The Cognitive Architecture of Spatial Navigation: Hippocampal and Striatal Contributions

Abstract

Spatial navigation can serve as a model system in cognitive neuroscience, in which specific neural representations, learning rules, and control strategies can be inferred from the vast experimental literature that exists across many species, including humans. Here, we review this literature, focusing on the contributions of hippocampal and striatal systems, and attempt to outline a minimal cognitive architecture that is consistent with the experimental literature and that synthesizes previous related computational modeling. The resulting architecture includes striatal reinforcement learning based on egocentric representations of sensory states and actions, incidental Hebbian association of sensory information with allocentric state representations in the hippocampus, and arbitration of the outputs of both systems based on confidence/uncertainty in medial prefrontal cortex. We discuss the relationship between this architecture and learning in model-free and model-based systems, episodic memory, imagery, and planning, including some open questions and directions for further experiments.

The application of a rodent-based Morris water maze (MWM) protocol to an investigation of age-related differences in human spatial learning.

“…Bohbot et al, 2012; Wiener, de Condappa, Harris, & Wolbers, 2013). At the neural level, the potential implementation of non-spatial egocentric strategies might have corresponded to an age-related shift in the locus of functional activity from the hippocampus toward the striatum when processing navigationally relevant information (Konishi et al, 2013; Schuck, Doeller, Polk, Lindenberger, & Li, 2015a; Bohbot et al, 2012; Chersi & Burgess, 2015). This has been proposed as an adaptive mechanism that frees up hippocampal-dependent resources for facilitating repetitive or stereotyped navigational behavior (Bohbot et al, 2012) and it may be worthwhile for future studies to ascertain whether the poor-performing older adults in the vMWM would exhibit a greater engagement of the striatum than of the hippocampus.…”

A virtual water maze revisited: Two-year changes in navigation performance and their neural correlates in healthy adults

“…Under conditions of uncertainty and potentially conflicting navigation strategies, the prefrontal cortex may act as arbiter (Chersi and Burgess, 2015). As the prefrontal cortex is viewed as a neural substrate of cognitive control in skill acquisition and maintenance as well as strategic aspects of procedural and episodic memory (Miller and Cohen 2001; Poldrack et al 2005; Seidler et al 2012; Ofen and Shing 2013), individuals with larger LPFC volume may be better able to adapt strategies that optimize path length and complexity (O’Keefe 1991).…”

Egocentric and allocentric visuospatial working memory in premotor Huntington's disease: A double dissociation with caudate and hippocampal volumes

“…The dorsolateral head of the caudate receives dense efferents from dorsolateral frontal regions important for executive and motor control, as well as from parietal sensory regions (Alexander et al, 1986). The dorsolateral head of the caudate may integrate perception and action by coding spatial information represented in parietal cortex in preparation for movement (Chersi and Burgess, 2015; Neggers et al, 2006; Voorn et al, 2004). Consistent with our findings, rats have shown impairment in coding egocentrically defined locations contralateral to dorsal striatum lesions (Brasted et al, 1997) and primates have shown metabolic activation in the caudate head during a right-left alternation spatial working memory test but not an object alternation working memory test, which was associated with caudate body activation (Levy et al, 1997).…”

“…The allocentric reference frame represents the spatial relationships between landmarks, independent of the position of the self, and is important for developing cognitive maps (Maguire et al, 1998; O’Keefe and Nadel, 1978). The egocentric reference frame represents object locations in reference to the position of the self, is updated during movement, and is important for navigating toward a visible landmark and learning stimulus-response associations and fixed routes (Aguirre and D’Esposito, 1999; Chersi and Burgess, 2015; Redish, 1999). These representational systems operate largely in parallel, and individuals favor one system or the other when solving a navigation task (Bohbot et al, 2007; Iaria et al, 2003).…”