An In-depth Exploration of the Interplay between fMRI Methods and Theory in Cognitive Neuroscience.

Functional magnetic resonance imaging (fMRI) has been a cornerstone of cognitive neuroscience since its invention in the 1990s. The methods that we use for fMRI data analysis allow us to test different theories of the brain, thus different analyses can lead us to different conclusions about how the brain produces cognition. There has been a centuries-long debate about the nature of neural processing, with some theories arguing for functional specialization or localization (e.g., face and scene processing) while other theories suggest that cognition is implemented in distributed representations across many neurons and brain regions. Importantly, these theories have received support via different types of analyses; therefore, having students implement hands-on data analysis to explore the results of different fMRI analyses can allow them to take a firsthand approach to thinking about highly influential theories in cognitive neuroscience. Moreover, these explorations allow students to see that there are not clearcut "right" or "wrong" answers in cognitive neuroscience, rather we effectively instantiate assumptions within our analytical approaches that can lead us to different conclusions. Here, I provide Python code that uses freely available software and data to teach students how to analyze fMRI data using traditional activation analysis and machine-learning-based multivariate pattern analysis (MVPA). Altogether, these resources help teach students about the paramount importance of methodology in shaping our theories of the brain, and I believe they will be helpful for introductory undergraduate courses, graduate-level courses, and as a first analysis for people working in labs that use fMRI.

Prolonged development of forced-choice recognition when targets are paired with non-corresponding lures.

Previous research suggests that mnemonic discrimination (i.e., the ability to discriminate between previously encountered and novel stimuli even when they are highly similar) improves substantially during childhood. To further understand the development of mnemonic discrimination during childhood, the current study had 4-year-old children, 6-year-old children, and young adults complete the forced-choice Mnemonic Similarity Task (MST). The forced-choice MST offers a significant advantage in the context of developmental research because it is not sensitive to age-related differences in response criteria and includes three test formats that are theorized to be supported by different cognitive processes. A target (i.e., a previously encountered item) is paired with either a novel item (A-X), a corresponding lure (A-A'; i.e., an item mnemonically similar to the target), or a non-corresponding lure (A-B'; i.e., an item mnemonically similar to a different previously encoded item). We observed that 4-year-olds performed more poorly than 6-year-olds on the A-X and A-A' test formats, whereas both 4- and 6-year-olds performed more poorly than young adults on the A-B' test format. The MINERVA 2.2 computational model effectively accounted for these age-related differences. The model suggested that 4-year-olds have a lower learning rate (i.e., probability of encoding stimulus features) than 6-year-olds and young adults and that both 4- and 6-year-olds have greater encoding variability than young adults. These findings provide new insight into possible mechanisms underlying memory development during childhood and serve as the basis for multiple avenues of future research.

Combining egoformative and alloformative cues in a novel tabletop navigation task.

Previous work has shown how different interfaces (i.e., route navigation, maps, or a combination of the two) influence spatial knowledge and recollection. To test for the existence of intermediate representations along an egocentric-to-allocentric continuum, we developed a novel task, tabletop navigation, to provide a mixture of cues that inform the emergence of egocentric and allocentric representations or strategies. In this novel tabletop task, participants navigated a remote-controlled avatar through a tabletop scale model of the virtual city. Participants learned virtual cities from either navigating routes, studying maps, or our new tabletop navigation task. We interleaved these learning tasks with either an in situ pointing task (the scene- and orientation-dependent pointing [SOP] task) or imagined judgements of relative direction (JRD) pointing. In Experiment 1, performance on each memory task was similar across learning tasks and performance on the route and map learning tasks correlated with more precise spatial recall on both the JRD and SOP tasks. Tabletop learning performance correlated with SOP performance only, suggesting a reliance on egocentric strategies, although increased utilization of the affordances of the tabletop task were related to JRD performance. In Experiment 2, using a modified criterion map learning task, participants who learned using maps provided more precise responses on the JRD compared to route or tabletop learning. Together, these findings provide mixed evidence for both optimization and egocentric predominance after learning from the novel tabletop navigation task.

An Important Step toward Understanding the Role of Body-based Cues on Human Spatial Memory for Large-Scale Environments.

Moving our body through space is fundamental to human navigation; however, technical and physical limitations have hindered our ability to study the role of these body-based cues experimentally. We recently designed an experiment using novel immersive virtual-reality technology, which allowed us to tightly control the availability of body-based cues to determine how these cues influence human spatial memory [Huffman, D. J., & Ekstrom, A. D. A modality-independent network underlies the retrieval of large-scale spatial environments in the human brain. Neuron, 104, 611-622, 2019]. Our analysis of behavior and fMRI data revealed a similar pattern of results across a range of body-based cues conditions, thus suggesting that participants likely relied primarily on vision to form and retrieve abstract, holistic representations of the large-scale environments in our experiment. We ended our paper by discussing a number of caveats and future directions for research on the role of body-based cues in human spatial memory. Here, we reiterate and expand on this discussion, and we use a commentary in this issue by A. Steel, C. E. Robertson, and J. S. Taube (Current promises and limitations of combined virtual reality and functional magnetic resonance imaging research in humans: A commentary on Huffman and Ekstrom (2019). Journal of Cognitive Neuroscience, 2020) as a helpful discussion point regarding some of the questions that we think will be the most interesting in the coming years. We highlight the exciting possibility of taking a more naturalistic approach to study the behavior, cognition, and neuroscience of navigation. Moreover, we share the hope that researchers who study navigation in humans and nonhuman animals will synergize to provide more rapid advancements in our understanding of cognition and the brain.

Landmarks: A solution for spatial navigation and memory experiments in virtual reality.

