Chapter 7 Mental Representation Mental Representation Mental representation is a systematic correspondence between some element of a target domain and some element of a modeling (or representation) domain. A representation, whether it be mental or any other, is a system of symbols. The system of symbols is isomorphic to another system (the represented system) so that conclusions drawn through the processing of the symbols in the representing system constitute valid inferences about the represented system. Isomorphic means `having the same form.' The following figure is a typical example of how we represent information mentally in our minds. Figure 8.12 A hierarchical network representation of concepts. Source: Collins and Quillian (1969) The cognitive psychologists have always agreed on the fact that human information processing depends on the mental representation of information; but there is a great disagreement with regard to the nature of this mental representation of information. Symbols are the representations that are amodal. They bear no necessary resemblance to the concept or percept they represent. The systematic correspondence between the two domains may be a matter of convention (only). For example, in algebra, we denote the different variables as x, y, z, and so on, but neither of these symbols have a direct resemblance to what they represent. Similarly, while solving a geometrical problem involving geometrical shapes, we might assign symbols such as A, B, or C to such geometrical shapes, even though these symbols do not have a direct resemblance to the shapes. Images are another way how the information can be represented in our minds. Images are basically representations that resemble what they represent in some non-arbitrary way. The systematic correspondence between the two domains is iconic. For example, a map of a particular city, or a caricature might resemble to what it is supposed to represent in terms of features iconically, but is not an exact replication of the same. Symbols and images are some basic ways by which information can be represented mentally. Some things such as visual percepts are almost obviously represented as images, while some other things such as abstract concepts are a difficult thing to represent as images. For example, if a person is feeling tired of listening about a particular movie, so then it would be difficult for that person to represent that feeling as an image. Furthermore, some things fall in between the two. For example, the concept of a dog in general; this concept of dogness can both be represented as a visual percept as well as an abstract thing. Over the years, a number of studies have provided evidence regarding the mental images. In one such study, the participants were presented with some pictures of abstract 3D objects. Then the participants were given an object-matching task, and were asked; “Are these two objects the same or not?” It was seen that on correct “same” responses, subject took longer when there was a greater angle of rotation between the two objects. These results suggest that the participants were rotating the images until they matched up in their “mind’s eye”. Subjects had to judge whether the two stimuli shown in Panel A are the same as each other ,but viewed from different perspectives;likewise for the pairs shown in B and C.Subjects seem to make these judgments by imagining one of the forms rotating until its position matches that of the other form.[After Shepard & Metzler,1971]. In another study, the participants were engaged in a property listing task in which they had to list a few properties of some object. The subjects were asked to name a few properties that they thought were true of that objects. In one such task, the subjects were asked to name a few properties of a watermelon. The reported answers of the subjects included the answers that the watermelon is green, heavy, round, is generally bought in summer, etc. In another task, the subjects were asked to list a few properties of half a watermelon. Now the answers included that it is pink, has seeds, and is sweet, watery, and so on. These differences in the answers about essentially a same object showed that the responses the participants gave were dependent in part, on the imagery. Imagery and Functional Equivalence Hypothesis Mental imagery (varieties of which are sometimes referred to as “visualizing,” “seeing in the mind's eye,” “hearing in the head,” “imagining the feel of,” etc.) is an experience that resembles perceptual experience, but occurs in the absence of the appropriate external stimuli. It is also generally understood to bear intentionality (i.e., mental images are always images of something or other), and thereby to function as a form of mental representation. Traditionally, visualmental imagery was thought to be caused by the presence of picture-like representations (mental images) in the mind, soul, or brain, but this is no longer universally accepted. The Functional Equivalence Hypothesis of imagery assumes that visual imagery, while not identical to perception, is mentally represented and functions the same as perception. This hypothesis was first suggested by Shepard and Kosslyn. According to this, an image is isomorphic to the referent object (second-order), meaning spatial relations are analogous, and an image is an analog representation of the object, as shown by mental rotation and image scanning. Propositions The proposition is the most basic unit of meaning in a representation. It is the smallest statement that can be judged either true or false. For example, “Fred is tall” is a single proposition coded as a relation with two arguments (is, Fred, tall). Similarly, “The ants ate the sweet jelly that was on the table” expresses four propositions. Furthermore, propositions are a means of specifying relationships between different concepts. Latent Semantic Analysis is a mathematical procedure for extracting and representing the meanings of propositions expressed by a text. It represents the co-occurrence of words and their contexts. Using a database of co-occurrence relations, it can compute the similarity in meaning of two words or texts. Figure 8.5 An imaginal and a propositional code for the concept of a robin Propositions differ from the images in the sense that they are abstract means of mental representation, whereas the images are the perceptual means of mental representation. Propositions are schematic and verbal, while the images are concrete and nonverbal. Furthermore, Propositions can be coded as a relation and arguments, and each proposition is an assertion which may be true or false. Figure 8.9 The star of David demonstration of the limitations of visual imagery The propositional theory to mental representation believes that there is a single code which is neither visual nor verbal but propositional in nature that is used to store and mentally represent all information. Propositions can be linked together in networks, with two closely related ideas joined by sharing a number of propositions. In 1973, Pylyshyn asserted that the propositional theory could explain the results of imagery experiments. He believed that all the information is mentally represented and stored by propositions, and further suggested that the participants in visual imagery experiments might look as if they were consulting or manipulating internal visual representations, but they would actually be using internal propositional representations, which are the same kind of representations that underlie their processing of verbal materials such as sentences or stories. Kosslyn in 1976, further attempted to test these assertions though experiments, but found that the propositional theory could not predict the performance of the participants when they reported to use imagery in the tasks. Semantic Network Models Vs Feature Comparison Models A semantic network, or frame network, is a network which represents semantic relations between concepts. This is often used as a form of knowledge representation. Collins and Quillian suggested that a network consists of a number of nodes, which correspond roughly to words or concepts. Each of these nodes is connected to related nodes by means of pointers or links that go on from one node to another. This collection of nodes associated with all the words or concepts one knows about is called the semantic network. The feature comparison model assumes that the meaning of any word or concept consists of a set of elements called features. These features are of two types; defining features and characteristic features. Defining features are those that must be present in every example of the concept, while the characteristic features are those which are usually but necessarily present. This model usually works in stages. In the first stage, the feature lists of the concerned concepts are accessed and comparisons are performed. If the lists show a great deal of overlap or if the overlap is very small, then the response is made quickly. However, if the overlap is neither very high nor very low, then the second stage of processing occurs where the comparison is made between the sets of defining features only. The feature comparison model also has explained the ‘category size effect’ which refers to the fact that if one term is a subcategory of another term, then people will generally be faster to verify the sentence with the smaller category. For example, people would be faster in verifying the sentence ‘A Labrador is a dog’ than to verify ‘A Labrador is an animal’. This happens because as categories grow larger, for example from Labrador to dog to animal to living things, they also become more abstract. And thus when the abstractness increases, the defining features become fewer in number. The two models, semantic network model and the feature comparison model differ from each other on a number of points.