Spatial cognition

Spatial cognition is concerned with the acquisition, organization, utilization, and revision of knowledge about spatial environments. These capabilities enable individuals to manage basic and high-level cognitive tasks in everyday life. Numerous disciplines (such as cognitive psychology, neuroscience, artificial intelligence, geographic Information science, cartography, etc.) work together to understand spatial cognition in different species, especially in humans. Thereby, spatial cognition studies also have helped to link cognitive psychology and neuroscience. Scientists in both fields work together to figure out what role spatial cognition plays in the brain as well as to determine the surrounding neurobiological infrastructure.

Spatial cognition is closely related to how people talk about their environment, find their way in new surroundings and plan routes. Thus a wide range of studies is based on participants reports, performance measures and similar, for example in order to determine cognitive reference frames that allow subjects to perform. In this context the implementation of Virtual Reality becomes more and more widespread among researchers, since it offers the opportunity to confront participants with unknown environments in a highly controlled manner.[1]

Spatial knowledge

A classical approach to the acquisition of spatial knowledge, proposed by Siegel & White in 1975, defines three types of spatial knowledge – landmarks, route knowledge and survey knowledge – and draws a picture of these three as stepstones in a successive development of spatial knowledge.[2]

Within this framework, landmarks can be understood as salient objects in the environment of an actor, which are memorized without information about any metric relations at first. By traveling between landmarks, route knowledge evolves, which can be seen as sequential information about the space which connects landmarks. Finally, increased familiarity with an environment allows the development of so-called survey knowledge, which integrates both landmarks and routes and relates it to a fixed coordinate system, i. e. in terms of metric relations and alignment to absolute categories like compass bearings etc. This results in abilities like taking shortcuts never taken before, for example.

More recently, newer findings challenged this stairway-like model of acquisition of spatial knowledge. Whereas familiarity with an environment seems to be a crucial predictor of navigational performance indeed,[3][4] in many cases even survey knowledge can be established after minimal exploration of a new environment.[5][6][7]

In this context, Daniel R. Montello proposed a new framework, indicating, that the changes in spatial knowledge ongoing with growing experience are rather quantitative than qualitative, i. e. different types of spatial knowledge become just more precise and confident.[8] Furthermore, the use of these different types seems to be predominantly task-dependent,[3][4] which leads to the conclusion that spatial navigation in everyday life requires multiple strategies with different emphasis on landmarks, routes and overall survey knowledge.

Reference frames

Which type of spatial knowledge is acquired in a special situation depends also from the respective source of information.

Active navigation appears to have a bigger impact on the establishment of route knowledge,[9][10][11] whereas the use of a map seemingly better supports survey knowledge about more large-scaled complex environments.[9][11][12]

In this context, a discussion came up about different reference frames, which are the frameworks wherein spatial information is encoded. In general, two of them can be distinguished as the egocentric (Latin ego: “I”) and the allocentric (ancient Greek allos: “another, external”) reference frame.

Within an egocentric reference frame, spatial information is encoded in terms of relations to the physical body of a navigator, whereas the allocentric reference frame defines relations of objects among each other, that is independent of the physical body of an “observer” and thus in a more absolute way, which takes metrical conditions and general alignments like cardinal directions into account.[13] This suggests, that route knowledge, which is supported by direct navigation, is more likey to be encoded within an egocentric reference frame[2][14] and survey knowledge, which is supported by map learning, to be more likely to be encoded within an allocentric reference frame in turn.[2][12]

Whilst spatial information can be stored into these different frames, they already seem to develop together in early stages of childhood[15] and appear to be necessarily used in combination in order to solve everyday life tasks.[16][17][9]

Coding

There are many strategies used to spatially encode the environment, and they are often used together within the same task. In a recent study, König et aliae[18] provided further evidence by letting participants learn the positions of streets and houses from an interactive map. Participants reproduced their knowledge in both relative and absolute terms by indicating the positions of houses and streets in relation to one another and their absolute locations using cardinal directions. Some participants were allowed three seconds to form their description, while others were not given a time limit. Their conclusions show that positions of houses were best remembered in relative tasks, while streets were best remembered in absolute tasks, and that increasing allotted time for cognitive reasoning improved performance for both.

