The importance of shape in early lexical learning

By Barbara Landau, Linda B. Smith, S. Jones. 1988

Subfields: Cognitive Science & Psychology, Linguistics

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Citations in the corpus

Finding Generalizable Evidence by Learning to Convince Q&A Models (2019, background): “, 2019), while humans rely on shape (Landau et al., 1988).”
Data-Efficient Image Recognition with Contrastive Predictive Coding (2019, background): “In contrast, humans and animals can learn about new classes of images from a small number of examples (Landau et al., 1988; Markman, 1989).”
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness (2018, background): “, 2014); and it is well-known that object shape is the single most important cue for human object recognition (Landau et al., 1988), much more than other cues like size or texture (which may explain the ease at which humans recognise line drawings or millennia-old cave paintings).”
Analyzing and Improving the Image Quality of StyleGAN (2019, background): “It was recently discovered that classifiers trained using ImageNet [30] tend to base their decisions much more on texture than shape [10], while humans strongly focus on shape [23].”
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition (2020, background): “Perceptual similarity for humans however is biased towards shape [25, 31], but also based on e.”
Few-Shot Learning with Metric-Agnostic Conditional Embeddings (2018, background): “While children, and some neural networks, share a bias to classify based on shape (Landau et al., 1988; Ritter et al., 2017), we can also recognize the potential that some classiﬁcations would require a re-weighting of attribute importance as in the classiﬁcation of non-solids where shape is less…”
Bayesian Model-Agnostic Meta-Learning (2018, background): “This is presumed to be because during early learning, a human brain develops foundational structures such as the “shape bias” in order to learn the learning procedure (Landau et al., 1988).”
Frustratingly Simple Few-Shot Object Detection (2020, background): “Even a toddler can easily recognize a new concept with very little instruction (Landau et al., 1988; Samuelson & Smith, 2005; Smith et al., 2002).”
Semantic Adversarial Examples (2018, background): “One such property is the “shape bias,” stating that humans prefer to categorize objects according to their shape rather than color [11].”
Cumulative cultural evolution in the laboratory: An experimental approach to the origins of structure in human language (2008, result): “The collapse of distinctions based on color (which eventually occurred in all 4 replications of the first experiment) in favor of distinctions based on shape and movement is compatible with the literature on a shape bias, an expectation that words will refer to shapes of objects rather than to prope”
Cognition (1999, unknown): “Languages displaying harmony between the verb phrase and the adposi-tional phrase are those which have head-initial Verb-Object (VO) order and prepositions (e.g., as in English, see 1a. below), or which have head-final Object-Verb (OV) order and postpositions (e.g., as in Turkish, see 1b. below).”
Detecting blickets: how young children use information about novel causal powers in categorization and induction. (2000, background): “Recently, a number of authors have suggested that children categorize and, in particular, name objects on the basis of perceptual features such as shape (Imai, Gentner, & Uchida, 1994; Landau, Smith, & Jones, 1988; Smith, Jones, & Landau, 1996).”
Understanding Grounded Language Learning Agents (2017, methodology): “(2017), we adapt various experimental techniques initially developed by experimental psychologists (Landau et al., 1988; Markman, 1990; Hollich et al., 2000; Colunga & Smith, 2005).”