@article {5526, title = {Causal implicatures from correlational statements}, journal = {PLOS ONE}, volume = {18}, year = {2023}, month = {05/2023}, pages = {e0286067}, abstract = {

Correlation does not imply causation, but this does not necessarily stop people from drawing causal inferences from correlational statements. We show that people do in fact infer causality from statements of association, under minimal conditions. In Study 1, participants interpreted statements of the form {\textquotedblleft}X is associated with Y{\textquotedblright} to imply that Y causes X. In Studies 2 and 3, participants interpreted statements of the form {\textquotedblleft}X is associated with an increased risk of Y{\textquotedblright} to imply that X causes Y. Thus, even the most orthodox correlational language can give rise to causal inferences.

}, doi = {10.1371/journal.pone.0286067}, url = {https://dx.plos.org/10.1371/journal.pone.0286067}, author = {Gershman, Samuel J. and Ullman, Tomer D.}, editor = {Goldwater, Micah B.} } @article {5368, title = {Non-commitment in mental imagery}, journal = {Cognition}, volume = {238}, year = {2023}, month = {09/2023}, pages = {105498}, abstract = {

We examine non-commitment in the imagination. Across 5 studies (N\ \>\ 1, 800), we find that most people are non-committal about basic aspects of their mental images, including features that would be readily apparent in real images. While previous work on the imagination has discussed the possibility of non-commitment, this paper is the first, to our knowledge, to examine this systematically and empirically. We find that people do not commit to basic properties of specified mental scenes (Studies 1 and 2), and that people report non-commitment rather than uncertainty or forgetfulness (Study 3). Such non-commitment is present even for people with generally vivid imaginations, and those who report imagining the specified scene very vividly (Studies 4a, 4b). People readily confabulate properties of their mental images when non-commitment is not offered as an explicit option (Study 5). Taken together, these results establish non-commitment as a pervasive component of mental imagery.

}, issn = {00100277}, doi = {10.1016/j.cognition.2023.105498}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0010027723001324}, author = {Bigelow, Eric J. and McCoy, John P. and Ullman, Tomer D.} } @article {5286, title = {Dangerous Ground: One-Year-Old Infants are Sensitive to Peril in Other Agents{\textquoteright} Action PlansAbstract}, journal = {Open Mind}, volume = {6}, year = {2022}, month = {10/2022}, pages = {211 - 231}, abstract = {

Do infants appreciate that other people{\textquoteright}s actions may fail, and that these failures endow risky actions with varying degrees of negative utility (i.e., danger)? Three experiments, including a pre-registered replication, addressed this question by presenting 12- to 15-month-old infants (N = 104, 52 female, majority White) with an animated agent who jumped over trenches of varying depth towards its goals. Infants expected the agent to minimize the danger of its actions, and they learned which goal the agent preferred by observing how much danger it risked to reach each goal, even though the agent{\textquoteright}s actions were physically identical and never failed. When we tested younger, 10-month-old infants (N = 102, 52 female, majority White) in a fourth experiment, they did not succeed consistently on the same tasks. These findings provide evidence that one-year-old infants use the height that other agents could fall from in order to explain and predict those agents{\textquoteright} actions.

}, keywords = {action understanding, agency, cognitive development, infancy, open data, open materials, pre-registered}, doi = {10.1162/opmi_a_00063}, url = {https://direct.mit.edu/opmi/article/doi/10.1162/opmi_a_00063/113342/Dangerous-Ground-One-Year-Old-Infants-are}, author = {Liu, Shari and Pepe, Bill and Ganesh Kumar, Manasa and Ullman, Tomer D. and Tenenbaum, Joshua B. and Spelke, Elizabeth S.} } @article {5311, title = {What Could Go Wrong: Adults and Children Calibrate Predictions and Explanations of Others{\textquoteright} Actions Based on Relative Reward and Danger}, journal = {Cognitive Science}, volume = {46}, year = {2022}, month = {06/2022}, abstract = {

