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Webb TW, Frankland SM, Altabaa A, Segert S, Krishnamurthy K, Campbell D, Russin J, Giallanza T, O'Reilly R, Lafferty J, Cohen JD. The relational bottleneck as an inductive bias for efficient abstraction. Trends Cogn Sci 2024; 28:829-843. [PMID: 38729852 DOI: 10.1016/j.tics.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 05/12/2024]
Abstract
A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This has often been framed in terms of a dichotomy between connectionist and symbolic cognitive models. Here, we highlight a recently emerging line of work that suggests a novel reconciliation of these approaches, by exploiting an inductive bias that we term the relational bottleneck. In that approach, neural networks are constrained via their architecture to focus on relations between perceptual inputs, rather than the attributes of individual inputs. We review a family of models that employ this approach to induce abstractions in a data-efficient manner, emphasizing their potential as candidate models for the acquisition of abstract concepts in the human mind and brain.
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2
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Piantadosi ST, Muller DCY, Rule JS, Kaushik K, Gorenstein M, Leib ER, Sanford E. Why concepts are (probably) vectors. Trends Cogn Sci 2024; 28:844-856. [PMID: 39112125 DOI: 10.1016/j.tics.2024.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/18/2024] [Accepted: 06/27/2024] [Indexed: 09/06/2024]
Abstract
For decades, cognitive scientists have debated what kind of representation might characterize human concepts. Whatever the format of the representation, it must allow for the computation of varied properties, including similarities, features, categories, definitions, and relations. It must also support the development of theories, ad hoc categories, and knowledge of procedures. Here, we discuss why vector-based representations provide a compelling account that can meet all these needs while being plausibly encoded into neural architectures. This view has become especially promising with recent advances in both large language models and vector symbolic architectures. These innovations show how vectors can handle many properties traditionally thought to be out of reach for neural models, including compositionality, definitions, structures, and symbolic computational processes.
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Affiliation(s)
- Steven T Piantadosi
- Department of Psychology, University of California, Berkeley, CA, USA; Department of Neuroscience, University of California, Berkeley, CA, USA.
| | - Dyana C Y Muller
- Department of Neuroscience, University of California, Berkeley, CA, USA
| | - Joshua S Rule
- Department of Psychology, University of California, Berkeley, CA, USA
| | | | - Mark Gorenstein
- Department of Neuroscience, University of California, Berkeley, CA, USA
| | - Elena R Leib
- Department of Psychology, University of California, Berkeley, CA, USA
| | - Emily Sanford
- Department of Psychology, University of California, Berkeley, CA, USA
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3
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Rule JS, Piantadosi ST, Cropper A, Ellis K, Nye M, Tenenbaum JB. Symbolic metaprogram search improves learning efficiency and explains rule learning in humans. Nat Commun 2024; 15:6847. [PMID: 39127796 DOI: 10.1038/s41467-024-50966-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
Throughout their lives, humans seem to learn a variety of rules for things like applying category labels, following procedures, and explaining causal relationships. These rules are often algorithmically rich but are nonetheless acquired with minimal data and computation. Symbolic models based on program learning successfully explain rule-learning in many domains, but performance degrades quickly as program complexity increases. It remains unclear how to scale symbolic rule-learning methods to model human performance in challenging domains. Here we show that symbolic search over the space of metaprograms-programs that revise programs-dramatically improves learning efficiency. On a behavioral benchmark of 100 algorithmically rich rules, this approach fits human learning more accurately than alternative models while also using orders of magnitude less search. The computation required to match median human performance is consistent with conservative estimates of human thinking time. Our results suggest that metaprogram-like representations may help human learners to efficiently acquire rules.
