How Limited Systematicity Emerges: A Computational Cognitive Neuroscience Approach (Author's Manuscript)

Is human cognition best characterized in terms of the systematic nature of classical symbol processing systems (as argued by Fodor & Pylyshyn, 1988), or in terms of the context-sensitive, embedded knowledge characteristic of classical connectionist or neural network systems? We attempt to bridge these contrasting perspectives in several ways. First, we argue that human cognition exhibits the full spectrum, from extreme context sensitivity to high levels of systematicity. Next, we leverage biologically-based computational modeling of different brain areas (and their interactions), at multiple levels of abstraction, to show how this full spectrum of behavior can be understood from a computational cognitive neuroscience perspective. In particular, recent computational modeling of the prefrontal cortex / basal ganglia circuit demonstrates a mechanism for variable binding that supports high levels of systematicity, in domains where traditional connectionist models fail. Thus, we find that this debate has helped advance our understanding of human cognition in many ways, and are optimistic that a careful consideration of the computational nature of neural processing can help bridge seemingly opposing viewpoints. 2 How Limited Systematicity Emerges Introduction In this chapter we address the claims made by Fodor & Pylyshyn (1988) (FP88 herafter). We strike a middle ground between classic symbolic and connectionist perspectives, arguing that cognition is less systematic than classicists claim, but that connectionist, neural-processing-based theories have yet to explain the extent to which it is systematic. We offer a sketch of an emerging understanding of the basis of human systematicity in terms of interactions between specialized brain systems, leveraging the computational principles identified and empirical work done in the quarter-century since the target work was published. We identify a full spectrum of processing mechanisms, arrayed along the continuum between context sensitivity and combinatorial, systematic processing, each associated with different parts of the human brain. We find that attempting to understand the role of these different brain areas through the lens of systematicity results in a rich picture of human cognitive abilities. FP88 make two central claims for what a classical symbol processing system must be capable of, which define a Classical model: 1. Mental representations have combinatorial syntax and semantics. Complex representations (“molecules”) can be composed of other complex representations (compositionality), or simpler “atomic” ones, and these combinations behave sensibly in terms of the constituents. 2. Structure sensitivity of processes. There is a separation between form and content, exemplified in the distinction between syntax and semantics, and processes can operate on the form (syntax) while ignoring the semantic content. Supported by NIMH grant MH079485, ONR grant N00014-13-1-0067, and by the Intelligence Advanced Research Projects Activity (IARPA) via Department of the Interior (DOI) contract number D10PC20021. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained hereon are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DOI, or the U.S. Government. O’Reilly et al. 3 Taken together, these abilities enable a system to be fully systematic and compositional. Systematicity comes directly from the ability to process the form or structure of something, independent of its specific contents: if you can process sentences with a given syntax (e.g., Noun Verb Object) then you can process any constituent words in such sentences – you do not have to relearn the syntax all over again for each new word. In Chomsky’s famous example, you can tell that “Colorless green ideas sleep furiously” is grammatically correct because you can encode its structural form, independent of the (lack of) meaning, while “Furiously sleep ideas green colorless” is not grammatically correct. FP88 made the point that connectionist models of that time failed to exhibit these features, and thus were insufficient models of the full power of human cognition (Fodor & Pylyshyn, 1988; Fodor & McLaughlin, 1990; McLaughlin, 1993). This debate remains active to this day, with various critical commentaries (Aizawa, 1997; Cummins, 1996; Hadley, 1994; Horgan & Tienson, 1996; Matthews, 1997; van Gelder, 1990), anthologies (Macdonald & Macdonald, 1995), and a book-length treatment (Aizawa, 2003). Recently, Bayesian symbolic modelers have raised similar critiques of neural network models (Kemp & Tenenbaum, 2008; Griffiths, Chater, Kemp, Perfors, & Tenenbaum, 2010), which are defended in return (McClelland, Botvinick, Noelle, Plaut, Rogers, Seidenberg, & Smith, 2010). Qualitatively, there are two opposing poles in the space of approaches one can take in