Testing AI models as cognitive models for abstract reasoning development

Abstract reasoning, the ability to solve large-scale problems by taking away unnecessary details (Clement et al., 2007), is essential for human cognition and behavior. However, there remains a lack of cognitive computational models available to study how abstract reasoning emerges and develops in early childhood. We seek to solve this knowledge gap by testing whether deep learning models can explain the key mechanisms that enable children to develop abstract reasoning. Specifically, we investigated whether the Emergent Symbol Binding Network (ESBN; Webb et al., 2021) would be a suitable model. Higher working memory capacity has been shown to facilitate the development of abstract visual reasoning (AVR) in humans. We explore whether ESBN can simulate AVR developmental phenomena by manipulating its memory architecture and training regime. To test this, we observed ESBN’s accuracy as it solved two abstract visual reasoning tasks with decreasing batch size per condition (32, 16, 8, 4). We also used two possible encoders: a random and convolutional encoder. We predicted the convolutional encoder should perform better than the random one, given it has more layers (Seijdel et al., 2020). Initial results do not show support for the ESBN model as a model of abstract visual reasoning development because the simpler, random encoder fared better than the convolutional encoder for all batch sizes. Further research will be performed to identify a suitable candidate model for explaining abstract visual reasoning development.