Imageability

Key takeaways

Imageability belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Imageability to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Imageability from memory before moving on to harder problems.

Reference excerpt

Imageability is a measure of how easily a physical object, word or environment will evoke a clear mental image in the mind of any person observing it. It is used in architecture and city planning, in psycholinguistics, and in automated computer vision research. In automated image recognition, training models to connect images with concepts that have low imageability can lead to biased and harmful results.

History and components Kevin A. Lynch first introduced the term, "imageability" in his 1960 book, The Image of the City. In the book, Lynch argues cities contain a key set of physical elements that people use to understand the environment, orient themselves inside of it, and assign it meaning. Lynch argues the five key elements that impact the imageability of a city are Paths, Edges, Districts, Nodes, and Landmarks.

Paths: channels in which people travel. Examples: streets, sidewalks, trails, canals, railroads. Edges: objects that form boundaries around space. Examples: walls, buildings, shoreline, curbstone, streets, and overpasses. Districts: medium to large areas people can enter into and out of that have a common set of identifiable characteristics. Nodes: large areas people can enter, that serve as the foci of the city, neighborhood, district, etc. Landmarks: memorable points of reference people cannot enter into. Examples: signs, mountains and public art. In 1914, half a century before The Image of the City was published, Paul Stern discussed a concept similar to imageability in the context of art. Stern, in Susan Langer's Reflections on Art, names the attribute that describes how vividly and intensely an artistic object could be experienced apparency.

In computer vision Automated image recognition was developed by using machine learning to find patterns in large, annotated datasets of photographs, like ImageNet. Images in ImageNet are labelled using concepts in WordNet. Concepts that are easily expressed verbally, like "early", are seen as less "imageable" than nouns referring to physical objects like "leaf". Training AI models to associate concepts with low imageability with specific images can lead to problematic bias in image recognition algorithms. This has particularly been critiqued as it relates to the "person" category of WordNet and therefore also ImageNet. Trevor Pagan and Kate Crawford demonstrated in their essay "Excavating AI" and their art project ImageNet Roulette how this leads to photos of ordinary people being labelled by AI systems as "terrorists" or "sex offenders". Images in datasets are often labelled as having a certain level of imageability. As described by Kaiyu Yang, Fei-Fei Li and co-authors, this is often done following criteria from Allan Paivio and collaborators' 1968 psycholinguistic study of nouns. Yang el.al. write that dataset annotators tasked with labelling imageability "see a list of words and rate each word on a 1-7 scale from 'low imagery' to 'high imagery'. To avoid biased or harmful image recognition and image generation, Yang et.al. recommend not training vision recognition models on concepts with low imageability, especially when the concepts are offensive (such as sexual or racial slurs) or sensitive (their examples for this category include "orphan", "separatist", "Anglo-Saxon" and "crossover voter"). Even "safe" concepts with low imageability, like "great-niece" or "vegetarian" can lead to misleading results and should be avoided.

See also Wayfinding Mental mapping Environmental psychology Speech perception Experimental psychology

Further reading Holahan, Charles J.; Sorenson, Paul F. (1985-09-01). "The role of figural organization in city imageability: An information processing analysis". Journal of Environmental Psychology. Smolík Filip (2019-05-21). "Imageability and Neighborhood Density Facilitate the Age of Word Acquisition in Czech". Journal of Speech, Language, and Hearing Research. Paivio, Allan; Yuille, John C.; Madigan, Stephen A. (1968). "Concreteness, imagery, and meaningfulness values for 925 nouns". Journal of Experimental Psychology. Hansen, Pernille; Holm, Elisabeth; Lind, Marianne; Simonsen, Hanne Gram (2012). "Name relatedness and imageability". Richardson, John T. E. (1975–05). "Concreteness and Imageability". Quarterly Journal of Experimental Psychology. Silva, Kapila Dharmasena (2015). "Developing Alternative Methods for Urban Imageability Research". McCunn, Lindsay J.; Gifford, Robert (2018-04-01). "Spatial navigation and place imageability in sense of place". Cities. Caplan, Jeremy B.; Madan, Christopher R. (2016-06-17). "Word Imageability Enhances Association-memory by Increasing Hippocampal Engagement". Journal of Cognitive Neuroscience Chmielewski S., Bochniak A., Natapov A., Wezyk P. (2020). "Introducing GEOBIA to Landscape Imageability Assessment". Remote Sensing.

References

Worked examples

Example 1 — a first encounter with Imageability

Start with the simplest possible case. Write down what Imageability claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Imageability before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Imageability ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Imageability

In research: Imageability appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Imageability in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Imageability is common in secondary-school and first-year university syllabi. It links to neighbouring topics Environmental psychology, Knowledge representation, Psychogeography, so understanding it makes those chapters shorter.
In everyday life: Look for Imageability outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

How to study Imageability in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Imageability means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Imageability out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Imageability in simple terms?

Imageability is a measure of how easily a physical object, word or environment will evoke a clear mental image in the mind of any person observing it. It is used in architecture and city planning, in psycholinguistics, and in automated computer vision research.

Why does Imageability matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Imageability?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Imageability.