Noah Goodman

All Publications

• Applying Probabilistic Programming to Affective Computing. IEEE transactions on affective computing Ong, D. C., Soh, H., Zaki, J., Goodman, N. D. 2021; 12 (2): 306-317

  Abstract

  Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models. To address this, we propose a probabilistic programming approach to affective computing, which models psychological-grounded theories as generative models of emotion, and implements them as stochastic, executable computer programs. We first review probabilistic approaches that integrate reasoning about emotions with reasoning about other latent mental states (e.g., beliefs, desires) in context. Recently-developed probabilistic programming languages offer several key desidarata over previous approaches, such as: (i) flexibility in representing emotions and emotional processes; (ii) modularity and compositionality; (iii) integration with deep learning libraries that facilitate efficient inference and learning from large, naturalistic data; and (iv) ease of adoption. Furthermore, using a probabilistic programming framework allows a standardized platform for theory-building and experimentation: Competing theories (e.g., of appraisal or other emotional processes) can be easily compared via modular substitution of code followed by model comparison. To jumpstart adoption, we illustrate our points with executable code that researchers can easily modify for their own models. We end with a discussion of applications and future directions of the probabilistic programming approach.

  View details for DOI 10.1109/taffc.2019.2905211

  View details for PubMedID 34055236

• The Division of Labor in Communication: Speakers Help Listeners Account for Asymmetries in Visual Perspective. Cognitive science Hawkins, R. D., Gweon, H., Goodman, N. D. 2021; 45 (3): e12926

  Abstract

  Recent debates over adults' theory of mind use have been fueled by surprising failures of perspective-taking in communication, suggesting that perspective-taking may be relatively effortful. Yet adults routinely engage in effortful processes when needed. How, then, should speakers and listeners allocate their resources to achieve successful communication? We begin with the observation that the shared goal of communication induces a natural division of labor: The resources one agent chooses to allocate toward perspective-taking should depend on their expectations about the other's allocation. We formalize this idea in a resource-rational model augmenting recent probabilistic weighting accounts with a mechanism for (costly) control over the degree of perspective-taking. In a series of simulations, we first derive an intermediate degree of perspective weighting as an optimal trade-off between expected costs and benefits of perspective-taking. We then present two behavioral experiments testing novel predictions of our model. In Experiment 1, we manipulated the presence or absence of occlusions in a director-matcher task. We found that speakers spontaneously modulated the informativeness of their descriptions to account for "known unknowns" in their partner's private view, reflecting a higher degree of speaker perspective-taking than previously acknowledged. In Experiment 2, we then compared the scripted utterances used by confederates in prior work with those produced in interactions with unscripted directors. We found that confederates were systematically less informative than listeners would initially expect given the presence of occlusions, but listeners used violations to adaptively make fewer errors over time. Taken together, our work suggests that people are not simply "mindblind"; they use contextually appropriate expectations to navigate the division of labor with their partner. We discuss how a resource-rational framework may provide a more deeply explanatory foundation for understanding flexible perspective-taking under processing constraints.

  View details for DOI 10.1111/cogs.12926

  View details for PubMedID 33686646

• Polite Speech Emerges From Competing Social Goals. Open mind : discoveries in cognitive science Yoon, E. J., Tessler, M. H., Goodman, N. D., Frank, M. C. 2020; 4: 71–87

  Abstract

  Language is a remarkably efficient tool for transmitting information. Yet human speakers make statements that are inefficient, imprecise, or even contrary to their own beliefs, all in the service of being polite. What rational machinery underlies polite language use? Here, we show that polite speech emerges from the competition of three communicative goals: to convey information, to be kind, and to present oneself in a good light. We formalize this goal tradeoff using a probabilistic model of utterance production, which predicts human utterance choices in socially sensitive situations with high quantitative accuracy, and we show that our full model is superior to its variants with subsets of the three goals. This utility-theoretic approach to speech acts takes a step toward explaining the richness and subtlety of social language use.

  View details for DOI 10.1162/opmi_a_00035

  View details for PubMedID 33225196

• When redundancy is useful: A Bayesian approach to "overinformative" referring expressions. Psychological review Degen, J., Hawkins, R. D., Graf, C., Kreiss, E., Goodman, N. D. 2020

  Abstract

  Referring is one of the most basic and prevalent uses of language. How do speakers choose from the wealth of referring expressions at their disposal? Rational theories of language use have come under attack for decades for not being able to account for the seemingly irrational overinformativeness ubiquitous in referring expressions. Here we present a novel production model of referring expressions within the Rational Speech Act framework that treats speakers as agents that rationally trade off cost and informativeness of utterances. Crucially, we relax the assumption that informativeness is computed with respect to a deterministic Boolean semantics, in favor of a nondeterministic continuous semantics. This innovation allows us to capture a large number of seemingly disparate phenomena within one unified framework: the basic asymmetry in speakers' propensity to overmodify with color rather than size; the increase in overmodification in complex scenes; the increase in overmodification with atypical features; and the increase in specificity in nominal reference as a function of typicality. These findings cast a new light on the production of referring expressions: rather than being wastefully overinformative, reference is usefully redundant. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

  View details for DOI 10.1037/rev0000186

  View details for PubMedID 32237876

• The Interactions of Rational, Pragmatic Agents Lead to Efficient Language Structure and Use. Topics in cognitive science Peloquin, B. N., Goodman, N. D., Frank, M. C. 2020; 12 (1): 433–45

  Abstract

  Despite their diversity, languages around the world share a consistent set of properties and distributional regularities. For example, the distribution of word frequencies, the distribution of syntactic dependency lengths, and the presence of ambiguity are all remarkably consistent across languages. We discuss a framework for studying how these system-level properties emerge from local, in-the-moment interactions of rational, pragmatic speakers and listeners. To do so, we derive a novel objective function for measuring the communicative efficiency of linguistic systems in terms of the interactions of speakers and listeners. We examine the behavior of this objective in a series of simulations focusing on the communicative function of ambiguity in language. These simulations suggest that rational pragmatic agents will produce communicatively efficient systems and that interactions between such agents provide a framework for examining efficient properties of language structure and use more broadly.

  View details for DOI 10.1111/tops.12489

  View details for PubMedID 32023005

• Analyzing Machine-Learned Representations: A Natural Language Case Study. Cognitive science Dasgupta, I. n., Guo, D. n., Gershman, S. J., Goodman, N. D. 2020; 44 (12): e12925

  Abstract

  As modern deep networks become more complex, and get closer to human-like capabilities in certain domains, the question arises as to how the representations and decision rules they learn compare to the ones in humans. In this work, we study representations of sentences in one such artificial system for natural language processing. We first present a diagnostic test dataset to examine the degree of abstract composable structure represented. Analyzing performance on these diagnostic tests indicates a lack of systematicity in representations and decision rules, and reveals a set of heuristic strategies. We then investigate the effect of training distribution on learning these heuristic strategies, and we study changes in these representations with various augmentations to the training set. Our results reveal parallels to the analogous representations in people. We find that these systems can learn abstract rules and generalize them to new contexts under certain circumstances-similar to human zero-shot reasoning. However, we also note some shortcomings in this generalization behavior-similar to human judgment errors like belief bias. Studying these parallels suggests new ways to understand psychological phenomena in humans as well as informs best strategies for building artificial intelligence with human-like language understanding.

  View details for DOI 10.1111/cogs.12925

  View details for PubMedID 33340161

• Characterizing the Dynamics of Learning in Repeated Reference Games. Cognitive science Hawkins, R. D., Frank, M. C., Goodman, N. D. 2020; 44 (6): e12845

  Abstract

  The language we use over the course of conversation changes as we establish common ground and learn what our partner finds meaningful. Here we draw upon recent advances in natural language processing to provide a finer-grained characterization of the dynamics of this learning process. We release an open corpus (>15,000 utterances) of extended dyadic interactions in a classic repeated reference game task where pairs of participants had to coordinate on how to refer to initially difficult-to-describe tangram stimuli. We find that different pairs discover a wide variety of idiosyncratic but efficient and stable solutions to the problem of reference. Furthermore, these conventions are shaped by the communicative context: words that are more discriminative in the initial context (i.e., that are used for one target more than others) are more likely to persist through the final repetition. Finally, we find systematic structure in how a speaker's referring expressions become more efficient over time: Syntactic units drop out in clusters following positive feedback from the listener, eventually leaving short labels containing open-class parts of speech. These findings provide a higher resolution look at the quantitative dynamics of ad hoc convention formation and support further development of computational models of learning in communication.

