Computational Developmental Psychology

By Thomas R. Shultz. 2003

160 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, neutral): “ring this kind of intuitive physics in deep learning systems? Connectionist models in psychology have previously been applied to physical reasoning tasks such as balance-beam rules (McClelland, 1988; Shultz, 2003) or rules relating distance, velocity, and time in motion (Buckingham & Shultz, 200”
Early lexical development in a self-organizing neural network (2004, background): “…and neurogenesis in the brain, recent evidence suggests that both types of neural genesis occur across the life span and have important developmental and cognitive consequences for the learning species (see Gould, Tanapat, Hastings, & Shors, 1999; Quinlan, 2003; Shultz, 2003 for discussion).”
Dynamic Development of Action and Thought (2007, methodology): “Many data sets show powerfully nonlinear individual growth as the norm in infant cognitive and emotional development as well as development at later ages (Fischer & Hogan, 1989; McCall, Eichorn, & Hogarty, 1977; Reznick & Goldfield, 1992; Ruhland & van Geert, 1998; Shultz, 2003).”
A constructivist connectionist model of transitions on false-belief tasks. (2013, unknown): “…no need to specify the internal network topology) and tend to cover developmental changes better (because hidden unit recruitment produces qualitative performance changes that can produce developmental transitions, e.g., Shultz, 2003, 2006; Shultz & Cohen, 2004; Shultz, Mareschal, & Schmidt, 1994).”
Cross-cultural, developmental psychology: integrating approaches and key insights (2020, unknown): “Of course, formal models of cognition are not at all new or radical in developmental science (see Shultz, 2003 and Mareschal & Thomas, 2007 for comprehensive reviews); we simply argue there is much to be gained from applying these existing methods and perspectives to the study of cross-societal…”
Building Machines that Learn and Think Like People (2018, background): “What are the prospects for embedding or acquiring this kind of intuitive physics in deep learning systems? Connectionist models in psychology have previously been applied to physical reasoning tasks such as balance-beam rules (McClelland, 1988; Shultz, 2003) or rules relating distance, velocity, and”
The shape of development (2005, methodology): “…in childhood, adolescence, and early adulthood (Andrich & Styles, 1994; Case, 1991; Draney, 1996; Fischer & Rose, 1994, 1999; Fischer & Silvern, 1985; Kitchener et al., 1993; Shultz, 2003; Thomas & Lohaus, 1993; van der Maas & Molenaar, 1992; van Geert, 1998; Walker et al., 2001; Wilson, 1989).”
The discovery of structural form (2008, background): “Analogous discoveries are made by children, who learn, for example, that social networks are often organized into cliques, that temporal categories such as the seasons and the days of the week can be arranged into cycles, that comparative relations such as ‘‘longer than’’ or ‘‘better than’’ are tran”
Learning overhypotheses with hierarchical Bayesian models. (2007, background): “Smith et al. (2002) report that the shape match was chosen 88% (66%) of the time in the test of first-order generalization, and 70% (65%) of the time in the second-order task (percentages in parentheses represent results when the task was replicated as part of Experiment 2).”
Neuroconstructivism - I: How the Brain Constructs Cognition (2007, background): “We assert that increases in representational complexity arise as a natural consequence of the processes of adaptation typical of complex biological systems like the brain (see also Quartz and Sejnowski, 1997; Shultz, 2003, for related views).”