Virtual reality is becoming increasingly present in our everyday lives, from online tours of homes for sale to high-tech headsets that immerse gamers in hyper-realistic digital worlds.
While its entertainment value is well-established, virtual reality also has vast potential for practical uses that are just beginning to be explored.
Arne Ekstrom, director of the Human Spatial Cognition Lab in the University of Arizona Department of Psychology, uses virtual reality to study spatial navigation and memory.
Among the lab’s interests are the technology’s potential for socially beneficial uses, such as training first responders, medical professionals and those who must navigate hazardous environments.
For those types of applications to be most effective, though, we need to better understand how people learn in virtual environments.
In a new study published in the journal Neuron, Ekstrom and co-author Derek Huffman, a post-doctoral researcher in the Center for Neuroscience at the University of California, Davis, advance that understanding by looking at whether or not being able to physically move through virtual spaces improves how we learn them.
“One of the big concerns or drawbacks with virtual reality is that it fails to capture the experience that we actually have when we navigate in the real world,” said Ekstrom, an associate professor of psychology and the study’s senior author.
“That’s what we were trying to address in this study: What information is sufficient for forming spatial representations that are useful in actually knowing where things are?”
The researchers had study participants explore three virtual cities while wearing virtual reality headsets. The participants navigated each city in one of three ways:
- Participants wore the headset while walking on an omnidirectional, or 360-degree, treadmill, which allows users to walk freely in any direction. In this condition, the participants could navigate through the virtual environment by walking and turning their heads.
- Participants navigated through the virtual environments using only a handheld joystick; they were not able to navigate by moving their heads or walking.
- Participants navigated by moving their bodies side to side and moving a joystick back and forth; they were not able to walk around.
Participants spent two to three hours, on average, exploring the virtual cities and locating certain shops they were instructed to find.
Once they’d had an opportunity to learn the environments well, they were asked a series of questions to test their spatial memory.
For example, they might be asked to imagine they were standing at the coffee shop, facing the bookstore. They would then be asked to point in the direction of the grocery store.
The accuracy of participants’ responses did not vary based on which condition they were in.
Participants then underwent an MRI scan while answering a similar set of questions.
This allowed the researchers to see what was happening in the brain as participants retrieved spatial memories.
The researchers found that the same areas of the brain were activated for participants in all three situations.
In addition, the patterns of interaction between different regions of the brain were similar among the three conditions.
“What we found was that the neural codes were identical between the different conditions,” Ekstrom said. “This suggests – as far as the brain is concerned and what we were also able to measure with behavior – that there is sufficient information with just seeing things in a virtual environment.
The information you get from moving your body, once you know the environment well enough, doesn’t really add that much.”
The findings address a long-standing scientific debate around whether or not body movements aid in learning physical spaces.
The researchers found that the same areas of the brain were activated for participants in all three situations.
In addition, the patterns of interaction between different regions of the brain were similar among the three conditions.
“There’s been this idea that how you learn might make a huge difference, and that if you don’t have body-based cues, then you’re lacking a big part of what might be important for forming memories of space,” said Huffman, the study’s first author. “Our research would suggest that once you have a well-formed memory of an environment, it doesn’t matter as much how you learned it.”
“We would say you don’t need body immersion, and you don’t need body cues to form complex spatial representations,” Ekstom added. “That can happen with sufficient exposure in simple virtual reality applications.”
From a practical standpoint, the research suggests that even basic virtual reality systems may be useful in instructional applications.
“Virtual reality has the potential to allow us to understand situations that we might not otherwise be able to directly experience,” Ekstrom said. “For example, what if we could train first responders to be able to find people after an attack on a building, without them actually ever having been to that building?
“Our findings suggest there’s promise for using virtual reality – even simple applications where you’re just movi
Detection of the state of self-motion, such as the instantaneous heading direction, the traveled trajectory and traveled distance or time, is critical for efficient spatial navigation. Numerous psychophysical studies have indicated that the vestibular system, originating from the otolith and semicircular canals in our inner ears, provides robust signals for different aspects of self-motion perception. In addition, vestibular signals interact with other sensory signals such as visual optic flow to facilitate natural navigation.