  View details for DOI 10.1111/cogs.12845

  View details for PubMedID 32496603

• Extracting Customer Perceptions of Product Sustainability From Online Reviews El Dehaibi, N., Goodman, N. D., MacDonald, E. F. ASME. 2019

  View details for DOI 10.1115/1.4044522

  View details for Web of Science ID 000506893800005

• A thousand studies for the price of one: Accelerating psychological science with Pushkin BEHAVIOR RESEARCH METHODS Hartshorne, J. K., de Leeuw, J. R., Goodman, N. D., Jennings, M., O'Donnell, T. J. 2019; 51 (4): 1782–1803

  View details for DOI 10.3758/s13428-018-1155-z

  View details for Web of Science ID 000481874200020

• The language of generalization. Psychological review Tessler, M. H., Goodman, N. D. 2019

  Abstract

  Language provides simple ways of communicating generalizable knowledge to each other (e.g., "Birds fly," "John hikes," and "Fire makes smoke"). Though found in every language and emerging early in development, the language of generalization is philosophically puzzling and has resisted precise formalization. Here, we propose the first formal account of generalizations conveyed with language that makes quantitative predictions about human understanding. The basic idea is that the language of generalization expresses that an event or a property occurs relatively often, where what counts as relatively often depends upon one's prior expectations. We formalize this simple idea in a probabilistic model of language understanding, which we test in 3 diverse case studies: generalizations about categories (generic language), events (habitual language), and causes (causal language). We find that the model explains the gradience in human endorsements that has perplexed previous attempts to formalize this swath of linguistic expressions. This work opens the door to understanding precisely how abstract knowledge is learned from language. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

  View details for PubMedID 30762385

• A thousand studies for the price of one: Accelerating psychological science with Pushkin. Behavior research methods Hartshorne, J. K., de Leeuw, J. R., Goodman, N. D., Jennings, M., O'Donnell, T. J. 2019

  Abstract

  Half of the world's population has internet access. In principle, researchers are no longer limited to subjects they can recruit into the laboratory. Any study that can be run on a computer or mobile device can be run with nearly any demographic anywhere in the world, and in large numbers. This has allowed scientists to effectively run hundreds of experiments at once. Despite their transformative power, such studies remain rare for practical reasons: the need for sophisticated software, the difficulty of recruiting so many subjects, and a lack of research paradigms that make effective use of their large amounts of data, due to such realities as that they require sophisticated software in order to run effectively. We present Pushkin: an open-source platform for designing and conducting massive experiments over the internet. Pushkin allows for a wide range of behavioral paradigms, through integration with the intuitive and flexible jsPsych experiment engine. It also addresses the basic technical challenges associated with massive, worldwide studies, including auto-scaling, extensibility, machine-assisted experimental design, multisession studies, and data security.

  View details for PubMedID 30746644

• Pyro: Deep Universal Probabilistic Programming JOURNAL OF MACHINE LEARNING RESEARCH Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P., Horsfall, P., Goodman, N. D. 2019; 20

  View details for Web of Science ID 000458670100001

• Shapeglot: Learning Language for Shape Differentiation Achlioptas, P., Fan, J., Hawkins, R., Goodman, N., Guihas, L., IEEE IEEE. 2019: 8937–46

  View details for DOI 10.1109/ICCV.2019.00903

  View details for Web of Science ID 000548549204006

• Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference Wu, M., Mosse, M., Goodman, N., Piech, C., AAAI ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2019: 782–90

  View details for Web of Science ID 000485292600097

• On the grammatical source of adjective ordering preferences SEMANTICS & PRAGMATICS Scontras, G., Degen, J., Goodman, N. D. 2019; 12

  View details for Web of Science ID 000498393400007

• Learning to Explain: Answering Why-Questions via Rephrasing Nie, A., Bennett, E. D., Goodman, N. D., Assoc Computat Linguist ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2019: 113–20

  View details for Web of Science ID 000538539200013

• Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference Wu, M., Goodman, N., Ermon, S., Chaudhuri, K., Sugiyama, M. MICROTOME PUBLISHING. 2019

  View details for Web of Science ID 000509687902095

• Variational Bayesian Optimal Experimental Design Fostert, A., Jankowiak, M., Bingham, E., Horsfall, P., Teh, Y., Rainforth, T., Goodman, N., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

  View details for Web of Science ID 000535866905068

• Learning from omission McDowell, B., Goodman, N. D., ACL, Korhonen, A., Traum, D., Marquez, L. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2019: 619–28

  View details for Web of Science ID 000493046101012

• DisSent: Learning Sentence Representations from Explicit Discourse Relations Nie, A., Bennett, E. D., Goodman, N. D., ACL, Korhonen, A., Traum, D., Marquez, L. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2019: 4497–4510

  View details for Web of Science ID 000493046106101

• Definitely, maybe: A new experimental paradigm for investigating the pragmatics of evidential devices across languages JOURNAL OF PRAGMATICS Degen, J., Trotzke, A., Scontras, G., Wittenberg, E., Goodman, N. D. 2019; 140: 33–48

  View details for DOI 10.1016/j.pragma.2018.11.015

  View details for Web of Science ID 000458097100004

• Cake or broccoli? Recency biases children's verbal responses. PloS one Sumner, E. n., DeAngelis, E. n., Hyatt, M. n., Goodman, N. n., Kidd, C. n. 2019; 14 (6): e0217207

  Abstract

  One of the greatest challenges of developmental psychology is figuring out what children are thinking. This is particularly difficult in early childhood, for children who are prelinguistic or are just beginning to speak their first words. In this stage, children's responses are commonly measured by presenting young children with a limited choice between one of a small number of options (e.g., "Do you want X or Y?"). A tendency to choose one response in these tasks may be taken as an indication of a child's preference or understanding. Adults' responses are known to exhibit order biases when they are asked questions. The current set of experiments looks into the following question: do children demonstrate response biases? Together, we show that 1) toddlers demonstrate a robust verbal recency bias when asked "or" questions in a lab-based task and a naturalistic corpus of caretaker-child speech interactions, 2) the recency bias weakens with age, and 3) the recency bias strengthens as the syllable-length of the choices gets longer. Taken together, these results indicate that children show a different type of response bias than adults, recency instead of primacy. Further, the results may suggest that this bias stems from increased constraints on children's working memory.

  View details for DOI 10.1371/journal.pone.0217207

  View details for PubMedID 31188864

• The Emergence of Social Norms and Conventions. Trends in cognitive sciences Hawkins, R. X., Goodman, N. D., Goldstone, R. L. 2018

  Abstract

  The utility of our actions frequently depends upon the beliefs and behavior of other agents. Thankfully, through experience, we learn norms and conventions that provide stable expectations for navigating our social world. Here, we review several distinct influences on their content and distribution. At the level of individuals locally interacting in dyads, success depends on rapidly adapting pre-existing norms to the local context. Hence, norms are shaped by complex cognitive processes involved in learning and social reasoning. At the population level, norms are influenced by intergenerational transmission and the structure of the social network. As human social connectivity continues to increase, understanding and predicting how these levels and time scales interact to produce new norms will be crucial for improving communities.

  View details for PubMedID 30522867

• Beyond Reward Prediction Errors: Human Striatum Updates Rule Values During Learning CEREBRAL CORTEX Ballard, I., Miller, E. M., Piantadosi, S. T., Goodman, N. D., McClure, S. M. 2018; 28 (11): 3965–75

  Abstract

  Humans naturally group the world into coherent categories defined by membership rules. Rules can be learned implicitly by building stimulus-response associations using reinforcement learning or by using explicit reasoning. We tested if the striatum, in which activation reliably scales with reward prediction error, would track prediction errors in a task that required explicit rule generation. Using functional magnetic resonance imaging during a categorization task, we show that striatal responses to feedback scale with a "surprise" signal derived from a Bayesian rule-learning model and are inconsistent with RL prediction error. We also find that striatum and caudal inferior frontal sulcus (cIFS) are involved in updating the likelihood of discriminative rules. We conclude that the striatum, in cooperation with the cIFS, is involved in updating the values assigned to categorization rules when people learn using explicit reasoning.

  View details for PubMedID 29040494

• Remembrance of inferences past: Amortization in human hypothesis generation COGNITION Dasgupta, I., Schulz, E., Goodman, N. D., Gershman, S. J. 2018; 178: 67–81

  Abstract

  Bayesian models of cognition assume that people compute probability distributions over hypotheses. However, the required computations are frequently intractable or prohibitively expensive. Since people often encounter many closely related distributions, selective reuse of computations (amortized inference) is a computationally efficient use of the brain's limited resources. We present three experiments that provide evidence for amortization in human probabilistic reasoning. When sequentially answering two related queries about natural scenes, participants' responses to the second query systematically depend on the structure of the first query. This influence is sensitive to the content of the queries, only appearing when the queries are related. Using a cognitive load manipulation, we find evidence that people amortize summary statistics of previous inferences, rather than storing the entire distribution. These findings support the view that the brain trades off accuracy and computational cost, to make efficient use of its limited cognitive resources to approximate probabilistic inference.

