4.3 Emergentist Cognitive Architectures 65 4.3 Emergentist Cognitive Architectures Another species of cognitive architecture expects abstract symbolic processing to emerge from lower-level “subsymbolic” dynamics, which sometimes (but not always) are designed to simu- late neural networks or other aspects of human brain function. These architectures are typically strong at recognizing patterns in high-dimensional data, reinforcement learning and associative memory; but no one has yet shown how to achieve high-level functions such as abstract reason- ing or complex language processing using a purely subsymbolic approach. A few of the more important subsymbolic, emergentist cognitive architectures are: e DeSTIN [ARK09a, ARCO9], which is part of CogPrime, may also be considered as an autonomous AGI architecture, in which case it is emergentist and contains mechanisms to encourage language, high-level reasoning and other abstract aspects of intelligent to emerge from hierarchical pattern recognition and related self-organizing network dynamics. In CogPrime DeSTIN is used as part of a hybrid architecture, which greatly reduces the reliance on DeSTIN’s emergent properties. e Hierarchical Temporal Memory (HTM) [I06] is a hierarchical temporal pattern recognition architecture, presented as both an AI approach and a model of the cortex. So far it has been used exclusively for vision processing and we will discuss its shortcomings later in the context of our treatment of DeSTIN. e SAL [JL08], based on the earlier and related IBCA (Integrated Biologically-based Cog- nitive Architecture) is a large-scale emergent architecture that seeks to model distributed information processing in the brain, especially the posterior and frontal cortex and the hippocampus. So far the architectures in this lineage have been used to simulate various human psychological and psycholinguistic behaviors, but haven’t been shown to give rise to higher-level behaviors like reasoning or subgoaling.