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Schulz L, Bhui R. Political reinforcement learners. Trends Cogn Sci 2024; 28:210-222. [PMID: 38195364 DOI: 10.1016/j.tics.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 12/09/2023] [Accepted: 12/11/2023] [Indexed: 01/11/2024]
Abstract
Politics can seem home to the most calculating and yet least rational elements of humanity. How might we systematically characterize this spectrum of political cognition? Here, we propose reinforcement learning (RL) as a unified framework to dissect the political mind. RL describes how agents algorithmically navigate complex and uncertain domains like politics. Through this computational lens, we outline three routes to political differences, stemming from variability in agents' conceptions of a problem, the cognitive operations applied to solve the problem, or the backdrop of information available from the environment. A computational vantage on maladies of the political mind offers enhanced precision in assessing their causes, consequences, and cures.
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Affiliation(s)
- Lion Schulz
- Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max-Planck-Ring 8-14, 72076 Tübingen, Germany.
| | - Rahul Bhui
- Sloan School of Management and Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA
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2
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Okumura R, Taniguchi T, Hagiwara Y, Taniguchi A. Metropolis-Hastings algorithm in joint-attention naming game: experimental semiotics study. Front Artif Intell 2023; 6:1235231. [PMID: 38116389 PMCID: PMC10728479 DOI: 10.3389/frai.2023.1235231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 11/13/2023] [Indexed: 12/21/2023] Open
Abstract
We explore the emergence of symbols during interactions between individuals through an experimental semiotic study. Previous studies have investigated how humans organize symbol systems through communication using artificially designed subjective experiments. In this study, we focused on a joint-attention-naming game (JA-NG) in which participants independently categorized objects and assigned names while assuming their joint attention. In the Metropolis-Hastings naming game (MHNG) theory, listeners accept provided names according to the acceptance probability computed using the Metropolis-Hastings (MH) algorithm. The MHNG theory suggests that symbols emerge as an approximate decentralized Bayesian inference of signs, which is represented as a shared prior variable if the conditions of the MHNG are satisfied. This study examines whether human participants exhibit behavior consistent with the MHNG theory when playing the JA-NG. By comparing human acceptance decisions of a partner's naming with acceptance probabilities computed in the MHNG, we tested whether human behavior is consistent with the MHNG theory. The main contributions of this study are twofold. First, we reject the null hypothesis that humans make acceptance judgments with a constant probability, regardless of the acceptance probability calculated by the MH algorithm. The results of this study show that the model with acceptance probability computed by the MH algorithm predicts human behavior significantly better than the model with a constant probability of acceptance. Second, the MH-based model predicted human acceptance/rejection behavior more accurately than four other models (i.e., Constant, Numerator, Subtraction, Binary). Among the models compared, the model using the MH algorithm, which is the only model with the mathematical support of decentralized Bayesian inference, predicted human behavior most accurately, suggesting that symbol emergence in the JA-NG can be explained by the MHNG.
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Affiliation(s)
- Ryota Okumura
- Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan
| | - Tadahiro Taniguchi
- College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan
| | - Yoshinobu Hagiwara
- Research Organization of Science and Technology, Ritsumeikan University, Kusatsu, Japan
| | - Akira Taniguchi
- College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan
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3
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Palma P, Lee S, Hodgins V, Titone D. From One Bilingual to the Next: An Iterated Learning Study on Language Evolution in Bilingual Societies. Cogn Sci 2023; 47:e13289. [PMID: 37183541 DOI: 10.1111/cogs.13289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 03/03/2023] [Accepted: 03/28/2023] [Indexed: 05/16/2023]
Abstract
Studies of language evolution in the lab have used the iterated learning paradigm to show how linguistic structure emerges through cultural transmission-repeated cycles of learning and use across generations of speakers . However, agent-based simulations suggest that prior biases crucially impact the outcome of cultural transmission. Here, we explored this notion through an iterated learning study of English-French bilingual adults (mostly sequential bilinguals dominant in English). Each participant learned two unstructured artificial languages in a counterbalanced fashion, one resembling English, another resembling French at the phono-orthographic level. The output of each participant was passed down to the next participant, forming diffusion chains of 10 generations per language. We hypothesized that artificial languages would become easier to learn and exhibit greater structure when they were aligned with participants' bilingual experience (i.e., English languages being easier to learn overall), or as a function of practice (i.e., languages learned second being easier to learn overall). Instead, we found that English-like languages became more structured over generations, but only when they were learned first. In contrast, French-like languages became more structured regardless of the order of learning, suggesting the presence of an asymmetric switch cost during artificial language learning. Moreover, individual differences in language usage modulated the amount of structure produced by the participants. Overall, these data suggest that bilingual experience impacts how novel languages are learned at an individual level, which can then scale up to cultural transmission of novel language at a group level.