  View details for PubMedID 29793110

• Learning physical parameters from dynamic scenes COGNITIVE PSYCHOLOGY Ullman, T. D., Stuhlmuller, A., Goodman, N. D., Tenenbaum, J. B. 2018; 104: 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, Kemp, Griffiths, & Goodman, 2011), we work with more expressive 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 simultaneously 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.

  View details for PubMedID 29653395

• Extremely costly intensifiers are stronger than quite costly ones. Cognition Bennett, E. D., Goodman, N. D. 2018; 178: 147–61

  Abstract

  We show that the wide range in strengths of intensifying degree adverbs (e.g. very and extremely) can be partly explained by pragmatic inference based on differing cost, rather than differing semantics. The pragmatic theory predicts a linear relationship between the meaning of intensifiers and their length and log-frequency. We first test this prediction in three studies, using two different dependent measures, finding that higher utterance cost (i.e. higher word length or surprisal) does predict stronger meanings. In two additional studies we confirm that the relationship between length and meaning is present even for novel words. We discuss the implications for adverbial meaning and the more general question of how extensive non-arbitrary form-meaning association may be in language.

  View details for PubMedID 29857283

• Empirical evidence for resource-rational anchoring and adjustment PSYCHONOMIC BULLETIN & REVIEW Lieder, F., Griffiths, T. L., Huys, Q. M., Goodman, N. D. 2018; 25 (2): 775–84

  Abstract

  People's estimates of numerical quantities are systematically biased towards their initial guess. This anchoring bias is usually interpreted as sign of human irrationality, but it has recently been suggested that the anchoring bias instead results from people's rational use of their finite time and limited cognitive resources. If this were true, then adjustment should decrease with the relative cost of time. To test this hypothesis, we designed a new numerical estimation paradigm that controls people's knowledge and varies the cost of time and error independently while allowing people to invest as much or as little time and effort into refining their estimate as they wish. Two experiments confirmed the prediction that adjustment decreases with time cost but increases with error cost regardless of whether the anchor was self-generated or provided. These results support the hypothesis that people rationally adapt their number of adjustments to achieve a near-optimal speed-accuracy tradeoff. This suggests that the anchoring bias might be a signature of the rational use of finite time and limited cognitive resources rather than a sign of human irrationality.

  View details for PubMedID 28484951

• Happier Than Thou? A Self-Enhancement Bias in Emotion Attribution EMOTION Ong, D. C., Goodman, N. D., Zaki, J. 2018; 18 (1): 116–26

  Abstract

  People tend to judge themselves as exhibiting above average levels of desirable traits-including competence, kindness, and life satisfaction-but does this self-enhancement extend to emotional responses? Here, we explore this question by having people attribute emotions to themselves and others following simple gambles. We demonstrate that people display an emotional self-enhancement bias that varies with the context of the emotion-eliciting situation. People judge themselves as experiencing more positive emotional reactions on average, and they also believed that others' emotions are more sensitive to gamble outcomes, such that people judge others to experience stronger negative affect in response to negative outcomes (Study 1). This self-enhancement bias further tracks social distance, such that people attribute less positive and more negative emotion to more dissimilar, as compared with more similar others (Study 2). People also predict less favorable emotional states for themselves and others experiencing events in the future, as compared with the present (Study 3), suggesting that this attribution bias extends across multiple dimensions of psychological distance. Broadly, these data suggest that people exhibit self-enhancement in emotion attribution, but do so in subtle ways that depend on situational and social factors. (PsycINFO Database Record

  View details for PubMedID 28406680

• The anchoring bias reflects rational use of cognitive resources PSYCHONOMIC BULLETIN & REVIEW Lieder, F., Griffiths, T. L., Huys, Q. M., Goodman, N. D. 2018; 25 (1): 322–49

  Abstract

  Cognitive biases, such as the anchoring bias, pose a serious challenge to rational accounts of human cognition. We investigate whether rational theories can meet this challenge by taking into account the mind's bounded cognitive resources. We asked what reasoning under uncertainty would look like if people made rational use of their finite time and limited cognitive resources. To answer this question, we applied a mathematical theory of bounded rationality to the problem of numerical estimation. Our analysis led to a rational process model that can be interpreted in terms of anchoring-and-adjustment. This model provided a unifying explanation for ten anchoring phenomena including the differential effect of accuracy motivation on the bias towards provided versus self-generated anchors. Our results illustrate the potential of resource-rational analysis to provide formal theories that can unify a wide range of empirical results and reconcile the impressive capacities of the human mind with its apparently irrational cognitive biases.

  View details for PubMedID 28484952

• Multimodal Generative Models for Scalable Weakly-Supervised Learning Wu, M., Goodman, N., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018

  View details for Web of Science ID 000461852000011

• Computational Models of Emotion Inference in Theory of Mind: A Review and Roadmap. Topics in cognitive science Ong, D. C., Zaki, J. n., Goodman, N. D. 2018

  Abstract

  Research on social cognition has fruitfully applied computational modeling approaches to explain how observers understand and reason about others' mental states. By contrast, there has been less work on modeling observers' understanding of emotional states. We propose an intuitive theory framework to studying affective cognition-how humans reason about emotions-and derive a taxonomy of inferences within affective cognition. Using this taxonomy, we review formal computational modeling work on such inferences, including causal reasoning about how others react to events, reasoning about unseen causes of emotions, reasoning with multiple cues, as well as reasoning from emotions to other mental states. In addition, we provide a roadmap for future research by charting out inferences-such as hypothetical and counterfactual reasoning about emotions-that are ripe for future computational modeling work. This framework proposes unifying these various types of reasoning as Bayesian inference within a common "intuitive Theory of Emotion." Finally, we end with a discussion of important theoretical and methodological challenges that lie ahead in modeling affective cognition.

  View details for PubMedID 30066475

• Bias and Generalization in Deep Generative Models: An Empirical Study Zhao, S., Ren, H., Yuan, A., Song, J., Goodman, N., Ermon, S., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018

  View details for Web of Science ID 000461852005038

• Eye-Tracking Causality PSYCHOLOGICAL SCIENCE Gerstenberg, T., Peterson, M. F., Goodman, N. D., Lagnado, D. A., Tenenbaum, J. B. 2017; 28 (12): 1731–44

  View details for DOI 10.1177/0956797617713053

  View details for Web of Science ID 000417871900003

• Eye-Tracking Causality. Psychological science Gerstenberg, T., Peterson, M. F., Goodman, N. D., Lagnado, D. A., Tenenbaum, J. B. 2017; 28 (12): 1731-1744

  Abstract

  How do people make causal judgments? What role, if any, does counterfactual simulation play? Counterfactual theories of causal judgments predict that people compare what actually happened with what would have happened if the candidate cause had been absent. Process theories predict that people focus only on what actually happened, to assess the mechanism linking candidate cause and outcome. We tracked participants' eye movements while they judged whether one billiard ball caused another one to go through a gate or prevented it from going through. Both participants' looking patterns and their judgments demonstrated that counterfactual simulation played a critical role. Participants simulated where the target ball would have gone if the candidate cause had been removed from the scene. The more certain participants were that the outcome would have been different, the stronger the causal judgments. These results provide the first direct evidence for spontaneous counterfactual simulation in an important domain of high-level cognition.

  View details for DOI 10.1177/0956797617713053

  View details for PubMedID 29039251

• Resolving uncertainty in plural predication COGNITION Scontras, G., Goodman, N. D. 2017; 168: 294–311

  Abstract

  Plural predications (e.g., "the boxes are heavy") are common sources of ambiguity in everyday language, allowing both distributive and collective interpretations (e.g., the boxes each are heavy vs. the boxes together are heavy). This paper investigates the role of context in the disambiguation of plural predication. We address the key phenomenon of "stubborn distributivity," whereby certain predicates (e.g., big, tall) are claimed to lack collective interpretations altogether. We first validate a new methodology for measuring the interpretation of plural predications. Using this method, we then analyze naturally-occurring plural predications from corpora. We find a role of context, but no evidence of a distinct class of predicates that resists collective interpretations. We further explore the role of context in our final experiments, showing that both the predictability of properties and the knowledgeability of the speaker affect disambiguation. This suggests a pragmatic account of how ambiguous plural predications are interpreted. In particular, stubbornly distributive predicates are so because the collective properties they name are unpredictable, or unstable, in most contexts; this unpredictability results in a noisy collective interpretation, something speakers and listeners recognize as ineffective for communicating efficiently about their world. We formalize the pragmatics of utterance disambiguation within the Bayesian Rational Speech Act framework.

