Cognitive Science Literature Research Articles

1070An online searchable database of example causal diagrams to make them easier to construct

Abstract Focus of Presentation Most researchers do not use causal diagrams, in this case meaning directed acyclic graphs (DAGs), despite being widely recommended in epidemiology. They can help to identify the biases that might lead to faulty conclusions or suggest variables for which data should be collected and included in a model. Seeking to understand this reluctance and develop alternative strategies that might increase the use of causal diagrams, we searched the cognitive science literature for potential reasons and suggestions. Findings Insights from cognitive psychology led to a better understanding of the barriers that might underlie the reluctance to use causal diagrams. This includes our built-in desire for cognitive ease and suggests that strategies which lower the effort required to create a diagram may help. We explain these findings using example projects from neuropsychiatry big data research and describe how an online resource we have created has helped. Conclusions/Implications A causal diagram website has been created that aims to lower the effort needed to create a diagram for a study. It contains tutorials and a terminology guide, as well as links to other tutorials; a guide to software and other resources that might be used; and a searchable database of example causal diagrams with links to published articles that include them. Key messages A website has been developed to help overcome barriers to the use of causal diagrams. With contributions welcome.

How and Why We Should Argue with Angry Uncle: A Defense of Fact Dumping and Consistency Checking

ABSTRACT How should we talk to Angry Uncle, or attempt to persuade any very ignorant audience? This paper discusses several strategies, including fact dumping, consistency checking, pandering, and just being friendly. It defends the continued value of fact dumping and consistency checking despite skeptical doubts rooted in recent cognitive science literature about their strategic efficacy. Pandering and friendliness often fail to confront our audience with epistemic resistance and so face serious limitations as means of responding to ignorance. Any reasonable view of how to talk to Angry Uncles must also consider how to meet relevant moral standards, such as showing respect for ourselves, our audience, and important social causes. Without some fact dumping and consistency checking, pandering and friendliness often fail to meet these standards. All in all, the various modes work best together, and it would be a mistake to conclude from unfavorable cognitive science research that we should avoid fact dumping and consistency checking in Angry Uncle exchanges.

Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks

In this paper we develop a framework for analysing the impact of Artificial Intelligence (AI) on occupations. This framework maps 59 generic tasks from worker surveys and an occupational database to 14 cognitive abilities (that we extract from the cognitive science literature) and these to a comprehensive list of 328 AI benchmarks used to evaluate research intensity across a broad range of different AI areas. The use of cognitive abilities as an intermediate layer, instead of mapping work tasks to AI benchmarks directly, allows for an identification of potential AI exposure for tasks for which AI applications have not been explicitly created. An application of our framework to occupational databases gives insights into the abilities through which AI is most likely to affect jobs and allows for a ranking of occupations with respect to AI exposure. Moreover, we show that some jobs that were not known to be affected by previous waves of automation may now be subject to higher AI exposure. Finally, we find that some of the abilities where AI research is currently very intense are linked to tasks with comparatively limited labour input in the labour markets of advanced economies (e.g., visual and auditory processing using deep learning, and sensorimotor interaction through (deep) reinforcement learning).
 This article appears in the special track on AI and Society.

Are representations glorified receptors? On use and usage of mental representations

Abstract In the paper I present an application of Jerzy Pelc’s functional semiotics to contemporary cognitive science. I argue that, even though the original theory addressed only linguistic representations, it can be fruitfully applied to mental representations. I show how Pelc’s distinction between use and usage helps us clarify the notion of mental representations and how it makes it immune to skeptical challenges presented in contemporary cognitive science literature.

Beneficial and harmful explanatory machine learning

Given the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong machine learning (USML). USML is demonstrated by a measurable increase in human performance of a task following provision to the human of a symbolic machine learned theory for task performance. A recent paper demonstrates the beneficial effect of a machine learned logic theory for a classification task, yet no existing work to our knowledge has examined the potential harmfulness of machine’s involvement for human comprehension during learning. This paper investigates the explanatory effects of a machine learned theory in the context of simple two person games and proposes a framework for identifying the harmfulness of machine explanations based on the Cognitive Science literature. The approach involves a cognitive window consisting of two quantifiable bounds and it is supported by empirical evidence collected from human trials. Our quantitative and qualitative results indicate that human learning aided by a symbolic machine learned theory which satisfies a cognitive window has achieved significantly higher performance than human self learning. Results also demonstrate that human learning aided by a symbolic machine learned theory that fails to satisfy this window leads to significantly worse performance than unaided human learning.

Modeling Human Morphological Competence.

