Human Spatial Memory is Made Up of Numerous Individual Maps

Summary: Researchers investigate how spatial memories are formed.

Source: Max Planck Institute.

Spatial memory is something we use and need in our everyday lives. Time for morning coffee? We head straight to the kitchen and know where to find the coffee machine and cups. To do this, we require a mental image of our home and its contents. If we didn’t have this information stored in our memory, we would have to search through the entire house every time we needed something. Exactly how this mental processing works is not clear. Do we use one big mental map of all of the objects we have in our home? Or do we have a bunch of small maps instead – perhaps one for each room? Tobias Meilinger and Marianne Strickrodt, cognitive scientists from the Max Planck Institute for Biological Cybernetics, investigated these questions in a research study.

In their study, the Max Planck researchers tested the spatial memory of volunteers in a virtual environment using 3D glasses. They were asked to memorize an arrangement of seven virtual objects placed in either of two spaces: an open, fully overseeable space or across multiple interlinked corridors. The objects were distributed in precisely the same way in both scenarios. In order to see all of the objects, along the interlinked corridors, referred to as the environmental space, participants had to walk through the environment. In the open vista space, they could see everything at a glance.

They were then asked: Where was the kettle, the banana, the hammer? Marianne Strickrodt and Tobias Meilinger examined how quickly and accurately participants remembered the location of the objects and in what order. “In a virtual world like the one in our study, we have perfect control over the conditions of the experiment. This enables us to alter individual parameters and measure the associated effects on memory performance,” explains Marianne Strickrodt.

Image shows a room and a map.
The objects in open space are arranged in the same manner as in the corridors. Volunteers, however, had to initially walk along the corridors to see all the items. Credit: Max Planck Institute.

Memory as far as the eye can see

The spatial memory trace for the layout of the seven objects depended on the space in which the participants had seen the objects. If they learned the location of the objects in the interlinked corridor environment, they immediately remembered objects in the corridor in which they themselves were located at the moment of survey. However, they needed more time to recall objects from the neighbouring corridor, and again longer for objects located two corridors away. They could therefore only access their spatial memory step-by-step, corridor-by-corridor.

Contrarily, participants who memorized the objects in vista space were able to remember all of the objects equally quickly and were more flexible when it came to reconstructing the order of the objects. A control experiment showed that these differences in the structure of the spatial memory were not due to the fact that the volunteers were walking through the environmental space or only got to see the objects one after the other. Instead, they were due to the segmentation and the limited visibility dictated by the corridor walls.

“Our findings do not support the idea that we construct a large comprehensive mental map of the environment, from which we can flexibly read information about all locations. Figuratively speaking, our spatial memory of the coffee machine in the kitchen doesn’t necessarily include the location of the hairbrush in the bathroom and vice versa. If we want to point from the kitchen to the hairbrush in the bathroom, the way we access our spatial memory follows our actual learning experience step-by-step: first the kitchen, then the hallway, and then the bathroom,” explains Marianne Strickrodt, summarizing the results.

Image shows a person in 3D glasses.
Volunteer wearing 3D glasses. image is credited to MPI.

It makes a fundamental difference whether we learn about the location of objects in vista or environmental spaces. We find it easy to remember the position of many items as one unit when arranged in large open spaces. Hence, large corridors, roads and entrance areas that provide a broad overall view enhance wayfinding.

“The study findings are relevant for the research on the neuronal basis of navigation. Many previous findings were obtained in the context of vista spaces. The extent to which these results are applicable to environmental spaces, or whether completely new mechanisms must be sought for, poses a fascinating question for future research,” says Tobias Meilinger, who headed the study.

