The origins of same/different discrimination in human infants

Hespos, Susan J.; Gentner, Dedre; Anderson, Erin M.; Shivaram, Apoorva

doi:10.1016/j.cobeha.2020.10.013

Cited by 12 publications

(11 citation statements)

References 39 publications

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“…This is consistent with the analogical reasoning research literature that suggests the ability to form abstract relationship amongst objects is linked to the ability to solve the same/different tasks (Gentner et al, 2021;Premack, 1983). This skill is acquired during the first months, fully developed in 7-months old infants, and strongly linked to higher form of abstractions (Hespos et al, 2021). Importantly, this approach does not require the complex process of object categorization through supervised learning that clearly does not apply at these early ages.…”

Section: Discussionsupporting

confidence: 85%

Learning Online Visual Invariances for Novel Objects via Supervised and Self-Supervised Training

Biscione,

Bowers

2021

Preprint

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CNNs can be trained to acquire online-invariance: the same internal representation is elicited following different transformations of the same, novel objects• We demonstrate on-line invariance for the following transformations: translation, rotation, scale, brightness, contrast, and viewpoint• Many different supervised networks acquire this property, as does a self-supervised network that solves the same/different task• As few as 50 images taken 10 object classes are needed to train for online invariance

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Section: Discussionsupporting

confidence: 85%

Learning Online Visual Invariances for Novel Objects via Supervised and Self-Supervised Training

Biscione,

Bowers

2021

Preprint

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“…Previous research on the visual reasoning capabilities of DNNs has focused heavily on the same-different task (Adeli, Ahn and Zelinsky, 2023b;Baker, Garrigan, Phillips and Kellman, 2023;Funke, Borowski, Stosio, Brendel, Wallis and Bethge, 2021;Kim, Ricci and Serre, 2018;Falchi, 2021, 2022;Puebla and Bowers, 2022;Ricci et al, 2021;Stabinger, Peer and Rodríguez-Sánchez, 2021;Tartaglini, Feucht, Lepori, Vong, Lovering, Lake and Pavlick, 2023;Vaishnav, Cadene, Alamia, Linsley, VanRullen and Serre, 2022;Webb, Mondal and Cohen, 2023c;Webb, Sinha and Cohen, 2021). This is due to the fact that the concept of sameness is considered to be fundamental to human thought (Hochmann, Wasserman and Carey, 2021), develops early in human infants (Hespos, Gentner, Anderson and Shivaram, 2021), and it is more sophisticated in humans in comparison to other species (Gentner, Shao, Simms and Hespos, 2021). Furthermore, as putative models of the human visual system, DNNs should be able to support not only object recognition, but also visual reasoning (Bowers, Malhotra, Dujmović, Llera Montero, Tsvetkov, Biscione, Puebla, Adolfi, Hummel, Heaton and et al, 2023b).…”

Section: Introductionmentioning

confidence: 99%

Can deep convolutional neural networks support relational reasoning in the same-different task?

Puebla

Bowers

2021

Preprint

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Same-different visual reasoning is a basic skill central to abstract combinatorial thought. This fact has lead neural networks researchers to test same-different classification on deep convolutional neural networks (DCNNs), which has resulted in a controversy regarding whether this skill is within the capacity of these models. However, most tests of same-different classification rely on testing on images that come from the same pixel-level distribution as the testing images, yielding the results inconclusive. In this study we tested relational same-different reasoning DCNNs. In a series of simulations we show that models based on the ResNet-50 architecture are capable of visual same-different classification, but only when the test images are similar to the training images at the pixel-level. In contrast, even when there are only subtle differences between the testing and training images, the performance of DCNNs drops substantially. This is true even when DCNNs' training regime is augmented with images from new versions of the same-different task or through multi-task learning on the test images. Furthermore, we show that the Relation Network, a deep learning architecture specifically designed to tackle visual relational reasoning problems, suffers the same kind of limitations than ResNet-50 classifiers.

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“…“Isomorphism” is derived from the Greek words isos –“same” or “equal”—and morphe —“form” or “shape.” Thus, isomorphism means “same form” or “same structure.” We argue that the mathematical concept of isomorphism is grounded in a psychological intuition of similarity or sameness. An ability to determine if a collection of objects or items are the same or different was described as the “keel and backbone” of our cognition by James (1890), but has also been demonstrated in infants (Hespos et al, 2021) and many nonhuman species including pigeons (Katz & Wright, 2006; Wasserman & Young, 2010), bees (Giurfa, 2021; Giurfa et al, 2001), crows (Smirnova et al, 2015), parrots (Pepperberg, 1987), and macaques and rhesus monkeys (Katz et al, 2002; Wright et al, 2003). Ducklings have been shown to imprint on a sameness versus difference relation (Martinho & Kacelnik, 2016; see also Versace et al, 2017, for similar results with chicks).…”

Section: Psychological Status Of Monotonicity Convexity Continuity An...mentioning

confidence: 99%

The psychological scaffolding of arithmetic.

Grice¹,

Kemp²,

Morton³

et al. 2024

Psychological Review

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Where does arithmetic come from, and why are addition and multiplication its fundamental operations? Although we know that arithmetic is true, no explanation that meets standards of scientific rigor is available from philosophy, mathematical logic, or the cognitive sciences. We propose a new approach based on the assumption that arithmetic has a biological origin: Many examples of adaptive behavior such as spatial navigation suggest that organisms can perform arithmetic-like operations on represented magnitudes. If so, these operations—nonsymbolic precursors of addition and multiplication—might be optimal due to evolution and thus identifiable according to an appropriate criterion. We frame this as a metamathematical question, and using an order-theoretic criterion, prove that four qualitative conditions—monotonicity, convexity, continuity, and isomorphism—are sufficient to identify addition and multiplication over the real numbers uniquely from the uncountably infinite class of possible operations. Our results show that numbers and algebraic structure emerge from purely qualitative conditions, and as a construction of arithmetic, provide a rigorous explanation for why addition and multiplication are its fundamental operations. We argue that these conditions are preverbal psychological intuitions or principles of perceptual organization that are biologically based and shape how humans and nonhumans alike perceive the world. This is a Kantian view and suggests that arithmetic need not be regarded as an immutable truth of the universe but rather as a natural consequence of our perception. Algebraic structure may be inherent in the representations of the world formed by our perceptual system.

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The origins of same/different discrimination in human infants

Cited by 12 publications

References 39 publications

Learning Online Visual Invariances for Novel Objects via Supervised and Self-Supervised Training

Learning Online Visual Invariances for Novel Objects via Supervised and Self-Supervised Training

Can deep convolutional neural networks support relational reasoning in the same-different task?

The psychological scaffolding of arithmetic.

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