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Affiliation(s)
- Pauline Palma
- Department of Psychology, McGill University
- Centre for Research on Brain, Language, and Music, McGill University
| | - Sarah Lee
- Department of Psychology, McGill University
- Centre for Research on Brain, Language, and Music, McGill University
| | - Vegas Hodgins
- Department of Psychology, McGill University
- Centre for Research on Brain, Language, and Music, McGill University
| | - Debra Titone
- Department of Psychology, McGill University
- Centre for Research on Brain, Language, and Music, McGill University
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Kvam PD, Alaukik A, Mims CE, Martemyanova A, Baldwin M. Rational inference strategies and the genesis of polarization and extremism. Sci Rep 2022; 12:7344. [PMID: 35513424 PMCID: PMC9072310 DOI: 10.1038/s41598-022-11389-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 04/19/2022] [Indexed: 11/17/2022] Open
Abstract
Polarization and extremism are often viewed as the product of psychological biases or social influences, yet they still occur in the absence of any bias or irrational thinking. We show that individual decision-makers implementing optimal dynamic decision strategies will become polarized, forming extreme views relative to the true information in their environment by virtue of how they sample new information. Extreme evidence enables decision makers to stop considering new information, whereas weak or moderate evidence is unlikely to trigger a decision and is thus under-sampled. We show that this information polarization effect arises empirically across choice domains including politically-charged, affect-rich and affect-poor, and simple perceptual decisions. However, this effect can be disincentivized by asking participants to make a judgment about the difference between two options (estimation) rather than deciding. We experimentally test this intervention by manipulating participants' inference goals (decision vs inference) in an information sampling task. We show that participants in the estimation condition collect more information, hold less extreme views, and are less polarized than those in the decision condition. Estimation goals therefore offer a theoretically-motivated intervention that could be used to alleviate polarization and extremism in situations where people traditionally intend to decide.
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Robin AN, Denton KK, Horna Lowell ES, Dulay T, Ebrahimi S, Johnson GC, Mai D, O’Fallon S, Philson CS, Speck HP, Zhang XP, Nonacs P. Major Evolutionary Transitions and the Roles of Facilitation and Information in Ecosystem Transformations. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.711556] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
A small number of extraordinary “Major Evolutionary Transitions” (METs) have attracted attention among biologists. They comprise novel forms of individuality and information, and are defined in relation to organismal complexity, irrespective of broader ecosystem-level effects. This divorce between evolutionary and ecological consequences qualifies unicellular eukaryotes, for example, as a MET although they alone failed to significantly alter ecosystems. Additionally, this definition excludes revolutionary innovations not fitting into either MET type (e.g., photosynthesis). We recombine evolution with ecology to explore how and why entire ecosystems were newly created or radically altered – as Major System Transitions (MSTs). In doing so, we highlight important morphological adaptations that spread through populations because of their immediate, direct-fitness advantages for individuals. These are Major Competitive Transitions, or MCTs. We argue that often multiple METs and MCTs must be present to produce MSTs. For example, sexually-reproducing, multicellular eukaryotes (METs) with anisogamy and exoskeletons (MCTs) significantly altered ecosystems during the Cambrian. Therefore, we introduce the concepts of Facilitating Evolutionary Transitions (FETs) and Catalysts as key events or agents that are insufficient themselves to set a MST into motion, but are essential parts of synergies that do. We further elucidate the role of information in MSTs as transitions across five levels: (I) Encoded; (II) Epigenomic; (III) Learned; (IV) Inscribed; and (V) Dark Information. The latter is ‘authored’ by abiotic entities rather than biological organisms. Level IV has arguably allowed humans to produce a MST, and V perhaps makes us a FET for a future transition that melds biotic and abiotic life into one entity. Understanding the interactive processes involved in past major transitions will illuminate both current events and the surprising possibilities that abiotically-created information may produce.
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Carcassi F, Steinert‐Threlkeld S, Szymanik J. Monotone Quantifiers Emerge via Iterated Learning. Cogn Sci 2021; 45:e13027. [PMID: 34379338 PMCID: PMC8459284 DOI: 10.1111/cogs.13027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 06/28/2021] [Accepted: 05/07/2021] [Indexed: 01/28/2023]
Abstract
Natural languages exhibit many semantic universals, that is, properties of meaning shared across all languages. In this paper, we develop an explanation of one very prominent semantic universal, the monotonicity universal. While the existing work has shown that quantifiers satisfying the monotonicity universal are easier to learn, we provide a more complete explanation by considering the emergence of quantifiers from the perspective of cultural evolution. In particular, we show that quantifiers satisfy the monotonicity universal evolve reliably in an iterated learning paradigm with neural networks as agents.
