Interest in knowledge and mental models from an HCI perspective is based on the idea that, by discovering what users know about systems and how they reason about how the system function, it may be possible to predict learning time, likely errors, and the relative ease with which users can perform their tasks.
The way knowledge is structured in memory is assumed to be highly organized, as I already said at the end of this article. Before looking in more detail at the way such knowledge is used when carrying out cognitive tasks like thinking and problem solving, however, it is important to understand how knowledge is represented in memory:
Analogical representations
Propositional representations
Distributed representations
Analogical representations are picture-like images, such as an image of an apple for example. Propositional representations are abstract and language-like statements that make assertions, like "the book is on the table". Distributed representations are networks of nodes where the knowledge is implicit in the connections between the nodes. The first two are generally viewed as symbolic representations, while the last-mentioned is considered sub-symbolic.
One of the main characteristics of knowledge is that it is highly organized. This can be demonstrated quite simply. Even though we have millions of facts and pieces of information stored in our memory, we are able to answer questions about that information very rapidly. Such a fast response would seem highly unlikely if semantic memory were not organized in some way. Exactly how knowledge is organized and used in memory has been a major debate in cognitive science. One of the most influential approaches has been to assume that knowledge is organized as some form of network. Concepts are arranged so that those that have something in common are linked in some way. Another theory views knowledge as consisting of numerous schemata. Essentially, a schema is a network of general knowledge based on previous experience that facilitates our understanding of commonplace events.
One of the main criticism of schema-based theories of knowledge is that they are too inflexible. Humans can make inferences in complex situations, predict future states and comprehend situations we have never personally experienced. Schema theory has not been able to explain this kind of flexible behavior. An alternative, but related, theoretical concept, which has been developed for these more dynamic aspects of cognitive activity is mental models.
In relation to schemata, mental models are assumed to be dynamically constructed - as creations of the moment - by activating stored schemata. They are distinct from, but related to, images. An important difference between images and mental models is in terms of their function. Mental models are usually constructed when we are required to make an inference or a prediction about a particular state of affairs. In constructing the mental model a conscious mental stimulation may be "run" from which conclusion about the predicted state of affairs. can be deduced. An image is considered to be a one-off representation.
The fact that we can "run" a model means that we can derive new predictions without having to test them out in the real world.
In the early 1980s, two main types of mental models that users employ when interacting with devices were identified: these are categorized as structural and functional models. A structural is one where it is assumed that the user has internalized the structure of how the device or system works in memory, while a functional model assumes that the user has internalized procedural knowledge about the device or system. A simple distinction is to consider structural models as models of "how-it-works" and functional models as models of "how-to-use-it".
The utility of mental models in HCI
A mental model is based on belief, not facts: it's a model of what users know (or think they know) about a system such as a website. Individual users each have their mental model. A mental model is internal to each user's brain, and different users might construct different mental models of the same UI. It's a prime goal for designers to make the user interface communicate the system's basic nature well enough that users form reasonably accurate (and thus useful) mental models.
Further, one of usability's big dilemmas is the common gap between designers' and users' mental models. Because designers know too much, they form lovely mental models of their creations, leading them to believe that each feature is easy to understand. Users' mental models of the UI are likely to be somewhat more deficient, making it more likely for people to make mistakes and find the design much more difficult to use.
Photo by Paweł Czerwiński on Unsplash