  View details for PubMedID 28756352

• Avoiding frostbite: It helps to learn from others BEHAVIORAL AND BRAIN SCIENCES Tessler, M., Goodman, N. D., Frank, M. C. 2017; 40: e279

  Abstract

  Machines that learn and think like people must be able to learn from others. Social learning speeds up the learning process and - in combination with language - is a gateway to abstract and unobservable information. Social learning also facilitates the accumulation of knowledge across generations, helping people and artificial intelligences learn things that no individual could learn in a lifetime.

  View details for PubMedID 29342698

• Learning Disentangled Representations with Semi-Supervised Deep Generative Models Siddharth, N., Paige, B., van de Meent, J., Desmaison, A., Goodman, N. D., Kohli, P., Wood, F., Torr, P. S., Guyon, Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017

  View details for Web of Science ID 000452649406001

• Pragmatic Language Interpretation as Probabilistic Inference. Trends in cognitive sciences Goodman, N. D., Frank, M. C. 2016; 20 (11): 818-829

  Abstract

  Understanding language requires more than the use of fixed conventions and more than decoding combinatorial structure. Instead, comprehenders make exquisitely sensitive inferences about what utterances mean given their knowledge of the speaker, language, and context. Building on developments in game theory and probabilistic modeling, we describe the rational speech act (RSA) framework for pragmatic reasoning. RSA models provide a principled way to formalize inferences about meaning in context; they have been used to make successful quantitative predictions about human behavior in a variety of different tasks and situations, and they explain why complex phenomena, such as hyperbole and vagueness, occur. More generally, they provide a computational framework for integrating linguistic structure, world knowledge, and context in pragmatic language understanding.

  View details for DOI 10.1016/j.tics.2016.08.005

  View details for PubMedID 27692852

• The Logical Primitives of Thought: Empirical Foundations for Compositional Cognitive Models PSYCHOLOGICAL REVIEW Piantadosi, S. T., Tenenbaum, J. B., Goodman, N. D. 2016; 123 (4): 392-424

  Abstract

  The notion of a compositional language of thought (LOT) has been central in computational accounts of cognition from earliest attempts (Boole, 1854; Fodor, 1975) to the present day (Feldman, 2000; Penn, Holyoak, & Povinelli, 2008; Fodor, 2008; Kemp, 2012; Goodman, Tenenbaum, & Gerstenberg, 2015). Recent modeling work shows how statistical inferences over compositionally structured hypothesis spaces might explain learning and development across a variety of domains. However, the primitive components of such representations are typically assumed a priori by modelers and theoreticians rather than determined empirically. We show how different sets of LOT primitives, embedded in a psychologically realistic approximate Bayesian inference framework, systematically predict distinct learning curves in rule-based concept learning experiments. We use this feature of LOT models to design a set of large-scale concept learning experiments that can determine the most likely primitives for psychological concepts involving Boolean connectives and quantification. Subjects' inferences are most consistent with a rich (nonminimal) set of Boolean operations, including first-order, but not second-order, quantification. Our results more generally show how specific LOT theories can be distinguished empirically. (PsycINFO Database Record

  View details for DOI 10.1037/a0039980

  View details for Web of Science ID 000379503900003

  View details for PubMedID 27077241

• A Computational Model of Linguistic Humor in Puns COGNITIVE SCIENCE Kao, J. T., Levy, R., Goodman, N. D. 2016; 40 (5): 1270-1285

  Abstract

  Humor plays an essential role in human interactions. Precisely what makes something funny, however, remains elusive. While research on natural language understanding has made significant advancements in recent years, there has been little direct integration of humor research with computational models of language understanding. In this paper, we propose two information-theoretic measures-ambiguity and distinctiveness-derived from a simple model of sentence processing. We test these measures on a set of puns and regular sentences and show that they correlate significantly with human judgments of funniness. Moreover, within a set of puns, the distinctiveness measure distinguishes exceptionally funny puns from mediocre ones. Our work is the first, to our knowledge, to integrate a computational model of general language understanding and humor theory to quantitatively predict humor at a fine-grained level. We present it as an example of a framework for applying models of language processing to understand higher level linguistic and cognitive phenomena.

  View details for DOI 10.1111/cogs.12269

  View details for Web of Science ID 000383383700008

  View details for PubMedID 26235596

  View details for PubMedCentralID PMC5042108

• Affective cognition: Exploring lay theories of emotion COGNITION Ong, D. C., Zaki, J., Goodman, N. D. 2015; 143: 141-162

  Abstract

  Humans skillfully reason about others' emotions, a phenomenon we term affective cognition. Despite its importance, few formal, quantitative theories have described the mechanisms supporting this phenomenon. We propose that affective cognition involves applying domain-general reasoning processes to domain-specific content knowledge. Observers' knowledge about emotions is represented in rich and coherent lay theories, which comprise consistent relationships between situations, emotions, and behaviors. Observers utilize this knowledge in deciphering social agents' behavior and signals (e.g., facial expressions), in a manner similar to rational inference in other domains. We construct a computational model of a lay theory of emotion, drawing on tools from Bayesian statistics, and test this model across four experiments in which observers drew inferences about others' emotions in a simple gambling paradigm. This work makes two main contributions. First, the model accurately captures observers' flexible but consistent reasoning about the ways that events and others' emotional responses to those events relate to each other. Second, our work models the problem of emotional cue integration-reasoning about others' emotion from multiple emotional cues-as rational inference via Bayes' rule, and we show that this model tightly tracks human observers' empirical judgments. Our results reveal a deep structural relationship between affective cognition and other forms of inference, and suggest wide-ranging applications to basic psychological theory and psychiatry.

  View details for DOI 10.1016/j.cognition.2015.06.010

  View details for Web of Science ID 000359885600017

  View details for PubMedID 26160501

• Controlling Procedural Modeling Programs with Stochastically-Ordered Sequential Monte Carlo ACM TRANSACTIONS ON GRAPHICS Ritchie, D., Mildenhall, B., Goodman, N. D., Hanrahan, P. 2015; 34 (4)

  View details for DOI 10.1145/2766895

  View details for Web of Science ID 000358786600071

• Generating Design Suggestions under Tight Constraints with Gradient-based Probabilistic Programming COMPUTER GRAPHICS FORUM Ritchie, D., Lin, S., Goodman, N. D., Hanrahan, P. 2015; 34 (2): 515-526

  View details for DOI 10.1111/cgf.12580

  View details for Web of Science ID 000358326600049

• The Strategic Use of Noise in Pragmatic Reasoning TOPICS IN COGNITIVE SCIENCE Bergen, L., Goodman, N. D. 2015; 7 (2): 336-350

  Abstract

  We combine two recent probabilistic approaches to natural language understanding, exploring the formal pragmatics of communication on a noisy channel. We first extend a model of rational communication between a speaker and listener, to allow for the possibility that messages are corrupted by noise. In this model, common knowledge of a noisy channel leads to the use and correct understanding of sentence fragments. A further extension of the model, which allows the speaker to intentionally reduce the noise rate on a word, is used to model prosodic emphasis. We show that the model derives several well-known changes in meaning associated with prosodic emphasis. Our results show that nominal amounts of actual noise can be leveraged for communicative purposes.

  View details for DOI 10.1111/tops.12144

  View details for Web of Science ID 000353954500014

  View details for PubMedID 25898999

• Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic TOPICS IN COGNITIVE SCIENCE Griffiths, T. L., Lieder, F., Goodman, N. D. 2015; 7 (2): 217-229

  Abstract

  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 key idea is to push the notion of rationality, often used in defining computational-level models, deeper toward the algorithmic level. We offer a simple recipe for reverse-engineering the mind's cognitive strategies by deriving optimal algorithms for a series of increasingly more realistic abstract computational architectures, which we call "resource-rational analysis."