About this neuroscience research article

Source: Tobias Meilinger – Max Planck Institute
Image Source: images are credited to MPI.
Original Research: Abstract for “Qualitative differences in memory for vista and environmental spaces are caused by opaque borders, not movement or successive presentation” by Tobias Meilinger, Marianne Strickrodt, and Heinrich H. Bülthoff in Cognition. Published online June 28 2016 doi:10.1016/j.cognition.2016.06.003

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[cbtabs][cbtab title=”MLA”]Max Planck Institute “Human Spatial Memory is Made Up of Numerous Individual Maps.” NeuroscienceNews. NeuroscienceNews, 6 September 2016.
<>.[/cbtab][cbtab title=”APA”]Max Planck Institute (2016, September 6). Human Spatial Memory is Made Up of Numerous Individual Maps. NeuroscienceNew. Retrieved September 6, 2016 from[/cbtab][cbtab title=”Chicago”]Max Planck Institute “Human Spatial Memory is Made Up of Numerous Individual Maps.” (accessed September 6, 2016).[/cbtab][/cbtabs]


Qualitative differences in memory for vista and environmental spaces are caused by opaque borders, not movement or successive presentation

Two classes of space define our everyday experience within our surrounding environment: vista spaces, such as rooms or streets which can be perceived from one vantage point, and environmental spaces, for example, buildings and towns which are grasped from multiple views acquired during locomotion. However, theories of spatial representations often treat both spaces as equal. The present experiments show that this assumption cannot be upheld. Participants learned exactly the same layout of objects either within a single room or spread across multiple corridors. By utilizing a pointing and a placement task we tested the acquired configurational memory. In Experiment 1 retrieving memory of the object layout acquired in environmental space was affected by the distance of the traveled path and the order in which the objects were learned. In contrast, memory retrieval of objects learned in vista space was not bound to distance and relied on different ordering schemes (e.g., along the layout structure). Furthermore, spatial memory of both spaces differed with respect to the employed reference frame orientation. Environmental space memory was organized along the learning experience rather than layout intrinsic structure. In Experiment 2 participants memorized the object layout presented within the vista space room of Experiment 1 while the learning procedure emulated environmental space learning (movement, successive object presentation). Neither factor rendered similar results as found in environmental space learning. This shows that memory differences between vista and environmental space originated mainly from the spatial compartmentalization which was unique to environmental space learning. Our results suggest that transferring conclusions from findings obtained in vista space to environmental spaces and vice versa should be made with caution.

“Qualitative differences in memory for vista and environmental spaces are caused by opaque borders, not movement or successive presentation” by Tobias Meilinger, Marianne Strickrodt, and Heinrich H. Bülthoff in Cognition. Published online June 28 2016 doi:10.1016/j.cognition.2016.06.003

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  1. You may find it useful to travel back in time…maybe somewhere around 1946:
    The First Seeds of Cognitive Psychology

    Edward Chance Tolman (Uh oh… we might be computers after all)
    A number of studies in the Berkeley laboratory of Edward Tolman appeared both to show flaws in the law of effect as well as radical Behaviorism as promoted by Skinner and his followers …and to require (gasp!!)mental representation in their explanation. For example, rats were allowed to explore a maze in which there were three routes of different lengths between the starting position and the goal. The rats behavior when the maze was blocked implied that they must have some sort of mental map of the maze. The rats prefer the routes according to their shortness, so, when the maze is blocked at point A, stopping them using the shortest route, they will choose the second shortest route. When, however, the maze is blocked at point B the rats does not retrace his steps and use route 2, which would be predicted according to the law of effect, but rather uses route 3 . The rat must be recognising that block B will stop him using route 2 by using some memory of the layout of the maze. Tolman’s group also showed that animals could use knowledge they gained learning a maze by running to navigate it swimming and that unexpected changes in the quality of reward could weaken learning even though the animal was still rewarded. This result was developed further by Crespi who, in 1942, showed that unexpected decreases in reward quantity caused rats temporarily to run a maze more slowly than normal while unexpected increases caused a temporary elevation in running speed (The animals are making stastical calculations, and using mathematical spacial navigation algorithims, and at the very least vector algebra/analytical geometry and trigonometry to a degree that would no doubt impress both Rene Descartes and Pythagoras).

