Interacting networks of brain regions underlie human spatial navigation: a review and novel synthesis of the literature

Abstract

Navigation is an inherently dynamic and multimodal process, making isolation of the unique cognitive components underlying it challenging. The assumptions of much of the literature on human spatial navigation are that 1) spatial navigation involves modality independent, discrete metric representations (i.e., egocentric vs. allocentric), 2) such representations can be further distilled to elemental cognitive processes, and 3) these cognitive processes can be ascribed to unique brain regions. We argue that modality-independent spatial representations, instead of providing exact metrics about our surrounding environment, more often involve heuristics for estimating spatial topology useful to the current task at hand. We also argue that egocentric (body centered) and allocentric (world centered) representations are better conceptualized as involving a continuum rather than as discrete. We propose a neural model to accommodate these ideas, arguing that such representations also involve a continuum of network interactions centered on retrosplenial and posterior parietal cortex, respectively. Our model thus helps explain both behavioral and neural findings otherwise difficult to account for with classic models of spatial navigation and memory, providing a testable framework for novel experiments.

Keywords: allocentric; cognitive map; egocentric; episodic memory; hippocampus; humans; path integration; retrosplenial cortex; spatial navigation.

Fig. 1.

Fundamental similarities between allocentric and egocentric coordinate systems. A: within an allocentric coordinate system, A, B, and C are landmark coordinates, N is the position of the navigator, and D is the displacement vector (movement of the navigator). AB, AC, and BC are therefore vectors that indicate both the direction and distance of landmarks to each other. The positions of landmarks stay stable with displacements of the navigator. B: in an egocentric coordinate system, in contrast, the positions of landmarks change continuously with movements of the navigator (i.e., movement from frame 1 to frame 2). The position of the navigator, however, is always centered at the origin. A′, B′, and C′ are positions of landmarks in egocentric frame 1, A″, B″, and C″ are positions of landmarks in egocentric frame 2. These correspond to vectors A′N, B′N, C′N, etc. Within this framework, egocentric to allocentric conversion can be accomplished many different ways. For example, AB, BC, AC in allocentric coordinates can be approximated with vector subtraction (A′N − B′N or A″N − B″N); see McNaughton et al. (1991) for more details. This conversion can also be solved using estimation of the Pythagorean theorem [e.g., AB = sqrt(A′N² + B′N²) or AB = sqrt(A″N² + B″N²)]. This is because vectors AB = A′B′ = A″B″ and the displacement vector (D) are the same in allocentric (A) and egocentric (B) space. This also means the two subspaces have the same basis set.

Fig. 2.

Canonical tasks used to assay egocentric and allocentric representations. A: the scene and orientation pointing (SOP) task. Components include 1) current location and bearing during navigation and 2) location of probed store (Ice Cream Shop). Whereas the SOP task is most easily solved based on knowledge of one’s egocentric position relative to the target, the SOP task can also be solved with knowledge of the current location relative to a second store and its relationship to the probe (Ice Cream Shop). In this way, allocentric knowledge can be used to solve the SOP task, in some situations. B: the judgments of relative direction (JRD) task. Components include 1) imagined location and direction of one target (Bookstore) relative to a second (Fast Food) and 2) imagined location and direction of a second target (Fast Food) relative to the probe (Camera Store). Whereas the JRD task is most easily solved based on knowledge about the relative positions of the three stores (allocentric knowledge), it can also be solved using one’s memory for a visual snapshot that includes the two imagined target stores (Bookstore, Fast Food) and probe (Camera Store); see Wolbers and Wiener (2014) for more details. In this way, egocentric knowledge can also contribute to solving the JRD task.

Fig. 3.

Heuristics override metric Euclidean knowledge of space. A: schematic of results from 2 different studies by Huttenlocher et al. (1991, 2004). These studies involved participants encoding and then drawing the locations of dots within a circle. Participants showed a significant tendency toward drawing dot locations that were encoded at or near 0°, 90°, 180°, or 270° as closer to 45°, 135°, 225°, and 315°. These findings demonstrated a tendency to use category heuristics (“somewhere in between 0° and 90°”) rather than precise metric knowledge. B: schematic of findings from Moar and Bower (1983). When participants estimated the angles of the intersections between 3 well-known streets, they tended to draw the angles as closer to 90° than their actual angles. Because the sum of angles of a triangle must not exceed 180°, this violates the rules of Euclidean geometry. C: Alignment effects, demonstrated in numerous studies cited within the main text. Participants pointed to the imagined locations of targets (using the JRD task) more accurately when imagining themselves facing parallel to main axes of a rectangle than when misaligned. These findings suggest that the category heuristic of “rectangle,” in this case, aids in the memory for the locations of stores, without which spatial memory is significantly less precise.

Fig. 4.

Schematic of our network model of allocentric and egocentric representation. Network conceptualization shows allocentric (A) and egocentric (B) representations centered on the retrosplenial region (RSR; retrosplenial cortex and retrosplenial complex) and posterior parietal cortex (PPC), respectively. EC, entorhinal cortex; HC, hippocampus; IT, inferotemporal cortex; PFC, prefrontal cortex; PR, parahippocampal region; Thal, thalamus; VC, visual cortex. Allocentric and egocentric representations involve information processing centered on different hubs yet still involve largely overlapping brain regions. The model thus allows for a continuum of allocentric and egocentric representations, as well as hybrid versions of the two, which will depend on the degree of hubness and shared computations across brain regions within the network. The model also allows for heuristics, which will involve additional contributions from areas such as IT.