An emergentist perspective on the origin of number sense. (19th February 2018)
- Record Type:
- Journal Article
- Title:
- An emergentist perspective on the origin of number sense. (19th February 2018)
- Main Title:
- An emergentist perspective on the origin of number sense
- Authors:
- Zorzi, Marco
Testolin, Alberto - Abstract:
- Abstract : The finding that human infants and many other animal species are sensitive to numerical quantity has been widely interpreted as evidence for evolved, biologically determined numerical capacities across unrelated species, thereby supporting a 'nativist' stance on the origin of number sense. Here, we tackle this issue within the 'emergentist' perspective provided by artificial neural network models, and we build on computer simulations to discuss two different approaches to think about the innateness of number sense. The first, illustrated by artificial life simulations, shows that numerical abilities can be supported by domain-specific representations emerging from evolutionary pressure. The second assumes that numerical representations need not be genetically pre-determined but can emerge from the interplay between innate architectural constraints and domain-general learning mechanisms, instantiated in deep learning simulations. We show that deep neural networks endowed with basic visuospatial processing exhibit a remarkable performance in numerosity discrimination before any experience-dependent learning, whereas unsupervised sensory experience with visual sets leads to subsequent improvement of number acuity and reduces the influence of continuous visual cues. The emergent neuronal code for numbers in the model includes both numerosity-sensitive (summation coding) and numerosity-selective response profiles, closely mirroring those found in monkey intraparietalAbstract : The finding that human infants and many other animal species are sensitive to numerical quantity has been widely interpreted as evidence for evolved, biologically determined numerical capacities across unrelated species, thereby supporting a 'nativist' stance on the origin of number sense. Here, we tackle this issue within the 'emergentist' perspective provided by artificial neural network models, and we build on computer simulations to discuss two different approaches to think about the innateness of number sense. The first, illustrated by artificial life simulations, shows that numerical abilities can be supported by domain-specific representations emerging from evolutionary pressure. The second assumes that numerical representations need not be genetically pre-determined but can emerge from the interplay between innate architectural constraints and domain-general learning mechanisms, instantiated in deep learning simulations. We show that deep neural networks endowed with basic visuospatial processing exhibit a remarkable performance in numerosity discrimination before any experience-dependent learning, whereas unsupervised sensory experience with visual sets leads to subsequent improvement of number acuity and reduces the influence of continuous visual cues. The emergent neuronal code for numbers in the model includes both numerosity-sensitive (summation coding) and numerosity-selective response profiles, closely mirroring those found in monkey intraparietal neurons. We conclude that a form of innatism based on architectural and learning biases is a fruitful approach to understanding the origin and development of number sense. This article is part of a discussion meeting issue 'The origins of numerical abilities'. … (more)
- Is Part Of:
- Philosophical transactions. Volume 373:Number 1740(2018)
- Journal:
- Philosophical transactions
- Issue:
- Volume 373:Number 1740(2018)
- Issue Display:
- Volume 373, Issue 1740 (2018)
- Year:
- 2018
- Volume:
- 373
- Issue:
- 1740
- Issue Sort Value:
- 2018-0373-1740-0000
- Page Start:
- Page End:
- Publication Date:
- 2018-02-19
- Subjects:
- number sense -- numerosity perception -- numerical development -- computational modelling -- deep learning -- artificial neural networks
Biology -- Periodicals
Science -- Periodicals
570 - Journal URLs:
- https://royalsocietypublishing.org/loi/rstb ↗
- DOI:
- 10.1098/rstb.2017.0043 ↗
- Languages:
- English
- ISSNs:
- 0962-8436
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library STI - ELD Digital store
- Ingest File:
- 25054.xml