Daphna Buchsbaum

dblp:15/5158 · DBLP profile ↗
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45ranked-venue papers
4as first author
19since 2021 · last 2025
0000-0002-8716-7756ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 45 · 4 first-author · 19 since 2021Applied, interdisciplinary, general and emerging computing · 42 · 3 first-author · 19 since 2021
YearPublicationVenuePosition
2025 Resource-rational belief revision can mitigate as well as amplify polarization
Rebekah Gelpi, Pablo León-Villagrá, William A. Cunningham, Christopher G. Lucas, Daphna Buchsbaum
CogSci5
2025 Examining Individual Differences in Within-Category Variability Reasoning
Olympia N. Mathiaparanam, Pablo León-Villagrá, Daphna Buchsbaum, Karl S. Rosengren
CogSci3
2025 Preschool Children's Learning and Generalization of Continuous Causal Functions
Caiqin Zhou, Rebekah Gelpi, Maria Iorini, Christopher G. Lucas, Daphna Buchsbaum
CogSci5
2024 How Red Is a Ladybeetle? Examining People's Notions of Biological Variability
Pablo León-Villagrá, Olympia N. Mathiaparanam, Karl S. Rosengren, Daphna Buchsbaum
CogSci4
2024 Find it like a dog: Using Gesture to Improve Object Search
Madeline H. Pelgrim, Ivy Xiao He, Kyle Lee, Falak Pabari, Stefanie Tellex, Daphna Buchsbaum
CogSci7
2024 Human Perceptions of Canine Intelligence
Miriam Ross, Daphna Buchsbaum, Bertram F. Malle
CogSci2
2024 Can Children Learn Functional Relations Through Active Information Sampling?
Caiqin Zhou, Rebekah Gelpi, Christopher G. Lucas, Daphna Buchsbaum
CogSci4
2023 Characterizing Shifts in Strategy in Active Function Learning
Rebekah Gelpi, Caiqin Zhou, Christopher G. Lucas, Daphna Buchsbaum
CogSci4
2023 Charting children's fruit categories with Markov-Chain Monte Carlo with People
Pablo León-Villagrá, Isaac Ehrlich, Christopher G. Lucas, Daphna Buchsbaum
CogSci4
2023 The Impact of Quality and Familiarity on Dogs' Food Preferences
Madeline H. Pelgrim, Julia Espinosa, Daphna Buchsbaum
CogSci3
2022 Uncovering children's concepts and conceptual change
Pablo León-Villagrá, Isaac Ehrlich, Christopher G. Lucas, Daphna Buchsbaum
CogSci4
2022 Uncovering Childrens' Category Representations with MCMCP
Pablo León-Villagrá, Isaac Ehrlich, Christopher G. Lucas, Daphna Buchsbaum
CogSci4
2022 Categorizing Dogs' Real World Visual Statistics
Madeline H. Pelgrim, Daphna Buchsbaum
CogSci2
2022 Dynamic Strategy Selection in Active Function Learning
Nayan Saxena, Rebekah Gelpi, Daphna Buchsbaum, Christopher G. Lucas
CogSci3
2021 Domestic dogs' gaze and behaviour in 2-alternative choice tasks
Julia Espinosa, Liyuzhi Dong, Daphna Buchsbaum
CogSci3
2021 Sampling Heuristics for Active Function Learning
Rebekah Gelpi, Nayan Saxena, George Lifchits, Daphna Buchsbaum, Christopher G. Lucas