When human adults make decisions (e.g., wearing a seat belt), we often consider the negative consequences that would ensue if our actions were to fail, even if we have never experienced such a failure. Do the same considerations guide our understanding of other people{\textquoteright}s decisions? In this paper, we investigated whether adults, who have many years of experience making such decisions, and 6- and 7-year-old children, who have less experience and are demonstrably worse at judging the consequences of their own actions, conceive others{\textquoteright} actions as motivated both by reward (how good reaching one{\textquoteright}s intended goal would be), and by what we call {\textquotedblleft}danger{\textquotedblright} (how badly one{\textquoteright}s action could end). In two pre-registered experiments, we tested whether adults and 6- and 7-year-old children tailor their predictions and explanations of an agent{\textquoteright}s action choices to the specific degree of danger and reward entailed by each action. Across four different tasks, we found that children and adults expected others to negatively appraise dangerous situations and minimize the danger of their actions. Children{\textquoteright}s and adults{\textquoteright} judgments varied systematically in accord with both the degree of danger the agent faced and the value the agent placed on the goal state it aimed to achieve. However, children did not calibrate their inferences about how much an agent valued the goal state of a successful action in accord with the degree of danger the action entailed, and adults calibrated these inferences more weakly than inferences concerning the agent{\textquoteright}s future action choices. These results suggest that from childhood, people use a degree of danger and reward to make quantitative, fine-grained explanations and predictions about other people{\textquoteright}s behavior, consistent with computational models on theory of mind that contain continuous representations of other agents{\textquoteright} action plans.

}, issn = {0364-0213}, doi = {10.1111/cogs.v46.710.1111/cogs.13163}, url = {https://onlinelibrary.wiley.com/toc/15516709/46/7}, author = {Gjata, Nensi N. and Ullman, Tomer D. and Spelke, Elizabeth S. and Liu, Shari} } @article {5062, title = {Plans or Outcomes: How Do We Attribute Intelligence to Others?}, journal = {Cognitive Science}, volume = {45}, year = {2021}, month = {09/2021}, issn = {0364-0213}, doi = {10.1111/cogs.v45.910.1111/cogs.13041}, url = {https://onlinelibrary.wiley.com/toc/15516709/45/9}, author = {Marta Kryven and Ullman, Tomer D. and Cowan, William and Joshua B. Tenenbaum} } @conference {5063, title = {Temporal and Object Quantification Networks}, booktitle = {Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence}, year = {2021}, month = {06/2021}, address = {Montreal, Canada}, abstract = {

We present Temporal and Object Quantification Networks (TOQ-Nets), a new class of neuro-symbolic networks with a structural bias that enables them to learn to recognize complex relational-temporal events. This is done by including reasoning layers that implement finite-domain quantification over objects and time. The structure allows them to generalize directly to input instances with varying numbers of objects in temporal sequences of varying lengths. We evaluate TOQ-Nets on input domains that require recognizing event-types in terms of complex temporal relational patterns. We demonstrate that TOQ-Nets can generalize from small amounts of data to scenarios containing more objects than were present during training and to temporal warpings of input sequences.

}, doi = {10.24963/ijcai.2021/386}, url = {https://www.ijcai.org/proceedings/2021}, author = {Mao, Jiayuan and Luo, Zhezheng and Gan, Chuang and Joshua B. Tenenbaum and Wu, Jiajun and Kaelbling, Leslie Pack and Ullman, Tomer D.}, editor = {Zhou, Zhi-Hua} } @article {4802, title = {Bayesian Models of Conceptual Development: Learning as Building Models of the World}, journal = {Annual Review of Developmental Psychology}, volume = {2}, year = {2020}, month = {12/2021}, pages = {533 - 558}, issn = {2640-7922}, doi = {10.1146/annurev-devpsych-121318-084833}, url = {https://www.annualreviews.org/doi/10.1146/annurev-devpsych-121318-084833}, author = {Ullman, Tomer D. and Joshua B. Tenenbaum} } @conference {4450, title = {People{\textquoteright}s perceptions of others{\textquoteright} risk preferences.}, booktitle = {Cognitive Science Society}, year = {2019}, author = {Shari Liu and John P. McCoy and Ullman, Tomer D.} } @article {3513, title = {Learning physical parameters from dynamic scenes.}, journal = {Cognitive Psychology}, volume = {104}, year = {2018}, month = {8/2018}, pages = {57-82}, abstract = {