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Affiliation(s)
- Joshua S Rule
- Psychology, University of California, Berkeley, Berkeley, CA, 94704, USA.
| | | | | | - Kevin Ellis
- Computer Science, Cornell University, Ithaca, NY, 14850, USA
| | - Maxwell Nye
- Adept AI Labs, San Francisco, CA, 94110, USA
| | - Joshua B Tenenbaum
- Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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4
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Murphy E, Holmes E, Friston K. Natural language syntax complies with the free-energy principle. SYNTHESE 2024; 203:154. [PMID: 38706520 PMCID: PMC11068586 DOI: 10.1007/s11229-024-04566-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 03/15/2024] [Indexed: 05/07/2024]
Abstract
Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating syntactic objects. We argue that recently proposed principles of economy in language design-such as "minimal search" criteria from theoretical syntax-adhere to the FEP. This affords a greater degree of explanatory power to the FEP-with respect to higher language functions-and offers linguistics a grounding in first principles with respect to computability. While we mostly focus on building new principled conceptual relations between syntax and the FEP, we also show through a sample of preliminary examples how both tree-geometric depth and a Kolmogorov complexity estimate (recruiting a Lempel-Ziv compression algorithm) can be used to accurately predict legal operations on syntactic workspaces, directly in line with formulations of variational free energy minimization. This is used to motivate a general principle of language design that we term Turing-Chomsky Compression (TCC). We use TCC to align concerns of linguists with the normative account of self-organization furnished by the FEP, by marshalling evidence from theoretical linguistics and psycholinguistics to ground core principles of efficient syntactic computation within active inference.
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Affiliation(s)
- Elliot Murphy
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Science Center, Houston, TX 77030 USA
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center, Houston, TX 77030 USA
| | - Emma Holmes
- Department of Speech Hearing and Phonetic Sciences, University College London, London, WC1N 1PF UK
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, WC1N 3AR UK
| | - Karl Friston
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, WC1N 3AR UK
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5
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Cammisuli DM, Tuena C, Riva G, Repetto C, Axmacher N, Chandreswaran V, Isella V, Pomati S, Zago S, Difonzo T, Pavanello G, Prete LA, Stramba-Badiale M, Mauro A, Cattaldo S, Castelnuovo G. Exploring the Remediation of Behavioral Disturbances of Spatial Cognition in Community-Dwelling Senior Citizens with Mild Cognitive Impairment via Innovative Technological Apparatus (BDSC-MCI Project): Protocol for a Prospective, Multi-Center Observational Study. J Pers Med 2024; 14:192. [PMID: 38392625 PMCID: PMC10890288 DOI: 10.3390/jpm14020192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/25/2024] [Accepted: 02/02/2024] [Indexed: 02/24/2024] Open
Abstract
Spatial navigation (SN) has been reported to be one of the first cognitive domains to be affected in Alzheimer's disease (AD), which occurs as a result of progressive neuropathology involving specific brain areas. Moreover, the epsilon 4 isoform of apolipoprotein-E (APOE-ε4) has been associated with both sporadic and familial late-onset AD, and patients with mild cognitive impairment (MCI) due to AD are more likely to progressively deteriorate. Spatial navigation performance will be examined on a sample of 76 community-dwelling senior citizens (25 healthy controls; 25 individuals with subjective cognitive decline (SCD); and 26 patients with MCI due to AD) via a virtual computer-based task (i.e., the AppleGame) and a naturalistic task (i.e., the Detour Navigation Test-modified version) for which a wearable device with sensors will be used for recording gait data and revealing physiological parameters that may be associated with spatial disorientation. We expect that patients with MCI due to AD and APOE-ε4 carriers will show altered SN performances compared to individuals with SCD and healthy controls in the experimental tasks, and that VR testing may predict ecological performance. Impaired SN performances in people at increased risk of developing AD may inform future cognitive rehabilitation protocols for counteracting spatial disorientation that may occur during elders' traveling to unfamiliar locations. The research protocol has been approved by the Ethics Committee of the Istituto Auxologico Italiano. Findings will be published in peer-reviewed medical journals and discussed in national and international congresses.