These behavioral results are consistent with recent findings in neurophysiological studies. In particular, vestibular activity in response to the translation or rotation of the head/body in darkness is revealed in a growing number of cortical regions, many of which are also sensitive to visual motion stimuli.
The temporal dynamics of the vestibular activity in the central nervous system can vary widely, ranging from acceleration-dominant to velocity-dominant. Different temporal dynamic signals may be decoded by higher level areas for different functions. For example, the acceleration signals during the translation of body in the horizontal plane may be used by the brain to estimate the heading directions.
Although translation and rotation signals arise from independent peripheral organs, that is, otolith and canals, respectively, they frequently converge onto single neurons in the central nervous system including both the brainstem and the cerebral cortex.
The convergent neurons typically exhibit stronger responses during a combined curved motion trajectory which may serve as the neural correlate for complex path perception. During spatial navigation, traveled distance or time may be encoded by different population of neurons in multiple regions including hippocampal-entorhinal system, posterior parietal cortex, or frontal cortex.
Accurate and precise detection of displacement of our head and body in space is critical for important functions including balance, posture control, gait, spatial orientation and self-motion perception.
It can be accomplished through the vestibular pathway that starts from two small but elegant organs embedded in our inner ears: the otolith and semicircular canals, which detect linear and angular acceleration of our head, respectively (Goldberg and Fernandez, 1971; Fernández and Goldberg, 1976a,b).
The encoded inertial motion signals in the peripheral system are propagated to the central nervous system for further processing.
While the neural circuits mediating automatic process such as vestibulo-ocular reflex (VOR) for maintaining visual stability and body balance are well known (Takakusaki, 2017), less is clear about how vestibular signals are coded by the brain for perception of self-motion and spatial orientation (Lopez, 2016).
Recent neurophysiological studies have discovered that robust vestibular signals are distributed broadly in sensory cortices, suggesting that the vestibular system may be involved in higher cognitive functions (Gu, 2018).
In this review article, we summarized recent progress on the involvement of the vestibular system in higher cognitive functions, particularly for self-motion. We will focus on three topics:
(1) how vestibular signals may contribute to estimating of one’s heading direction through space;
(2) how a more complex path trajectory may be coded by convergent translation and rotation signals arising independently from the otolith and horizontal canals; and
(3) how traveled distance or time is possibly coded by the vestibular system.
For each topic, we first reviewed results from psychophysical studies on humans and monkeys, and then pointed to the neurophysiological studies that may provide insights for the underlying neural mechanisms.
We finally discussed remained issues that need to be addressed in future studies. Note that we have focused primarily on recent progress in the cortical system.
Numerous studies conducted in subcortical areas including thalamus, hippocampus, and the limbic system including the entorhinal cortex, retrosplenial cortex can be referred elsewhere, and are only briefly mentioned in the current review article.
Both human and monkey can judge heading directions accurately and precisely based on vestibular cues (Telford et al., 1995; Ohmi, 1996; Gu et al., 2007; Fetsch et al., 2009, 2011; Crane, 2012; Drugowitsch et al., 2014).
The intact vestibular system is crucial for heading estimation. For example, bilateral labyrinthectomy led to dramatic increase in the psychophysical threshold in a vestibular heading discrimination task, in which the monkeys were instructed to report their perceived heading directions delivered through a motion platform under a two-alternative-forced-choice experimental paradigm (Gu et al., 2007).
However, psychophysical threshold decreased gradually after labyrinthectomy, suggesting that the animals may learn to use other sensory inputs, for example, somatosensory or proprioceptive cues to compensate the deficiency in the vestibular system. This hypothesis is consistent with the phenomenon that the animals began to lean their hands against the wall of the cage when moving around after labyrinthectomy. Note that, the psychophysical threshold remained about 10 times worse than the baseline (i.e., before labyrinthectomy), demonstrating that the function of the vestibular system could not be fully compensated by other sensory systems (Gu et al., 2007).
