Thomas L Griffiths
Dados Biográficos
| ID | 3862162 |
|---|---|
| NOME | Thomas L Griffiths |
| PRENOMES | Thomas L |
| SOBRENOME | Griffiths |
| ASSINATURA | GRIFFITHS T L |
| AFILIAÇÕES | Princeton University |
| ORCID | 0000-0002-5138-7255 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 53 |
| TOTAL DE CITAÇÕES | 55 |
| TOTAL COMO AUTOR | 53 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2001 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2026 |
| ÍNDICE H | 5 |
Considering Psychological Mechanisms Can Change the Interpretation of Bayesian Models
A significant challenge for Bayesian models of cognition is understanding how the abstract assumptions behind these models connect to psychological mechanisms. We examine the consequences of considering one of the psychological processes involved in inductive inference: generating hypotheses. We analyze the predictions of a simple model that separates the processes of generating hypotheses and evaluating those hypotheses. This analysis shows that…
A Resource-Rational Account of Human Eye Movements During Immersive Visual Search
The nature of eye movements during visual search has been widely studied in cognitive science. Virtual reality (VR) paradigms are an opportunity to test whether computational models of search can predict naturalistic search behavior. However, existing ideal observer models are constrained by strong assumptions about the structure of the world, rendering them impractical for modeling the complexity of environments which can be studied in VR. To ad…
Embodied LLM Agents Learn to Cooperate in Organized Teams
Large language models (LLMs) have emerged as integral tools for reasoning, planning, and decision-making, drawing upon their extensive world knowledge and proficiency in language-related tasks. LLMs thus hold tremendous potential for natural language interaction within multiagent systems to foster cooperation. However, LLM agents tend to over-report and comply with any instruction, which may result in information redundancy and confusion in multi…
A folk taxonomy of magic
Magic tricks provide a uniquely powerful window onto the boundaries of what people consider possible, and the ways in which those boundaries can be challenged. Although there exists an effectively infinite variety of magic tricks, relatively little is known about how different magical effects relate to one another at a psychological level. Previous attempts to classify magic have largely relied on the judgments of a small number of expert magicia…
Aha! moments correspond to metacognitive prediction errors
Psychologists have long been fascinated with understanding the nature of Aha! moments, moments when we transition from not knowing to suddenly realizing the solution to a problem. In this work, we present a theoretical framework that explains why we experience Aha! moments. Our theory posits that during problem-solving, in addition to solving the problem, people also maintain a metacognitive model of their ability to solve the problem as well as …
Considering What We Know and What We Don’t Know
When making decisions, we often have more information about some options than others. Previous work has shown that people are more likely to choose options that they look at more and those that they are more confident in. But should one always prefer options one knows more about? Intuition suggests not. Rather, how additional information impacts our preferences should depend critically on how valuable we expect the options to be. Here, we formali…
People make suboptimal decisions about existential risks
Allocating resources to maximize the probability that humanity survives a set of existential risks has a different structure from many decision problems, as the objective is the product of the probabilities of desired outcomes rather than the sum. We derive the optimal solution to this problem and use this solution to evaluate the choices that people make when presented with decisions that have this multiplicative structure. Our participants (tot…
People evaluate idle collaborators based on their impact on task efficiency
Humans collaborate to improve productivity, but when is it acceptable for a collaborator to remain idle? Theories from distributed computer systems suggest that, depending on the task structure, division of labor leads to diminishing returns in efficiency as group size increases. We examine whether people are aware of these limitations to collaboration, and how considerations of task efficiency may affect the perceived acceptability of idleness, …
Exploring the hierarchical structure of human plans via program generation
Characterizing the Large‐Scale Structure of Multimodal Semantic Networks
Humans organize semantic knowledge into complex networks that encode relations between concepts. The structure of those networks has broad implications for human cognitive processes, and for theories of semantic development. Evidence from large lexical networks such as those derived from word associations suggest that semantic networks are characterized by high sparsity and clustering while maintaining short average paths between concepts, a phen…
Time Spent Thinking in Online Chess Reflects the Value of Computation
Human planning tends to be efficient, focusing on a relatively small number of options when considering future paths. Recent proposals have suggested that this efficiency reflects intelligent deployment of the limited resources available for planning. A prediction of this and related proposals is that when individuals spend time thinking should depend on the benefits and costs of additional computation. We tested this hypothesis by measuring how …
Teaching Recombinable Motifs Through Simple Examples
A hallmark of effective teaching is that it grants learners not just a collection of facts about the world, but also a toolkit of abstractions that can be applied to solve new problems. How do humans teach abstractions from examples? Here, we applied Bayesian models of pedagogy to a necklace‐building task where teachers create necklaces to teach a learner “motifs” that can be flexibly recombined to create new necklaces. In Experiment 1 ( N = 151)…
Capturing the complexity of human strategic decision-making with machine learning
Binary climate data visuals amplify perceived impact of climate change
Predicting human decisions with behavioural theories and machine learning
Reconciling categorization and memory via environmental statistics
Learning From Aggregated Opinion
The capacity to leverage information from others’ opinions is a hallmark of human cognition. Consequently, past research has investigated how we learn from others’ testimony. Yet a distinct form of social information— aggregated opinion—increasingly guides our judgments and decisions. We investigated how people learn from such information by conducting three experiments with participants recruited online within the United States ( N = 886) compar…
AI-generated visuals of car-free US cities help improve support for sustainable policies
Building machines that learn and think with people
Using games to understand the mind
Iterated learning reveals stereotypes of facial trustworthiness that propagate in the absence of evidence
Resampling reduces bias amplification in experimental social networks
Machine culture
A Pragmatic Account of the Weak Evidence Effect