  View details for DOI 10.1111/tops.12142

  View details for Web of Science ID 000353954500005

  View details for PubMedID 25898807

• Relevant and robust: a response to Marcus and Davis (2013). Psychological science Goodman, N. D., Frank, M. C., Griffiths, T. L., Tenenbaum, J. B., Battaglia, P. W., Hamrick, J. B. 2015; 26 (4): 539-541

  View details for DOI 10.1177/0956797614559544

  View details for PubMedID 25749699

• How many kinds of reasoning? Inference, probability, and natural language semantics. Cognition Lassiter, D., Goodman, N. D. 2015; 136: 123-134

  Abstract

  The "new paradigm" unifying deductive and inductive reasoning in a Bayesian framework (Oaksford & Chater, 2007; Over, 2009) has been claimed to be falsified by results which show sharp differences between reasoning about necessity vs. plausibility (Heit & Rotello, 2010; Rips, 2001; Rotello & Heit, 2009). We provide a probabilistic model of reasoning with modal expressions such as "necessary" and "plausible" informed by recent work in formal semantics of natural language, and show that it predicts the possibility of non-linear response patterns which have been claimed to be problematic. Our model also makes a strong monotonicity prediction, while two-dimensional theories predict the possibility of reversals in argument strength depending on the modal word chosen. Predictions were tested using a novel experimental paradigm that replicates the previously-reported response patterns with a minimal manipulation, changing only one word of the stimulus between conditions. We found a spectrum of reasoning "modes" corresponding to different modal words, and strong support for our model's monotonicity prediction. This indicates that probabilistic approaches to reasoning can account in a clear and parsimonious way for data previously argued to falsify them, as well as new, more fine-grained, data. It also illustrates the importance of careful attention to the semantics of language employed in reasoning experiments.

  View details for DOI 10.1016/j.cognition.2014.10.016

  View details for PubMedID 25497521

• Inferring word meanings by assuming that speakers are informative. Cognitive psychology Frank, M. C., Goodman, N. D. 2014; 75: 80-96

  Abstract

  Language comprehension is more than a process of decoding the literal meaning of a speaker's utterance. Instead, by making the assumption that speakers choose their words to be informative in context, listeners routinely make pragmatic inferences that go beyond the linguistic data. If language learners make these same assumptions, they should be able to infer word meanings in otherwise ambiguous situations. We use probabilistic tools to formalize these kinds of informativeness inferences-extending a model of pragmatic language comprehension to the acquisition setting-and present four experiments whose data suggest that preschool children can use informativeness to infer word meanings and that adult judgments track quantitatively with informativeness.

  View details for DOI 10.1016/j.cogpsych.2014.08.002

  View details for PubMedID 25238461

• Nonliteral understanding of number words PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Kao, J. T., Wu, J. Y., Bergen, L., Goodman, N. D. 2014; 111 (33): 12002-12007

  Abstract

  One of the most puzzling and important facts about communication is that people do not always mean what they say; speakers often use imprecise, exaggerated, or otherwise literally false descriptions to communicate experiences and attitudes. Here, we focus on the nonliteral interpretation of number words, in particular hyperbole (interpreting unlikely numbers as exaggerated and conveying affect) and pragmatic halo (interpreting round numbers imprecisely). We provide a computational model of number interpretation as social inference regarding the communicative goal, meaning, and affective subtext of an utterance. We show that our model predicts humans' interpretation of number words with high accuracy. Our model is the first to our knowledge to incorporate principles of communication and empirically measured background knowledge to quantitatively predict hyperbolic and pragmatic halo effects in number interpretation. This modeling framework provides a unified approach to nonliteral language understanding more generally.

  View details for DOI 10.1073/pnas.1407479111

  View details for Web of Science ID 000340438800037

  View details for PubMedID 25092304

  View details for PubMedCentralID PMC4143012

• A rational account of pedagogical reasoning: Teaching by, and learning from, examples COGNITIVE PSYCHOLOGY Shafto, P., Goodman, N. D., Griffiths, T. L. 2014; 71: 55-89

  Abstract

  Much of learning and reasoning occurs in pedagogical situations--situations in which a person who knows a concept chooses examples for the purpose of helping a learner acquire the concept. We introduce a model of teaching and learning in pedagogical settings that predicts which examples teachers should choose and what learners should infer given a teacher's examples. We present three experiments testing the model predictions for rule-based, prototype, and causally structured concepts. The model shows good quantitative and qualitative fits to the data across all three experiments, predicting novel qualitative phenomena in each case. We conclude by discussing implications for understanding concept learning and implications for theoretical claims about the role of pedagogy in human learning.

  View details for DOI 10.1016/j.cogpsych.2013.12.004

  View details for Web of Science ID 000336108500003

  View details for PubMedID 24607849

• One and Done? Optimal Decisions From Very Few Samples COGNITIVE SCIENCE Vul, E., Goodman, N., Griffiths, T. L., Tenenbaum, J. B. 2014; 38 (4): 599-637

  View details for DOI 10.1111/cogs.12101

  View details for Web of Science ID 000337529500001

• Some arguments are probably valid: Syllogistic reasoning as communication Proceedings of the Thirty-Sixth Annual Conference of the Cognitive Science Society Tessler, M. H., Goodman, N. D. 2014
• Uncertainty and denial: a resource-rational model of the value of information. PloS one Pierson, E., Goodman, N. 2014; 9 (11)

  Abstract

  Classical decision theory predicts that people should be indifferent to information that is not useful for making decisions, but this model often fails to describe human behavior. Here we investigate one such scenario, where people desire information about whether an event (the gain/loss of money) will occur even though there is no obvious decision to be made on the basis of this information. We find a curious dual trend: if information is costless, as the probability of the event increases people want the information more; if information is not costless, people's desire for the information peaks at an intermediate probability. People also want information more as the importance of the event increases, and less as the cost of the information increases. We propose a model that explains these results, based on the assumption that people have limited cognitive resources and obtain information about which events will occur so they can determine whether to expend effort planning for them.

  View details for DOI 10.1371/journal.pone.0113342

  View details for PubMedID 25426631

  View details for PubMedCentralID PMC4245129

• The strategic use of noise in pragmatic reasoning Proceedings of the Thirty-Sixth Annual Conference of the Cognitive Science Society Bergen, L., Goodman, N. D. 2014
• Lost your marbles? The puzzle of dependent measures in experimental pragmatics Proceedings of the Thirty-Sixth Annual Conference of the Cognitive Science Society Degen, J., Goodman, N. D. 2014
• Solve For Standing Ovation: Should AI Researchers Bother Building A TED-Bot? Popular Science Goodman, N. D. 2014
• Forget the Turing Test: Here’s How We Could Actually Measure AI WIRED Goodman, N. D. 2014
• From counterfactual simulation to causal judgment Proceedings of the Thirty-Sixth Annual Conference of the Cognitive Science Society Gerstenberg, T., Goodman, N. D., Lagnado, D. A., Tenenbaum, J. B. 2014
• Formalizing the pragmatics of metaphor understanding Proceedings of the Thirty-Sixth Annual Conference of the Cognitive Science Society Kao, J., Bergen, L., Goodman, N. D. 2014
• Generating efficient MCMC kernels from probabilistic programs AISTATS 2014 Yang, L., Hanrahan, P., Goodman, N. D. 2014
• Amortized inference in probabilistic reasoning Proceedings of the Thirty-Sixth Annual Conference of the Cognitive Science Society Gershman, S., Goodman, N. D. 2014
• Rational reasoning in pedagogical contexts Cognitive Psychology Shafto, P., Goodman, N. D., Griffiths, T. L. 2014
• The mentalistic basis of core social cognition: experiments in preverbal infants and a computational model DEVELOPMENTAL SCIENCE Hamlin, J. K., Ullman, T., Tenenbaum, J., Goodman, N., Baker, C. 2013; 16 (2): 209-226

  View details for DOI 10.1111/desc.12017

  View details for Web of Science ID 000315384700006

• The mentalistic basis of core social cognition: experiments in preverbal infants and a computational model. Developmental science Kiley Hamlin, J., Ullman, T., Tenenbaum, J., Goodman, N., Baker, C. 2013; 16 (2): 209-226

  Abstract

  Evaluating individuals based on their pro- and anti-social behaviors is fundamental to successful human interaction. Recent research suggests that even preverbal infants engage in social evaluation; however, it remains an open question whether infants' judgments are driven uniquely by an analysis of the mental states that motivate others' helpful and unhelpful actions, or whether non-mentalistic inferences are at play. Here we present evidence from 10-month-olds, motivated and supported by a Bayesian computational model, for mentalistic social evaluation in the first year of life.A video abstract of this article can be viewed at http://youtu.be/rD_Ry5oqCYE.

  View details for DOI 10.1111/desc.12017

  View details for PubMedID 23432831

  View details for PubMedCentralID PMC4100482

• Did She Jump Because She Was the Big Sister or Because the Trampoline Was Safe? Causal Inference and the Development of Social Attribution CHILD DEVELOPMENT Seiver, E., Gopnik, A., Goodman, N. D. 2013; 84 (2): 443-454

  Abstract

  Children rely on both evidence and prior knowledge to make physical causal inferences; this study explores whether they make attributions about others' behavior in the same manner. A total of one hundred and fifty-nine 4- and 6-year-olds saw 2 dolls interacting with 2 activities, and explained the dolls' actions. In the person condition, each doll acted consistently across activities, but differently from each other. In the situation condition, the two dolls acted differently for each activity, but both performed the same actions. Both age groups provided more "person" explanations (citing features of the doll) in the person condition than in the situation condition. In addition, 6-year-olds showed an overall bias toward "person" explanations. As in physical causal inference, social causal inference combines covariational evidence and prior knowledge.

