Bilingual
Volume 38, Issue 3 (2023)                   GeoRes 2023, 38(3): 381-389 | Back to browse issues page
Article Type:
Original Research |
Subject:

Print XML Persian Abstract PDF HTML


History

How to cite this article
Mijani M, Gharehbeiglu M, Reshad L, Nejadebrahimi A. Evaluation of Students' Cognitive Maps from an Urban Perspective as a Mental Image Output. GeoRes 2023; 38 (3) :381-389
URL: http://georesearch.ir/article-1-1509-en.html
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Rights and permissions
1- Department of Architecture, Faculty of Architecture and Urban Planning, University of Islamic Art, Tabriz, Iran
* Corresponding Author Address: Department of Architecture, Faculty of Architecture and Urban Planning, University of Islamic Art, Saat Square, Tabriz, Iran. Postal Code: 5137753497 (m.gharehbaglou@tabriziau.ac.ir)
Full-Text (HTML)   (121 Views)
Introduction
The subject of spatial perception has long been a focus of scholarly works in various disciplines, including geography, sociology, psychology, architecture, and urban studies. In different studies, the concept of space is regarded as a function of intersubjective experiences; in simpler terms, space carries a mental dimension. Particularly in the field of urban sciences, the perceptions of all those who use and engage with urban spaces are essential for urban planning [Özgece et al., 2022; Nikšič, 2006]. The necessity of addressing people’s mental images of the city stems from the fact that studies evaluating the visual environment aim to demonstrate the interaction between humans and their surroundings, thereby providing data to improve living environments in terms of both social and physical convenience, factors that contribute to enhancing urban habitability [Topcu & Topcu, 2012]. Mental images are cognitive structures that guide users’ behaviors and activities in urban space. Lynch argued that by recognizing urban visual elements and forming mental images of the city, people develop a natural sense of orientation and wayfinding [Hartanti et al., 2016; Lynch, 1984]. Boulding asserted that understanding human actions requires understanding their thoughts, and that mental images are key to comprehending human behavior [Markey-Towler, 2017; Boulding, 1956].
The peak of research on mental mapping emerged in the 1960s, coinciding with the behavioral revolution in geography, where space was conceived as a linkage between human activities and the environment. Mental maps were first introduced by Trowbridge as “imaginary maps” [Trowbridge, 1913], later appearing in geography as “cognitive maps” [De Alba, 2011]. In 1930, Tolman and Honzik, based on animal experiments, proposed the theory of response sequences, suggesting that animal behavior is holistic and demonstrates purposiveness and cognition [Tsagareli, 2022; Tuan, 1975; Tolman & Honzik, 1930]. Lynch formulated one of the most influential theories of spatial cognition and behavioral geography in 1959, explaining how people perceive and represent cities [Filomena et al., 2019; Lynch, 1959], with the aim of exploring people’s feelings and knowledge about their environment [Mohamed, 2012]. According to Lynch, individuals represent the content and structure of a city in their minds based on personal experience. He emphasized the reciprocal interaction between observer and environment, which he termed the “image of the surroundings.” Observers, relying on their own experiences, selectively organize and edit the multitude of images offered by the physical environment, thereby attributing meaning to it. In this sense, urban images intertwine with mental images to reflect the identity of the city [Alptekin, 2017; Lynch, 1959]. Özgece employed the term “mental images,” which in other studies is also referred to as “sense of place,” describing how physical objects are endowed with qualities that evoke vivid impressions in the observer’s mind [Özgece et al., 2022]. Mental images, or sense of place, may vary across individuals, depending on their backgrounds, experiences, and familiarity with a location. Alongside collective experiences and shared images derived from physical reality and human physiology, mental images also encapsulate individual identities [Hartanti et al., 2016].
Mental images of space are formed both through direct experiences and indirect exposure via other sources. In both cases, they involve a complex process of acquiring, processing, and interpreting information [Nikšič, 2006]. However, the principles by which this process is intensified or weakened depend on various factors identified in different theories. Rossi highlighted the role of collective memory, arguing that cities are places of life and memory, and emphasized the historical value of the relationship between community and place. For Rossi, the city’s basic structures such as historic buildings, residential areas, and open spaces are key elements perceived at first glance [Gojnić, 2018; Gao et al., 2021; Rossi, 1982]. Bacon emphasized continuity in urban perception, which parallels Cullen’s theory of serial vision. Cullen viewed the city as a “sequence of revelations” shaped and experienced through “sudden contrasts” between buildings and spaces [Shanken, 2018; Cullen, 1961; Bacon, 1976].