CogSci4
2021 Recovering human category structure across development using sparse judgments
Pablo León-Villagrá, Isaac Ehrlich, Christopher G. Lucas, Daphna Buchsbaum
CogSci4
2021 Modelling Recognition in Human Puzzle Solving
Ben Prystawski, Rebekah Gelpi, Christopher G. Lucas, Daphna Buchsbaum
CogSci4
2021 Can 1- and 2-year-old toddlers learn causal action sequences?
Emma Tecwyn, Nafisa Mahbub, Nishat Kazi, Daphna Buchsbaum
CogSci4
2020 Does looking time predict choice in domestic dogs? Examining visual attention in man's best friend
Liyuzhi Dong, Julia Espinosa, Daphna Buchsbaum
CogSci3
2020 Domestic dogs' understanding of spatial temporal priority
Julia Espinosa, Katherine McGinn, Madeline H. Pelgrim, Daphna Buchsbaum
CogSci4
2020 Incremental Hypothesis Revision in Causal Reasoning Across Development
Rebekah Gelpi, Ben Prystawski, Christopher G. Lucas, Daphna Buchsbaum
CogSci4
2020 Exploring Category Structure in Children and Adults
Pablo León-Villagrá, Isaac Ehrlich, Christopher G. Lucas, Daphna Buchsbaum
CogSci4
2020 Uncovering Category Representations with Linked MCMC with People
Pablo León-Villagrá, Kay Otsubo, Christopher G. Lucas, Daphna Buchsbaum
CogSci4
2020 Balancing Personal and Social Outcomes: Cultural Differences in Children's Moral Decision-Making
Yiqi Luo, Theodore Cheung, Daphna Buchsbaum
CogSci3
2020 Can toddlers learn causal action sequences?
Emma Tecwyn, Nafisa Mahbub, Nishat Kazi, Daphna Buchsbaum
CogSci4
2020 Sensitivity to ostension is not sufficient for pedagogical reasoning by toddlers
Emma Tecwyn, Amanda Seed, Daphna Buchsbaum
CogSci3
2019 Children's exploration as a window into their causal learning
Sophie Bridgers, Yvonne Wang, Daphna Buchsbaum
CogSci3
2019 Modeling the Costly Rejection of Wrongdoers by Children using a Bayesian Approach
Theodore Cheung, Rachel Eng, Daphna Buchsbaum
CogSci3
2019 Domestic dog understanding of containment and occlusion events
Julia Espinosa, Daphna Buchsbaum
CogSci2
2019 Exploring the use of overhypotheses by children and capuchin monkeys
Elisa Felsche, Patience Stevens, Christoph Voelter, Daphna Buchsbaum, Amanda Seed
CogSci4
2019 Domestic Dogs' Sensitivity to the Accuracy of Human Informants
Madeline H. Pelgrim, Emma Tecwyn, Julia Espinosa, Angie Johnston, Sarah MacKay Marton, Daphna Buchsbaum
CogSci6
2017 Investigating the Explore/Exploit Trade-off in Adult Causal Inferences
Erik Herbst, Christopher G. Lucas, Daphna Buchsbaum
CogSci3
2017 Investigating Sensitivity to Shared Information and Personal Experience in Children's Use of Majority Information
Kay Otsubo, Andrew Whalen, Daphna Buchsbaum
CogSci3
2017 Pragmatics Influence Children's Use of Majority Information
Theresa Pham, Jane C. Hu, Daphna Buchsbaum
CogSci3
2016 Investigating the Explore/Exploit Trade-off in Adult Causal Inferences
Erik Herbst, Christopher G. Lucas, Daphna Buchsbaum
CogSci3
2015 Can children balance the size of a majority with the quality of their information?
Jane C. Hu, Andrew Whalen, Daphna Buchsbaum, Thomas L. Griffiths 0001
CogSci3
2013 What if? Counterfactual reasoning, pretense, and the role of possible worlds
Daphna Buchsbaum, Caren M. Walker, Alison Gopnik, Nick Chater, David Danks, Christopher G. Lucas, Charles Kemp, Eva Rafetseder, Josef Perner
CogSci1
2013 When does the majority rule? Preschoolers' trust in majority informants varies by task domain
Jane C. Hu, Daphna Buchsbaum, Thomas L. Griffiths 0001
CogSci2
2013 How do you know that? Sensitivity to statistical dependency in social learning
Andrew Whalen, Daphna Buchsbaum, Thomas L. Griffiths 0001
CogSci2
2012 Do I know that you know what you know? Modeling testimony in causal inference
Daphna Buchsbaum, Sophie Bridgers, Andrew Whalen, Elizabeth Seiver, Thomas L. Griffiths 0001, Alison Gopnik
CogSci1
2011 Segmenting and Recognizing Human Action using Low-level Video Features
Daphna Buchsbaum, Kevin Robert Canini, Thomas L. Griffiths 0001
CogSci1
2005 Designing collective behavior in a group of humans using a real-time polling system and interactive evolution
abstract
Interactive evolutionary design, a powerful technique where one marries the exploratory capabilities of evolutionary computation with the aesthetic skills and domain knowledge of the human as selective agent, has been demonstrated to be an extremely powerful exploratory design method. One of interactive evolutions most promising uses is in discovering individual-level rules of behavior and interaction that will produce a desired collective pattern in a group of human or non-human agents. The problem of finding micro-rules that produce interesting macro-behavior poses significant challenges, all the more so when what constitutes "interesting" macro-behavior may not be known ahead of time. Here, the system at our disposal is a real-time crowd polling and display system, whose potential for generating interesting group behavior remains largely untapped. Additionally, because the system is capable of polling large crowds of people in real time, it presents an ideal framework within which to take advantage of a relatively unexplored form of interactive evolution - collective evolution, where the opinions of the entire group are taken into account in the design of the next generation. Collective evolution has a broad range of potential applications, including marketing research and logo and brand name design.
