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  1. Article ; Online: Counterfactuals and the logic of causal selection.

    Quillien, Tadeg / Lucas, Christopher G

    Psychological review

    2023  

    Abstract: Everything that happens has a multitude of causes, but people make causal judgments effortlessly. How do people select one particular cause (e.g., the lightning bolt that set the forest ablaze) out of the set of factors that contributed to the event (the ...

    Abstract Everything that happens has a multitude of causes, but people make causal judgments effortlessly. How do people select one particular cause (e.g., the lightning bolt that set the forest ablaze) out of the set of factors that contributed to the event (the oxygen in the air, the dry weather … )? Cognitive scientists have suggested that people make causal judgments about an event by simulating alternative ways things could have happened. We argue that this counterfactual theory explains many features of human causal intuitions, given two simple assumptions. First, people tend to imagine counterfactual possibilities that are both a priori likely and similar to what actually happened. Second, people judge that a factor C caused effect E if C and E are highly correlated across these counterfactual possibilities. In a reanalysis of existing empirical data, and a set of new experiments, we find that this theory uniquely accounts for people's causal intuitions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
    Language English
    Publishing date 2023-06-08
    Publishing country United States
    Document type Journal Article
    ZDB-ID 209907-x
    ISSN 1939-1471 ; 0033-295X
    ISSN (online) 1939-1471
    ISSN 0033-295X
    DOI 10.1037/rev0000428
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  2. Book ; Online: Non-Compositionality in Sentiment

    Dankers, Verna / Lucas, Christopher G.

    New Data and Analyses

    2023  

    Abstract: When natural language phrases are combined, their meaning is often more than the sum of their parts. In the context of NLP tasks such as sentiment analysis, where the meaning of a phrase is its sentiment, that still applies. Many NLP studies on sentiment ...

    Abstract When natural language phrases are combined, their meaning is often more than the sum of their parts. In the context of NLP tasks such as sentiment analysis, where the meaning of a phrase is its sentiment, that still applies. Many NLP studies on sentiment analysis, however, focus on the fact that sentiment computations are largely compositional. We, instead, set out to obtain non-compositionality ratings for phrases with respect to their sentiment. Our contributions are as follows: a) a methodology for obtaining those non-compositionality ratings, b) a resource of ratings for 259 phrases -- NonCompSST -- along with an analysis of that resource, and c) an evaluation of computational models for sentiment analysis using this new resource.

    Comment: Published in EMNLP Findings 2023; 13 pages total (5 in the main paper, 3 pages with limitations, acknowledgments and references, 5 pages with appendices)
    Keywords Computer Science - Computation and Language
    Publishing date 2023-10-31
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: A model of conceptual bootstrapping in human cognition.

    Zhao, Bonan / Lucas, Christopher G / Bramley, Neil R

    Nature human behaviour

    2023  Volume 8, Issue 1, Page(s) 125–136

    Abstract: To tackle a hard problem, it is often wise to reuse and recombine existing knowledge. Such an ability to bootstrap enables us to grow rich mental concepts despite limited cognitive resources. Here we present a computational model of conceptual ... ...

    Abstract To tackle a hard problem, it is often wise to reuse and recombine existing knowledge. Such an ability to bootstrap enables us to grow rich mental concepts despite limited cognitive resources. Here we present a computational model of conceptual bootstrapping. This model uses a dynamic conceptual repertoire that can cache and later reuse elements of earlier insights in principled ways, modelling learning as a series of compositional generalizations. This model predicts systematically different learned concepts when the same evidence is processed in different orders, without any extra assumptions about previous beliefs or background knowledge. Across four behavioural experiments (total n = 570), we demonstrate strong curriculum-order and conceptual garden-pathing effects that closely resemble our model predictions and differ from those of alternative accounts. Taken together, this work offers a computational account of how past experiences shape future conceptual discoveries and showcases the importance of curriculum design in human inductive concept inferences.
    MeSH term(s) Humans ; Concept Formation ; Cognition ; Learning ; Curriculum ; Knowledge
    Language English
    Publishing date 2023-10-16
    Publishing country England
    Document type Journal Article
    ISSN 2397-3374
    ISSN (online) 2397-3374
    DOI 10.1038/s41562-023-01719-1
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  4. Article ; Online: Designing optimal behavioral experiments using machine learning.

    Valentin, Simon / Kleinegesse, Steven / Bramley, Neil R / Seriès, Peggy / Gutmann, Michael U / Lucas, Christopher G

    eLife

    2024  Volume 13

    Abstract: Computational models are powerful tools for understanding human cognition and behavior. They let us express our theories clearly and precisely and offer predictions that can be subtle and often counter-intuitive. However, this same richness and ability ... ...