In his seminal study on urban perception, Lynch found that people retain mental images of spaces under the influence of clarity, visibility, and spatial coherence. He classified urban representations into districts, paths, nodes, landmarks, and edges as components of the built environment [Lynch, 1959]. He argues that the environment must be clearly organized and easily recognizable, enabling citizens to attribute meanings and connections to it [Kalin & Yilmaz, 2012]. Lynch further emphasizes the importance of singularity, simplicity, continuity, dominance, clarity, directional distinctiveness, visual scope, movement awareness, and temporal sequence in shaping urban legibility [Nurgandarum & Anjani, 2020; Lynch, 1959]. Legibility, in fact, is defined as the ease with which the mind can organize the environment into a coherent mental pattern. Bentley described legibility and clarity as the ability to interpret the environment in terms of options, enabling individuals to form distinct and accurate images of it [Bentley, 1985]. Legibility is an environmental property that allows people to explore their surroundings without becoming lost [Kaplan & Kaplan, 1981]. It enables them to grasp the layout of a place and the activities associated with it [Carmona et al., 2003]. Simplicity, coherence, and comprehensibility are also often regarded as qualities of legible environments [Thombre & Kapshe, 2022]. Relph highlightes the importance of building appearances in shaping urban perception, arguing that place identity is a function of architectural forms, urban landscapes, and the similarities and differences among them [Relph, 1976]. Steinitz argues for a deeper understanding of the interaction between urban form and activity, and the role of this interaction in conveying meaning within significant environments. He further notes that environments should articulate the type of activities associated with particular places, enabling individuals to locate, recognize, and describe them. Thus, the alignment between physical form and activities renders cities more comprehensible and meaningful for their residents [Özgece et al., 2022; Steinitz, 1968].
Based on the above, it can be concluded that access to factors influencing urban environments from the perspective of residents’ perceptions can be achieved by examining their mental images of the city. The present study seeks to explore the influential factors shaping the imageability of urban spaces through the mental images of first-semester urban planning students at Tabriz Islamic Art University. Accordingly, the objectives of this research are twofold: first, to identify the structural components influencing students’ mental representations of urban spaces, and second, to examine the role of gender in shaping these perceptions.


Methodology
This study was applied in its purpose, as it sought to investigate cognitive maps with an emphasis on mental images. However, its nature was quantitative. The research was conducted in 2022 in Tabriz, using a sketch-based questionnaire as the data collection instrument. First, in the theoretical section, the operational model was extracted based on previous studies to guide the present research. Three structures of physical, functional, and sense of time along with their respective subcomponents, were identified as the criteria for measuring mental images.
For data collection, sketch-based questionnaires were employed (i.e., the analysis of students’ cognitive sketch maps, which represent the reproduction of individuals’ internal images of the environment). The statistical population consisted of students of the Faculty of Architecture and Urban Planning at Tabriz Islamic Art University. The sketches were drawn by first-semester urban planning students in the “Introductory Urban Sketching” course in 2022. The study sample included 50 participants, selected through purposive and convenient sampling. The sample size was determined according to the principle of data saturation. Students were asked to visit the city of Tabriz, select an area of their choice, and conduct an on-site visit. In the following class session, they were requested to present a perspective drawing based on the mental images formed from their visit.
The first group of 25 participants comprised male students admitted in Fall 2022, and the second group of 25 comprised female students admitted in Spring 2023. Given their academic background, all participants possessed basic sketching skills. After analyzing the 25th sketch in each group, it became evident that the identified concepts were beginning to recur, and from the 20th to the 25th sketch, few new elements emerged. Nevertheless, the analysis continued up to the 25th sketch in each group. Since presenting all cognitive maps (sketches) exceeded the scope of the paper.
The data derived from students’ mental images were analyzed using confirmatory factor analysis (CFA) and correlation methods, with SmartPLS and IBM SPSS 25.0 software. The KMO (Kaiser-Meyer-Olkin) test confirmed sampling adequacy, indicating satisfactory correlations among items; thus, proceeding with factor analysis was justified.
The reliability of the data collection tool was assessed using the partial least squares (PLS) method. The composite reliability (CR) values exceeded 0.70 overall, with the following results: sense of time=1.00, physical structure=0.859, and functional structure=0.737, all of which indicated acceptable reliability. Cronbach’s alpha values for the overall construct and all components were above 0.70, further confirming reliability.
