Social navigation: distance and grid-like codes support navigation of abstract social space in human brain

Reviewer #1 (Public Review):

This study examines whether the human brain uses a hexagonal grid-like representation to navigate in a non-spatial space constructed by competence and trustworthiness. To test this, the authors asked human participants to learn the levels of competence and trustworthiness for six faces by associating them with specific lengths of bar graphs that indicate their levels in each trait. After learning, participants were asked to extrapolate the location from the partially observed morphing bar graphs. Using fMRI, the authors identified brain areas where activity is modulated by the angles of morphing trajectories in six-fold symmetry. The strength of this paper lies in the question it attempts to address. Specifically, the question of whether and how the human brain uses grid-like representations not only for spatial navigation but also for navigating abstract concepts, such as social space, and guiding everyday decision-making. This question is of emerging importance.

The weak points of this paper are that its findings are not sufficiently supporting their arguments, and there are several reasons for this:

1. Does the grid-like activity reflect 'navigation over the social space' or 'navigation in sensory feature space'? The grid-like representation in this study could simply reflect the transition between stimuli (the length of bar graphs). Participants in this study associated each face with a specific length of two bars, and the 'navigation' was only guided by the morphing of a bar graph image. Moreover, any social cognition was not required to perform the task where they estimate the grid-like activity. To make social decision-making that was conducted separately, we do not know if participants needed to navigate between faces in a social space. Instead, they can recall bar graphs associated with faces and compute the decision values by comparing the length of bars. Notably, in the trust game in this study, competence and trustworthiness are not equally important to make a decision (Equation 1). The expected value is more sensitive to one over the other. This also suggests that the space might not reflect social values but perceptual differences.

2. Does the brain have a common representation of faces in a social space? In this study, participants don't need to have a map-like representation of six faces according to their levels of social traits. Instead, they can remember the values of each trait. The evidence of neural representations of the faces in a 2-dimensional social space is lacking. The authors argued that the relationship between the reaction times and the distances between faces provides evidence of the formation of internal representations. However, this can be found without the internal representation of the relationships between faces. If the authors seek internal representations of the faces in the brain, it would be important to show that this representation is not simply driven by perceptual differences between bar graphs that participants may recall in association with each face.

Considering these caveats, it is hard for me to agree if the authors provide evidence to support their claims.

Reviewer #2 (Public Review):

Summary:
In this work, Liang et al. investigate whether an abstract social space is neurally represented by a grid-like code. They trained participants to 'navigate' around a two-dimensional space of social agents characterized by the traits of warmth and competence, then measured neural activity as participants imagined navigating through this space. The primary neural analysis consisted of three procedures: 1) identifying brain regions exhibiting the hexagonal modulation characteristic of a grid-like code, 2) estimating the orientation of each region's grid, and 3) testing whether the strength of the univariate neural signal increases when a participant is navigating in a direction aligned with the grid, compared to a direction that is misaligned with the grid.
From these analyses, the authors find the clearest evidence of a grid-like code in the prefrontal cortex and weaker evidence in the entorhinal cortex.

Strengths:
The work demonstrates the existence of a grid-like neural code for a socially-relevant task, providing evidence that such coding schemes may be relevant for a variety of two-dimensional task spaces.

Weaknesses:
In various parts of this manuscript, the authors appear to use a variety of terms to refer to the (ostensibly) same neural regions: prefrontal cortex, frontal pole, ventromedial prefrontal cortex (vmPFC), and orbitofrontal cortex (OFC). It would be useful for the authors to use more consistent terminology to avoid confusing readers.

Claims about a grid code in the entorhinal cortex are not well-supported by the analyses presented. The whole-brain analysis does not suggest that the entorhinal cortex exhibits hexagonal modulation; the strength of the entorhinal BOLD signal does not track the putative alignment of the grid code there; multivariate analyses do not reveal any evidence of a grid-like representational geometry.

On a conceptual level, it is not entirely clear how this work advances our understanding of grid-like encoding of two-dimensional abstract spaces, or of social cognition. The study design borrows heavily from Constantinescu et al. 2016, which is itself not an inherent weakness, but the Constantinescu et al. study already suggests that grid codes are likely to underlie two-dimensional spaces, no matter how abstract or arbitrary. If there were a hypothesis that there is something unique about how grid codes operate in the social domain, that would help motivate the search for social grid codes specifically, but no such theory is provided. The authors do note that warmth and competence likely have ecological importance as social traits, but other past studies have used slightly different social dimensions without any apparent loss of generality (e.g., Park et al. 2021). There are some (seemingly) exploratory analyses examining how individual difference measures like social anxiety and avoidance might affect the brain and behavior in this study, but a strong theoretical basis for examining these particular measures is lacking.