Other sensory inputs, in particular, visual cues, do help the vestibular system for more accurate and precise heading estimate. Provided with congruent vestibular and visual optic flow cues, both humans (Butler et al., 2010, 2011, 2015; Crane, 2017; Ramkhalawansingh et al., 2018) and monkeys (Gu et al., 2008; Fetsch et al., 2009; Chen et al., 2013) can judge smaller heading directions compared to the condition when only one sensory input is available.
Interestingly, the decrement in psychophysical threshold during cue combined condition is consistent with the prediction from the optimal cue integration theory (Ernst and Banks, 2002), indicating that our brain makes full use of the information when summing sensory evidence from different sensory modalities.
The optimal performance is verified under conditions when a conflict heading angle between vestibular and visual cues is introduced (Fetsch et al., 2009; Butler et al., 2015), or when subjects performed a reaction-time version of the task in which they do not have to wait and accumulate sensory evidence for a long and fixed duration (Drugowitsch et al., 2014).
The neural substrate for heading perception has been extensively explored within the last three decades.
Most of the studies have largely focused on areas within the cerebral cortex because neurons in many of these areas are modulated by complex optic flow that is typically experienced during natural navigation.
For example, Duffy and colleagues have shown that neurons in the dorsal portion of the medial superior temporal sulcus (MSTd) are sensitive to global-field optic flow simulating real self-motion (Duffy and Wurtz, 1991, 1995), as well as to transient whole body movement in darkness (Duffy, 1998; Page and Duffy, 2003).
Later on, Angelaki and DeAngelis further characterized heading selectivity of MSTd neurons in three-dimensional (3D) space using a six degree of freedom (6-DOF) motion platform (Gu et al., 2006; Takahashi et al., 2007; Morgan et al., 2008). They found that nearly all MSTd neurons are significantly modulated by optic flow and two thirds are significantly tuned to vestibular stimuli.
Labyrinthectomy largely diminished the vestibular activity but not visual activity in MSTd, suggesting the responses measured during the physical motion condition in darkness really arise from the vestibular source (Gu et al., 2007; Takahashi et al., 2007).
Interestingly, for neurons significantly modulated by both optic flow and inertial motion, about half prefers congruent heading direction, and these “congruent” neurons typically exhibit higher heading selectivity when both cues are provided in a congruent way, constituting an ideal substrate for more robust heading estimate during natural navigation.
However, note that the other half neurons tend to carry conflict visual and vestibular heading information, producing weaker heading selectivity during cue combination. Thus, this population of neurons is unlikely to account for more robust heading estimate under congruent vestibular-visual inputs. The exact functional implications of these neurons remain a mystery at this stage.
Using the same paradigm, researchers have examined a number of areas in the cerebral sensory cortices and cerebellum. Many of these areas exhibit similar neuronal properties as those found in MSTd, including the ventral parietal area (VIP; Chen et al., 2011c), the smooth eye movement area of the frontal eye field (FEFsem; Gu et al., 2016), the visual posterior sylvian area (VPS; Chen et al., 2011b), and the cerebellar nodulus and uvula (Yakusheva et al., 2013).
However, some areas exhibit different properties. For example, most neurons in the posterior insular vestibular cortex (PIVC) are only tuned to vestibular stimuli, but not to optic flow (Chen et al., 2010). By contrast, most neurons in the middle temporal area (MT; Chowdhury et al., 2009) and V6 (Fan et al., 2015) only respond to visual stimuli, but not to inertial motion.
Note in this map, sensory information is hypothesized to further transmit to decision-related areas such as the frontal and parietal lobes (e.g., FEFsac and LIP) in which the evidence of sensory inputs is accumulated and transformed to form decision and generate motor output.
How momentary vestibular evidence is accumulated during this process is still unverified. For example, is the vestibular acceleration accumulated for heading estimate (Drugowitsch et al., 2014)? Future physiological experiments need to be conducted to examine this hypothesis.
