In Proceedings of the 15th International FLAIRS Conference (Special Track 'Categorization and Concept Representation: Models and Implications'), Pensacola Florida, May 14-17, 2002, American Association for Artificial Intelligence. (Note: due to page limit somne things are not fully explained. For details see forthcoming invited paper to Journal of Experimental and Theoretical Artificial Intelligence.)

Contextualizing Concepts

Liane Gabora and Diederik Aerts
Center Leo Apostel for Interdisciplinary Studies (CLEA)
Free University of Brussels (VUB)
Krijgskundestraat 33, Brussels
B1160, Belgium, EUROPE , ,

ABSTRACT: To cope with problems arising in the description of (1) contextual interactions, and (2) the generation of new states with new properties when quantum entities become entangled, the mathematics of quantum mechanics was developed. Similar problems arise with concepts. We use a generalization of standard quantum mechanics, the mathematical lattice theoretic formalism, to develop a formal description of the contextual manner in which concepts are evoked, used, and combined to generate meaning.

1. The Problem of Conjunctions
According to the classical theory of concepts, there exists for each concept a set of defining features that are singly necessary and jointly sufficient (e.g. Sutcliffe 1993). Extensive evidence has been provided against this theory (see Komatsu 1992 and Smith & Medin 1981 for overviews). Two major alternatives have been put forth. According to the prototype theory (Rosch, 1975a, 1978, 1983; Rosch and Mervis 1975), concepts are represented by a set of, not defining, but characteristic features, which are weighted in the definition of the prototype. A new item is categorized as an instance of the concept if it is sufficiently similar to this prototype. According to the exemplar theory, (e.g., Heit and Barsalou 1996; Medin et al. 1984; Nosofsky 1988, 1992) a concept is represented by, not defining or characteristic features, but a set of instances of it stored in memory. A new item is categorized as an instance of a concept if it is sufficiently similar to one or more of these previous instances. We use the term representational theories to refer to both prototype and exemplar theories since concepts take the form of fixed representations (as opposed to changing according to context).

Representational theories are adequate for predicting experimental results for many dependent variables including typicality ratings, latency of category decision, exemplar generation frequencies, and category naming frequencies. However, they run into problems trying to account for the creative generation of, and membership assessment for, conjunctions of concepts. They cannot account for phenomena such as the so-called guppy effect, where guppy is not rated as a good example of pet, nor of fish, but it is rated as a good example of pet fish (Osherson & Smith 1981). This is problematic because if (1) activation of pet does not cause activation of guppy, and (2) activation of fish does not cause activation of guppy, how is it that (3) pet fish, which activates both pet AND fish, causes activation of guppy? (In fact, it has been demonstrated experimentally that other conjunctions are better examples of the ‘guppy effect’ than pet fish (Storms et al. 1998), but since the guppy example is well-known we will continue to use it here as an example.)

Zadeh (1965, 1982) tried, unsuccessfully, to solve the conjunction problem using a minimum rule model, where the typicality of an item as a conjunction of two concepts (conjunction typicality) equals the minimum of the typicalities of the two constituents. Storms et al. (2000) showed that a weighted and calibrated version of this model can account for a substantial proportion of the variance in typicality ratings for conjunctions exhibiting the guppy effect, suggesting the effect could be due to the existence of contrast categories. However, another study provided negative evidence for contrast categories (Verbeemen et al., in press).

Conjunction cannot be described with the mathematics of classical physical theories because it only allows one to describe a composite or joint entity by means of the product state space of the state spaces of the two subentities. Thus if X1 is the state space of the first subentity, and X2 the state space of the second subentity, the state space of the joint entity is the Cartesian product space X1 * X2. So if the first subentity is ‘door’ and the second is ‘bell’, one can give a description of the two at once, but they are still two. The classical approach cannot even describe the situation wherein two entities generate a new entity that has all the properties of its subentities, let alone a new entity with certain properties of one subentity and certain of the properties of the other. The problem can be solved ad hoc by starting all over again with a new state space each time there appears a state that was not possible given the previous state space. However, in so doing we fail to include exactly those changes of state that involve the generation of novelty. Another possibility would be to make the state space infinitely large to begin with. However, since we hold only a small number of items in mind at any one time, this is not a viable solution to the problem of describing what happens in cognition.