I found it difficult to understand the analyses examining whether behavior (i.e., reaction times) and individual difference measures (i.e., social anxiety and avoidance) can be predicted by the hexagonal modulation strength in some region X, conditional on region X having a similar estimated grid alignment with some other region Y. It is possible that I have misunderstood the authors' logic and/or methodology, but I do not feel comfortable commenting on the correctness or implications of this approach given the information provided in the current version of this manuscript.

It was puzzling to see passing references to multivariate analyses using representational similarity analysis (RSA) in the main text, given that RSA is only used in analyses presented in the supplementary material.

References:
Constantinescu, A. O., O'Reilly, J. X., & Behrens, T. E. (2016). Organizing conceptual knowledge in humans with a gridlike code. Science, 352(6292), 1464-1468.

Park, S. A., Miller, D. S., & Boorman, E. D. (2021). Inferences on a multidimensional social hierarchy use a grid-like code. Nature Neuroscience, 24(9), 1292-1301.

Reviewer #3 (Public Review):

Liang and colleagues set out to test whether the human brain uses distance and grid-like codes in social knowledge using a design where participants had to navigate in a two-dimensional social space based on competence and warmth during an fMRI scan. They showed that participants were able to navigate the social space and found distance-based codes as well as grid-like codes in various brain regions, and the grid-like code correlated with behavior (reaction times).

On the whole, the experiment is designed appropriately for testing for distant-based and grid-like codes and is relatively well-powered for this type of study, with a large amount of behavioral training per participant. They revealed that a number of brain regions correlated positively or negatively with distance in the social space, and found grid-like codes in the frontal polar cortex and posterior medial entorhinal cortex, the latter in line with prior findings on grid-like activity in the entorhinal cortex. The current paper seems quite similar conceptually and in design to previous work, most notably by Park et al., 2021, Nature Neuroscience.

Below, I raise a few issues and questions on the evidence presented here for a grid-like code as the basis of navigating abstract social space or social knowledge.

1. The authors claim that this study provides evidence that humans use a spatial / grid code for abstract knowledge like social knowledge.

This data does specifically not add anything new to this argument. As with almost all studies that test for a grid code in a similar "conceptual" space (not only the current study), the problem is that when the space is not a uniform, square/circular space, and 2-dimensional then there is no reason the code will be perfectly grid-like, i.e., show six-fold symmetry. In real-world scenarios of social space (as well as navigation, semantic concepts), it must be higher dimensional - or at least more than two-dimensional. It is unclear if this generalizes to larger spaces where not all part of the space is relevant. Modelling work from Tim Behrens' lab (e.g., Whittington et al., 2020) and Bradley Love's lab (e.g., Mok & Love, 2019) have shown/argued this to be the case. In experimental work, like in mazes from the Mosers' labs (e.g., Derdikman et al., 2009), or trapezoid environments from the O'Keefe lab (Krupic et al., 2015), there are distortions in mEC cells, and would not pass as grid cells in terms of the six-fold symmetry criterion.

The authors briefly discuss the limitations of this at the very end but do not really say how this speaks to the goal of their study and the claim that social space or knowledge is organized as a grid code and if it is in fact used in the brain in their study and beyond. This issue deserves to be discussed in more depth, possibly referring to prior work that addressed this, and raising the issue for future work to address the problem - or if the authors think it is a problem at all.

Data and analysis

2. Concerning the negative correlation of distance with activation in the fusiform gyrus and visual cortex: this is a slightly puzzling but potentially interesting finding. However, could this be related to reaction times? The larger the distance, the longer the reaction times, so the original finding might reflect larger activations with smaller distances.

3. Concerning the correlation of grid-like activity with behavior: is the correlation with reaction time just about how long people took (rather than a task-related neural signal)? The authors have only reported correlations with reaction time. The issue here is that the duration of reaction times also relates to the starting positions of each trial and where participants will navigate to. Considering the speed-accuracy tradeoff, could performance accuracy be negatively correlated with these grid consistency metrics? Or it could be positively correlated, which would suggest the grid signal reflects a good representation of the task.