The exact information flow across the heading network is currently unknown. However, there are hints from some properties of the neurons recorded from these areas (Figures 1B,C).
First, the strength of vestibular heading selectivity tends to increase from visual dominant areas (e.g., V6) to vestibular dominant areas (e.g., PIVC; Figure Figure1B).1B). Second, the vestibular temporal dynamics are heterogeneous in the brain.
In the peripheral otolith organs, vestibular signals predominantly encode the acceleration component of the inertial motion. Yet these signals are integrated more or less after propagating to the central nervous system, leading to temporal dynamics varied from acceleration to velocity dominant profiles (Laurens et al., 2017; see review by Gu, 2018). Across sensory cortices, the proportion of velocity dominant neurons tends to decrease gradually from area MSTd to PIVC, whereas the proportion of acceleration dominant neurons shows an opposite trend (Chen et al., 2011a). These results suggest that PIVC may lie most proximally to the vestibular periphery, followed by VPS and FEFsem, and then VIP and MSTd.
Although the vestibular and visual heading signals are broadly distributed within the brain network, it is unclear about which areas are really involved in heading estimate. Recently some studies were conducted to address this issue.
At the same time, neural activities in certain areas were artificially manipulated to test their causal roles in heading perception. For example, researchers injected chemical drugs (such as muscimol) into the brain to suppress neuronal activity, and found that inactivation of PIVC greatly diminishes the animals’ heading performance based on vestibular cues, but not much in the optic flow condition (Chen et al., 2016).
On contrary, inactivation of MSTd greatly diminishes the animals’ heading performance based on optic flow, but not much in the vestibular condition (Gu et al., 2012). These results suggest that the vestibular-dominant area PIVC plays a critical role in heading perception based on inertial motion, whereas the visual-dominant area MSTd is key to heading based on optic flow.
Different from PIVC and MSTd, inactivation of area VIP does not generate significant effects on the animals’ heading performance based on either the vestibular or visual cues (Chen et al., 2016). Such a result is surprising because VIP is similar to MSTd in many aspects (Britten, 2008; Maciokas and Britten, 2010).
For example, both areas carry robust vestibular and visual heading signals (Chen et al., 2011c).
In addition, neuronal activity in VIP also co-varies with the animal’s choice on a trial to trial basis, and this choice-correlation effect is even larger compared to that in MSTd (Chen et al., 2013; Zaidel et al., 2017; Yu and Gu, 2018).
Hence, the exact functional implications of the motion directional signals in VIP remain unclear and require further investigation, probably by using other techniques or other behavioral paradigms.
For example, by delivering weak electrical currents into the brain to selectively activate a cluster of neurons, researchers examine whether the animals’ perceptual judgments are biased in the direction that is predicted from the artificially stimulated neurons (e.g., Salzman et al., 1992).
Such an effect implies that the examined area plays a sufficient role in the perceptual decision making task. Using this technique, researcher found that microstimulation in MSTd produces significant effects on the animals’ heading performance based on optic flow (Gu et al., 2012; Yu and Gu, 2018; Yu et al., 2018), but not for VIP (Yu and Gu, 2018). However, this effect becomes significant when smooth eye movements were simultaneously accompanied the presented heading stimuli (Zhang and Britten, 2011). In another study, electrical stimulation in VIP could even directly evoke complex facial movements (Cooke et al., 2003). Thus, compared to other sensory cortices (e.g., MSTd), VIP seems to carry more motor-related signals and may causally contribute to behavior only when more complex behavior is involved.
Trajectory of Self-Motion
Our motion trajectory through the space can be complex, typically composed of both translation and rotation components rather than only one of them.
For example, when animals run away from their predators, they may make turns while remain heading forward at the same time, resulting in a curved motion trajectory.
Curved motion also frequently happens in human world, for example, vehicle driving, ski and running race in sports. How could complex motion trajectories be represented by the vestibular system?
Recent studies begin to address this issue by focusing on interactions of translation and rotation signals arising from otolith and semicircular canals respectively, particularly in the horizontal plane.