2. The Problem of Contextuality
The problems that arise with conjunctions reflects a more general problem with representational theories (see Riegler, Peschl and von Stein 1999 for overview). As Rosch (1999) puts it, they do not account for the fact that concepts "have a participatory, not an identifying function in situations"; that is, they cannot explain the contextual way in which concepts are evoked and used (see also Gerrig and Murphy 1992; Hampton, 1987; Komatsu, 1992; Medin and Shoben, 1988; Murphy and Medin, 1985). Not only does a concept give meaning to a stimulus or situation, but the situation evokes meaning in the concept, and when more than one is active they evoke meaning in each other. It is this contextuality that makes them difficult to model when two or more arise together, or follow one another, as in a creative construction such as a conjunction, invention, or sentence.

3. The Formalism
This story has a precedent. The same two problems–that of conjunctions of entities, and that of contextuality–arose in physics in the last century. Classical physics could not describe what happens when quantum entities interact. According to the dynamical evolution described by the Schrödinger equation, quantum entities spontaneously enter an entangled state that contains new properties the original entities did not have. To describe the birth of new states and new properties it was necessary to develop the formalism of quantum mechanics.

The shortcomings of classical mechanics were also revealed when it came to describing the measurement process. It could describe situations where the effect of the measurement was negligible, but not situations where the measurement intrinsically influenced the evolution of the entity; it could not incorporate the context generated by a measurement directly into the formal description of the quantum entity. This too required the quantum formalism.

First we describe the pure quantum formalism, and then we briefly describe the generalization of it that we apply to the description of concepts.

3.1 Pure Quantum Formalism
As in any mathematical model, we begin by cutting out a piece of reality and say this is the entity of interest, and these are its properties. The set of actual properties constitute the state of the entity. We also define a state space, which delineates, given how the properties can change, the possible states of the entity. A quantum entity is described using not just a state space but also a set of measurement contexts. The algebraic structure of the state space is given by the vector space structure of the complex Hilbert space: states are represented by unit vectors, and measurement contexts by self-adjoint operators.

One says a quantum entity is entangled if it is a composite of subentities that can only be individuated by a separating measurement. When such a measurement is performed on an entangled quantum entity, its state changes probabilistically, and this change of state is called quantum collapse.

In pure quantum mechanics, if H1 is the Hilbert space representing the state space of the first subentity, and H2 the Hilbert space representing the state space of the second subentity, the state space of the composite is not the Cartesian product, as in classical physics, but the tensor product, i.e., H1 Ä H2. The tensor product always generates new states with new properties, specifically the entangled states. Thus it is possible to describe the spontaneous generation of new states with new properties. However, in the pure quantum formalism, a state can only collapse to itself with a probability equal to one; thus it cannot describe situations of intermediate contextuality.

3.2 Generalized Quantum Formalism
The standard quantum formalism has been generalized, making it possible to describe entities with any degree of contextuality (Aerts 1993; Aerts and Durt 1994a, 1994b; Foulis and Randall 1981; Foulis et al. 1983; Jauch 1968; Mackey 1963; Piron 1976, 1989, 1990; Pitowsky 1989; Randall and Foulis 1976, 1978). The generalized formalisms use lattice theory to describe the states and properties of physical entities, and the result is referred to as a state property system. The approach is sufficiently general to be used to describe the different context-dependent states in which a concept can exist, and the features of the concept manifested in these various states.

4. Incorporating Contextuality into a Theory of Concepts
One of the first applications of these generalized formalisms to cognition was modeling the decision making process. Aerts and Aerts (1996) proved that in situations where one moves from a state of indecision to a decided state (or vice versa), the probability distribution necessary to describe this change of state is non-Kolmogorovian, and therefore a classical probability model cannot be used. Moreover, they proved that such situations can be accurately described using these generalized quantum mathematical formalisms. Their mathematical treatment also applies to the situation where the state of the mind changes from thinking about a concept to an instantiation of that concept, or vice versa. Once again, context induces a nondeterministic change of the state of the mind which introduces a non-Kolmogorivian probability on the state space. Thus, a nonclassical (quantum) formalism is necessary.