One of the central debates in the cognitive science of language has revolved around the nature of human linguistic competence. Whether syntactic competence should be characterized by abstract hierarchical structures or reduced to surface linear strings has been actively debated, but the nature of morphological competence has been insufficiently appreciated despite the parallel question in the cognitive science literature. In this paper, in order to investigate whether morphological competence should be characterized by abstract hierarchical structures, we conducted a crowdsourced acceptability judgment experiment on morphologically complex words and evaluated five computational models of morphological competence against human acceptability judgments: Character Markov Models (Character), Syllable Markov Models (Syllable), Morpheme Markov Models (Morpheme), Hidden Markov Models (HMM), and Probabilistic Context-Free Grammars (PCFG). Our psycholinguistic experimentation and computational modeling demonstrated that “morphous” computational models with morpheme units outperformed “amorphous” computational models without morpheme units and, importantly, PCFG with hierarchical structures most accurately explained human acceptability judgments on several evaluation metrics, especially for morphologically complex words with nested morphological structures. Those results strongly suggest that human morphological competence should be characterized by abstract hierarchical structures internally generated by the grammar, not reduced to surface linear strings externally attested in large corpora.

Modelling cyclists’ comfort zones from obstacle avoidance manoeuvres

This paper introduces a framework for modelling the cyclist’s comfort zone. Unlike the driver’s comfort zone, little is known about the cyclist’s. The framework draws on existing literature in cognitive science about driver behaviour to explain experimental results from cycling field trials, and the modelling of these results. We modelled braking and steering manoeuvres from field data of cyclists’ obstacle avoidance within their comfort zone. Results show that when cyclists avoided obstacles by braking, they kept a constant deceleration; as speed increased, they started to brake earlier, farther from the obstacle, maintaining an almost constant time to collision. When cyclists avoided obstacles by steering, they maintained a constant distance from the object, independent of speed. Overall, the higher the speed, the more the steering manoeuvres were temporally delayed compared to braking manoeuvres. We discuss these results and other similarities between cyclist and driver behaviour during obstacle avoidance. Implications for the design of acceptable active safety and infrastructure design are also addressed.

Explainable Agency in Reinforcement Learning Agents

This thesis explores how reinforcement learning (RL) agents can provide explanations for their actions and behaviours. As humans, we build causal models to encode cause-effect relations of events and use these to explain why events happen. Taking inspiration from cognitive psychology and social science literature, I build causal explanation models and explanation dialogue models for RL agents. By mimicking human-like explanation models, these agents can provide explanations that are natural and intuitive to humans.

Cognitive Behavioral Science behind the Value of Play: Leveraging Everyday Experiences to Promote Play, Learning, and Positive Interactions

ABSTRACT Participating in play affords physical, social, and cognitive benefits. Here, we review the cognitive behavioral science literature highlighting the value of play and describe the different types of play along with the evidence linking play to positive outcomes for children in areas such as social-emotional, cognitive, academic, and social-emotional development. Several case studies demonstrate how educators, caregivers, and community members can integrate low-cost, evidenced-based playful learning interventions into community settings to impact children where they live.

Research-based instructional strategies in a flipped principles of microeconomics classroom

Principles of economics students often struggle to move beyond memorization of key terms and simple application of concepts to higher order thinking, such as evaluation and concept transfer. This higher order thinking is necessary for students to answer fundamental questions in economics. The primary advantage of the blend and flip format is that students can achieve low levels of learning, memorization of terms, and mastery of simple applications of concepts on their own before meeting for a face-to-face class. Previous research confirms that students in flipped-blended classes show larger gains in learning than students in moderately-blended classes. However, the gains we find are modest compared to those reported in related research in the math and science disciplines, which use research-based teaching practices, proven in the cognitive science literature. We determine the effects of using research-based instructional strategies on student learning in principles of microeconomics courses. Specifically, after controlling for a number of key background variables, we test whether students in flipped-blended classes who learn with research-based strategies in face-to-face classes are capable of applying knowledge and skills better than their counterparts in flipped-blended classes who are assigned practice problems similar to those on exams during face-to-face classes. Our findings indicate that students exposed to RBIS show significantly greater learning gains than students in the control group, and that these gains are most prevalent in the later part of the semester.

Story Quality as a Matter of Perception: Using Word Embeddings to Estimate Cognitive Interest

Storytelling is a capable tool for interactive agents and better stories can enable better interactions. Many existing automated evaluation techniques are either focused on textual features that are not necessarily reflective of perceived interestingness (e.g. coherence), or are domain-specific, relying on a priori semantics models (e.g. in a game). However, the effectiveness of storytelling depends both on its versatility to adapt to new domains and the perceived interestingness of its generated stories. In this paper, drawing from cognitive science literature, we propose and evaluate a method for estimating cognitive interest in stories based on the level of predictive inference they cause during perception.