  View details for DOI 10.1111/j.1467-8624.2012.01865.x

  View details for Web of Science ID 000316805900005

  View details for PubMedID 23002946

• The Principles and Practice of Probabilistic Programming ACM SIGPLAN NOTICES Goodman, N. D. 2013; 48 (1): 399-401

  View details for DOI 10.1145/2480359.2429117

  View details for Web of Science ID 000318629900034

• The Funny Thing About Incongruity: A Computational Model of Humor in Puns Proceedings of the Thirty-Fifth Annual Conference of the Cognitive Science Society Kao, J. T., Levy, R., Goodman, N. D. 2013
• Learned helplessness and generalization Proceedings of the Thirty-Fifth Annual Conference of the Cognitive Science Society Lieder, F., Goodman, N. D., Huys, Q. M. 2013
• Reasoning about Reasoning by Nested Conditioning: Modeling Theory of Mind with Probabilistic Programs J. Cognitive Systems Research Stuhlmüller, A., Goodman, N. D. 2013
• Context, scale structure, and statistics in the interpretation of positive-form adjectives SALT23 Lassiter, D., Goodman, N. D. 2013
• Learning and using language via recursive pragmatic reasoning about other agents Advances in Neural Information Processing Systems, 25 Smith, N. J., Goodman, N. D., Frank, M. C. 2013
• Learning Stochastic Inverses Advances in Neural Information Processing Systems, 25 Stuhlmueller, A., Taylor, J., Goodman, N. D. 2013
• Knowledge and Implicature: Modeling Language Understanding as Social Cognition TOPICS IN COGNITIVE SCIENCE Goodman, N. D., Stuhlmueller, A. 2013; 5 (1): 173-184

  Abstract

  Is language understanding a special case of social cognition? To help evaluate this view, we can formalize it as the rational speech-act theory: Listeners assume that speakers choose their utterances approximately optimally, and listeners interpret an utterance by using Bayesian inference to "invert" this model of the speaker. We apply this framework to model scalar implicature ("some" implies "not all," and "N" implies "not more than N"). This model predicts an interaction between the speaker's knowledge state and the listener's interpretation. We test these predictions in two experiments and find good fit between model predictions and human judgments.

  View details for DOI 10.1111/tops.12007

  View details for Web of Science ID 000313754300009

  View details for PubMedID 23335578

• Theory learning as stochastic search in the language of thought COGNITIVE DEVELOPMENT Ullman, T. D., Goodman, N. D., Tenenbaum, J. B. 2012; 27 (4): 455-480

  View details for DOI 10.1016/j.cogdev.2012.07.005

  View details for Web of Science ID 000312575300009

• Synthesizing Open Worlds with Constraints using Locally Annealed Reversible Jump MCMC ACM TRANSACTIONS ON GRAPHICS Yeh, Y., Yang, L., Watson, M., Goodman, N. D., Hanrahan, P. 2012; 31 (4)

  View details for DOI 10.1145/2185520.2185552

  View details for Web of Science ID 000308250300032

• Learning From Others: The Consequences of Psychological Reasoning for Human Learning. Perspectives on psychological science : a journal of the Association for Psychological Science Shafto, P., Goodman, N. D., Frank, M. C. 2012; 7 (4): 341-51

  Abstract

  From early childhood, human beings learn not only from collections of facts about the world but also from social contexts through observations of other people, communication, and explicit teaching. In these contexts, the data are the result of human actions-actions that come about because of people's goals and intentions. To interpret the implications of others' actions correctly, learners must understand the people generating the data. Most models of learning, however, assume that data are randomly collected facts about the world and cannot explain how social contexts influence learning. We provide a Bayesian analysis of learning from knowledgeable others, which formalizes how learners may use a person's actions and goals to make inferences about the actor's knowledge about the world. We illustrate this framework using two examples from causal learning and conclude by discussing the implications for cognition, social reasoning, and cognitive development.

  View details for DOI 10.1177/1745691612448481

  View details for PubMedID 26168471

• Learning From Others: The Consequences of Psychological Reasoning for Human Learning PERSPECTIVES ON PSYCHOLOGICAL SCIENCE Shafto, P., Goodman, N. D., Frank, M. C. 2012; 7 (4): 341-351

  Abstract

  From early childhood, human beings learn not only from collections of facts about the world but also from social contexts through observations of other people, communication, and explicit teaching. In these contexts, the data are the result of human actions-actions that come about because of people's goals and intentions. To interpret the implications of others' actions correctly, learners must understand the people generating the data. Most models of learning, however, assume that data are randomly collected facts about the world and cannot explain how social contexts influence learning. We provide a Bayesian analysis of learning from knowledgeable others, which formalizes how learners may use a person's actions and goals to make inferences about the actor's knowledge about the world. We illustrate this framework using two examples from causal learning and conclude by discussing the implications for cognition, social reasoning, and cognitive development.

  View details for DOI 10.1177/1745691612448481

  View details for Web of Science ID 000305837300003

• Predicting Pragmatic Reasoning in Language Games SCIENCE Frank, M. C., Goodman, N. D. 2012; 336 (6084): 998-998

  Abstract

  One of the most astonishing features of human language is its capacity to convey information efficiently in context. Many theories provide informal accounts of communicative inference, yet there have been few successes in making precise, quantitative predictions about pragmatic reasoning. We examined judgments about simple referential communication games, modeling behavior in these games by assuming that speakers attempt to be informative and that listeners use Bayesian inference to recover speakers' intended referents. Our model provides a close, parameter-free fit to human judgments, suggesting that the use of information-theoretic tools to predict pragmatic reasoning may lead to more effective formal models of communication.

  View details for DOI 10.1126/science.1218633

  View details for Web of Science ID 000304406800035

  View details for PubMedID 22628647

• Bootstrapping in a language of thought: A formal model of numerical concept learning COGNITION Piantadosi, S. T., Tenenbaum, J. B., Goodman, N. D. 2012; 123 (2): 199-217

  Abstract

  In acquiring number words, children exhibit a qualitative leap in which they transition from understanding a few number words, to possessing a rich system of interrelated numerical concepts. We present a computational framework for understanding this inductive leap as the consequence of statistical inference over a sufficiently powerful representational system. We provide an implemented model that is powerful enough to learn number word meanings and other related conceptual systems from naturalistic data. The model shows that bootstrapping can be made computationally and philosophically well-founded as a theory of number learning. Our approach demonstrates how learners may combine core cognitive operations to build sophisticated representations during the course of development, and how this process explains observed developmental patterns in number word learning.

  View details for DOI 10.1016/j.cognition.2011.11.005

  View details for Web of Science ID 000303178000001

  View details for PubMedID 22284806

• Comparing pluralities COGNITION Scontras, G., Graff, P., Goodman, N. D. 2012; 123 (1): 190-197

  Abstract

  What does it mean to compare sets of objects along a scale, for example by saying "the men are taller than the women"? We explore comparison of pluralities in two experiments, eliciting comparison judgments while varying the properties of the members of each set. We find that a plurality is judged as "bigger" when the mean size of its members is larger than the mean size of the competing plurality. These results are incompatible with previous accounts, in which plural comparison is inferred from many instances of singular comparison between the members of the sets (Matushansky & Ruys, 2006). Our results suggest the need for a type of predication that ascribes properties to plural entities, not just individuals, based on aggregate statistics of their members. More generally, these results support the idea that sets and their properties are actively represented as single units.