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Affiliation(s)
| | - Cosimo Tuena
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, 20145 Milan, Italy
| | - Giuseppe Riva
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, 20145 Milan, Italy
- Human Technology Lab, Catholic University, 20145 Milan, Italy
| | - Claudia Repetto
- Department of Psychology, Catholic University, 20123 Milan, Italy
| | - Nikolai Axmacher
- Department of Neuropsychology, Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr University, 44801 Bochum, Germany
| | - Varnan Chandreswaran
- Department of Neuropsychology, Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr University, 44801 Bochum, Germany
| | - Valeria Isella
- Department of Neurology, School of Medicine, University of Milano-Bicocca, 20126 Milan, Italy
- Milan Center for Neurosciences, 20133 Milan, Italy
| | - Simone Pomati
- Neurology Unit, Luigi Sacco University Hospital, 20157 Milan, Italy
| | - Stefano Zago
- Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, University of Milan, 20122 Milan, Italy
| | - Teresa Difonzo
- Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, University of Milan, 20122 Milan, Italy
| | - Giada Pavanello
- School of Specialization in Clinical Psychology, Catholic University, 20123 Milan, Italy
| | - Lorenzo Augusto Prete
- School of Specialization in Clinical Psychology, Catholic University, 20123 Milan, Italy
| | - Marco Stramba-Badiale
- Department of Geriatrics and Cardiovascular Medicine, IRCCS Istituto Auxologico Italiano, 20145 Milan, Italy
| | - Alessandro Mauro
- "Rita Levi Montalcini" Department of Neurosciences, University of Turin, 10126 Turin, Italy
- Neurology and Neurorehabilitation Unit, IRCCS Istituto Auxologico Italiano, "San Giuseppe" Hospital, 33081 Piancavallo, Italy
| | - Stefania Cattaldo
- Clinic Neurobiology Laboratory, IRCCS Istituto Auxologico Italiano, "San Giuseppe" Hospital, 33081 Piancavallo, Italy
| | - Gianluca Castelnuovo
- Department of Psychology, Catholic University, 20123 Milan, Italy
- Clinical Psychology Research Laboratory, IRCCS Istituto Auxologico Italiano, 20149 Milan, Italy
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6
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Zhao B, Lucas CG, Bramley NR. A model of conceptual bootstrapping in human cognition. Nat Hum Behav 2024; 8:125-136. [PMID: 37845519 PMCID: PMC11349578 DOI: 10.1038/s41562-023-01719-1] [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: 01/24/2023] [Accepted: 09/08/2023] [Indexed: 10/18/2023]
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.
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Affiliation(s)
- Bonan Zhao
- Department of Psychology, University of Edinburgh, Edinburgh, UK.
| | | | - Neil R Bramley
- Department of Psychology, University of Edinburgh, Edinburgh, UK
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7
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Piantadosi ST. The algorithmic origins of counting. Child Dev 2023; 94:1472-1490. [PMID: 37984061 DOI: 10.1111/cdev.14031] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 09/16/2023] [Accepted: 09/19/2023] [Indexed: 11/22/2023]
Abstract
The study of how children learn numbers has yielded one of the most productive research programs in cognitive development, spanning empirical and computational methods, as well as nativist and empiricist philosophies. This paper provides a tutorial on how to think computationally about learning models in a domain like number, where learners take finite data and go far beyond what they directly observe or perceive. To illustrate, this paper then outlines a model which acquires a counting procedure using observations of sets and words, extending the proposal of Piantadosi et al. (2012). This new version of the model responds to several critiques of the original work and outlines an approach which is likely appropriate for acquiring further aspects of mathematics.
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8
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Bramley NR, Zhao B, Quillien T, Lucas CG. Local Search and the Evolution of World Models. Top Cogn Sci 2023. [PMID: 37850714 DOI: 10.1111/tops.12703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 10/02/2023] [Accepted: 10/03/2023] [Indexed: 10/19/2023]
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 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.
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Affiliation(s)
| | - Bonan Zhao
- Department of Psychology, University of Edinburgh
| | - Tadeg Quillien
- Institute of Language, Cognition & Computation, Informatics University of Edinburgh
| | - Christopher G Lucas
- Institute of Language, Cognition & Computation, Informatics University of Edinburgh
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9
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Cesana-Arlotti N. The reemergence of the language-of-thought hypothesis: Consequences for the development of the logic of thought. Behav Brain Sci 2023; 46:e268. [PMID: 37766621 DOI: 10.1017/s0140525x23001802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Quilty-Dunn et al. defended the reemergence of language-of-thought hypothesis (LoTH). My commentary builds up implications for the study of the development of our logical capacities. Empirical support for logically augmented LoT systems calls for the investigation of their logical primitives and developmental origin. Furthermore, Quilty-Dunn et al.'s characterization of LoT helps the quest for the foundation of logic by dissociating logical cognition from natural language.