    Abstract Computational models are powerful tools for understanding human cognition and behavior. They let us express our theories clearly and precisely and offer predictions that can be subtle and often counter-intuitive. However, this same richness and ability to surprise means our scientific intuitions and traditional tools are ill-suited to designing experiments to test and compare these models. To avoid these pitfalls and realize the full potential of computational modeling, we require tools to design experiments that provide clear answers about what models explain human behavior and the auxiliary assumptions those models must make. Bayesian optimal experimental design (BOED) formalizes the search for optimal experimental designs by identifying experiments that are expected to yield informative data. In this work, we provide a tutorial on leveraging recent advances in BOED and machine learning to find optimal experiments for any kind of model that we can simulate data from, and show how by-products of this procedure allow for quick and straightforward evaluation of models and their parameters against real experimental data. As a case study, we consider theories of how people balance exploration and exploitation in multi-armed bandit decision-making tasks. We validate the presented approach using simulations and a real-world experiment. As compared to experimental designs commonly used in the literature, we show that our optimal designs more efficiently determine which of a set of models best account for individual human behavior, and more efficiently characterize behavior given a preferred model. At the same time, formalizing a scientific question such that it can be adequately addressed with BOED can be challenging and we discuss several potential caveats and pitfalls that practitioners should be aware of. We provide code to replicate all analyses as well as tutorial notebooks and pointers to adapt the methodology to different experimental settings.
    MeSH term(s) Humans ; Bayes Theorem ; Machine Learning ; Cognition ; Awareness ; Computer Simulation
    Language English
    Publishing date 2024-01-23
    Publishing country England
    Document type Journal Article
    ZDB-ID 2687154-3
    ISSN 2050-084X ; 2050-084X
    ISSN (online) 2050-084X
    ISSN 2050-084X
    DOI 10.7554/eLife.86224
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  5. Book ; Online: Actively learning to learn causal relationships

    Jiang, Chentian / Lucas, Christopher G.

    2022  

    Abstract: How do people actively learn to learn? That is, how and when do people choose actions that facilitate long-term learning and choosing future actions that are more informative? We explore these questions in the domain of active causal learning. We propose ...

    Abstract How do people actively learn to learn? That is, how and when do people choose actions that facilitate long-term learning and choosing future actions that are more informative? We explore these questions in the domain of active causal learning. We propose a hierarchical Bayesian model that goes beyond past models by predicting that people pursue information not only about the causal relationship at hand but also about causal overhypotheses$\unicode{x2014}$abstract beliefs about causal relationships that span multiple situations and constrain how we learn the specifics in each situation. In two active "blicket detector" experiments with 14 between-subjects manipulations, our model was supported by both qualitative trends in participant behavior and an individual-differences-based model comparison. Our results suggest when there are abstract similarities across active causal learning problems, people readily learn and transfer overhypotheses about these similarities. Moreover, people exploit these overhypotheses to facilitate long-term active learning.
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
    Subject code 501
    Publishing date 2022-06-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Local Search and the Evolution of World Models.

    Bramley, Neil R / Zhao, Bonan / Quillien, Tadeg / Lucas, Christopher G

    Topics in cognitive science

    2023  

    Abstract: An open question regarding how people develop their models of the world is how new candidates are generated for consideration out of infinitely many possibilities. We discuss the role that evolutionary mechanisms play in this process. Specifically, we ... ...

    Abstract An open question regarding how people develop their models of the world is how new candidates are generated for consideration out of infinitely many possibilities. We discuss the role that evolutionary mechanisms play in this process. Specifically, we argue that when it comes to developing a global world model, innovation is necessarily incremental, involving the generation and selection among random local mutations and recombinations of (parts of) one's current model. We argue that, by narrowing and guiding exploration, this feature of cognitive search is what allows human learners to discover better theories, without ever grappling directly with the problem of finding a "global optimum," or best possible world model. We suggest this aspect of cognitive processing works analogously to how blind variation and selection mechanisms drive biological evolution. We propose algorithms developed for program synthesis provide candidate mechanisms for how human minds might achieve this. We discuss objections and implications of this perspective, finally suggesting that a better process-level understanding of how humans incrementally explore compositional theory spaces can shed light on how we think, and provide explanatory traction on fundamental cognitive biases, including anchoring, probability matching, and confirmation bias.
    Language English
    Publishing date 2023-10-18
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2482883-X
    ISSN 1756-8765 ; 1756-8757
    ISSN (online) 1756-8765
    ISSN 1756-8757
    DOI 10.1111/tops.12703
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  7. Article ; Online: Naïve information aggregation in human social learning.

    Fränken, J-Philipp / Valentin, Simon / Lucas, Christopher G / Bramley, Neil R

    Cognition

    2023  Volume 242, Page(s) 105633

    Abstract: To glean accurate information from social networks, people should distinguish evidence from hearsay. For example, when testimony depends on others' beliefs as much as on first-hand information, there is a danger of evidence becoming inflated or ignored ... ...