Language is not only used to transmit neutral information; we often seek to persuade by arguing in favor of a particular view. Persuasion raises a number of challenges for classical accounts of belief updating, as information cannot be taken at face value. How should listeners account for a speaker’s “hidden agenda” when incorporating new information? Here, we extend recent probabilistic models of recursive social reasoning to allow for persuasiv…
Memory transmission in small groups and large networks
Can being scared cause tummy aches? Naive theories, ambiguous evidence, and preschoolers' causal inferences
Causal learning requires integrating constraints provided by domain-specific theories with domain-general statistical learning. In order to investigate the interaction between these factors, the authors presented preschoolers with stories pitting their existing theories against statistical evidence. Each child heard 2 stories in which 2 candidate causes co-occurred with an effect. Evidence was presented in the form: AB?E; CA?E; AD?E; and so forth…
How the Bayesians got their beliefs (and what those beliefs actually are)
Bowers and Davis (2012) criticize Bayesian modelers for telling "just so" stories about cognition and neuroscience. Their criticisms are weakened by not giving an accurate characterization of the motivation behind Bayesian modeling or the ways in which Bayesian models are used and by not evaluating this theoretical framework against specific alternatives. We address these points by clarifying our beliefs about the goals and status of Bayesian mod…
Machine culture
Rational use of cognitive resources in human planning
Children’s causal inferences from conflicting testimony and observations
Preschoolers use both direct observation of statistical data and informant testimony to learn causal relationships. Can children integrate information from these sources, especially when source reliability is uncertain? We investigate how children handle a conflict between what they hear and what they see. In Experiment 1, 4-year-olds were introduced to a machine and 2 blocks by a knowledgeable informant who claimed to know which block was better…
Sources of developmental change in the efficiency of information search
Children are active learners: they learn not only from the information people offer and the evidence they happen to observe, but by actively seeking information. However, children's information search strategies are typically less efficient than those of adults. In two studies, we isolate potential sources of developmental change in how children (7- and 10-year-olds) and adults search for information. To do so, we develop a hierarchical version o…
Using games to understand the mind
Building machines that learn and think with people
Resampling reduces bias amplification in experimental social networks
A rational model of the Dunning-Kruger effect supports insensitivity to evidence in low performers
AI-generated visuals of car-free US cities help improve support for sustainable policies
Cognitive prostheses for goal achievement
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…
Finding scientific topics
A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We th…
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 …
Language Evolution by Iterated Learning With Bayesian Agents
Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute a posterior distribution over languages by combining a prior (representing their inductive biases) wi…
Can being scared cause tummy aches? Naive theories, ambiguous evidence, and preschoolers' causal inferences
Causal learning requires integrating constraints provided by domain-specific theories with domain-general statistical learning. In order to investigate the interaction between these factors, the authors presented preschoolers with stories pitting their existing theories against statistical evidence. Each child heard 2 stories in which 2 candidate causes co-occurred with an effect. Evidence was presented in the form: AB?E; CA?E; AD?E; and so forth…
Probabilistic models of cognition
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…
How the Bayesians got their beliefs (and what those beliefs actually are)
Bowers and Davis (2012) criticize Bayesian modelers for telling "just so" stories about cognition and neuroscience. Their criticisms are weakened by not giving an accurate characterization of the motivation behind Bayesian modeling or the ways in which Bayesian models are used and by not evaluating this theoretical framework against specific alternatives. We address these points by clarifying our beliefs about the goals and status of Bayesian mod…
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…
Natural speech reveals the semantic maps that tile human cerebral cortex
Children’s causal inferences from conflicting testimony and observations
Preschoolers use both direct observation of statistical data and informant testimony to learn causal relationships. Can children integrate information from these sources, especially when source reliability is uncertain? We investigate how children handle a conflict between what they hear and what they see. In Experiment 1, 4-year-olds were introduced to a machine and 2 blocks by a knowledgeable informant who claimed to know which block was better…
Sources of developmental change in the efficiency of information search
Children are active learners: they learn not only from the information people offer and the evidence they happen to observe, but by actively seeking information. However, children's information search strategies are typically less efficient than those of adults. In two studies, we isolate potential sources of developmental change in how children (7- and 10-year-olds) and adults search for information. To do so, we develop a hierarchical version o…
Toward a Rational and Mechanistic Account of Mental Effort
In spite of its familiar phenomenology, the mechanistic basis for mental effort remains poorly understood. Although most researchers agree that mental effort is aversive and stems from limitations in our capacity to exercise cognitive control, it is unclear what gives rise to those limitations and why they result in an experience of control as costly. The presence of these control costs also raises further questions regarding how best to allocate…
Cognitive prostheses for goal achievement
Resource-rational analysis
Modeling human cognition is challenging because there are infinitely many mechanisms that can generate any given observation. Some researchers address this by constraining the hypothesis space through assumptions about what the human mind can and cannot do, while others constrain it through principles of rationality and adaptation. Recent work in economics, psychology, neuroscience, and linguistics has begun to integrate both approaches by augmen…
Parallelograms revisited
What the Baldwin Effect affects depends on the nature of plasticity
Integrating explanation and prediction in computational social science
Evaluating models of robust word recognition with serial reproduction
A rational reinterpretation of dual-process theories
Intuitions about magic track the development of intuitive physics
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 …
A rational model of people’s inferences about others’ preferences based on response times
A rational model of the Dunning-Kruger effect supports insensitivity to evidence in low performers
Psychology (39 obras) · Computer Science (37 obras) · Cognition (22 obras) · Artificial Intelligence (21 obras) · Cognitive psychology (20 obras) · Artificial Intelligence (19 obras) · Epistemology (13 obras) · Child and Animal Learning Development (12 obras) · Cognitive science (12 obras) · Social Psychology (12 obras)