attempting to reconcile the FP88 and subsequent critiques with the fact that the human brain is, in fact, made of networks of neurons. One could argue that this systematic, compositional behavior is a defining feature of human cognition, and figure out some way that networks of neurons can implement it (the “mere implementation” approach). Alternatively, one could argue that the kind of systematicity championed by FP88 is actually not a very good characterization of human cognition, and that a closer examination of actual human behavior shows that people behave much more as would be expected from networks of neurons, and not much as would be expected from a classical symbol processing system (the “dismissive” approach). Few connectionist researchers have shown much enthusiasm for the project of merely implementing a symbolic system, although proof-of-concept demonstrations do exist (Touretzky, 1990). Instead, there have been numerous attempts to demonstrate systematic generalization with neural networks (Bodén & Niklasson, 2000; Chalmers, 1990; Christiansen & Chater, 1994; Hadley, 1997; 4 How Limited Systematicity Emerges Hadley & Hayward, 1997; Niklasson & van Gelder, 1994; Smolensky, 1988, 1990b; Smolensky & Legendre, 2006). Also, careful examinations of language (Johnson, 2004) and various aspects of human behavior have questioned whether human language, thought, and behavior really are as systematic as it is commonly assumed (van Gelder & Niklasson, 1994). An intermediate approach is to attempt to implement a symbolic system using neural networks with the intent of finding out which symbolic aspects of systematicity are plausible from a neural perspective and which are not (Lebiere & Anderson, 1993). This attempt to implement the ACT-R cognitive architecture using standard neural network constructs such as Hopfield networks and feedforward networks resulted in a considerable simplification of the architecture. This included both the outright removal of some of its most luxuriant symbolic features as neurally implausible, such as chunks of information in declarative memory that could contain lists of items and production rules that could perform arbitrarily complex pattern-matching over those chunks. More fundamentally, neural constraints on the architecture led to a modular organization that combines massive parallelism within each component (procedural control, declarative memory, visual processing, etc.) with serial synchronization of information transfers between components. That organization in turn has been validated using neural localization of architectural modules using neural imaging techniques (Anderson, 2007). In general, this hybrid approach has resulted in an architecture that largely preserves the systematicity of the original one while greatly improving its neural plausibility. It should be pointed out though that systematicity in ACT-R is limited both by the need to acquire the skills and knowledge needed to perform any of the tasks in which it is demonstrated, but more fundamentally by the combination of the symbolic level with a subsymbolic level that controls every aspect of its operations (procedural action selection, information retrieval from memory, etc.) The reason the systematicity debate has persisted for so long is that both positions have merit. In this paper, we take a “middle way” approach, arguing that purely systematic symbol processing systems do not provide a good description of much of human cognition, but nevertheless that there are some clear examples where people can approximate the systematicity of symbol processing systems, and we need to understand how the human brain can achieve this feat. Going further, we argue that a careful consideration O’Reilly et al. 5 of all the ways in which the human brain can support systematicity actually deals with important limitations of the pure symbol processing approach, while providing a useful window into the nature of human cognition. From a neural mechanisms perspective, we emphasize the role that interactions between brain systems including the more “advanced” brain areas, specifically the prefrontal cortex / basal ganglia (PFC/BG) system, play in enabling the systematic aspects of human cognition. In so doing, we move beyond the limitations of traditional “connectionist” neural network models, while remaining committed to only considering neural mechanisms that have strong biological support. While the overall space of issues relevant to this systematicity debate is quite high-dimensional and complex, one very important principal component can be boiled down to the issue of the context sensitivity vs combinatoriality tradeoff. At the extreme context sensitivity end of the spectrum, the system maintains a lookup table that simply memorizes each instance or exemplar, and the appropriate interpretation or response to it. Such a system is highly context sensitive,