The PLS method was selected due to its variance-based approach and its greater flexibility compared to covariance-based structural models. Advantages of PLS include suitability for small sample sizes, no requirement for data normality, applicability to formative measurement models, strong predictive power, compatibility with complex models (with numerous constructs and indicators), theory development, use with categorical parameters, and hypothesis testing [Sarstedt & Chah, 2019]. Normality of quantitative parameters was examined using the Shapiro–Wilk test. Independent-sample t-tests were used to compare the two groups, and chi-square tests were employed to examine associations between qualitative parameters. IBM SPSS 25.0 was applied, and a 5% significance level was adopted.
The average variance extracted (AVE) was 0.50 or higher, indicating that, on average, the construct explained more than half of the variance of its indicators. The calculations showed that the validity of the constructs exceeded 0.67, confirming acceptable validity. To assess model fit, multiple indices were employed. The results obtained from the software confirmed that the model exhibited good overall fit.


Findings
In this section, the conceptual model of the study was examined using partial least squares structural equation modeling (PLS-SEM). The analysis aimed to evaluate the relationships among the research parameters, as well as the reliability, validity, and overall quality of the model. First, the model related to path coefficients or factor loadings is reported. Factor loadings indicate the strength of the relationship between a latent parameter and its observed indicators, with values ranging from zero to one. Based on the software analysis, it was found that all observed parameters adequately explained their respective latent parameters. Since factor loadings above 0.40 indicate that the latent parameter can reliably account for the observed parameter, all loadings were deemed acceptable.
Confirmatory factor analysis revealed that the factor loadings of all components were above 0.42, which, being higher than 0.40, confirms their acceptability. Thus, the model successfully explained the relationships between latent and observed parameters. Considering the t-statistics, all three main constructs of the study were confirmed at the 99% confidence level.
Overall, the physical structure construct demonstrated the strongest influence on students’ mental images of urban space, with an effect coefficient of 0.930, followed by the functional structure construct with an effect coefficient of 0.733. In contrast, the sense of time construct showed a negative effect coefficient of –0.084, indicating an inverse relationship with mental image formation.
At the component level, the results showed that within the physical structure, the subcomponents of geometry and orientation/axis had the highest explanatory power, with factor loadings of 0.681 and 0.639, respectively. Conversely, the subcomponents of scale/proportion and sequence/visual scope demonstrated the lowest explanatory power. Within the functional structure, the subcomponent of human events and activities exhibited the greatest influence on students’ mental images of urban space. Finally, the negative loading of the sense of time construct indicated that the historical buildings component, with a loading of –1.000, had an inverse effect.
The effect of gender on students’ cognitive maps of urban space was further examined using correlation analysis. The chi-square value for the physical structure construct between male and female students was 0.47, with a p-value of 0.389. Since this result was not statistically significant, no meaningful difference was observed between genders in this construct. Similarly, chi-square tests for the functional structure and sense of time constructs revealed no statistically significant gender-based differences.


Discussion
The aim of the present study was to evaluate and identify the components influencing the registration of students’ mental images of urban landscapes, using the output of their cognitive maps.
According to the findings of the confirmatory factor analysis, the results indicate that in the “physical structure domain”, the built environment and its associated elements play a significant role in shaping mental images of urban landscapes. Specifically, the component of “geometry”, with emphasis on building form, rhythm, and symmetry, ranked first, followed by “direction and axis”, with emphasis on urban pathways, as the second most determinant factors in students’ mental images. Similar studies, such as those by Huang et al., Yu et al., and Jin & Wang aligned with our findings, highlighting that formal characteristics, such as the shape and distinctiveness of buildings, play an essential role in the structure of visual landscapes and their imageability. The use of distinctive architectural forms fosters mental differentiation of environmental elements [Huang et al., 2018; Yu et al., 2022; Jin & Wang, 2021].
Kuliga et al. note that wayfinding encompasses sensory perception and spatial cognition (e.g., spatial orientation, route planning, and path selection between origin and destination), monitoring progress while moving toward the destination, and recognizing it upon arrival [Kuliga et al., 2021].
The third-ranked component was “distinction and similarity”, emphasizing unity and continuity and open and closed spaces, followed by “simplicity and diversity”, emphasizing details and architectural ornamentation. Imani and Tabaeian have emphasized that buildings with strong distinctiveness, such as those with unusual shapes, are more memorable; when buildings differ in appearance, they are easier to distinguish, thus facilitating wayfinding, whereas uniform building appearances increase the risk of disorientation [Imani & Tabaeian, 2012]. Similarly, Jin et al. stress that complex urban landscapes with diverse and continuous details are easier to comprehend [Jin et al., 2021]. Van den Berg et al. also have emphasized the stimulating effect of architectural details on residents’ visual perception [Van den Berg et al., 2016].