For example, researcher have designed experiments in which human subjects were instructed to navigate along a curved motion trajectory through passive driving or active walking (Ivanenko et al., 1997; Israël et al., 2005; Nooij et al., 2016).
The subjects were then required to reproduce the experienced path by drawing, walking or driving a vehicle.
This is not a trivial task because to reproduce the exact profile of the experienced motion trajectory, the subjects need to discriminate the relative translation and rotation components over time during navigation (Li and Cheng, 2011).
It showed that the blindfolded subjects were quite good at recovering the traveled path either under the straight or curved motion conditions, suggesting that similar to visual optic flow cues (Li and Cheng, 2011), vestibular signals could also be reliable enough for path perception. However, subjects could not effectively distinguish real curved self-motion from a straight motion trajectory accompanied by a yaw rotation of the head or whole body at the same time (i.e., illusorily perceived curved motion; Ivanenko et al., 1997; Israël et al., 2005).
Thus, signals arising from horizontal canals seem to play a critical role in complex path perception. Indeed, a recent study examined the detection threshold for head translation and rotation respectively during combined, i.e., curvilinear motion (MacNeilage et al., 2010).
It is found that the detection threshold for rotation was unaffected under the presence of translation, while the detection threshold for translation was significantly increased under the presence of rotation.
In a different study, researcher found that yaw rotations could significantly bias the subjects’ perceived sway of the body, but a reversed effect did not happen (Crane, 2016). Finally, when asked to reproduce a triangle path, some patients with vestibular deficits could replicate the traveled distance, but not the traveled angle (Glasauer et al., 2002), indicating a causal role of the vestibular signals, particularly for the rotation signals in complex path perception.
How do neurons in the brain carry out computations that could underlie the curvilinear self-motion perception?
To address this issue, researchers recorded single-unit activity from neurons in the central nervous system of macaques under translation only, yaw rotation only and convergent translation plus yaw rotation conditions.
In the vestibular nucleus (VN) in brainstem, neurons integrate translation and rotation inputs in a sub-additive (Carriot et al., 2015) or near additive way (Newlands et al., 2018) when both signals co-exist in the curvilinear motion condition.
Researchers also have examined several cortical areas including MSTd, VIP and VPS, and found that a group of convergent neurons receiving both translation and rotation inputs tended to integrate the two signals sub-additively (Figure (Figure2),2), suggesting that this property may arise from the subcortical areas, e.g., the brainstem (Cheng and Gu, 2016). However, the weight assigned to the translation and rotation signals in cortices is not consistent with what has been reported in the brainstem, suggesting that additional integration may also happen when the vestibular signals are propagated to the cortex.
Curved motion trajectory in the horizontal plane would potentially produce centripetal force that may also mediate curvilinear self-motion perception.
However, using a straight linear path with simultaneous head rotation paradigm (Ivanenko et al., 1997), researchers found that human subjects reported almost the same “curved” motion experience as in the curved path condition with head rotation (Figure (Figure2C,2C, middle panel vs. left panel).
Because the magnitude of the centripetal force is quite different between these two experimental conditions, it is unlikely that the centripetal force would be a key to curved motion sensation. Indeed, in a third experimental conditions in which subjects experienced curved motion path but without head rotations, they did not report curved self-motion any more although the centrifugal force was now present as in the curved path condition with head rotation (Figure (Figure2C,2C, right panel vs. left panel).
On the neural level, recently researcher recorded neurons in a number of cortical areas under a similar paradigm as in the above psychophysical study (Cheng and Gu, 2016). Interestingly, similar to the behavior, the firing patterns of the cortical neurons are analogous under the curved-path-with-rotation condition and straight-path-with-rotation condition, but are different from the condition when yaw rotation is absent (Figure (Figure2B).2B).
Thus, neurons receiving inputs from both otolith and horizontal canals in the brain may mediate curvilinear self-motion perception. Note, that physiological properties including the proportion of different types of neurons, tuning strength and sensory summation rules are similar across the examined cortical areas, suggesting that the complex motion trajectory may be widely represented in the brain. However, future work is required to dissect the exact role of individual areas in self-motion.