Research into the behavioral and neural correlates of spatial cognition and navigation has benefited greatly from recent advances in virtual reality (VR) technology. Devices such as head-mounted displays (HMDs) and omnidirectional treadmills provide research participants with access to a more complete range of body-based cues, which facilitate the naturalistic study of learning and memory in three-dimensional (3D) spaces. One limitation to using these technologies for research applications is that they almost ubiquitously require integration with video game development platforms, also known as game engines. While powerful, game engines do not provide an intrinsic framework for experimental design and require at least a working proficiency with the software and any associated programming languages or integrated development environments (IDEs). Here, we present a new asset package, called Landmarks, for designing and building 3D navigation experiments in the Unity game engine. Landmarks combines the ease of building drag-and-drop experiments using no code, with the flexibility of allowing users to modify existing aspects, create new content, and even contribute their work to the open-source repository via GitHub, if they so choose. Landmarks is actively maintained and is supplemented by a wiki with resources for users including links, tutorials, videos, and more. We compare several alternatives to Landmarks for building navigation experiments and 3D experiments more generally, provide an overview of the package and its structure in the context of the Unity game engine, and discuss benefits relating to the ongoing and future development of Landmarks.

Grid coding, spatial representation, and navigation: Should we assume an isomorphism?

Grid cells provide a compelling example of a link between cellular activity and an abstract and difficult to define concept like space. Accordingly, a representational perspective on grid coding argues that neural grid coding underlies a fundamentally spatial metric. Recently, some theoretical proposals have suggested extending such a framework to nonspatial cognition as well, such as category learning. Here, we provide a critique of the frequently employed assumption of an isomorphism between patterns of neural activity (e.g., grid cells), mental representation, and behavior (e.g., navigation). Specifically, we question the strict isomorphism between these three levels and suggest that human spatial navigation is perhaps best characterized by a wide variety of both metric and nonmetric strategies. We offer an alternative perspective on how grid coding might relate to human spatial navigation, arguing that grid coding is part of a much larger conglomeration of neural activity patterns that dynamically tune to accomplish specific behavioral outputs.

The influence of low-level stimulus features on the representation of contexts, items, and their mnemonic associations.

Since the earliest attempts to characterize the "receptive fields" of neurons, a central aim of many neuroscience experiments is to elucidate the information that is represented in various regions of the brain. Recent studies suggest that, in the service of memory, information is represented in the medial temporal lobe in a conjunctive or associative form with the contextual aspects of the experience being the primary factor or highest level of the conjunctive hierarchy. A critical question is whether the information that has been observed in these studies reflects notions such as a cognitive representation of context or whether the information reflects the low-level sensory differences between stimuli. We performed two functional magnetic resonance imaging experiments to address this question and we found that associative representations observed between context and item (and order) in the human brain can be highly influenced by low-level sensory differences between stimuli. Our results place clear constraints on the experimental design of studies that aim to investigate the representation of contexts and items during performance of associative memory tasks. Moreover, our results raise interesting theoretical questions regarding the disambiguation of memory-related representations from processing-related representations.

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

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.

Limbic Tract Integrity Contributes to Pattern Separation Performance Across the Lifespan.

Accurate memory for discrete events is thought to rely on pattern separation to orthogonalize the representations of similar events. Previously, we reported that a behavioral index of pattern separation was correlated with activity in the hippocampus (dentate gyrus, CA3) and with integrity of the perforant path, which provides input to the hippocampus. If the hippocampus operates as part of a broader neural network, however, pattern separation would likely also relate to integrity of limbic tracts (fornix, cingulum bundle, and uncinate fasciculus) that connect the hippocampus to distributed brain regions. In this study, healthy adults (20-89 years) underwent diffusion tensor imaging and completed the Behavioral Pattern Separation Task-Object Version (BPS-O) and Rey Auditory Verbal Learning Test (RAVLT). After controlling for global effects of brain aging, exploratory skeleton-wise and targeted tractography analyses revealed that fornix integrity (fractional anisotropy, mean diffusivity, and radial diffusivity; but not mode) was significantly related to pattern separation (measured using BPS-O and RAVLT tasks), but not to recognition memory. These data suggest that hippocampal disconnection, via individual- and age-related differences in limbic tract integrity, contributes to pattern separation performance. Extending our earlier work, these results also support the notion that pattern separation relies on broad neural networks interconnecting the hippocampus.

Multivariate pattern analysis of the human medial temporal lobe revealed representationally categorical cortex and representationally agnostic hippocampus.

Contemporary theories of the medial temporal lobe (MTL) suggest that there are functional differences between the MTL cortex and the hippocampus. High-resolution functional magnetic resonance imaging and multivariate pattern analysis were utilized to study whether MTL subregions could classify categories of images, with the hypothesis that the hippocampus would be less representationally categorical than the MTL cortex. Results revealed significant classification accuracy for faces versus objects and faces versus scenes in MTL cortical regions-parahippocampal cortex (PHC) and perirhinal cortex (PRC)-with little evidence for category discrimination in the hippocampus. MTL cortical regions showed significantly greater classification accuracy than the hippocampus. The hippocampus showed significant classification accuracy for images compared to a nonmnemonic baseline task, suggesting that it responded to the images. Classification accuracy in a region of interest encompassing retrosplenial cortex (RSC) and the posterior cingulate cortex (PCC) posterior to RSC, showed a similar pattern of results to PHC, supporting the hypothesis that these regions are functionally related. The results suggest that PHC, PRC, and RSC/PCC are representationally categorical and the hippocampus is more representationally agnostic, which is concordant with the hypothesis of the role of the hippocampus in pattern separation.