•  3
    Replies to commentaries on beyond playing 20 questions with nature
    with Abdullah Almaatouq, Jordan W. Suchow, Mark E. Whiting, James Evans, and Duncan J. Watts
    Behavioral and Brain Sciences 47. 2024.
    Commentaries on the target article offer diverse perspectives on integrative experiment design. Our responses engage three themes: (1) Disputes of our characterization of the problem, (2) skepticism toward our proposed solution, and (3) endorsement of the solution, with accompanying discussions of its implementation in existing work and its potential for other domains. Collectively, the commentaries enhance our confidence in the promise and viability of integrative experiment design, while highl…Read more
  •  17
    Beyond playing 20 questions with nature: Integrative experiment design in the social and behavioral sciences
    with Abdullah Almaatouq, Jordan W. Suchow, Mark E. Whiting, James Evans, and Duncan J. Watts
    Behavioral and Brain Sciences 47. 2024.
    The dominant paradigm of experiments in the social and behavioral sciences views an experiment as a test of a theory, where the theory is assumed to generalize beyond the experiment's specific conditions. According to this view, which Alan Newell once characterized as “playing twenty questions with nature,” theory is advanced one experiment at a time, and the integration of disparate findings is assumed to happen via the scientific publishing process. In this article, we argue that the process o…Read more
  •  5
    On the hazards of relating representations and inductive biases
    with Sreejan Kumar and R. Thomas McCoy
    Behavioral and Brain Sciences 46. 2023.
    The success of models of human behavior based on Bayesian inference over logical formulas or programs is taken as evidence that people employ a “language-of-thought” that has similarly discrete and compositional structure. We argue that this conclusion problematically crosses levels of analysis, identifying representations at the algorithmic level based on inductive biases at the computational level.
  •  17
    Optimal metacognitive control of memory recall
    with Frederick Callaway, Kenneth A. Norman, and Qiong Zhang
    Psychological Review. forthcoming.
  •  10
    How do Humans Overcome Individual Computational Limitations by Working Together?
    with Natalia Vélez, Brian Christian, Mathew Hardy, and Bill D. Thompson
    Cognitive Science 47 (1). 2023.
    Since the cognitive revolution, psychologists have developed formal theories of cognition by thinking about the mind as a computer. However, this metaphor is typically applied to individual minds. Humans rarely think alone; compared to other animals, humans are curiously dependent on stores of culturally transmitted skills and knowledge, and we are particularly good at collaborating with others. Rather than picturing the human mind as an isolated computer, we can imagine each mind as a node in a…Read more
  •  8
    The Computational Challenges of Means Selection Problems: Network Structure of Goal Systems Predicts Human Performance
    with Daniel Reichman, Falk Lieder, David D. Bourgin, and Nimrod Talmon
    Cognitive Science 47 (8). 2023.
    We study human performance in two classical NP‐hard optimization problems: Set Cover and Maximum Coverage. We suggest that Set Cover and Max Coverage are related to means selection problems that arise in human problem‐solving and in pursuing multiple goals: The relationship between goals and means is expressed as a bipartite graph where edges between means and goals indicate which means can be used to achieve which goals. While these problems are believed to be computationally intractable in gen…Read more
  •  13
    Reconciling truthfulness and relevance as epistemic and decision-theoretic utility
    with Theodore R. Sumers, Mark K. Ho, and Robert D. Hawkins
    Psychological Review 131 (1): 194-230. 2024.
  •  21
    Sensitivity to Shared Information in Social Learning
    with Andrew Whalen and Daphna Buchsbaum
    Cognitive Science 42 (1): 168-187. 2018.
    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 …Read more
  •  71
    One and Done? Optimal Decisions From Very Few Samples
    with Edward Vul, Noah Goodman, and Joshua B. Tenenbaum
    Cognitive Science 38 (4): 599-637. 2014.
    In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sam…Read more
  •  17
    Reconciling intuitive physics and Newtonian mechanics for colliding objects
    with Adam N. Sanborn and Vikash K. Mansinghka
    Psychological Review 120 (2): 411-437. 2013.
  •  12
    Faster Teaching via POMDP Planning
    with Anna N. Rafferty, Emma Brunskill, and Patrick Shafto
    Cognitive Science 40 (6): 1290-1332. 2016.
    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 t…Read more
  •  11
    Greater learnability is not sufficient to produce cultural universals
    with Anna N. Rafferty and Marc Ettlinger
    Cognition 129 (1): 70-87. 2013.