What people learn about how people learn: An analysis of citation behavior and the multidisciplinary flow of knowledge

We explore the contention that the seminal US National Academies consensus report, How People Learn (HPL), played a major role in bridging the flow of knowledge from Cognitive Science to Education. Our paper yielded four important results: First, HPL is, on a number of bibliometric measures, an unusually interdisciplinary work. Focusing on the fields of particular interest here, our citation analysis shows the Education, Cognitive Science, and Border field (e.g., Educational Psychology, Learning Sciences, and Learning Technology and Human-Computer Interaction) literatures all to have been major influences on it. Second, we found HPL to be unusually highly cited – and by publications from an unusually diverse set of disciplines. Beyond Education, Cognitive Science, and Border field publications, HPL was also relatively highly cited by publications in Medical/Health-related, Engineering, and other Discipline-Based Education Research fields. Third, undermining the claim that HPL served as a gateway to the Cognitive Science literature, we found Education articles citing HPL not to be more likely to have Cognitive Science as a major influence than are Education articles more generally, as indicated by their cited references. Finally, the Education publications that cited HPL were far more likely to refer to concepts in HPL that were already prevalent in the Education literature rather than to concepts from Cognitive Science. Conversely, the Cognitive Science publications that cited HPL were more apt to refer to concepts already in the Cognitive Science literature. Taken together, these results are a caution that, even for a highly regarded multidisciplinary work cited widely by publications from multiple disciplines, its direct influence could be largely disciplinary. Implications for the policy goals of fostering interdisciplinary research and the role of National Academies consensus reports are discussed.

The relationship between forward and backward diversity in CORE datasets

In this paper we seek to better understand the relationship between forward diversity in the Cognitive Science and Educational Research literature, as well as what we call Border fields (i.e. those fields which exist at the intersection of Cognitive Science and Education Research). We find a clear and convincing relationship between forward and backward diversity in the datasets we study. Among all available explanatory variables, Integration scores claim the strongest correlation in terms of their ability to account for forward diversity. When comparing results from this study to benchmark results from a prior study (using the same indicators) the datasets in this study show a tendency to be both more integrative and diffuse.

A logical study of group-size based social network creation

This paper is part of an on-going research programme on the logical aspects of social network creation. In our proposed setting we make use of a notion of distance between agents, defined as the number of features distinguishing them. Following the ideas of the cognitive science literature, we build the social network by adopting a group-size approach: each agent's new social space will contain the λ agents that are closer to her. The paper studies the properties of the resulting networks, providing a sound and complete axiom system for a logical language which describes the changes that the social network creation operation brings about. This is done not only for the basic case in which the distance is relative to all basic features and the more general one in which the distance is relative to a given subset of them, but also for the more interesting scenario in which the distance is relative to a set of more complex social features.

The overlooked ubiquity of first-person experience in the cognitive sciences

Science aims to transform the subjectivity of individual observations and ideas into more objective and universal knowledge. Yet if there is any area in which first-person experience holds a particularly special and delicate role, it is the sciences of the mind. According to a widespread view, first-person methods were largely discarded from psychology after the fall of introspectionism a century ago and replaced by more objective behavioral measures, a step that some authors have begun to criticize. To examine whether these views are sufficiently informed by actual scientific practice, we conducted a review of methodological approaches in the cognitive science literature. We found that reports of subjective experience are in fact still widely used in a broad variety of different experimental paradigms, both in studies that focus on subjective experience, and in those that make no explicit reference to it. Across these studies, we documented a diverse collection of approaches that leveraged first-person reports, ranging from button presses to unstructured interviews, while continuing to maximise experimental reproducibility. Common to these studies were subjects acting as sensors, intentionally communicating their experience to the experimenter, which we termed “second-person” methods. We conclude that, despite views to the contrary, first-person experience has always been and is still central to investigations of the mind even if it is not recognized as such. We suggest that the conversation ought to be reframed: instead of debating whether to accept subjects’ first-person knowledge we should discuss how best to do so.

Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics

Network science provides a set of quantitative methods to investigate complex systems, including human cognition. Although cognitive theories in different domains are strongly based on a network perspective, the application of network science methodologies to quantitatively study cognition has so far been limited in scope. This review demonstrates how network science approaches have been applied to the study of human cognition and how network science can uniquely address and provide novel insight on important questions related to the complexity of cognitive systems and the processes that occur within those systems. Drawing on the literature in cognitive network science, with a focus on semantic and lexical networks, we argue three key points. (i) Network science provides a powerful quantitative approach to represent cognitive systems. (ii) The network science approach enables cognitive scientists to achieve a deeper understanding of human cognition by capturing how the structure, i.e., the underlying network, and processes operating on a network structure interact to produce behavioral phenomena. (iii) Network science provides a quantitative framework to model the dynamics of cognitive systems, operationalized as structural changes in cognitive systems on different timescales and resolutions. Finally, we highlight key milestones that the field of cognitive network science needs to achieve as it matures in order to provide continued insights into the nature of cognitive structures and processes.