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Affiliation(s)
- Nicolò Cesana-Arlotti
- Department of Psychology, Yale University, New Haven, CT, USA. ; www.nicolocesanaarlotti.com
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10
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Chalmers DJ. The computational and the representational language-of-thought hypotheses. Behav Brain Sci 2023; 46:e269. [PMID: 37766631 DOI: 10.1017/s0140525x23001796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
There are two versions of the language-of-thought hypothesis (LOT): Representational LOT (roughly, structured representation), introduced by Ockham, and computational LOT (roughly, symbolic computation) introduced by Fodor. Like many others, I oppose the latter but not the former. Quilty-Dunn et al. defend representational LOT, but they do not defend the strong computational LOT thesis central to the classical-connectionist debate.
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Affiliation(s)
- David J Chalmers
- Department of Philosophy, New York University, New York, NY, USA. ; consc.net/chalmers
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11
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Lupyan G. Is language-of-thought the best game in the town we live? Behav Brain Sci 2023; 46:e281. [PMID: 37766628 DOI: 10.1017/s0140525x23001814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
There are towns in which language-of-thought (LoT) is the best game. But do we live in one? I go through three properties that characterize the LoT hypothesis: Discrete constituents, role-filler independence, and logical operators, and argue that in each case predictions from the LoT hypothesis are a poor fit to actual human cognition. As a hypothesis of what human cognition ought to be like, LoT departs from empirical reality.
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Affiliation(s)
- Gary Lupyan
- University of Wisconsin-Madison, Madison, WI, USA. ; http://sapir.psych.wisc.edu
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12
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Bramley NR, Xu F. Active inductive inference in children and adults: A constructivist perspective. Cognition 2023; 238:105471. [PMID: 37236019 DOI: 10.1016/j.cognition.2023.105471] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 02/27/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023]
Abstract
A defining aspect of being human is an ability to reason about the world by generating and adapting ideas and hypotheses. Here we explore how this ability develops by comparing children's and adults' active search and explicit hypothesis generation patterns in a task that mimics the open-ended process of scientific induction. In our experiment, 54 children (aged 8.97±1.11) and 50 adults performed inductive inferences about a series of causal rules through active testing. Children were more elaborate in their testing behavior and generated substantially more complex guesses about the hidden rules. We take a 'computational constructivist' perspective to explaining these patterns, arguing that these inferences are driven by a combination of thinking (generating and modifying symbolic concepts) and exploring (discovering and investigating patterns in the physical world). We show how this framework and rich new dataset speak to questions about developmental differences in hypothesis generation, active learning and inductive generalization. In particular, we find children's learning is driven by less fine-tuned construction mechanisms than adults', resulting in a greater diversity of ideas but less reliable discovery of simple explanations.
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Affiliation(s)
- Neil R Bramley
- Department of Psychology, University of Edinburgh, Scotland, United Kingdom.
| | - Fei Xu
- Psychology Department, University of California, Berkeley, USA
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13
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Poli F, Ghilardi T, Mars RB, Hinne M, Hunnius S. Eight-Month-Old Infants Meta-Learn by Downweighting Irrelevant Evidence. Open Mind (Camb) 2023; 7:141-155. [PMID: 37416070 PMCID: PMC10320826 DOI: 10.1162/opmi_a_00079] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 04/06/2023] [Indexed: 07/08/2023] Open
Abstract
Infants learn to navigate the complexity of the physical and social world at an outstanding pace, but how they accomplish this learning is still largely unknown. Recent advances in human and artificial intelligence research propose that a key feature to achieving quick and efficient learning is meta-learning, the ability to make use of prior experiences to learn how to learn better in the future. Here we show that 8-month-old infants successfully engage in meta-learning within very short timespans after being exposed to a new learning environment. We developed a Bayesian model that captures how infants attribute informativity to incoming events, and how this process is optimized by the meta-parameters of their hierarchical models over the task structure. We fitted the model with infants' gaze behavior during a learning task. Our results reveal how infants actively use past experiences to generate new inductive biases that allow future learning to proceed faster.