    At the same time as this work was appearing in the USA the Polish psychologists Konorski and Miller began the first cognitive analyses of classical conditioning – the forerunners of the work of Rescorla, Wagner, Dickinson and Mackintosh. In case you had forgotten here is a very basic review of the Rescorla/Wagner reinterpretation of Pavlovian conditioning as Cognitive Neuroscience in the Information Processing tradition: According to Rescorla and Kamin, associations are only learned when a surprising event accompanies a CS. In a normal simple conditioning experiment the US is surprising the first few times it is experienced so it is associated with salient stimuli which immediately precede it. In a blocking experiment once the association between the CS (CS1) presented in the first phase of the procedure and the US has been made the US is no longer surprising (since it is predicted by CS1). In the second phase, where both CS1 and CS2 are experienced, as the US is no longer surprising it does not induce any further learning and so no association is made between the US and CS2. This explanation was presented by Rescorla and Wagner (1972) as a formal model of conditioning which expresses the capacity a CS has to become associated with a US at any given time. This associative strength of the US to the CS is referred to by the letter V and the change in this strength which occurs on each trial of conditioning is called dV. The more a CS is associated with a US the less additional association the US can induce. This informal explanation of the role of US surprise and of CS (and US) salience in the process of conditioning can be stated as follows:
    dV = ab(L – V)
    where a is the salience (intensity) of the US, b is the salience (intensity) of the CS and L is the amount of processing given to a completely unpredicted US. In words: when the US is first encountered the CS has no association to it so V is zero. On the first trial the CS gains a strength of abL in its association with the US which is proportional to the saliences of the CS and the US and to the initial amount of processing given to the US. As we start trial two the associative strength is V is abL so the change in strength that occurs with the second pairing of the CS and US is ab(L – abL). It is smaller than the amount learned on the first trial and this reduction in amount that is learned reflects the fact that the CS now has some association with the US, so the US is less surprising (cute…very cute–oops I’m not supposed to impose my opinions). As more trials ensue, the equation predicts a gradually decreasing rate of learning which reaches an asymptote at L.
    However, the diagram below shows: this is not what is seen when the development CS-US associations is measured over time. Instead the learning curve is sigmoidal. Rescorla has argued that the equation is consistent with observed behavior if one assumes that very small changes in associative strength are undetectable and that there is a limit to the amount of effect that very large changes can have on behavior.

    CS-US acquisition
    There are other respects, however, where the model performs better in predicting experimental outcomes. It can also be applied to a number of CSs each of which contributes to an overall associative strength V of the US in the right hand side of the equation. It is reasonably clear that the presence of the CS salience term b in the equation lets it account for overshadowing. The meaning of the equation is clearest if the specific dVs on the left hand side are seen as referring to the increments in association between specific CSs while V on the right hand side is referring to the predictability of the US and so is the sum of all the different CS-US associations. If the conditioning strength accrued to CS1 is denoted by dV1 and that to CS2 by dV2 then our equations are:
    dV1 = ab1(L – V)
    dV2 = ab2(L – V)
    and both dV1 and dV2 accrue to V on each trial. The amount of association directed to each CS is proportional to their salience.
    The equation also models blocking well. During the initial phase of a blocking experiment the associative strength of the US is increased so later, when a second CS is presented the amount of associative strength it can gain has been reduced.
    The critical question is, however, does the model predict experimental outcomes it was not explicitly devised for, i.e. can it be generalized? In one example the model predicts the effects of pairing two previously learned CSs on learning about a third new stimulus. If on separate occasions (not as compound stimuli) two CSs of equal salience have both been completely associated with a US then V=L for both stimuli and dV on subsequent trials is zero for both. Now a third CS in conjunction with the original pair is presented so three CSs are presented together whereas only two of them were presented singly in the past. The overall associative strength of the US is now 2L, a contribution of L from both of the original CSs. The equation predicts that there will be a negative change in associative strength on this trial proportional to the salience of the CSs:
    dV = ab(L – 2L)
    dV = -abL
    Conducting the experiment shows: the third stimulus becomes a conditioned inhibitor of the US – it provokes a CR of the opposite quality to that produced by the other two CSs.
    It was obviously only a matter of time before The elegant science of behaviorism began to be co-opted by the “cognitive neuroscience” movement, AI, Neural Networking, Holographic models of Neuronal connections… etc.

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