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Josserand M, Allassonnière-Tang M, Pellegrino F, Dediu D. Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks. Front Psychol 2021; 12:626118. [PMID: 34234707 PMCID: PMC8257003 DOI: 10.3389/fpsyg.2021.626118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 05/12/2021] [Indexed: 01/28/2023] Open
Abstract
Treating the speech communities as homogeneous entities is not an accurate representation of reality, as it misses some of the complexities of linguistic interactions. Inter-individual variation and multiple types of biases are ubiquitous in speech communities, regardless of their size. This variation is often neglected due to the assumption that “majority rules,” and that the emerging language of the community will override any such biases by forcing the individuals to overcome their own biases, or risk having their use of language being treated as “idiosyncratic” or outright “pathological.” In this paper, we use computer simulations of Bayesian linguistic agents embedded in communicative networks to investigate how biased individuals, representing a minority of the population, interact with the unbiased majority, how a shared language emerges, and the dynamics of these biases across time. We tested different network sizes (from very small to very large) and types (random, scale-free, and small-world), along with different strengths and types of bias (modeled through the Bayesian prior distribution of the agents and the mechanism used for generating utterances: either sampling from the posterior distribution [“sampler”] or picking the value with the maximum probability [“MAP”]). The results show that, while the biased agents, even when being in the minority, do adapt their language by going against their a priori preferences, they are far from being swamped by the majority, and instead the emergent shared language of the whole community is influenced by their bias.
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Affiliation(s)
- Mathilde Josserand
- Laboratoire Dynamique Du Langage UMR 5596, Université Lumière Lyon 2, Lyon, France
| | | | - François Pellegrino
- Laboratoire Dynamique Du Langage UMR 5596, Université Lumière Lyon 2, Lyon, France
| | - Dan Dediu
- Laboratoire Dynamique Du Langage UMR 5596, Université Lumière Lyon 2, Lyon, France
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Mesoudi A. Cultural selection and biased transformation: two dynamics of cultural evolution. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200053. [PMID: 33993764 DOI: 10.1098/rstb.2020.0053] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Here, I discuss two broad versions of human cultural evolution which currently exist in the literature and which emphasize different underlying dynamics. One, which originates in population-genetic-style modelling, emphasizes how cultural selection causes some cultural variants to be favoured and gradually increase in frequency over others. The other, which draws more from cognitive science, holds that cultural change is driven by the biased transformation of cultural variants by individuals in non-random and consistent directions. Despite claims that cultural evolution is characterized by one or the other of these dynamics, these are neither mutually exclusive nor a dichotomy. Different domains of human culture are likely to be more or less strongly weighted towards cultural selection or biased transformation. Identifying cultural dynamics in real-world cultural data is challenging given that they can generate the same population-level patterns, such as directional change or cross-cultural stability, and the same cognitive and emotional mechanisms may underlie both cultural selection and biased transformation. Nevertheless, fine-grained historical analysis and laboratory experiments, combined with formal models to generate quantitative predictions, offer the best way of distinguishing them. This article is part of the theme issue 'Foundations of cultural evolution'.
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Affiliation(s)
- Alex Mesoudi
- Human Behaviour and Cultural Evolution Group, Biosciences, University of Exeter, Penryn TR10 9FE, UK
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Jin Y, Jiang A, Jiang W, Wu W, Ye L, Kong X, Liu L, Jin Z. Self-produced audio-visual animation introduction alleviates preoperative anxiety in pediatric strabismus surgery: a randomized controlled study. BMC Ophthalmol 2021; 21:163. [PMID: 33827488 PMCID: PMC8028828 DOI: 10.1186/s12886-021-01922-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 03/25/2021] [Indexed: 11/23/2022] Open
Abstract
Background Hospital anxiety caused by strabismus surgery has an unpleasant and disturbing feeling for both children and their parents. This study aimed to determine the effect of viewing a self-produced audio-visual animation introduction on preoperative anxiety and emergence agitation of pediatric patients undergoing strabismus surgery. Methods In this prospective randomized study, 1 hundred children scheduled for strabismus surgery with aged 3 ~ 6 years. The children were randomly divided into 2 groups (n = 50 for each), Group A: using a self-produced audio-visual animation introduction; Group C: controlled group without audio-visual animation introduction. Children’s preoperative anxiety was determined by the modified Yale Preoperative Anxiety Scale (mYPAS) at different time points: the night before surgery(T1), at pre-anesthetic holding room(T2), and just before anesthesia induction(T3). The Spielberger State-Trait Anxiety Inventory (STAI) was used to record the anxiety of parents at T1,T2 and T3. The incidence and the degree of emergence agitation were recorded. Results The mYPAS scores at T2 and T3 were higher than T1(p < 0.05) in both groups. The average score of mYPAS in Group A was significantly lower than in Group C at T2 and T3(p < 0.05). The STAI scores in Group A at T2 and T3 were significantly lower than in Group C(p < 0.05). The incidence of agitation in Group A was lower than that in Group C(p < 0.05). Conclusions Based on the findings, viewing a self-produced audio-visual animation can effectively alleviate the preoperative anxiety for both children and their parents in pediatric strabismus surgery, and it was effective for reducing emergence agitation as well. Trial registration The trial was prospectively registered before patient enrollment at Chinese Clinical Trial Registry (Clinical Trial Number: ChiCTR1900025116, Date: 08/12/2019). Supplementary Information The online version contains supplementary material available at 10.1186/s12886-021-01922-6.