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Affiliation(s)
- Francesco Poli
- Donders Center for Cognition, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Tommaso Ghilardi
- Donders Center for Cognition, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Rogier B. Mars
- Donders Center for Cognition, Radboud University Nijmegen, Nijmegen, The Netherlands
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
| | - Max Hinne
- Donders Center for Cognition, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Sabine Hunnius
- Donders Center for Cognition, Radboud University Nijmegen, Nijmegen, The Netherlands
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14
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Aguado-Orea J. Estimations of child linguistic productivity controlling for vocabulary and sample size. Front Psychol 2022; 13:996610. [PMID: 36329751 PMCID: PMC9624188 DOI: 10.3389/fpsyg.2022.996610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/21/2022] [Indexed: 11/30/2022] Open
Abstract
Children's use of present tense suffixes is less productive than that of their parents, after correcting for sample size and lexical knowledge, according to a recently established approach for the study of inflectional productivity. This article expands on this technique by providing precise estimates of early grammatical productivity through systematic random sampling and allowing for developmental assessment. Two cross-linguistic comparisons are given in the results of this study. Two Spanish-speaking children and their parents are compared with four English-speaking children and their parents. The second comparison examines potential differences in productivity throughout developmental stages using the same six children's speech. The findings indicate that Spanish-acquiring children are less productive than their parents while utilising the paradigm under study, but that productivity levels increase over time. In contrast, the English-speaking children's morphosyntactic production mirrors that of their parents. Although the primary focus of this research is methodological, these findings have consequences for theoretical theories arguing either rule abstraction or a restricted generalisation of early exemplars.
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Affiliation(s)
- Javier Aguado-Orea
- Centre for Behavioural Science and Applied Psychology (CeBSAP), Sheffield Hallam University, Sheffield, United Kingdom
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15
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Facchin M. Troubles with mathematical contents. PHILOSOPHICAL PSYCHOLOGY 2022. [DOI: 10.1080/09515089.2022.2119952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Marco Facchin
- Linguistics and Philosophy IUSS Center, Istituto Universitario di Studi Superiori IUSS Pavia, Pavia, Italy
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16
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Kruszewski G, Mikolov T. Emergence of Self-Reproducing Metabolisms as Recursive Algorithms in an Artificial Chemistry. ARTIFICIAL LIFE 2022; 27:1-23. [PMID: 35286388 DOI: 10.1162/artl_a_00355] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
One of the main goals of Artificial Life is to research the conditions for the emergence of life, not necessarily as it is, but as it could be. Artificial chemistries are one of the most important tools for this purpose because they provide us with a basic framework to investigate under which conditions metabolisms capable of reproducing themselves, and ultimately, of evolving, can emerge. While there have been successful attempts at producing examples of emergent self-reproducing metabolisms, the set of rules involved remain too complex to shed much light on the underlying principles at work. In this article, we hypothesize that the key property needed for self-reproducing metabolisms to emerge is the existence of an autocatalyzed subset of Turing-complete reactions. We validate this hypothesis with a minimalistic artificial chemistry with conservation laws, which is based on a Turing-complete rewriting system called combinatory logic. Our experiments show that a single run of this chemistry, starting from a tabula rasa state, discovers-with no external intervention-a wide range of emergent structures including ones that self-reproduce in each cycle. All of these structures take the form of recursive algorithms that acquire basic constituents from the environment and decompose them in a process that is remarkably similar to biological metabolisms.
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Affiliation(s)
| | - Tomáš Mikolov
- Czech Institute of Informatics, Robotics, and Cybernetics, Prague
- Czech Technical University.