  •  24
    Analyzing the Rate at Which Languages Lose the Influence of a Common Ancestor
    with Anna N. Rafferty and Dan Klein
    Cognitive Science 38 (7): 1406-1431. 2014.
    Analyzing the rate at which languages change can clarify whether similarities across languages are solely the result of cognitive biases or might be partially due to descent from a common ancestor. To demonstrate this approach, we use a simple model of language evolution to mathematically determine how long it should take for the distribution over languages to lose the influence of a common ancestor and converge to a form that is determined by constraints on language learning. We show that model…Read more
  •  7
    What the Baldwin Effect affects depends on the nature of plasticity
    with Thomas J. H. Morgan and Jordan W. Suchow
    Cognition 197 (C): 104165. 2020.
  •  38
    Determining the knowledge that guides human judgments is fundamental to understanding how people reason, make decisions, and form predictions. We use an experimental procedure called ‘‘iterated learning,’’ in which the responses that people give on one trial are used to generate the data they see on the next, to pinpoint the knowledge that informs people's predictions about everyday events (e.g., predicting the total box office gross of a movie from its current take). In particular, we use this …Read more
  •  77
    Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic
    with Falk Lieder and Noah D. Goodman
    Topics in Cognitive Science 7 (2): 217-229. 2015.
    Marr's levels of analysis—computational, algorithmic, and implementation—have served cognitive science well over the last 30 years. But the recent increase in the popularity of the computational level raises a new challenge: How do we begin to relate models at different levels of analysis? We propose that it is possible to define levels of analysis that lie between the computational and the algorithmic, providing a way to build a bridge between computational- and algorithmic-level models. The ke…Read more
  •  30
    From mere coincidences to meaningful discoveries
    Cognition 103 (2): 180-226. 2007.
  •  28
    Exploring Human Cognition Using Large Image Databases
    with Joshua T. Abbott and Anne S. Hsu
    Topics in Cognitive Science 8 (3): 569-588. 2016.
    Most cognitive psychology experiments evaluate models of human cognition using a relatively small, well-controlled set of stimuli. This approach stands in contrast to current work in neuroscience, perception, and computer vision, which have begun to focus on using large databases of natural images. We argue that natural images provide a powerful tool for characterizing the statistical environment in which people operate, for better evaluating psychological theories, and for bringing the insights…Read more
  •  77
    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…Read more
  •  48
    Testing the Efficiency of Markov Chain Monte Carlo With People Using Facial Affect Categories
    with Jay B. Martin and Adam N. Sanborn
    Cognitive Science 36 (1): 150-162. 2012.
    Exploring how people represent natural categories is a key step toward developing a better understanding of how people learn, form memories, and make decisions. Much research on categorization has focused on artificial categories that are created in the laboratory, since studying natural categories defined on high-dimensional stimuli such as images is methodologically challenging. Recent work has produced methods for identifying these representations from observed behavior, such as reverse corre…Read more
  •  61
    A Bayesian framework for word segmentation: Exploring the effects of context
    with Sharon Goldwater and Mark Johnson
    Cognition 112 (1): 21-54. 2009.
  •  9
    How to Be Helpful to Multiple People at Once
    with Vael Gates and Anca D. Dragan
    Cognitive Science 44 (6). 2020.
    When someone hosts a party, when governments choose an aid program, or when assistive robots decide what meal to serve to a family, decision‐makers must determine how to help even when their recipients have very different preferences. Which combination of people’s desires should a decision‐maker serve? To provide a potential answer, we turned to psychology: What do people think is best when multiple people have different utilities over options? We developed a quantitative model of what people co…Read more
  •  41
  •  66
    Modeling human performance in statistical word segmentation
    with Michael C. Frank, Sharon Goldwater, and Joshua B. Tenenbaum
    Cognition 117 (2): 107-125. 2010.
  •  40
    Word-level information influences phonetic learning in adults and infants
    with Naomi H. Feldman, Emily B. Myers, Katherine S. White, and James L. Morgan
    Cognition 127 (3): 427-438. 2013.
  •  48
    When Absence of Evidence Is Evidence of Absence: Rational Inferences From Absent Data
    with Anne S. Hsu, Andy Horng, and Nick Chater
    Cognitive Science 41 (S5): 1155-1167. 2017.
    Identifying patterns in the world requires noticing not only unusual occurrences, but also unusual absences. We examined how people learn from absences, manipulating the extent to which an absence is expected. People can make two types of inferences from the absence of an event: either the event is possible but has not yet occurred, or the event never occurs. A rational analysis using Bayesian inference predicts that inferences from absent data should depend on how much the absence is expected t…Read more