From the mathematical point of view, double integration of the vestibular acceleration signals provides information about the distance we have traveled, which appears to be more challenging than estimating heading direction during spatial navigation.
Researchers have investigated the role of vestibular signals in distance perception by requiring blindfolded human subjects to report their linear or angular displacement of the body through a number of methods including pointing (Ivanenko et al., 1997; Nooij et al., 2016), saccade (Berthoz et al., 1987; Israël and Berthoz, 1989), pressing button (Israël et al., 1993; Harris et al., 2000), walking (Mittelstaedt and Mittelstaedt, 2001; Campos et al., 2010, 2012, 2014), or controlling vehicles (Grasso et al., 1999; Tremblay et al., 2013). Normal human subjects could accurately recover their traveled distance, as well as the motion velocity profile regardless of reporting methods. By contrast, performance from vestibulopathy subjects were typically impaired in estimating time and distance when they were instructed to walk forward and make turns at a particular point under a blindfolded condition (Cohen, 2000), suggesting a causal role of vestibular signals in distance perception.
Similar to heading perception, information from other sensory modalities such as visual and proprioceptive cues, also contribute to the estimation of traveled distance (Jürgens et al., 2003; Jürgens and Becker, 2006).
For example, a number of studies have illustrated that subjects can accurately estimate the traveled distance from optic flow (Bremmer and Lappe, 1999; Redlick et al., 2001; Frenz and Lappe, 2005; Dacke and Srinivasan, 2007). When different sensory inputs are provided at the same time, information from different sources is summed with a weight that is proportional to the reliability of each cue (Sun et al., 2004; Campos et al., 2010, 2012, 2014; ter Horst et al., 2015). However, some work proposed that the vestibular signals could dominate the visual signals (Harris et al., 2000), similar to a prior of the vestibular signals as observed in heading discrimination tasks (Fetsch et al., 2011; Butler et al., 2015).
Unlike the extensive studies exploring the contribution of vestibular signals in heading perception, little is known about the role of vestibular signals underlying distance perception. There is evidence suggesting that the temporoparietal junction, which carries prominent vestibular signals, may be involved in distance perception.
For example, patients with lesions in the temporoparietal region tended to underestimate the traveled distance and stimulus duration, whereas the ability to detect onset of motion was unaffected (Kaski et al., 2016). When using repetitive transcranial magnetic stimulation (rTMS) to interfere the temporoparietal junction, subjects could replicate the motion velocity profile, but could not replicate the traveled distance (Seemungal et al., 2008a,b, 2009; Ventre-Dominey, 2014). This result suggests that temporoparietal junction plays an important role in distance perception, and moreover, distance perception (related to integration of velocity information over time) and heading perception (related to detection of motion direction over time) are two separate processes implemented in the brain.
How exactly vestibular signals in cortex contribute to the estimation of traveled distance or time remains unclear.
Recently a study characterizing the spatial-temporal properties of vestibular responses in MSTd found that nearly half of the neurons exhibited a statistically significant position component, yet it was much weaker compared to the velocity as well as the acceleration component (Chen et al., 2011a).
More works need to be conducted in the future to characterize how neurons in different cortical areas (see Figure Figure1)1) may encode the moving distance. For example, neurons in the sensory-motor transformation areas including the parietal and frontal lobes exhibit ramping activity over time (Kim and Shadlen, 1999; Gold and Shadlen, 2000; Shadlen and Newsome, 2001; Ding and Gold, 2012), which may serve as a neural correlate for distance coding. Indeed, it has been indicated that parietal neurons may encode the elapsed time (Jazayeri and Shadlen, 2015), thus, these neurons may also encode the traveled distance as the product of the time and moving speed. Such neurons have also been reported in subcortical areas such as the rodents’ hippocampus when the animals performed a spatial navigation task (Kraus et al., 2013).