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17
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Yang Y, Piantadosi ST. One model for the learning of language. Proc Natl Acad Sci U S A 2022; 119:e2021865119. [PMID: 35074868 PMCID: PMC8812683 DOI: 10.1073/pnas.2021865119] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 11/18/2021] [Indexed: 01/29/2023] Open
Abstract
A major goal of linguistics and cognitive science is to understand what class of learning systems can acquire natural language. Until recently, the computational requirements of language have been used to argue that learning is impossible without a highly constrained hypothesis space. Here, we describe a learning system that is maximally unconstrained, operating over the space of all computations, and is able to acquire many of the key structures present in natural language from positive evidence alone. We demonstrate this by providing the same learning model with data from 74 distinct formal languages which have been argued to capture key features of language, have been studied in experimental work, or come from an interesting complexity class. The model is able to successfully induce the latent system generating the observed strings from small amounts of evidence in almost all cases, including for regular (e.g., an , [Formula: see text], and [Formula: see text]), context-free (e.g., [Formula: see text], and [Formula: see text]), and context-sensitive (e.g., [Formula: see text], and xx) languages, as well as for many languages studied in learning experiments. These results show that relatively small amounts of positive evidence can support learning of rich classes of generative computations over structures. The model provides an idealized learning setup upon which additional cognitive constraints and biases can be formalized.
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Affiliation(s)
- Yuan Yang
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30332
| | - Steven T Piantadosi
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720
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18
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Akhlaghpour H. An RNA-Based Theory of Natural Universal Computation. J Theor Biol 2021; 537:110984. [PMID: 34979104 DOI: 10.1016/j.jtbi.2021.110984] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 09/30/2021] [Accepted: 12/07/2021] [Indexed: 12/15/2022]
Abstract
Life is confronted with computation problems in a variety of domains including animal behavior, single-cell behavior, and embryonic development. Yet we currently do not know of a naturally existing biological system that is capable of universal computation, i.e., Turing-equivalent in scope. Generic finite-dimensional dynamical systems (which encompass most models of neural networks, intracellular signaling cascades, and gene regulatory networks) fall short of universal computation, but are assumed to be capable of explaining cognition and development. I present a class of models that bridge two concepts from distant fields: combinatory logic (or, equivalently, lambda calculus) and RNA molecular biology. A set of basic RNA editing rules can make it possible to compute any computable function with identical algorithmic complexity to that of Turing machines. The models do not assume extraordinarily complex molecular machinery or any processes that radically differ from what we already know to occur in cells. Distinct independent enzymes can mediate each of the rules and RNA molecules solve the problem of parenthesis matching through their secondary structure. In the most plausible of these models all of the editing rules can be implemented with merely cleavage and ligation operations at fixed positions relative to predefined motifs. This demonstrates that universal computation is well within the reach of molecular biology. It is therefore reasonable to assume that life has evolved - or possibly began with - a universal computer that yet remains to be discovered. The variety of seemingly unrelated computational problems across many scales can potentially be solved using the same RNA-based computation system. Experimental validation of this theory may immensely impact our understanding of memory, cognition, development, disease, evolution, and the early stages of life.
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Affiliation(s)
- Hessameddin Akhlaghpour
- Laboratory of Integrative Brain Function, The Rockefeller University, New York, NY, 10065, USA
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Do Q, Hasselmo ME. Neural circuits and symbolic processing. Neurobiol Learn Mem 2021; 186:107552. [PMID: 34763073 PMCID: PMC10121157 DOI: 10.1016/j.nlm.2021.107552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 10/14/2021] [Accepted: 11/02/2021] [Indexed: 11/29/2022]
Abstract
The ability to use symbols is a defining feature of human intelligence. However, neuroscience has yet to explain the fundamental neural circuit mechanisms for flexibly representing and manipulating abstract concepts. This article will review the research on neural models for symbolic processing. The review first focuses on the question of how symbols could possibly be represented in neural circuits. The review then addresses how neural symbolic representations could be flexibly combined to meet a wide range of reasoning demands. Finally, the review assesses the research on program synthesis and proposes that the most flexible neural representation of symbolic processing would involve the capacity to rapidly synthesize neural operations analogous to lambda calculus to solve complex cognitive tasks.
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Affiliation(s)
- Quan Do
- Center for Systems Neuroscience, Boston University, 610 Commonwealth Ave, Boston, MA 02215, United States.
| | - Michael E Hasselmo
- Center for Systems Neuroscience, Boston University, 610 Commonwealth Ave, Boston, MA 02215, United States.
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