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Hodgetts CJ, Close JOE, Hahn U. Similarity and structured representation in human and nonhuman apes. Cognition 2023; 236:105419. [PMID: 37104894 DOI: 10.1016/j.cognition.2023.105419] [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: 06/23/2022] [Revised: 02/17/2023] [Accepted: 02/18/2023] [Indexed: 04/29/2023]
Abstract
How we judge the similarity between objects in the world is connected ultimately to how we represent those objects. It has been argued extensively that object representations in humans are 'structured' in nature, meaning that both individual features and the relations between them can influence similarity. In contrast, popular models within comparative psychology assume that nonhuman species appreciate only surface-level, featural similarities. By applying psychological models of structural and featural similarity (from conjunctive feature models to Tversky's Contrast Model) to visual similarity judgements from adult humans, chimpanzees, and gorillas, we demonstrate a cross-species sensitivity to complex structural information, particularly for stimuli that combine colour and shape. These results shed new light on the representational complexity of nonhuman apes, and the fundamental limits of featural coding in explaining object representation and similarity, which emerge strikingly across both human and nonhuman species.
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Affiliation(s)
- Carl J Hodgetts
- Department of Psychology, Royal Holloway, University of London, Egham, Surrey TW20 0EX, UK; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff CF24 4HQ, UK.
| | - James O E Close
- Department of Developmental and Comparative Psychology, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany; School of Psychology and Sport Science, Anglia Ruskin University, East Road, Cambridge CB1 1PT, UK
| | - Ulrike Hahn
- Department of Psychological Sciences, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK
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Epping GP, Fisher EL, Zeleznikow-Johnston AM, Pothos EM, Tsuchiya N. A Quantum Geometric Framework for Modeling Color Similarity Judgments. Cogn Sci 2023; 47:e13231. [PMID: 36655940 DOI: 10.1111/cogs.13231] [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: 01/13/2022] [Revised: 11/30/2022] [Accepted: 12/02/2022] [Indexed: 01/20/2023]
Abstract
Since Tversky argued that similarity judgments violate the three metric axioms, asymmetrical similarity judgments have been particularly challenging for standard, geometric models of similarity, such as multidimensional scaling. According to Tversky, asymmetrical similarity judgments are driven by differences in salience or extent of knowledge. However, the notion of salience has been difficult to operationalize, especially for perceptual stimuli for which there are no apparent differences in extent of knowledge. To investigate similarity judgments between perceptual stimuli, across three experiments, we collected data where individuals would rate the similarity of a pair of temporally separated color patches. We identified several violations of symmetry in the empirical results, which the conventional multidimensional scaling model cannot readily capture. Pothos et al. proposed a quantum geometric model of similarity to account for Tversky's findings. In the present work, we extended this model to a more general framework that can be fit to similarity judgments. We fitted several variants of quantum and multidimensional scaling models to the behavioral data and concluded in favor of the quantum approach. Without further modifications of the model, the best-fit quantum model additionally predicted violations of the triangle inequality that we observed in the same data. Overall, by offering a different form of geometric representation, the quantum geometric framework of similarity provides a viable alternative to multidimensional scaling for modeling similarity judgments, while still allowing a convenient, spatial illustration of similarity.
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Affiliation(s)
- Gunnar P Epping
- Department of Psychological and Brain Sciences, Indiana University
| | - Elizabeth L Fisher
- School of Psychological Sciences, Monash University.,Cognition and Philosophy Lab, Philosophy Department, School of Philosophy, Historical and International Studies, Monash University
| | - Ariel M Zeleznikow-Johnston
- School of Psychological Sciences, Monash University.,Turner Institute for Brain and Mental Health, Monash University
| | | | - Naotsugu Tsuchiya
- School of Psychological Sciences, Monash University.,Turner Institute for Brain and Mental Health, Monash University.,Center for Information and Neural Networks (CiNet).,Advanced Telecommunications Research Computational Neuroscience Laboratories
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Enriched category as a model of qualia structure based on similarity judgements. Conscious Cogn 2022; 101:103319. [DOI: 10.1016/j.concog.2022.103319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 01/11/2022] [Accepted: 03/24/2022] [Indexed: 11/22/2022]
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Maier A, Tsuchiya N. Growing evidence for separate neural mechanisms for attention and consciousness. Atten Percept Psychophys 2021; 83:558-576. [PMID: 33034851 PMCID: PMC7886945 DOI: 10.3758/s13414-020-02146-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/08/2020] [Indexed: 11/08/2022]
Abstract
Our conscious experience of the world seems to go in lockstep with our attentional focus: We tend to see, hear, taste, and feel what we attend to, and vice versa. This tight coupling between attention and consciousness has given rise to the idea that these two phenomena are indivisible. In the late 1950s, the honoree of this special issue, Charles Eriksen, was among a small group of early pioneers that sought to investigate whether a transient increase in overall level of attention (alertness) in response to a noxious stimulus can be decoupled from conscious perception using experimental techniques. Recent years saw a similar debate regarding whether attention and consciousness are two dissociable processes. Initial evidence that attention and consciousness are two separate processes primarily rested on behavioral data. However, the past couple of years witnessed an explosion of studies aimed at testing this conjecture using neuroscientific techniques. Here we provide an overview of these and related empirical studies on the distinction between the neuronal correlates of attention and consciousness, and detail how advancements in theory and technology can bring about a more detailed understanding of the two. We argue that the most promising approach will combine ever-evolving neurophysiological and interventionist tools with quantitative, empirically testable theories of consciousness that are grounded in a mathematically formalized understanding of phenomenology.
