1,721,115 research outputs found
Time in causal structure learning
Get PDFA large body of research has explored how the time between two events affects judgments of causal strength between them. In this article, we extend this work in 4 experiments that explore the role of temporal information in causal structure induction with multiple variables. We distinguish two qualitatively different types of information: The order in which events occur, and the temporal intervals between those events. We focus on one-shot learning in Experiment 1. In Experiment 2, we explore how people integrate evidence from multiple observations of the same causal device. Participants’ judgments are well predicted by a Bayesian model that rules out causal structures that are inconsistent with the observed temporal order, and favors structures that imply similar intervals between causally connected components. In Experiments 3 and 4, we look more closely at participants’ sensitivity to exact event timings. Participants see three events that always occur in the same order, but the variability and correlation between the timings of the events is either more consistent with a chain or a fork structure. We show, for the first time, that even when order cues do not differentiate, people can still make accurate causal structure judgments on the basis of interval variability alone. (PsycInfo Database Record (c) 2020 APA, all rights reserved
Active causal structure learning in continuous time
Get PDFResearch on causal cognition has largely focused on learning and reasoning about contingency data aggregated across discrete observations or experiments. However, this setting represents only the tip of the causal cognition iceberg. A more general problem lurking beneath is that of learning the latent causal structure that connects events and actions as they unfold in continuous time. In this paper, we examine how people actively learn about causal structure in a continuous-time setting, focusing on when and where they intervene and how this shapes their learning. Across two experiments, we find that participants' accuracy depends on both the informativeness and evidential complexity of the data they generate. Moreover, participants' intervention choices strike a balance between maximizing expected information and minimizing inferential complexity. People time and target their interventions to create simple yet informative causal dynamics. We discuss how the continuous-time setting challenges existing computational accounts of active causal learning, and argue that metacognitive awareness of one's inferential limitations plays a critical role for successful learning in the wild
Decompose, deduce, and dispose:A memory-limited metacognitive model of human problem solving
Get PDFMany real-world problems are defined by complex systems of interlocking constraints. How people are able to solve these problems with such limited working memory capacity remains poorly understood. We propose a formal model of human problem-solving under memory constraints that uses metacognitive knowledge of its own memory limits to guide subproblem choice. We compare our model to human gameplay in two experiments using a variant of the classic game Minesweeper. In Experiment 1, we find that participants' accuracy was influenced both by the order of subproblems and their ability to externalize intermediate results, indicative of a memory bottleneck in reasoning. In Experiment 2, we used a mouse-tracking paradigm to assess participants' subproblem choice and time allocation. The model captures key patterns of subproblem ordering, error, and time allocation. Our results point toward memory limits and strategies for navigating those limits as central elements of human problem-solving
Decompose, deduce, and dispose:A memory-limited metacognitive model of human problem solving
Get PDFMany real-world problems are defined by complex systems of interlocking constraints. How people are able to solve these problems with such limited working memory capacity remains poorly understood. We propose a formal model of human problem-solving under memory constraints that uses metacognitive knowledge of its own memory limits to guide subproblem choice. We compare our model to human gameplay in two experiments using a variant of the classic game Minesweeper. In Experiment 1, we find that participants' accuracy was influenced both by the order of subproblems and their ability to externalize intermediate results, indicative of a memory bottleneck in reasoning. In Experiment 2, we used a mouse-tracking paradigm to assess participants' subproblem choice and time allocation. The model captures key patterns of subproblem ordering, error, and time allocation. Our results point toward memory limits and strategies for navigating those limits as central elements of human problem-solving
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Whom can we trust? Examining competence assessments in a Multi-armed bandit framework
No full textIn figuring out how to best optimize their performance and maximise their rate of success,
individuals must consider what is known as the exploration-exploitation dilemma, a decision-making
trade-off where exploration of new options is balanced with exploitation of our pre-existing
knowledge. To better inform these kinds of decisions, we must make competence assessments of
others in the environment. The Multi-Armed bandit problem, a simple, decision-based framework
that encapsulates this dilemma, requires a learner to figure out which of a number of options (or
arms) will yield the highest rate of reward. In our experiment, we use this framework to investigate
how good individuals are at making social inferences on the competence of others, asking the
question “How capable are we at assessing the competence of others from their behaviour?” The
level of ambiguity of the conditions was manipulated in order to test how good participants were at
accurately judging the competence of the learners when they had incomplete information.
Participants (N = 48) observed four cognitive agents interact with a bandit and provided a Likertscale
competence judgement on their performance as well as a confidence rating. The findings
demonstrate that individuals are more accurate in their competence ratings when the individuals
behaved in a competent manner and that they were more confident in the conditions where they
had full information
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Think Fast: The effect of time pressure on moral decision-making within an automotive domain
No full textObjectives. To determine whether time pressure shapes moral decision-making within an automotive accident domain. A secondary goal is to determine if dual process theories are a good fit or if an alternative theory is a better fit for moral decision-making.
Methods. Visual sacrificial moral dilemmas were improved based on criticism of the typical dilemmas used in the field. Participants had to choose whether they would swerve onto the other side of the road or stay in their lane when confronted by pedestrians in the road and roadworks in the other lane. Time pressure of 7 s was applied to participants through a within-participant design.
Results. Under time pressure participants were 1.23 times more likely to avoid pedestrians and 1.32 times more likely to stay in their lane. Participants in general displayed a significant preference to avoid pedestrians, having no significant preference for following a utilitarian framework. Participants were more likely to avoid pedestrians as their number increased or when the car was moving faster. However, they were almost 5 times more likely to hit pedestrians if there was a passenger in the car.
Conclusions. The dual process theory is too crude to fully encapsulate the nuance of moral decision-making, the conflict theory proposed by Gürçay and Baron better accounts for the findings of this paper. Autonomous vehicles should consider the number of car occupants, speed of the car and number of pedestrians at risk when performing accident avoidance manoeuvres. A continued focus should be on the improvement of the typical dilemmas of this field
Pedagogical Compatibility: The Effect of Human Teaching Strategies on Inductive Learning for Simple Logical Rules
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