Daphna Buchsbaum, Pablo Funes, Julien Budynek, Heiner Kopperman, Eric Bonabeau
SIS1
2005 Learning From and About Others: Towards Using Imitation to Bootstrap the Social Understanding of Others by Robots
abstract
We want to build robots capable of rich social interactions with humans, including natural communication and cooperation. This work explores how imitation as a social learning and teaching process may be applied to building socially intelligent robots, and summarizes our progress toward building a robot capable of learning how to imitate facial expressions from simple imitative games played with a human, using biologically inspired mechanisms. It is possible for the robot to bootstrap from this imitative ability to infer the affective reaction of the human with whom it interacts and then use this affective assessment to guide its subsequent behavior. Our approach is heavily influenced by the ways human infants learn to communicate with their caregivers and come to understand the actions and expressive behavior of others in intentional and motivational terms. Specifically, our approach is guided by the hypothesis that imitative interactions between infant and caregiver, starting with facial mimicry, are a significant stepping-stone to developing appropriate social behavior, to predicting others' actions, and ultimately to understanding people as social beings.
Cynthia Breazeal, Daphna Buchsbaum, Jesse Gray, David Gatenby, Bruce Blumberg
Artif. Life2
2003 Lessons from ethology for computational models of development
abstract
Summary form only given. Recent results from the ethological literature challenge some widely held views about the development of animal behavior and offer new directions for research into the organization of synthetic behavior systems. The process of development is often seen as one of continual refinement, with infantile behaviors representing the primitive precursors of adult behavior. While this notion is intuitively pleasing, there are two ways in which it may be incorrect. First, Coppinger (Coppinger and Smith 1990) argues that infantile behaviors are just as well adapted as adult behaviors, but adapted to the challenges that the organism faces during its early life and early social context. In his view, development is best understood as a process of, 'swapping' elements in the behavioral repertoire rather than refining them; the adult version of a behavior, in other words, is a distinct replacement for its juvenile counterpart, not an outgrowth of it. The second problem with the traditional view of behavioral development is that infantile behaviors are often expressed in an entirely different motivational context than their adult counterparts. Indeed, infantile behaviors that satisfy a particular motivational system such as hunger may be controlled by entirely different motivational systems. Hall and Willams have shown, for example, that during the first several weeks of life, a rat pup's suckling rate is independent of how hungry it is. That is, rat pups suckle because they are innately motivated to do so, not because they are hungry. Similarly, Hogan (Hogan 1999) suggests that during the first 48 hours of a chick's life, the amount that it pecks has nothing to do with its level of hunger. Leyhausen (Lorenz and Leyhausen 1973) has shown that the behaviors that make up the sequence of predatory behaviors in cats are refined through play, often well before the cat ever makes 'the connection' between the acquisition of prey and a subsequent reduction in hunger. What is going on here? In the first two cases, the juvenile behaviors act as 'fail-safe' mechanisms to ensure that the creature's needs are met without needing an innate or learned connection between the behaviors and the critical motivational systems that they act to satisfy. In the latter case, behaviors that will be critically important in adulthood are initially expressed and refined in a context in which the costs of failure are low, i.e. the creature's life doesn't depend on how well they perform the behavior at that given moment. Both of these concerns suggest that by re-examining our assumptions about development in animals, we may be able to glean generally useful organizing principles for the design of adaptive computational systems.
Bruce Blumberg, Matt Berlin, Daphna Buchsbaum, Marc Downie, Derek Lyons, Jennie Cochran
IJCNN3