Understanding and Evaluating Eyewitness Recall of Events

Human factors practitioners in litigation sometimes base event reconstructions upon information from witnesses. The witnesses can be those who actively performed the at-issue tasks or those who merely observed the events. During this alternative-format interactive session, attendees will watch three event scenarios (two video and one role play). Attendees will then be queried about their recall of those scenarios, which will then be compared to the veridical information from the videos. The facilitators will help identify the particular eyewitness memory factors at play with the objectives of providing attendees (1) an experiential learning experience in which relevant eyewitness memory dynamics are demonstrated and discussed; (2) a summary of some of the cognitive-science literature concerning eyewitness memory for events and a framework to assist in evaluating and weighing the accuracy and completeness of eyewitness accounts; and (3) the general judicial posture toward expert testimony about eyewitness dynamics.

Supply Chain Expedience: Unraveling the Double Helix of Delivery Delays

We build on a metaphor where supply chain delivery delays result from the intertwined double helix of motor carrier errors and driver errors. Agency theory provides a foundation for understanding the relationship between a shipping company, a motor carrier and a truck driver, in cargo transportation in a supply chain. However, agency theory’s moral hazard construct is insufficient for fully explaining delays in this context. We integrate the literature on organizational expedience with agency theory to develop an explanation for actions by an agent that knowingly breaks rules in order to support both its own interests and the principal’s goals, further speculating that the principal may turn a blind eye to such actions. The cognitive science literature is then used to develop an understanding of motor carrier errors and truck driver errors, to develop hypotheses based on expedience as a way of compensating for motor carrier and truck driver errors. Logit analysis and survival analysis, using a Cox proportional hazards model and ROC curve, are used to analyze an archival data set of over 200,000 recent supply chain deliveries at a Fortune 500 company, which we linked to data about motor carrier behavior and truck driver behavior in a U.S. Department of Transportation (DOT) database. The results are relevant to shipping companies that rely on large trucks to transport their cargo, motor carriers providing transportation services and academics seeking to understand the nuances of agency relationships in supply chains and interested in incorporating archival data into their research.

Cognitive challenges of changeability: adjustment to system changes and transfer of knowledge in modular chemical plants

In the chemical industry, highly changeable modular plants allow for system reconfigurations on shortest timescales: a number of processing units can be combined to optimize the plant setup for current demands. As a consequence, human operators are frequently confronted with newly assembled systems that differ from previous ones in some ways while not differing in others. This partial overlap creates a number of challenges with regard to operator performance and learning. Both differentiation and generalization of knowledge are needed, which leads to a goal conflict: on the one hand, operators have to know the specifics of the current system, update their understanding of functional relations between system parameters, and use operation procedures that are tailored to the requirements of the current situation. On the other hand, they need to apply the knowledge they have acquired in previous plant setups to solve problems in a new one. While unwanted carryover must be avoided, appropriate transfer is essential. The present article provides an overview of the challenges and potentials of learning and transfer in changing environments as discussed in the cognitive science and situated cognition literatures. This overview is of prescriptive nature: it presents results and theories that should be considered when analyzing operator performance and designing interfaces or training in modular plants. To this end, the article considers how learning is adapted to the volatility of the environment, how mental representations are updated, how conceptual and procedural knowledge is transferred to new situations, and how learning is shaped by interactions with the environment.

Parameterized Complexity of Theory of Mind Reasoning in Dynamic Epistemic Logic

Theory of mind refers to the human capacity for reasoning about others’ mental states based on observations of their actions and unfolding events. This type of reasoning is notorious in the cognitive science literature for its presumed computational intractability. A possible reason could be that it may involve higher-order thinking (e.g., ‘you believe that I believe that you believe’). To investigate this we formalize theory of mind reasoning as updating of beliefs about beliefs using dynamic epistemic logic, as this formalism allows to parameterize ‘order of thinking.’ We prove that theory of mind reasoning, so formalized, indeed is intractable (specifically, PSPACE-complete). Using parameterized complexity we prove, however, that the ‘order parameter’ is not a source of intractability. We furthermore consider a set of alternative parameters and investigate which of them are sources of intractability. We discuss the implications of these results for the understanding of theory of mind.