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  1. Origins of Hierarchical Logical Reasoning.Abhishek M. Dedhe, Hayley Clatterbuck, Steven T. Piantadosi & Jessica F. Cantlon - 2023 - Cognitive Science 47 (2):13250.
    Hierarchical cognitive mechanisms underlie sophisticated behaviors, including language, music, mathematics, tool-use, and theory of mind. The origins of hierarchical logical reasoning have long been, and continue to be, an important puzzle for cognitive science. Prior approaches to hierarchical logical reasoning have often failed to distinguish between observable hierarchical behavior and unobservable hierarchical cognitive mechanisms. Furthermore, past research has been largely methodologically restricted to passive recognition tasks as compared to active generation tasks that are stronger tests of hierarchical rules. We argue (...)
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  • Cognitive Mechanisms Underlying Recursive Pattern Processing in Human Adults.Abhishek M. Dedhe, Steven T. Piantadosi & Jessica F. Cantlon - 2023 - Cognitive Science 47 (4):e13273.
    The capacity to generate recursive sequences is a marker of rich, algorithmic cognition, and perhaps unique to humans. Yet, the precise processes driving recursive sequence generation remain mysterious. We investigated three potential cognitive mechanisms underlying recursive pattern processing: hierarchical reasoning, ordinal reasoning, and associative chaining. We developed a Bayesian mixture model to quantify the extent to which these three cognitive mechanisms contribute to adult humans’ performance in a sequence generation task. We further tested whether recursive rule discovery depends upon relational (...)
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  • Non‐adjacent Dependency Learning in Humans and Other Animals.Benjamin Wilson, Michelle Spierings, Andrea Ravignani, Jutta L. Mueller, Toben H. Mintz, Frank Wijnen, Anne van der Kant, Kenny Smith & Arnaud Rey - 2018 - Topics in Cognitive Science 12 (3):843-858.
    Wilson et al. focus on one class of AGL tasks: the cognitively demanding task of detecting non‐adjacent dependencies (NADs) among items. They provide a typology of the different types of NADs in natural languages and in AGL tasks. A range of cues affect NAD learning, ranging from the variability and number of intervening elements to the presence of shared prosodic cues between the dependent items. These cues, important for humans to discover non‐adjacent dependencies, are also found to facilitate NAD learning (...)
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  • Non‐adjacent Dependency Learning in Humans and Other Animals.Benjamin Wilson, Michelle Spierings, Andrea Ravignani, Jutta L. Mueller, Toben H. Mintz, Frank Wijnen, Anne Kant, Kenny Smith & Arnaud Rey - 2020 - Topics in Cognitive Science 12 (3):843-858.
    Wilson et al. focus on one class of AGL tasks: the cognitively demanding task of detecting non‐adjacent dependencies (NADs) among items. They provide a typology of the different types of NADs in natural languages and in AGL tasks. A range of cues affect NAD learning, ranging from the variability and number of intervening elements to the presence of shared prosodic cues between the dependent items. These cues, important for humans to discover non‐adjacent dependencies, are also found to facilitate NAD learning (...)
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  • Dendrophobia in Bonobo Comprehension of Spoken English.Truswell Robert - 2017 - Mind and Language 32 (4):395-415.
    Data from a study by Savage-Rumbaugh and colleagues on comprehension of spoken English requests by a bonobo and a human infant supports Fitch's hypothesis that humans exhibit dendrophilia, or a propensity to manipulate tree structures, to a greater extent than other species. However, findings from language acquisition suggest that human infants do not show an initial preference for certain hierarchical syntactic structures. Infants are slow to acquire and generalize the structures in question, but they can eventually do so. Kanzi, in (...)
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  • Regularity Extraction Across Species: Associative Learning Mechanisms Shared by Human and Non‐Human Primates.Arnaud Rey, Laure Minier, Raphaëlle Malassis, Louisa Bogaerts & Joël Fagot - 2019 - Topics in Cognitive Science 11 (3):573-586.
    One of the themes that has been widely addressed in both the implicit learning and statistical learning literatures is that of rule learning. While it is widely agreed that the extraction of regularities from the environment is a fundamental facet of cognition, there is still debate about the nature of rule learning. Rey and colleagues show that the comparison between human and non‐human primates can contribute important insights to this debate.
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  • More than one way to see it: Individual heuristics in avian visual computation.Andrea Ravignani, Gesche Westphal-Fitch, Ulrike Aust, Martin M. Schlumpp & W. Tecumseh Fitch - 2015 - Cognition 143 (C):13-24.
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  • Chimpanzees process structural isomorphisms across sensory modalities.Andrea Ravignani & Ruth Sonnweber - 2017 - Cognition 161 (C):74-79.
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  • What baboons can (not) tell us about natural language grammars.Fenna H. Poletiek, Hartmut Fitz & Bruno R. Bocanegra - 2016 - Cognition 151 (C):108-112.
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  • Structured Sequence Learning: Animal Abilities, Cognitive Operations, and Language Evolution.Christopher I. Petkov & Carel ten Cate - 2020 - Topics in Cognitive Science 12 (3):828-842.
    Human language is a salient example of a neurocognitive system that is specialized to process complex dependencies between sensory events distributed in time, yet how this system evolved and specialized remains unclear. Artificial Grammar Learning (AGL) studies have generated a wealth of insights into how human adults and infants process different types of sequencing dependencies of varying complexity. The AGL paradigm has also been adopted to examine the sequence processing abilities of nonhuman animals. We critically evaluate this growing literature in (...)
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  • The Temporal Dynamics of Regularity Extraction in Non‐Human Primates.Laure Minier, Joël Fagot & Arnaud Rey - 2016 - Cognitive Science 40 (4):1019-1030.
    Extracting the regularities of our environment is one of our core cognitive abilities. To study the fine-grained dynamics of the extraction of embedded regularities, a method combining the advantages of the artificial language paradigm and the serial response time task was used with a group of Guinea baboons in a new automatic experimental device. After a series of random trials, monkeys were exposed to language-like patterns. We found that the extraction of embedded patterns positioned at the end of larger patterns (...)
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  • The nature of the memory buffer in implicit learning: Learning Chinese tonal symmetries.Feifei Li, Shan Jiang, Xiuyan Guo, Zhiliang Yang & Zoltan Dienes - 2013 - Consciousness and Cognition 22 (3):920-930.
    Previous research has established that people can implicitly learn chunks, which do not require a memory buffer to process. The present study explores the implicit learning of nonlocal dependencies generated by higher than finite-state grammars, specifically, Chinese tonal retrogrades and inversions , which do require buffers . People were asked to listen to and memorize artificial poetry instantiating one of the two grammars; after this training phase, people were informed of the existence of rules and asked to classify new poems, (...)
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  • How Did Language Evolve? Some Reflections on the Language Parasite Debate.Tzu-wei Hung - 2019 - Biological Theory 14 (4):214-223.
    The language parasite approach refers to the view that language, like a parasite, is an adaptive system that evolves to fit its human hosts. Supported by recent computer simulations, LPA proponents claim that the reason that humans can use languages with ease is not because we have evolved with genetically specified linguistic instincts but because languages have adapted to the preexisting brain structures of humans. This article examines the LPA. It argues that, while the LPA has advantages over its rival, (...)
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