These findings suggest, that circumscribed objects like houses, which would be sensory available at one moment during an active exploration, are more likely to be encoded in a relative/binary coded way and that time for cognitive reasoning allows the conversion into an absolute/unitary coded format, which is the deduction of their absolute position in line with cardinal directions, compass bearings etc. Contrary, bigger and more abstract objects like streets are more likely to be encoded in an absolute manner from the beginning.

That confirms the view of mixed strategies, in this case that spatial information of different objects is coded in distinct ways within the same task. Moreover, the orientation and location of objects like houses seems to be primarily learned in an action-oriented way, which is also in line with an enactive framework for human cognition.

Spatial cognition in genders

In a study of two congeneric rodent species, sex differences in hippocampal size were predicted by sex-specific patterns of spatial cognition. Hippocampal size is known to correlate positively with maze performance in laboratory mouse strains and with selective pressure for spatial memory among passerine bird species. In polygamous vole species (Rodentia: Microtus), males range more widely than females in the field and perform better on laboratory measures of spatial ability; both of these differences are absent in monogamous vole species. Ten females and males were taken from natural populations of two vole species, the polygamous meadow vole, M. pennsylvanicus, and the monogamous pine vole, M. pinetorum. Only in the polygamous species do males have larger hippocampi relative to the entire brain than do females.[19] This study shows that spatial cognition can vary depending on your gender.

Our study aimed to determine whether male cuttlefish (Sepia officinalis; cephalopod mollusc) range over a larger area than females and whether this difference is associated with a cognitive dimorphism in orientation abilities. First, we assessed the distance travelled by sexually immature and mature cuttlefish of both sexes when placed in an open field (test 1). Second, cuttlefish were trained to solve a spatial task in a T-maze, and the spatial strategy preferentially used (right/left turn or visual cues) was determined (test 2). Our results showed that sexually mature males travelled a longer distance in test 1, and were more likely to use visual cues to orient in test 2, compared with the other three groups.[20]

See also

References
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  2. Siegel, A. W. & White, S. H. (1975) (1975). "The development of spatial representations of large scale environments". Advances in Child Development and Behavior. 10: 9–55. doi:10.1016/s0065-2407(08)60007-5. ISBN 9780120097104. PMID 1101663.CS1 maint: multiple names: authors list (link)
  3. Nori, R. & Piccardi, L. (2010). "Familiarity and spatial cognitive style: how important are they for spatial representation?". In: Spatial Memory: Visuospatial Processes, Cognitive Performance and Developmental Effects, ed. J. B. Thomas (New York, NY: NovaPublisher), 123 – 144.CS1 maint: multiple names: authors list (link)
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  8. Montello, D. R. (1998). "A new framework for understanding the acquisition of spatial knowledge in large-scale environments" (PDF). In: Spatial and Temporal Reasoning in Geographic Information Systems, ed. R. Golledge (Oxford: Oxford University Press): 143–154.
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  12. Montello, D. R., Hegarty, M. & Richardson, A. E. (2004). "Spatial memory of real environments, virtual environments, and maps". In: Human Spatial Memory: Remembering Where, ed. G. L. Allen (Boca Raton, FL: CRC Press): 251–285.CS1 maint: multiple names: authors list (link)
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  17. Gramann, Klaus (2013). "Embodiment of Spatial Reference Frames and Individual Differences in Reference Frame Proclivity". Spatial Cognition & Computation. 13 (1): 1–25. doi:10.1080/13875868.2011.589038. ISSN 1387-5868. S2CID 14690762.
  18. König, Sabine U.; Goeke, Caspar; Meilinger, Tobias; König, Peter (2019). "Are allocentric spatial reference frames compatible with theories of Enactivism?". Psychological Research. 83 (3): 498–513. doi:10.1007/s00426-017-0899-x. ISSN 0340-0727. PMID 28770385. S2CID 4032915.
  19. Jacobs, Lucia F.; Gaulin, Steven J. C.; Sherry, David F.; Hoffman, Gloria E. (1990). "Evolution of Spatial Cognition: Sex-Specific Patterns of Spatial Behavior Predict Hippocampal Size". Proceedings of the National Academy of Sciences of the United States of America. 87 (16): 6349–6352. Bibcode:1990PNAS...87.6349J. doi:10.1073/pnas.87.16.6349. ISSN 0027-8424. JSTOR 2356205. PMC 54531. PMID 2201026.
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