In fact, via the anterior part of thalamus, vestibular peripheral inputs project to the limbic system which has been illustrated to be critical for self-motion based path integration (Cullen and Taube, 2017). For example, rotation signals arising from the semicircular canals are necessary for formation of head direction cells (Valerio and Taube, 2016). Translation signals from otolith may be critical for place cells, grid cells, and speed cells in the hippocampal-entorhinal system (Yoder and Kirby, 2014). It remains unclear how exactly the cortical self-motion system is connected with the subcortical and limbic systems, for example, through retrosplenial cortex (Vann et al., 2009). Future studies need to be conducted to fully understand how a complete neural network in the brain code self-motion during spatial navigation.
Convergent evidence from behavioral, neurophysiological and computational studies reveals that the vestibular system plays a critical role in different aspects of self-motion perception, such as heading, path, and traveled distance or time. Particularly for heading estimation, a series of physiological studies have been conducted in recent years to address the underlying neural mechanisms.
These studies have provided us with valuable information about how the brain may code motion signals to guide spatial navigation. At the same time, these studies also provoke many important issues to be addressed in the future.
First, vestibular signals are widely distributed in the central nervous system. Recent studies have revealed many areas conveying robust vestibular signals in the cerebral cortex. It is likely that more areas will be continually discovered in the future. Thus, it is important to address both the homogeneity and heterogeneity of the functional implications of each area in self-motion perception.
Second, the temporal dynamics of vestibular signals, especially those arise from the otolith organs, vary broadly in the central nervous system. Future studies need to identify exact functions of the neurons with different temporal dynamics. For example, it has been proposed that the momentary vestibular acceleration evidence could be accumulated by decision making neurons e.g., LIP neuron to generate the final behavioral output for heading discrimination task (Drugowitsch et al., 2014). In contrast, velocity information may be used for other functions such as distance perception, or maintenance of visual stability during head or body movements.
Third, vestibular signals arising from the inner ears are encoded in a head-centered reference frame, yet spatial navigation in the environment is basically a body-centered behavior. Recent neurophysiological studies have provided evidence suggesting that vestibular reference frame may be gradually transformed along the signal propagation pathway, for example, from largely head-centered in the rostral regions of the VN (Shaikh et al., 2004), to mixed head- and body-centered in the cerebellar rFN (Kleine et al., 2004; Martin et al., 2018) and the cerebral PIVC (Chen et al., 2013a), and to predominantly body-centered in the cortical area of VIP (Chen et al., 2013a). Future studies need to explore the possible role of neurons with gain modulated activity in reference frame transformation (Zipser and Andersen, 1988; Siegel, 1998; Xing and Andersen, 2000; Gu et al., 2006; Pesaran et al., 2006; Fetsch et al., 2007; Chen et al., 2013b; Hadjidimitrakis et al., 2014; Fan et al., 2015; Yang and Gu, 2017).
Fourth, vestibular signals have been recently discovered in a number of sensory cortices that also carry robust visual motion signals, suggesting that interactions between sensory modalities may exist. It is possible that vestibular and visual signals are integrated by the brain for more robust heading estimate. In addition, it is also possible that these signals may interact with each other for other functions such as maintaining visual stability when smooth pursuit eye movements are accompanied during head or body movements. Future works need to explore these potential functions, as well as the computational rules underlying the integration or interaction process.
Finally, the current review article focuses data mainly collected in the passive self-motion conditions. However, researchers have shown that active self-motion largely diminishes vestibular activity in the brainstem and cerebellum (see review Cullen and Taube, 2017). Recent theoretical studies suggest that a single sensory internal model can combine motor commands with the vestibular and proprioceptive signals optimally to recover accurate self-motion during active head movements (Laurens and Angelaki, 2017). Thus, it would be important to explore the vestibular signals in the cerebral cortex, including sensory cortices, sensory-motor transformation areas, and motor areas under active self-motion conditions.
University of Arizona
Alexis Blue – University of Arizona
The image is in the public domain.
Original Research: Closed access
“A Modality-Independent Network Underlies the Retrieval of Large-Scale Spatial Environments in the Human Brain”. Derek J. Huffman and Arne D. Ekstrom.