The fifth-ranked component was “sequence and visual boundary”, with emphasis on spatial depth, followed by “scale and proportion”, emphasizing building height, as the sixth determinant of urban landscape imageability. In a comparable study, Tara et al. have stressed that height and proportions are dominant factors influencing environmental visual effects [Tara et al., 2021]. Ma et al. likewise have considered these attributes as enhancing environmental attractiveness and residents’ visual perception [Ma et al., 2021].
In the functional structure domain, the findings showed that “human activities” ranked first and “functional types” second in importance in the registration of urban landscape images in students’ minds. Al-Alwan et al. similarly stated that urban landscapes, as places of human life, are inherently diverse, reflecting complex daily lives, and thus aligned with human activities. People typically perceive places with multiple land uses as behavioral settings for specific activities such as social interactions, mental health uses, or green spaces [Al-Alwan et al., 2022]. Zheng et al. also have emphasized that urban functional types and human activities are complementary and serve as tools for reflecting urban imagery [Zheng et al., 2022].
Finally, in the sense of time domain”, the findings revealed that the component of “valuable buildings” did not significantly contribute to the registration of students’ mental images of the urban landscape. This result contrasts with other studies. For example, Askarizad et al. and Caffo et al. have argued that urban landmarks with historical, social, or cultural value can revive a sense of place, enhance spatial legibility, and ultimately facilitate wayfinding in such environments [Askarizad et al., 2022; Caffo et al., 2018].
The results of correlation analysis further indicated that gender had no significant effect on students’ mental image registration of urban landscapes. However, other studies, such as those by Martin & Megan and Mirgholami et al. have suggested gender differences in spatial cognition, noting that males generally demonstrate higher spatial perception abilities, whereas females prefer route-based knowledge and experience greater spatial anxiety. These differences are linked to males’ stronger deductive reasoning (whole-to-part) and females’ stronger inductive reasoning (part-to-whole). Men’s holistic perception also leads them to pay less attention to details [Martin & Megan, 2017; Mirgholami et al., 2021].
Regarding the limitations, it should be noted that the findings were derived from students’ mental images represented in cognitive maps. Given the data constraints, this approach is considered a survey method. Participants were selected based on their drawing skills, though they lacked specialized knowledge in urban planning, thereby minimizing potential confounding factors. The outcomes of such research, in addition to contributing to urban design knowledge, can enhance the integration between environmental psychology and urban design practice.


Conclusion
The components influencing the formation of students’ mental images of urban landscapes are directly shaped by three main factors: physical structure, functional structure, and sense of time. Among these, the physical structure with its components of geometry, direction and axis, distinction and similarity, simplicity and diversity, scale and proportion, and sequence and visual boundary was identified as the most influential factor. Conversely, the sense of time, represented by the component of historical buildings, showed no significant impact on the registration of students’ mental images. This finding highlights that urban spaces are primarily the product of functional–physical structures, rather than temporal dimensions.

Acknowledgments: None declared by the authors.
Ethical Permission: The authors adhered to the principles of academic ethics, including honesty, confidentiality, integrity, and related standards.
Conflict of Interest: This article is derived from the first author’s thesis entitled “Visible and Invisible: Designing a Book-Passage Based on Place-Based Learning”, supervised by the second author and advised by the third author at Tabriz Islamic Art University.
Authors’ Contributions: Mijani M (first author), Principal Researcher (25%); Gharehbeiglu M (second author), Discussion Writer (25%); Reshad L (third author), Methodologist/Statistical Analyst (25%); Nejadebrahimi A (fourth author), Introduction Writer (25%)
Funding: None declared by the authors.
