Joshua B Tenenbaum
Dados Biográficos
| ID | 916395 |
|---|---|
| NOME | Joshua B Tenenbaum |
| PRENOMES | Joshua B |
| SOBRENOME | Tenenbaum |
| ASSINATURA | TENENBAUM J B |
| AFILIAÇÕES | Massachusetts Institute of Technology |
| ORCID | 0000-0002-1925-2035 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 38 |
| TOTAL DE CITAÇÕES | 36 |
| TOTAL COMO AUTOR | 38 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2001 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2026 |
| ÍNDICE H | 4 |
Language Is Not All You Need … but Language, Probabilistic Programs & Bayesian Models of Cognition Will Get You Pretty Far
Since their origins in the 1950s, cognitive science and artificial intelligence have made slow but steady progress together toward afunctional understanding of human intelligence. The arrival of large language models (LLMs) has upended this dynamic, with unprecedented commercial investment driven by the bet that superhuman AI could emerge simply from learning patterns in language at sufficient scale. While language is a singular tool for human th…
Approximating Human-Level 3D Visual Inferences With Deep Neural Networks
Humans make rich inferences about the geometry of the visual world. While deep neural networks (DNNs) achieve human-level performance on some psychophysical tasks (e.g., rapid classification of object or scene categories), they often fail in tasks requiring inferences about the underlying shape of objects or scenes. Here, we ask whether and how this gap in 3D shape representation between DNNs and humans can be closed. First, we define the problem…
Dissociating language and thought in large language models
Electrophysiology Reveals That Intuitive Physics Guides Visual Tracking and Working Memory
Starting in early infancy, our perception and predictions are rooted in strong expectations about the behavior of everyday objects. These intuitive physics expectations have been demonstrated in numerous behavioral experiments, showing that even pre-verbal infants are surprised when something impossible happens (e.g., when objects magically appear or disappear). However, it remains unclear whether and how physical expectations shape different asp…
Lifelong learning of cognitive styles for physical problem-solving
‘Embodied cognition’ suggests that our bodily experiences broadly shape our cognitive capabilities. We study how embodied experience affects the abstract physical problem-solving styles people use in a virtual task where embodiment does not affect action capabilities. We compare how groups with different embodied experience – 25 children and 35 adults with congenital limb differences versus 45 children and 40 adults born with two hands – perform …
When rules are over-ruled
Rules help guide our behavior-particularly in complex social contexts. But rules sometimes give us the "wrong" answer. How do we know when it is okay to break the rules? In this paper, we argue that we sometimes use contractualist (agreement-based) mechanisms to determine when a rule can be broken. Our model draws on a theory of social interactions - "virtual bargaining" - that assumes that actors engage in a simulated bargaining process when nav…
Building machines that learn and think with people
Using games to understand the mind
Perception of 3D shape integrates intuitive physics and analysis-by-synthesis
Self-orienting in human and machine learning
Flexible social inference facilitates targeted social learning when rewards are not observable
Empowerment contributes to exploration behaviour in a creative video game
Dangerous Ground
Do infants appreciate that other people’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 …
Foundations of intuitive power analyses in children and adults
Predicting responsibility judgments from dispositional inferences and causal attributions
Moral dynamics
Bayesian collective learning emerges from heuristic social learning
Researchers across cognitive science, economics, and evolutionary biology have studied the ubiquitous phenomenon of social learning-the use of information about other people's decisions to make your own. Decision-making with the benefit of the accumulated knowledge of a community can result in superior decisions compared to what people can achieve alone. However, groups of people face two coupled challenges in accumulating knowledge to make good …
The Naïve Utility Calculus as a unified, quantitative framework for action understanding
Social Pragmatics
Four experiments show that 4- and 5-year-olds (total N = 112) can identify the referent of underdetermined utterances through their Naïve Utility Calculus—an intuitive theory of people’s behavior structured around an assumption that agents maximize utilities. In Experiments 1–2, a puppet asked for help without specifying to whom she was talking (“Can you help me?”). In Experiments 3–4, a puppet asked the child to pass an object without specifying…
Machine behaviour
Drivers are blamed more than their automated cars when both make mistakes
A critical period for second language acquisition
Building machines that learn and think like people
Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligen…
Ten-month-old infants infer the value of goals from the costs of actions
Ranking valuations on the basis of observed choices Obliged to make a choice between two goals, we evaluate the benefits of achieving the goals compared with the costs of the actions required before deciding what to do. This seems perfectly straightforward, and it is unsurprising to learn that we can also apply this reasoning to others; that is, someone that we see choosing a goal that requires a more costly action must value that goal more highl…
Rational quantitative attribution of beliefs, desires and percepts in human mentalizing
The Structure and Dynamics of Scientific Theories
Hierarchical Bayesian models (HBMs) provide an account of Bayesian inference in a hierarchically structured hypothesis space. Scientific theories are plausibly regarded as organized into hierarchies in many cases, with higher levels sometimes called ‘paradigms’ and lower levels encoding more specific or concrete hypotheses. Therefore, HBMs provide a useful model for scientific theory change, showing how higher-level theory change may be driven by…
Rational quantitative attribution of beliefs, desires and percepts in human mentalizing
Using games to understand the mind
Drivers are blamed more than their automated cars when both make mistakes
Building machines that learn and think with people
Flexible social inference facilitates targeted social learning when rewards are not observable
Empowerment contributes to exploration behaviour in a creative video game
Generalization, similarity, and Bayesian inference
Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formula…
The Large‐Scale Structure of Semantic Networks
We present statistical analyses of the large‐scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small‐world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale‐free pattern of connectivity, with most nodes ha…
Optimal Predictions in Everyday Cognition
Human perception and memory are often explained as optimal statistical inferences that are informed by accurate prior probabilities. In contrast, cognitive judgments are usually viewed as following error-prone heuristics that are insensitive to priors. We examined the optimality of human cognition in a more realistic context than typical laboratory studies, asking people to make predictions about the duration or extent of everyday phenomena such …
Topics in semantic representation.
Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document and use that gist to predict related concepts and disambiguate words. This article analyzes the abstract computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference. This …
Word learning as Bayesian inference
The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word's referents, by making rational inductive inferences that integrate prior knowledge about plausible word meanings with the statistical structure of the observed examples. The theory addresses shortcomings of the two best…
Action understanding as inverse planning
Probabilistic models of cognition
Variability, negative evidence, and the acquisition of verb argument constructions
We present a hierarchical Bayesian framework for modeling the acquisition of verb argument constructions. It embodies a domain-general approach to learning higher-level knowledge in the form of inductive constraints (or overhypotheses), and has been used to explain other aspects of language development such as the shape bias in learning object names. Here, we demonstrate that the same model captures several phenomena in the acquisition of verb co…
The Structure and Dynamics of Scientific Theories
Hierarchical Bayesian models (HBMs) provide an account of Bayesian inference in a hierarchically structured hypothesis space. Scientific theories are plausibly regarded as organized into hierarchies in many cases, with higher levels sometimes called ‘paradigms’ and lower levels encoding more specific or concrete hypotheses. Therefore, HBMs provide a useful model for scientific theory change, showing how higher-level theory change may be driven by…
How to Grow a Mind
In coming to understand the world—in learning concepts, acquiring language, and grasping causal relations—our minds make inferences that appear to go far beyond the data available. How do we do it? This review describes recent approaches to reverse-engineering human learning and cognitive development and, in parallel, engineering more humanlike machine learning systems. Computational models that perform probabilistic inference over hierarchies of…
One and Done? Optimal Decisions From Very Few Samples
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, rat…
Computational rationality
After growing up together, and mostly growing apart in the second half of the 20th century, the fields of artificial intelligence (AI), cognitive science, and neuroscience are reconverging on a shared view of the computational foundations of intelligence that promotes valuable cross-disciplinary exchanges on questions, methods, and results. We chart advances over the past several decades that address challenges of perception and action under unce…
The Naïve Utility Calculus
Building machines that learn and think like people
Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligen…
Ten-month-old infants infer the value of goals from the costs of actions
Ranking valuations on the basis of observed choices Obliged to make a choice between two goals, we evaluate the benefits of achieving the goals compared with the costs of the actions required before deciding what to do. This seems perfectly straightforward, and it is unsurprising to learn that we can also apply this reasoning to others; that is, someone that we see choosing a goal that requires a more costly action must value that goal more highl…
Rational quantitative attribution of beliefs, desires and percepts in human mentalizing
A critical period for second language acquisition
Machine behaviour
Drivers are blamed more than their automated cars when both make mistakes
The Naïve Utility Calculus as a unified, quantitative framework for action understanding
Social Pragmatics
Four experiments show that 4- and 5-year-olds (total N = 112) can identify the referent of underdetermined utterances through their Naïve Utility Calculus—an intuitive theory of people’s behavior structured around an assumption that agents maximize utilities. In Experiments 1–2, a puppet asked for help without specifying to whom she was talking (“Can you help me?”). In Experiments 3–4, a puppet asked the child to pass an object without specifying…
Predicting responsibility judgments from dispositional inferences and causal attributions
Moral dynamics
Bayesian collective learning emerges from heuristic social learning
Researchers across cognitive science, economics, and evolutionary biology have studied the ubiquitous phenomenon of social learning-the use of information about other people's decisions to make your own. Decision-making with the benefit of the accumulated knowledge of a community can result in superior decisions compared to what people can achieve alone. However, groups of people face two coupled challenges in accumulating knowledge to make good …
Dangerous Ground
Do infants appreciate that other people’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 …
Psychology (32 obras) · Computer Science (26 obras) · Cognitive psychology (20 obras) · Artificial Intelligence (18 obras) · Cognition (18 obras) · Cognitive science (15 obras) · Child and Animal Learning Development (13 obras) · Artificial Intelligence (11 obras) · Inference (11 obras) · Social Psychology (11 obras)