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Affiliation(s)
- Yuexi Jin
- Department of Anesthesiology, Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Xueyuan Road #270, Wenzhou, Zhejiang, China
| | - Aifen Jiang
- Department of Anesthesiology, Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Xueyuan Road #270, Wenzhou, Zhejiang, China
| | - Wanna Jiang
- Department of Anesthesiology, Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Xueyuan Road #270, Wenzhou, Zhejiang, China
| | - Wenxin Wu
- Department of Anesthesiology, Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Xueyuan Road #270, Wenzhou, Zhejiang, China
| | - Lisha Ye
- Department of Anesthesiology, Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Xueyuan Road #270, Wenzhou, Zhejiang, China
| | - Xiaojiang Kong
- Wenzhou Medical University, Wenzhou Chashan Senior education park, Ouhai District, Wenzhou, Zhejiang, China
| | - Le Liu
- Department of Anesthesiology, The First Affiliated Hospital of Wenzhou Medical University, South Baixiang Town, Wenzhou, Zhejiang, China
| | - Zhousheng Jin
- Department of Anesthesiology, The First Affiliated Hospital of Wenzhou Medical University, South Baixiang Town, Wenzhou, Zhejiang, China.
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Krafft PM, Shmueli E, Griffiths TL, Tenenbaum JB, Pentland AS. Bayesian collective learning emerges from heuristic social learning. Cognition 2021; 212:104469. [PMID: 33770743 DOI: 10.1016/j.cognition.2020.104469] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 09/14/2020] [Accepted: 09/16/2020] [Indexed: 11/28/2022]
Abstract
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 decisions: (1) aggregating information and (2) addressing an informational public goods problem known as the exploration-exploitation dilemma. Here, we show how a Bayesian social sampling model can in principle simultaneously optimally aggregate information and nearly optimally solve the exploration-exploitation dilemma. The key idea we explore is that Bayesian rationality at the level of a population can be implemented through a more simplistic heuristic social learning mechanism at the individual level. This simple individual-level behavioral rule in the context of a group of decision-makers functions as a distributed algorithm that tracks a Bayesian posterior in population-level statistics. We test this model using a large-scale dataset from an online financial trading platform.
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Affiliation(s)
- P M Krafft
- Creative Computing Institute, University of Arts London, London, England, United Kingdom.
| | - Erez Shmueli
- Department of Industrial Engineering, Tel-Aviv University, Tel-Aviv, Israel
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Thompson B, Griffiths TL. Human biases limit cumulative innovation. Proc Biol Sci 2021; 288:20202752. [PMID: 33715436 PMCID: PMC7944091 DOI: 10.1098/rspb.2020.2752] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 02/08/2021] [Indexed: 01/05/2023] Open
Abstract
Is technological advancement constrained by biases in human cognition? People in all societies build on discoveries inherited from previous generations, leading to cumulative innovation. However, biases in human learning and memory may influence the process of knowledge transmission, potentially limiting this process. Here, we show that cumulative innovation in a continuous optimization problem is systematically constrained by human biases. In a large (n = 1250) behavioural study using a transmission chain design, participants searched for virtual technologies in one of four environments after inheriting a solution from previous generations. Participants converged on worse solutions in environments misaligned with their biases. These results substantiate a mathematical model of cumulative innovation in Bayesian agents, highlighting formal relationships between cultural evolution and distributed stochastic optimization. Our findings provide experimental evidence that human biases can limit the advancement of knowledge in a controlled laboratory setting, reinforcing concerns about bias in creative, scientific and educational contexts.
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Affiliation(s)
- Bill Thompson
- Departments of Psychology and Computer Science, Princeton University, Princeton, NJ 08544, USA
| | - Thomas L. Griffiths
- Departments of Psychology and Computer Science, Princeton University, Princeton, NJ 08544, USA
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