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Affiliation(s)
- Alexander Maier
- Department of Psychology, Vanderbilt University, Nashville, TN, USA.
| | - Naotsugu Tsuchiya
- Turner Institute for Brain and Mental Health & School of Psychological Sciences, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, VIC, Australia
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita, Osaka, 565-0871, Japan
- Advanced Telecommunications Research Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan
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Schleif FM, Raab C, Tino P. Sparsification of core set models in non-metric supervised learning. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2019.10.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Hiatt LM, Trafton JG. Familiarity, Priming, and Perception in Similarity Judgments. Cogn Sci 2016; 41:1450-1484. [PMID: 27766669 DOI: 10.1111/cogs.12418] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Revised: 06/02/2016] [Accepted: 06/13/2016] [Indexed: 11/28/2022]
Abstract
We present a novel way of accounting for similarity judgments. Our approach posits that similarity stems from three main sources-familiarity, priming, and inherent perceptual likeness. Here, we explore each of these constructs and demonstrate their individual and combined effectiveness in explaining similarity judgments. Using these three measures, our account of similarity explains ratings of simple, color-based perceptual stimuli that display asymmetry effects, as well as more complicated perceptual stimuli with structural properties; more traditional approaches to similarity solve one or the other and have difficulty accounting for both. Overall, our work demonstrates the importance of each of these components of similarity in explaining similarity judgments, both individually and together, and suggests important implications for other similarity approaches.
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Abstract
Efficient learning of a data analysis task strongly depends on the data representation. Most methods rely on (symmetric) similarity or dissimilarity representations by means of metric inner products or distances, providing easy access to powerful mathematical formalisms like kernel or branch-and-bound approaches. Similarities and dissimilarities are, however, often naturally obtained by nonmetric proximity measures that cannot easily be handled by classical learning algorithms. Major efforts have been undertaken to provide approaches that can either directly be used for such data or to make standard methods available for these types of data. We provide a comprehensive survey for the field of learning with nonmetric proximities. First, we introduce the formalism used in nonmetric spaces and motivate specific treatments for nonmetric proximity data. Second, we provide a systematization of the various approaches. For each category of approaches, we provide a comparative discussion of the individual algorithms and address complexity issues and generalization properties. In a summarizing section, we provide a larger experimental study for the majority of the algorithms on standard data sets. We also address the problem of large-scale proximity learning, which is often overlooked in this context and of major importance to make the method relevant in practice. The algorithms we discuss are in general applicable for proximity-based clustering, one-class classification, classification, regression, and embedding approaches. In the experimental part, we focus on classification tasks.
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Affiliation(s)
| | - Peter Tino
- University of Birmingham, School of Computer Science, B15 2TT, Birmingham, U.K.
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Hahn U. Similarity. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2014; 5:271-80. [DOI: 10.1002/wcs.1282] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2013] [Revised: 01/08/2014] [Accepted: 01/12/2014] [Indexed: 11/06/2022]
Affiliation(s)
- Ulrike Hahn
- Department of Psychological Sciences; Birkbeck, University of London; London UK
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Maggiora G, Vogt M, Stumpfe D, Bajorath J. Molecular similarity in medicinal chemistry. J Med Chem 2013; 57:3186-204. [PMID: 24151987 DOI: 10.1021/jm401411z] [Citation(s) in RCA: 365] [Impact Index Per Article: 33.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Similarity is a subjective and multifaceted concept, regardless of whether compounds or any other objects are considered. Despite its intrinsically subjective nature, attempts to quantify the similarity of compounds have a long history in chemical informatics and drug discovery. Many computational methods employ similarity measures to identify new compounds for pharmaceutical research. However, chemoinformaticians and medicinal chemists typically perceive similarity in different ways. Similarity methods and numerical readouts of similarity calculations are probably among the most misunderstood computational approaches in medicinal chemistry. Herein, we evaluate different similarity concepts, highlight key aspects of molecular similarity analysis, and address some potential misunderstandings. In addition, a number of practical aspects concerning similarity calculations are discussed.
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Affiliation(s)
- Gerald Maggiora
- College of Pharmacy and BIO5 Institute, University of Arizona , 1295 North Martin, P.O. Box 210202, Tucson, Arizona 85721, United States
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