Selective neural coding of object, feature, and geometry spatial cues in humans

Abstract

Orienting in space requires the processing of visual spatial cues. The dominant hypothesis about the brain structures mediating the coding of spatial cues stipulates the existence of a hippocampal-dependent system for the representation of geometry and a striatal-dependent system for the representation of landmarks. However, this dual-system hypothesis is based on paradigms that presented spatial cues conveying either conflicting or ambiguous spatial information and that used the term landmark to refer to both discrete three-dimensional objects and wall features. Here, we test the hypothesis of complex activation patterns in the hippocampus and the striatum during visual coding. We also postulate that object-based and feature-based navigation are not equivalent instances of landmark-based navigation. We examined how the neural networks associated with geometry-, object-, and feature-based spatial navigation compared with a control condition in a two-choice behavioral paradigm using fMRI. We showed that the hippocampus was involved in all three types of cue-based navigation, whereas the striatum was more strongly recruited in the presence of geometric cues than object or feature cues. We also found that unique, specific neural signatures were associated with each spatial cue. Object-based navigation elicited a widespread pattern of activity in temporal and occipital regions relative to feature-based navigation. These findings extend the current view of a dual, juxtaposed hippocampal-striatal system for visual spatial coding in humans. They also provide novel insights into the neural networks mediating object versus feature spatial coding, suggesting a need to distinguish these two types of landmarks in the context of human navigation.

Keywords: functional MRI; geometry; hippocampus; landmark; navigation; spatial cues; striatum.

FIGURE 1

The virtual navigation paradigm. (I) Schematic overhead perspectives of the virtual environment for the object, geometry, and feature conditions. (a) In the object condition all arms were 18 virtual meters (vm) long and equiangular and three light gray objects were placed in the center of the maze. (b) In the geometry condition the arms were approximately 18 vm long, separated by two 140° angles and one 80° angle and there were no objects in the center of the maze. (c) In the feature condition, the arms were 18 vm long and equiangular, and there were three differently colored walls in the center of the maze. Of note, the overhead perspective was never seen by subjects. (II) The design of one condition. (a) The encoding phase took place only once at the beginning of a condition. Participants were instructed to search for the hidden goal. Once they had found it they needed to learn its position with respect to the available visual spatial cues. A 15‐s pause marked the end of the encoding phase. (b) The retrieval phase comprised eight trials. In each trial, participants started from the end of an arm and they needed to retrieve the hidden goal as quickly and as accurately as possible. Each trial was separated by an interval lasting 2.68 or 5.36 s.

FIGURE 2

Behavioral results for the virtual navigation task across cue‐based conditions. (a) Proportion of trials in which the correct corridor was chosen (success rate). (b) Time taken to reach the goal averaged across eight trials (navigation time). (c) Navigation time across trial number for the object condition (OBJ), geometry condition (GEO) and feature condition (FEAT). The r and p‐values correspond to Spearman rank correlations (top right). All error bars represent standard errors of the mean.

FIGURE 3

Cerebral regions whose activity was elicited by contrasting (a) the object condition with the control condition [OBJ > CTRL], (b) the geometry condition with the control condition [GEO > CTRL], and (c) the feature condition with the control condition [FEAT > CTRL]. The neural activity is projected onto 3D inflated anatomical templates and 2D slices for the cerebellum (p < .001 uncorrected, k = 10 voxels). CTRL, control condition; FEAT, feature condition; GEO, geometry condition; L, left hemisphere; OBJ, object condition; R, right hemisphere.

FIGURE 4

Cerebral regions whose activity was elicited by the fMRI contrasts (a) [GEO > OBJ] and (b) [GEO > FEAT]. The neural activity is projected onto 2D slices (p < .001 uncorrected, k = 10 voxels). CN, caudate nucleus; FEAT, feature; GEO, geometry; IOG, inferior occipital gyrus; IX, lobule IX cerebellum; L, left hemisphere; MFG, middle frontal gyrus; MTG, middle temporal gyrus; OBJ, object; Pu, putamen; R, right hemisphere; SFG, superior frontal gyrus; SMG, supramarginal gyrus; SPG, superior parietal gyrus.

FIGURE 5

Results of the ROI analyses. (a) FMRI parameter estimates in the bilateral hippocampus and striatum for the fMRI contrasts [OBJ > CTRL], [GEO > CTRL], and [FEAT > CTRL]. (b) Representational similarity matrices for the hippocampus (left) and striatum (right) averaged across subjects. Similarity scores are Fisher z‐transformed Pearson correlation coefficients, and they represent the overlap in terms of neural patterns between the object, geometry, and feature conditions. (c) FMRI parameter estimates in the hippocampus and striatum for fMRI contrasts comparing neural activity in each trial of the object condition to that in one trial of the control condition (e.g., [OBJ t1 > CTRL t1]), neural activity in each trial of the geometry condition to that in one trial of the control condition (e.g., [GEO t1 > CTRL t1]) and neural activity in each trial of the feature condition to that in one trial of the control condition (e.g., [FEAT t1 > CTRL t1]). All error bars reflect standard errors of the mean.