Humans acquire their most basic physical concepts early in development, and continue to enrich and expand their intuitive physics throughout life as they are exposed to more and varied dynamical environments. We introduce a hierarchical Bayesian framework to explain how people can learn physical parameters at multiple levels. In contrast to previous Bayesian models of theory acquisition (Tenenbaum et al., 2011), we work with more ex- pressive probabilistic program representations suitable for learning the forces and properties that govern how objects interact in dynamic scenes unfolding over time. We compare our model to human learners on a challenging task of estimating multiple physical parameters in novel microworlds given short movies. This task requires people to reason simultane- ously about multiple interacting physical laws and properties. People are generally able to learn in this setting and are consistent in their judgments. Yet they also make systematic errors indicative of the approximations people might make in solving this computationally demanding problem with limited computational resources. We propose two approximations that complement the top-down Bayesian approach. One approximation model relies on a more bottom-up feature-based inference scheme. The second approximation combines the strengths of the bottom-up and top-down approaches, by taking the feature-based inference as its point of departure for a search in physical-parameter space.

}, keywords = {intuitive physics, intuitive theory, learning, physical reasoning, probabilistic inference}, doi = {10.1016/j.cogpsych.2017.05.006}, url = {https://www-sciencedirect-com.libproxy.mit.edu/science/article/pii/S0010028517301822}, author = {Ullman, Tomer D. and Stuhlm{\"u}ller, Andreas and Noah D. Goodman and Joshua B. Tenenbaum} } @article {3514, title = {Lucky or clever? From changed expectations to attributions of responsibility}, journal = {Cognition}, year = {2018}, month = {08/2018}, author = {Tobias Gerstenberg and Ullman, Tomer D. and Nagel, Jonas and Max Kleiman-Weiner and D. A. Lagnado and Joshua B. Tenenbaum} } @article {4197, title = {A Minimal Turing Test}, journal = {Journal of Experimental Social Psychology}, volume = {79}, year = {2018}, month = {11/2018}, pages = {1 - 8}, abstract = {

We introduce the Minimal Turing Test, an experimental paradigm for studying perceptions and meta-perceptions of different social groups or kinds of agents, in which participants must use a single word to convince a judge of their identity. We illustrate the paradigm by having participants act as contestants or judges in a Minimal Turing Test in which contestants must convince a judge they are a human, rather than an artificial intelligence. We embed the production data from such a large-scale Minimal Turing Test in a semantic vector space, and construct an ordering over pairwise evaluations from judges. This allows us to identify the semantic structure in the words that people give, and to obtain quantitative measures of the importance that people place on different attributes. Ratings from independent coders of the production data provide additional evidence for the agency and experience dimensions discovered in previous work on mind perception. We use the theory of Rational Speech Acts as a framework for interpreting the behavior of contestants and judges in the Minimal Turing Test.

}, keywords = {Meta-stereotypes, Mind perception, Natural language processing, Stereotypes, Turing Test}, issn = {00221031}, doi = {10.1016/j.jesp.2018.05.007}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0022103117303980}, author = {John P. McCoy and Ullman, Tomer D.} } @article {3512, title = {Mind Games: Game Engines as an Architecture for Intuitive Physics}, journal = {Trends in Cognitive Science}, volume = {21}, year = {2017}, month = {09/2017}, pages = {649 - 665}, chapter = {649}, issn = {1364-6613}, doi = {10.1016/j.tics.2017.05.012}, url = {https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(17)30113-4}, author = {Ullman, Tomer D. and Elizabeth S Spelke and Battaglia, Peter and Joshua B. Tenenbaum} } @article {3485, title = {Ten-month-old infants infer the value of goals from the costs of actions}, journal = {Science}, volume = {358}, year = {2017}, month = {11/2017}, pages = {1038-1041}, chapter = {1038}, abstract = {

Infants understand that people pursue goals, but how do they learn which goals people prefer? We tested whether infants solve this problem by inverting a mental model of action planning, trading off the costs of acting against the rewards actions bring. After seeing an agent attain two goals equally often at varying costs, infants expected the agent to prefer the goal it attained through costlier actions. These expectations held across three experiments that conveyed cost through different physical path features (height, width, and incline angle), suggesting that an abstract variable{\textemdash}such as {\textquotedblleft}force,{\textquotedblright} {\textquotedblleft}work,{\textquotedblright} or {\textquotedblleft}effort{\textquotedblright}{\textemdash}supported infants{\textquoteright} inferences. We modeled infants{\textquoteright} expectations as Bayesian inferences over utility-theoretic calculations, providing a bridge to recent quantitative accounts of action understanding in older children and adults.

}, author = {Shari Liu and Ullman, Tomer D. and Joshua B. Tenenbaum and Elizabeth S Spelke} }