Keywords:

References
1. Al-Alwan HAS, Al-Bazzaz IA, Mohammed Ali YH (2022). The potency of architectural probabilism in shaping cognitive environments: A psychophysical approach. Ain Shams Engineering Journal. 13(1):101522. [Link] [DOI:10.1016/j.asej.2021.06.008]
2. Alptekin O (2017). A reading attempt of the urban memory of Eskisehir Osmangazi University Meselik campus via cognitive mapping. In: IOP Conference Series: Materials Science and Engineering. 245(5):052016 .Bristol: IOP Publishing. [Link] [DOI:10.1088/1757-899X/245/5/052016]
3. Askarizad R, He J, Khotbehsara EM (2022). The legibility efficacy of historical neighborhoods in creating a cognitive map for citizens. Sustainability. 14(15):9010. [Link] [DOI:10.3390/su14159010]
4. Bacon EN (1976). Design of cities. Revised Edition. London: Penguin books. [Link]
5. Bentley I (1985). Responsive environments: A manual for designers. Butterworth Architecture. Philadelphia: Routledge. [Link]
6. Boulding KE (1956). The image: Knowledge in life and society. Michigan: University of Michigan Press. [Link] [DOI:10.3998/mpub.6607]
7. Caffo A, Lopez A, Spano G, Serino S, Cipresso E, Stasolla S, et al (2018). Spatial reorientation decline in aging: The combination of geometry and landmarks. Aging & Mental Health. 22(10):1372-1383. [Link] [DOI:10.1080/13607863.2017.1354973]
8. Carmona M, Heath T, Tiesdell S, Oc T (2003). Public places, urban spaces: the dimensions of urban design. Oxford: Architectural Press. [Link]
9. Cullen G (1961). Concise Townscape. 1st Edition. London: Routledge. [Link]
10. De Alba M (2011). Social representations of urban spaces: A comment on mental maps of Paris. Papers on Social Representations. 20(2):29-1. [Link]
11. Filomena G, Verstegen JA, Manley ED (2019). A computational approach to 'The Image of the City'. Cities. 89:14-25. [Link] [DOI:10.1016/j.cities.2019.01.006]
12. Gao S, Han L, Li C, Zhao L (2021). Detecting the evolution of collective memory space using a space syntax-based analysis method in Beiyuanmen historical and cultural block. Current Urban Studies. 9(4):744-758. [Link] [DOI:10.4236/cus.2021.94044]
13. Gojnić AB (2018). The collective and the architecture of the city in postwar modernism. Histories of Postwar Architecture. (2):1968. [Link]
14. Hartanti NB, Martokusumo W, Lubis BU, Poerbo HW (2016). The quest for urban identity: Influence of urban morphological development to the imageability of Bogor city streets. International Journal of Research in Engineering and Science (IJRES). 4(7):49-58. [Link]
15. Huang B, Zhao B, Song Y (2018). Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sensing of Environment. 214:73-86. [Link] [DOI:10.1016/j.rse.2018.04.050]
16. Imani F, Tabaeian M (2012). Recreating mental image with the aid of cognitive maps and its role in environmental perception. Procedia-Social and Behavioral Sciences. 32:53-62. [Link] [DOI:10.1016/j.sbspro.2012.01.010]
17. Jin X, Wang J (2021). Assessing linear urban landscape from dynamic visual perception based on urban morphology. Frontiers of Architectural Research. 10(1):202-219. [Link] [DOI:10.1016/j.foar.2021.01.001]
18. Kalin A, Yilmaz D (2012). A study on visibility analysis of urban landmarks: The case of Hagia Sophia (Ayasofya) in Trabzon. Journal of the Faculty of Architecture. 29(1):241-271. [Link]
19. Kaplan S, Kaplan R (1981). Cognition and environment: Functioning in an uncertain world. Ann Arbor: Ulrich's. [Link]
20. Kuliga S, Berwig M, Roes M (2021). Wayfinding in people with Alzheimer's disease: Perspective taking and architectural cognition-A vision paper on future dementia care research opportunities. Sustainability. 13(3):1084. [Link] [DOI:10.3390/su13031084]
21. Lynch K (1959). The image of the city. Massachusetts: MIT press. [Link]
22. Lynch K (1984). Reconsidering the image of the city. In: Rodwin L, Hollister RM, editors. Cities of the Mind. Environment, development, and public policy. Boston: Springer. p. 151-161. [Link] [DOI:10.1007/978-1-4757-9697-1_9]
23. Ma X, Ma C, Wu C, Xi Y, Yang R, Peng N, et al (2021). Measuring human perceptions of streetscapes to better inform urban renewal: A perspective of scene semantic parsing. Cities. 110:103086. [Link] [DOI:10.1016/j.cities.2020.103086]
