Computational perspectives on cognitive development

Mareschal, Denis

doi:10.1002/wcs.67

Cited by 8 publications

(8 citation statements)

References 108 publications

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“…Whilst some of these models have investigated different aspects of visual orienting in infancy (Carlson & Triesch, 2004;Schlesinger, Amso, & Johnson, 2007;Domsch et al, 2010;Sirois & Mareschal, 2002; for a review see Mareschal, 2010), to date no computational model has attempted to explain the specific mechanisms underlying saccadic control in infancy.…”

Section: Modeling Infant Fds With the Crisp Modelmentioning

confidence: 99%

Disentangling the mechanisms underlying infant fixation durations in scene perception: A computational account

et al. 2017

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The goal of this article is to investigate the unexplored mechanisms underlying the development of saccadic control in infancy by determining the generalizability and potential limitations of extending the CRISP theoretical framework and computational model of fixation durations (FDs) in adult scene-viewing to infants. The CRISP model was used to investigate the underlying mechanisms modulating FDs in 6-month-olds by applying the model to empirical eye-movement data gathered from groups of infants and adults during free-viewing of naturalistic and semi-naturalistic videos. Participants also performed a gap-overlap task to measure their disengagement abilities. Results confirmed the CRISP model's applicability to infant data. Specifically, model simulations support the view that infant saccade programming is completed in two stages: an initial labile stage, followed by a non-labile stage. Moreover, results from the empirical data and simulation studies highlighted the influence of the material viewed on the FD distributions in infants and adults, as well as the impact that the developmental state of the oculomotor system can have on saccade programming and execution at 6 months. The present work suggests that infant FDs reflect on-line perceptual and cognitive activity in a similar way to adults, but that the individual developmental state of the oculomotor system affects this relationship at 6 months. Furthermore, computational modeling filled the gaps of psychophysical studies and allowed the effects of these two factors on FDs to be simulated in infant data providing greater insights into the development of oculomotor and attentional control than can be gained from behavioral results alone.

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Section: Modeling Infant Fds With the Crisp Modelmentioning

confidence: 99%

Disentangling the mechanisms underlying infant fixation durations in scene perception: A computational account

et al. 2017

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“…For the last 2.5 decades, there has been active literature on the computational modeling of early psychological development with artificial neural networks, well documented in both review articles (Elman, 2005; Mareschal, 2010; Munakata & McClelland, 2003; Shultz, 2012, 2013, 2017; Shultz & Sirois, 2008; Westermann et al, 2006) and books (Elman et al, 1996; Mareschal et al, 2007; Shultz, 2003). This article contributes to that research, with implications for further integration with Bayesian approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

A computational model of infant learning and reasoning with probabilities.

Shultz¹,

Nobandegani²

2022

Psychological Review

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Recent experiments reveal that 6- to 12-month-old infants can learn probabilities and reason with them. In this work, we present a novel computational system called Neural Probability Learner and Sampler (NPLS) that learns and reasons with probabilities, providing a computationally sufficient mechanism to explain infant probabilistic learning and inference. In 24 computer simulations, NPLS shows how probability distributions can emerge naturally from neural-network learning of event sequences, providing a novel explanation of infant probabilistic learning and reasoning. Three mathematical proofs show how and why NPLS simulates the infant results so accurately. The results are situated in relation to seven other active research lines. This work provides an effective way to integrate Bayesian and neural-network approaches to cognition.

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“…Some researchers have pursued the goal of constructing computational models of cognitive development using different computational approaches (for a review, see Mareschal, 2010). However, as the author of this review remarks, all the models have explored cognition “as an isolated phenomenon”, i.e., they did not consider the physical and social context in which development unfolds.…”

Section: The Study Of Developmentmentioning

confidence: 99%

The Place of Development in the History of Psychology and Cognitive Science

Airenti

2019

Front. Psychol.

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In this article, I analyze how the relationship of developmental psychology with general psychology and cognitive science has unfolded. This historical analysis will provide a background for a critical examination of the present state of the art. I shall argue that the study of human mind is inherently connected with the study of its development. From the beginning of psychology as a discipline, general psychology and developmental psychology have followed parallel and relatively separated paths. This separation between adult and child studies has also persisted with the emergence of cognitive science. The reason is due essentially to methodological problems that have involved not only research methods but also the very object of inquiry. At present, things have evolved in many ways. Psychology and cognitive science have enlarged their scope to include change process and the interaction between mind and environment. On the other hand, the possibility of using experimental methods to study infancy has allowed us to realize the complexity of young humans. These facts have paved the way for new possibilities of convergence, which are eliciting interesting results, despite a number of ongoing problems related to methods.

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Computational perspectives on cognitive development

Cited by 8 publications

References 108 publications

Disentangling the mechanisms underlying infant fixation durations in scene perception: A computational account

Disentangling the mechanisms underlying infant fixation durations in scene perception: A computational account

A computational model of infant learning and reasoning with probabilities.

The Place of Development in the History of Psychology and Cognitive Science

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