task performance. However, the exploration of individual differences in the components of the decision process-such as cognitive processing speed, response caution, and motor execution speed-in previous research has been limited. Here, we assess the heritability of the components of the decision process, with heritability having been a common aspect of individual differences research within other areas of cognition.

Bayesian models of cognition revisited: Setting optimality aside and letting data drive psychological theory.

Recent debates in the psychological literature have raised questions about the assumptions that underpin Bayesian models of cognition and what inferences they license about human cognition. In this paper we revisit this topic, arguing that there are 2 qualitatively different ways in which a Bayesian model could be constructed. The most common approach uses a Bayesian model as a normative standard upon which to license a claim about optimality.

A generative joint model for spike trains and saccades during perceptual decision-making.

Theory development in both psychology and neuroscience can benefit by consideration of both behavioral and neural data sets. However, the development of appropriate methods for linking these data sets is a difficult statistical and conceptual problem. Over the past decades, different linking approaches have been employed in the study of perceptual decision-making, beginning with rudimentary linking of the data sets at a qualitative, structural level, culminating in sophisticated statistical approaches with quantitative links.