24. Markey-Towler B (2017). How to win customers and influence people: Ameliorating the barriers to inducing behavioural change. Journal of Behavioral Economics for Policy. 1(S):27-32. [Link]
25. Martin M (2017). The relationship between way-finding strategies, spatial anxiety and prior experiences [dissertation]. Brescia: Brescia University. [Link]
26. Mirgholami M, Ketabollahi K, Azadi M, Oskoyi B (2021). Perception of city entrances in term of user's sexual variety (Case study: Sanandaj city's entrance-from Hamedan city). Journal of Applied Arts, 1(2):51-71.‏ [Link]
27. Mohamed AA (2012). Evaluating way-finding ability within urban environment. Proceedings of the 8th International Space Syntax Symposium. Santiago: PUC. p.3-6. [Link]
28. Nikšič M (2006). The dimensions of urban public space in user's mental image. Urbani izziv. 17(1-2):200-204. [Link] [DOI:10.5379/urbani-izziv-en-2006-17-01-02-007]
29. Nurgandarum D, Anjani CF (2020). Legibility of building facades and imageability of historical city center, case study: Bukittinggi city center. In IOP Conference Series: Earth and Environmental Science. 452:012158. Bristol: IOP Publishing. [Link] [DOI:10.1088/1755-1315/452/1/012158]
30. Özgece N, Edgü E, Ayıran N (2022). Assessing imageability of port cities through the visibility of public spaces: The cases of Famagusta and Limassol. Space and Culture. 25(4):535-552. [Link] [DOI:10.1177/1206331220944063]
31. Relph EC (1976). Place and placelessness. London: Pion. [Link]
32. Sarstedt M, Cheah JH (2019). Partial least squares structural equation modeling using SmartPLS: A software review. 7(3): 196-202. [Link] [DOI:10.1057/s41270-019-00058-3]
33. Thombre L, Kapshe C (2022). Verification of connection between legibility and conviviality of public open spaces- a case of new market, Bhopal. Ecology, Environment and Conservation Paper. 28(1):219-226. [Link] [DOI:10.53550/EEC.2022.v28i01.029]
34. Qharehbaglou M, Reshad L (2021). Characterizing imageability in Gajar houses of Tabriz. Geographical Research. 36(3):233-241. [Persian] [Link]
35. Rossi A (1982). The architecture of the city. Cambridge: MIT press. [Link]
36. Shanken AM (2018). The visual culture of planning. Journal of Planning History. 17(4):300-319. [Link] [DOI:10.1177/1538513218775122]
37. Steinitz C (1968). Meaning and the congruence of urban form and activity. Journal of the American Institute of planners. 34(4):233-248. [Link] [DOI:10.1080/01944366808977812]
38. Tara A, Lawson G, Renata A (2021). Measuring magnitude of change by high-rise buildings in visual amenity conflicts in Brisbane. Landscape and Urban Planning. 205:103930. [Link] [DOI:10.1016/j.landurbplan.2020.103930]
39. Topcu KD, Topcu M (2012). Visual presentation of mental images in urban design education: Cognitive maps. Procedia-Social and Behavioral Sciences. 51:573-582. [Link] [DOI:10.1016/j.sbspro.2012.08.208]
40. Tolman EC, Honzik CH (1930). Introduction and removal of reward and maze performance in rats. University of California Publication in Psychology. 4:257-275. [Link] [DOI:10.1080/00221309.1930.9918318]
41. Trowbridge CC (1913). On fundamental methods of orientation and imaginary maps. Science. 38(990):888-897. [Link] [DOI:10.1126/science.38.990.888]
42. Tsagareli MG (2022). Pioneering studies of spatial behavior in animals: Ivane Beritashvili and Edward Tolman. Psychology Research. 12(8):563-574. [Link] [DOI:10.17265/2159-5542/2022.08.001]
43. Tuan YF (1975). Images and mental maps. Annals of the Association of American geographers. 65(2):205-212. [Link] [DOI:10.1111/j.1467-8306.1975.tb01031.x]
44. Van den Berg AE, Joye Y, Koole SL (2016). Why viewing nature is more fascinating and restorative than viewing buildings: A closer look at perceived complexity. Urban Forestry & Urban Greening. 20:397-401. [Link] [DOI:10.1016/j.ufug.2016.10.011]
45. Yu Z, Xiao Z, Liu X (2022). A data-driven perspective for sensing urban functional images: Place-based evidence in Hong Kong. Habitat International. 130:102707. [Link] [DOI:10.1016/j.habitatint.2022.102707]
46. Zheng L, Pan H, Kong L (2022). Ripple attention for visual perception with sub-quadratic complexity. Proceedings of the 39th International Conference on Machine Learning. 162:26993-27010. PMLR. [Link]