  View details for DOI 10.1016/j.cognition.2011.12.012

  View details for Web of Science ID 000301474000015

  View details for PubMedID 22245032

• Learning Design Patterns with Bayesian Grammar Induction 25th Annual ACM Symposium on User Interface Software and Technology (UIST) Talton, J. O., Yang, L., Kumar, R., Lim, M., Goodman, N., Mech, R. ASSOC COMPUTING MACHINERY. 2012: 63–73

  View details for Web of Science ID 000324815300008

• Learning from others: The consequences of social context for human learning Perspectives on Psychological Science Shafto, P., Goodman, N. D., Frank, M. 2012
• Did she jump because she was the big sister or because the trampoline was safe? Causal inference and the development of social attribution Child Development Seiver, E., Gopnik, A., Goodman, N. D. 2012
• Context is key to making computers better conversationalists WIRED.uk Goodman, N. D. 2012
• Artificial Intelligence Could Be on Brink of Passing Turing Test WIRED Goodman, N. D. 2012
• How many kinds of reasoning? Inference, probability, and natural language semantics Proceedings of the Thirty-Fourth Annual Conference of the Cognitive Science Society Lassiter, D., Goodman, N. D. 2012
• A dynamic programming algorithm for inference in recursive probabilistic programs Second Statistical Relational AI workshop at UAI 2012 Stuhlmuller, A., Goodman, N. D. 2012
• Quantifying pragmatic inference in language games Science Frank, M. C., Goodman, N. D. 2012
• Noisy Newtons: Unifying process and dependency accounts of causal attribution Proceedings of the Thirty-Fourth Annual Conference of the Cognitive Science Society Gerstenberg, T., Goodman, N. D., Lagnado, D. A., Tenenbaum, J. B. 2012
• Ping Pong in Church: Productive use of concepts in human probabilistic inference Proceedings of the Thirty-Fourth Annual Conference of the Cognitive Science Society Gerstenberg, T., Goodman, N. D. 2012
• That’s what she (could have) said: How alternative utterances affect language use Proceedings of the Thirty-Fourth Annual Conference of the Cognitive Science Society Bergen, L., Goodman, N. D., Levy, R. 2012
• Knowledge and implicature: Modeling language understanding as social cognition Proceedings of the Thirty-Fourth Annual Conference of the Cognitive Science Society Goodman, N. D., Stuhlmüller, A. 2012
• Burn-in, bias, and the rationality of anchoring Advances in Neural Information Processing Systems, 24 Lieder, F., Griffiths, T. L., Goodman, N. D. 2012
• The double-edged sword of pedagogy: Instruction limits spontaneous exploration and discovery COGNITION Bonawitz, E., Shafto, P., Gweon, H., Goodman, N. D., Spelke, E., Schulz, L. 2011; 120 (3): 322-330

  Abstract

  Motivated by computational analyses, we look at how teaching affects exploration and discovery. In Experiment 1, we investigated children's exploratory play after an adult pedagogically demonstrated a function of a toy, after an interrupted pedagogical demonstration, after a naïve adult demonstrated the function, and at baseline. Preschoolers in the pedagogical condition focused almost exclusively on the target function; by contrast, children in the other conditions explored broadly. In Experiment 2, we show that children restrict their exploration both after direct instruction to themselves and after overhearing direct instruction given to another child; they do not show this constraint after observing direct instruction given to an adult or after observing a non-pedagogical intentional action. We discuss these findings as the result of rational inductive biases. In pedagogical contexts, a teacher's failure to provide evidence for additional functions provides evidence for their absence; such contexts generalize from child to child (because children are likely to have comparable states of knowledge) but not from adult to child. Thus, pedagogy promotes efficient learning but at a cost: children are less likely to perform potentially irrelevant actions but also less likely to discover novel information.

  View details for DOI 10.1016/j.cognition.2010.10.001

  View details for Web of Science ID 000293312400003

  View details for PubMedID 21216395

• Where science starts: Spontaneous experiments in preschoolers' exploratory play COGNITION Cook, C., Goodman, N. D., Schulz, L. E. 2011; 120 (3): 341-349

  Abstract

  Probabilistic models of expected information gain require integrating prior knowledge about causal hypotheses with knowledge about possible actions that might generate data relevant to those hypotheses. Here we looked at whether preschoolers (mean: 54 months) recognize "action possibilities" (affordances) in the environment that allow them to isolate variables when there is information to be gained. By manipulating the physical properties of the stimuli, we were able to affect the degree to which candidate variables could be isolated; by manipulating the base rate of candidate causes, we were able to affect the potential for information gain. Children's exploratory play was sensitive to both manipulations: given unambiguous evidence children played indiscriminately and rarely tried to isolate candidate causes; given ambiguous evidence, children both selected (Experiment 1) and designed (Experiment 2) informative interventions.

  View details for DOI 10.1016/j.cognition.2011.03.003

  View details for Web of Science ID 000293312400005

  View details for PubMedID 21561605

• The imaginary fundamentalists: The unshocking truth about Bayesian cognitive science BEHAVIORAL AND BRAIN SCIENCES Chater, N., Goodman, N., Griffiths, T. L., Kemp, C., Oaksford, M., Tenenbaum, J. B. 2011; 34 (4): 194-?

  View details for DOI 10.1017/S0140525X11000239

  View details for Web of Science ID 000294347600008

• How to Grow a Mind: Statistics, Structure, and Abstraction SCIENCE Tenenbaum, J. B., Kemp, C., Griffiths, T. L., Goodman, N. D. 2011; 331 (6022): 1279-1285

  Abstract

  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 flexibly structured representations can address some of the deepest questions about the nature and origins of human thought: How does abstract knowledge guide learning and reasoning from sparse data? What forms does our knowledge take, across different domains and tasks? And how is that abstract knowledge itself acquired?

  View details for DOI 10.1126/science.1192788

  View details for Web of Science ID 000288215200035

  View details for PubMedID 21393536

• Learning and the Language of Thought IEEE International Conference on Computer Vision (ICCV) Goodman, N. D. IEEE. 2011

  View details for Web of Science ID 000300056700098

• More Than Child’s Play: Ability to Think Scientifically Declines as Kids Grow Up Scientific American Goodman, N. D. 2011
• I, algorithm New Scientist Goodman, N. D. 2011
• Productivity and reuse in language Proceedings of the Thirty-Third Annual Conference of the Cognitive Science Society O’Donnell, T. J., Snedeker, J., Tenenbaum, J. B., Goodman, N. D. 2011
• Nonstandard Interpretations of Probabilistic Programs for Efficient Inference Advances in Neural Information Processing Systems, 23 Wingate, D., Goodman, N. D., Stuhlmüller, A., Siskind, J. 2011
• Bayesian Policy Search with Policy Priors IJCAI 2011 Wingate, D., Kaelbling, L., Roy, D., Goodman, N. D., Tenenbaum, J. B. 2011
• Ad-hoc scalar implicature in adults and children Proceedings of the Thirty-Third Annual Conference of the Cognitive Science Society Stiller, A., Goodman, N. D., Frank, M. C. 2011
• Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation Artificial Intelligence and Statistics 2011 Wingate, D., Stuhlmüller, A., Goodman, N. D. 2011
• Learning a Theory of Causality PSYCHOLOGICAL REVIEW Goodman, N. D., Ullman, T. D., Tenenbaum, J. B. 2011; 118 (1): 110-119

  Abstract

  The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be learned from co-occurrence of events. We begin by phrasing the causal Bayes nets theory of causality and a range of alternatives in a logical language for relational theories. This allows us to explore simultaneous inductive learning of an abstract theory of causality and a causal model for each of several causal systems. We find that the correct theory of causality can be learned relatively quickly, often becoming available before specific causal theories have been learned--an effect we term the blessing of abstraction. We then explore the effect of providing a variety of auxiliary evidence and find that a collection of simple perceptual input analyzers can help to bootstrap abstract knowledge. Together, these results suggest that the most efficient route to causal knowledge may be to build in not an abstract notion of causality but a powerful inductive learning mechanism and a variety of perceptual supports. While these results are purely computational, they have implications for cognitive development, which we explore in the conclusion.

  View details for DOI 10.1037/a0021336

  View details for Web of Science ID 000286560500007

  View details for PubMedID 21244189

• Optimal habits can develop spontaneously through sensitivity to local cost PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Desrochers, T. M., Jin, D. Z., Goodman, N. D., Graybiel, A. M. 2010; 107 (47): 20512-20517

  Abstract

  Habits and rituals are expressed universally across animal species. These behaviors are advantageous in allowing sequential behaviors to be performed without cognitive overload, and appear to rely on neural circuits that are relatively benign but vulnerable to takeover by extreme contexts, neuropsychiatric sequelae, and processes leading to addiction. Reinforcement learning (RL) is thought to underlie the formation of optimal habits. However, this theoretic formulation has principally been tested experimentally in simple stimulus-response tasks with relatively few available responses. We asked whether RL could also account for the emergence of habitual action sequences in realistically complex situations in which no repetitive stimulus-response links were present and in which many response options were present. We exposed naïve macaque monkeys to such experimental conditions by introducing a unique free saccade scan task. Despite the highly uncertain conditions and no instruction, the monkeys developed a succession of stereotypical, self-chosen saccade sequence patterns. Remarkably, these continued to morph for months, long after session-averaged reward and cost (eye movement distance) reached asymptote. Prima facie, these continued behavioral changes appeared to challenge RL. However, trial-by-trial analysis showed that pattern changes on adjacent trials were predicted by lowered cost, and RL simulations that reduced the cost reproduced the monkeys' behavior. Ultimately, the patterns settled into stereotypical saccade sequences that minimized the cost of obtaining the reward on average. These findings suggest that brain mechanisms underlying the emergence of habits, and perhaps unwanted repetitive behaviors in clinical disorders, could follow RL algorithms capturing extremely local explore/exploit tradeoffs.