    Abstract To glean accurate information from social networks, people should distinguish evidence from hearsay. For example, when testimony depends on others' beliefs as much as on first-hand information, there is a danger of evidence becoming inflated or ignored as it passes from person to person. We compare human inferences with an idealized rational account that anticipates and adjusts for these dependencies by evaluating peers' communications with respect to the underlying communication pathways. We report on three multi-player experiments examining the dynamics of both mixed human-artificial and all-human social networks. Our analyses suggest that most human inferences are best described by a naïve learning account that is insensitive to known or inferred dependencies between network peers. Consequently, we find that simulated social learners that assume their peers behave rationally make systematic judgment errors when reasoning on the basis of actual human communications. We suggest human groups learn collectively through naïve signaling and aggregation that is computationally efficient and surprisingly robust. Overall, our results challenge the idea that everyday social inference is well captured by idealized rational accounts and provide insight into the conditions under which collective wisdom can emerge from social interactions.
    MeSH term(s) Humans ; Social Learning ; Learning ; Judgment ; Communication
    Language English
    Publishing date 2023-10-26
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1499940-7
    ISSN 1873-7838 ; 0010-0277
    ISSN (online) 1873-7838
    ISSN 0010-0277
    DOI 10.1016/j.cognition.2023.105633
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  8. Book ; Online: Bayesian Optimisation Against Climate Change

    Hellan, Sigrid Passano / Lucas, Christopher G. / Goddard, Nigel H.

    Applications and Benchmarks

    2023  

    Abstract: Bayesian optimisation is a powerful method for optimising black-box functions, popular in settings where the true function is expensive to evaluate and no gradient information is available. Bayesian optimisation can improve responses to many optimisation ...

    Abstract Bayesian optimisation is a powerful method for optimising black-box functions, popular in settings where the true function is expensive to evaluate and no gradient information is available. Bayesian optimisation can improve responses to many optimisation problems within climate change for which simulator models are unavailable or expensive to sample from. While there have been several feasibility demonstrations of Bayesian optimisation in climate-related applications, there has been no unifying review of applications and benchmarks. We provide such a review here, to encourage the use of Bayesian optimisation in important and well-suited application domains. We identify four main application domains: material discovery, wind farm layout, optimal renewable control and environmental monitoring. For each domain we identify a public benchmark or data set that is easy to use and evaluate systems against, while being representative of real-world problems. Due to the lack of a suitable benchmark for environmental monitoring, we propose LAQN-BO, based on air pollution data. Our contributions are: a) identifying a representative range of benchmarks, providing example code where necessary; b) introducing a new benchmark, LAQN-BO; and c) promoting a wider use of climate change applications among Bayesian optimisation practitioners.
    Keywords Computer Science - Machine Learning
    Subject code 333
    Publishing date 2023-06-07
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Selective imitation on the basis of reward function similarity

    Taylor-Davies, Max / Droop, Stephanie / Lucas, Christopher G.

    2023  

    Abstract: Imitation is a key component of human social behavior, and is widely used by both children and adults as a way to navigate uncertain or unfamiliar situations. But in an environment populated by multiple heterogeneous agents pursuing different goals or ... ...

    Abstract Imitation is a key component of human social behavior, and is widely used by both children and adults as a way to navigate uncertain or unfamiliar situations. But in an environment populated by multiple heterogeneous agents pursuing different goals or objectives, indiscriminate imitation is unlikely to be an effective strategy -- the imitator must instead determine who is most useful to copy. There are likely many factors that play into these judgements, depending on context and availability of information. Here we investigate the hypothesis that these decisions involve inferences about other agents' reward functions. We suggest that people preferentially imitate the behavior of others they deem to have similar reward functions to their own. We further argue that these inferences can be made on the basis of very sparse or indirect data, by leveraging an inductive bias toward positing the existence of different \textit{groups} or \textit{types} of people with similar reward functions, allowing learners to select imitation targets without direct evidence of alignment.

    Comment: 7 pages, 3 figures, to appear in CogSci 2023
    Keywords Quantitative Biology - Neurons and Cognition ; Computer Science - Machine Learning
    Subject code 120
    Publishing date 2023-05-12
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: DreamDecompiler

    Palmarini, Alessandro B. / Lucas, Christopher G. / Siddharth, N.

    Bayesian Program Learning by Decompiling Amortised Knowledge

    2023  

    Abstract: Solving program induction problems requires searching through an enormous space of possibilities. DreamCoder is an inductive program synthesis system that, whilst solving problems, learns to simplify search in an iterative wake-sleep procedure. The cost ... ...

    Abstract Solving program induction problems requires searching through an enormous space of possibilities. DreamCoder is an inductive program synthesis system that, whilst solving problems, learns to simplify search in an iterative wake-sleep procedure. The cost of search is amortised by training a neural search policy, reducing search breadth and effectively "compiling" useful information to compose program solutions across tasks. Additionally, a library of program components is learnt to express discovered solutions in fewer components, reducing search depth. In DreamCoder, the neural search policy has only an indirect effect on the library learnt through the program solutions it helps discover. We present an approach for library learning that directly leverages the neural search policy, effectively "decompiling" its amortised knowledge to extract relevant program components. This provides stronger amortised inference: the amortised knowledge learnt to reduce search breadth is now also used to reduce search depth. We integrate our approach with DreamCoder and demonstrate faster domain proficiency with improved generalisation on a range of domains, particularly when fewer example solutions are available.
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Machine Learning ; Computer Science - Software Engineering
    Subject code 006
    Publishing date 2023-06-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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