  View details for DOI 10.1073/pnas.1013470107

  View details for Web of Science ID 000284529000067

  View details for PubMedID 20974967

• Learning to Learn Causal Models COGNITIVE SCIENCE Kemp, C., Goodman, N. D., Tenenbaum, J. B. 2010; 34 (7): 1185-1243

  Abstract

  Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning.

  View details for DOI 10.1111/j.1551-6709.2010.01128.x

  View details for Web of Science ID 000281554700004

  View details for PubMedID 21564248

• The Structure and Dynamics of Scientific Theories: A Hierarchical Bayesian Perspective PHILOSOPHY OF SCIENCE Henderson, L., Goodman, N. D., Tenenbaum, J. B., Woodward, J. F. 2010; 77 (2): 172-200

  View details for Web of Science ID 000276017100002

• Beyond Boolean logic: exploring representation languages for learning complex concepts Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society Piantadosi, S. T., Tenenbaum, J. B., Goodman, N. D. 2010
• Learning Structured Generative Concepts Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society Stuhlmüller, A., Tenenbaum, J. B., Goodman, N. D. 2010
• Help or hinder: Bayesian models of social goal inference Advances in Neural Information Processing Systems Ullman, T., Baker, C. L., Macindoe, O., Evans, O., Goodman, N. D., Tenenbaum, J. B. 2010
• Theory learning as stochastic search Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society Ullman, T. D., Goodman, N. D., Tenenbaum, J. B. 2010
• Prior expectations in pedagogical situations Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society Shafto, P., Goodman, N. D., Gerstle, B., Ladusaw, F. 2010
• Using Speakers' Referential Intentions to Model Early Cross-Situational Word Learning PSYCHOLOGICAL SCIENCE Frank, M. C., Goodman, N. D., Tenenbaum, J. B. 2009; 20 (5): 578-585

  Abstract

  Word learning is a "chicken and egg" problem. If a child could understand speakers' utterances, it would be easy to learn the meanings of individual words, and once a child knows what many words mean, it is easy to infer speakers' intended meanings. To the beginning learner, however, both individual word meanings and speakers' intentions are unknown. We describe a computational model of word learning that solves these two inference problems in parallel, rather than relying exclusively on either the inferred meanings of utterances or cross-situational word-meaning associations. We tested our model using annotated corpus data and found that it inferred pairings between words and object concepts with higher precision than comparison models. Moreover, as the result of making probabilistic inferences about speakers' intentions, our model explains a variety of behavioral phenomena described in the word-learning literature. These phenomena include mutual exclusivity, one-trial learning, cross-situational learning, the role of words in object individuation, and the use of inferred intentions to disambiguate reference.

  View details for DOI 10.1111/j.1467-9280.2009.02335.x

  View details for Web of Science ID 000265774700011

  View details for PubMedID 19389131

• Informative communication in word production and word learning Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society Frank, M. C., Goodman, N. D., Lai, P., Tenenbaum, J. B. 2009
• Continuity of discourse provides information for word learning Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society Frank, M. C., Goodman, N. D., Tenenbaum, J. B., Fernald, A. 2009
• One and done: Globally optimal behavior from locally suboptimal decisions Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society Vul, E., Goodman, N. D., Griffiths, T. L., Tenenbaum, J. B. 2009
• How tall Is tall? Compositionality, statistics, and gradable adjectives Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society Schmidt, L., Goodman, N. D., Barner, D., Tenenbaum, J. B. 2009
• The infinite latent events model Uncertainty in Artificial Intelligence 2009 Wingate, D., Goodman, N. D., Roy, D. M., Tenenbaum, J. B. 2009
• Cause and intent: Social reasoning in causal learning Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society Goodman, N. D., Baker, C. L., Tenenbaum, J. B. 2009
• Learning a theory of causality Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society Goodman, N. D., Ullman, T., Tenenbaum, J. B. 2009
• Going beyond the evidence: Abstract laws and preschoolers' responses to anomalous data COGNITION Schulz, L. E., Goodman, N. D., Tenenbaum, J. B., Jenkins, A. C. 2008; 109 (2): 211-223

  Abstract

  Given minimal evidence about novel objects, children might learn only relationships among the specific entities, or they might make a more abstract inference, positing classes of entities and the relations that hold among those classes. Here we show that preschoolers (mean: 57 months) can use sparse data about perceptually unique objects to infer abstract physical causal laws. These newly inferred abstract laws were robust to potentially anomalous evidence; in the face of apparent counter-evidence, children (correctly) posited the existence of an unobserved object rather than revise the abstract laws. This suggests that children's ability to learn robust, abstract principles does not depend on extensive prior experience but can occur rapidly, on-line, and in tandem with inferences about specific relations.

  View details for DOI 10.1016/j.cognition.2008.07.017

  View details for Web of Science ID 000261756000003

  View details for PubMedID 18930186

• Compositionality in rational analysis: Grammar-based induction for concept learning The probabilistic mind: Prospects for Bayesian cognitive science Goodman, N. D., Tenenbaum, J. B., Griffiths, T. L., Feldman, J. edited by Oaksford, M., Chater, N. 2008
• Teaching games: statistical sampling assumptions for learning in pedagogical situations Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society Shafto, P., Goodman, N. D. 2008
• Theory-based social goal induction Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society Baker, C. L., Goodman, N. D., Tenenbaum, J. B. 2008
• Learning relational theories Advances in Neural Information Processing Systems Kemp, C., Goodman, N. D., Tenenbaum, J. B. 2008
• A Bayesian framework for cross-situational word-learning Advances in Neural Information Processing Systems, 20 Frank, M. C., Goodman, N. D., Tenenbaum, J. B. 2008
• Theory acquisition and the language of thought Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society Kemp, C., Goodman, N. D., Tenenbaum, J. B. 2008
• Church: a language for generative models Uncertainty in Artificial Intelligence 2008 Goodman, N. D., Mansighka, V. K., Roy, D., Bonawitz, K., Tenenbaum, J. B. 2008
• Modeling semantic cognition as logical dimensionality reduction Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society Katz, Y., Goodman, N. D., Kersting, K., Kemp, C., Tenenbaum, J. B. 2008
• Bayesian model of compositional semantics acquisition Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society Piantadosi, S. T., Goodman, N. D., Ellis, B. A., Tenenbaum, J. B. 2008
• Structured correlation from the causal background Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society Mayrhofer, R., Goodman, N. D., Waldmann, M., Tenenbaum, J. B. 2008
• A rational analysis of rule-based concept learning 29th Annnual Conference of the Cognitive-Science-Society Goodman, N. D., Tenenbaum, J. B., Feldman, J., Griffiths, T. L. PSYCHOLOGY PRESS. 2008: 108–54

  Abstract

  This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space-a concept language of logical rules. This article compares the model predictions to human generalization judgments in several well-known category learning experiments, and finds good agreement for both average and individual participant generalizations. This article further investigates judgments for a broad set of 7-feature concepts-a more natural setting in several ways-and again finds that the model explains human performance.

  View details for DOI 10.1080/03640210701802071

  View details for Web of Science ID 000254296700005

  View details for PubMedID 21635333

• Learning causal schemata Proceedings of the Twenty- Ninth Annual Conference of the Cognitive Science Society Kemp, C., Goodman, N. D., Tenenbaum, J. B. 2007
• Frameworks in science: a Bayesian approach LSE-Pitt Conference: Confirmation, Induction and Science Henderson, L., Goodman, N. D., Tenenbaum, J. B., Woodward, J. 2007
• A rational analysis of rule-based concept learning Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society Goodman, N. D., Griffiths, T. L., Feldman, J., Tenenbaum, J. B. 2007
• Learning grounded causal models Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society Goodman, N. D., Mansinghka, V. K., Tenenbaum, J. B. 2007
• Intuitive theories of mind: A rational approach to false belief Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society Goodman, N. D., Baker, C. L., Bonawitz, E. B., Mansinghka, V. K., Gopnik, A., Wellman, H., Schulz, L., Tenenbaum, J. B. 2006
• On the stable equivalence of open books in three-manifolds GEOMETRY & TOPOLOGY Giroux, E., Goodman, N. 2006; 10: 97-114

  View details for DOI 10.2140/gt.2006.10.97

  View details for Web of Science ID 000236064000004

• Overtwisted open books from sobering arcs ALGEBRAIC AND GEOMETRIC TOPOLOGY Goodman, N. 2005; 5: 1173-1195

  View details for Web of Science ID 000241347800046