In our approach, concepts are described using what to a first approximation can be viewed as an entangled states of exemplars, though this is not precisely accurate. For technical reasons (see Gabora 2001), the term potentiality state is used instead of entangled state. For a given stimulus, the probability that a potentiality state representing a certain concept will, in a given context, collapse to another state representing another concept is related to the algebraic structure of the total state space, and to how the context is represented in this space. The state space where concepts ‘live’ is not limited a priori to only those dimensions which appear to be most relevant; thus concepts retain in their representation the contexts in which they have, or even could potentially be, evoked or collapsed to. It is this that allows their contextual character to be expressed. The stimulus situation plays the role of the measurement context by determining which state is collapsed upon. Stimuli are categorized as instances of a concept not according to how well they match a static prototype or set of typical exemplars, but according to the extent to which they correspond to, and thereby actualize or collapse upon, one the potential interpretations of the concept. (As a metaphorical explanatory aid, if concepts were apples, and the stimulus a knife, then the qualities of the knife determine not just which apple to slice, but which direction to slice through it: changing the context in which a stimulus situation is embedded can cause a different version of the concept to be elicited.) This approach has something in common with both prototype and exemplar theories. Like exemplar theory, concepts consist of exemplars, but the exemplars are in a sense ‘woven together’ like a prototype.

5. Preliminary Theoretical Evidence of the Utility of the Approach
We present three sources of theoretical evidence of the utility of the approach.

5.1 A Proof that Bell Inequalities can be Violated by Concepts
The presence of entanglement can be tested for by determining whether correlation experiments on the joint entity violate Bell inequalities (Bell 1964). Using an example involving the concept cat and specific instances of cats we proved that Bell inequalities are violated in the relationship between a concept, and specific instances of this concept (Aerts et al. 2000a; Gabora 2001). Thus we have evidence that this approach indeed reflects the underlying structure of concepts.

5.2 Application to the Pet Fish Problem
We have applied the contextualized approach to the Pet Fish Problem (Aerts et al. 2000b; Gabora 2001). Conjunctions such as this are dealt with by incorporating context-dependency, as follows: (1) activation of pet still rarely causes activation of guppy, and likewise (2) activation of fish still rarely causes activation of guppy. But now (3) pet fish causes activation of the potentiality states petin the context of pet fish AND fish in the context of pet fish. Since for both, the probability of collapsing onto the state guppy is high, it is very likely to be activated. Thus we have a formalism for describing concepts that is not stumped by a situation wherein an entity that is neither a good instance of A nor B is nevertheless a good instance of A AND B.

Note that whereas in representational approaches relations between concepts arise through overlapping context-independent distributions, in the present approach, the closeness of one concept to another (expressed as the probability that its potentiality state will collapse to an actualized state of the other) is context-dependent. Thus it is possible for two states to be far apart from each other with respect to a one context (for example ‘fish’ and ‘guppy’ in the context of just being asked to name a fish), and close to one another with respect to another context (for example ‘fish’ and ‘guppy’ in the context of both ‘pet’ and being asked to name a ‘fish’). Examples such as this are evidence that the mind handles nondisjunction (as well as negation) in a nonclassical manner (Aerts et al. 2000b).

5.3 Describing Impossibilist Creativity
Boden (1990) uses the term impossibilist creativity to refer to creative acts that not only explore the existing state space but transform that state space. In other words, it involves the spontaneous generation of new states with new properties. In (Gabora 2001) the contextual lattice approach is used to generate a mathematical description of impossibilist creativity using as an example the invention of the torch. This example involves the spontaneous appearance of a new state (the state of mind that conceives of the torch) with a new property (the property of being able to move fire).

6. Research in Progress
We are comparing the performance of the contextualized theory of concepts with prototype and examplar theories using previous data sets for typicality ratings, latency of category decision, exemplar generation frequencies, category naming frequencies on everyday natural language concepts, such as ‘trees’, ‘furniture’, or ‘games’. The purpose of these initial investigations is to make sure that the proposed formalism is at least as successful as representational approaches for the simple case of single concepts. Assuming this to be the case, we will concentrate our efforts on conjunctions of concepts, since this is where the current approach is expected to supercede representational theories. We will re-analyze previously collected data for noun-noun conjunctions such as ‘pet fish’, and relative clause conjunctions such as ‘pets that are also fish’ (Storms et al. 1996). A new study is being prepared which will compare the proposed approach with representational approaches at predicting the results of studies using situations that are highly contextual. Typicality ratings for conjunctions will be compared with, not just their components, but with other conjunctions that share these components. (Thus, for example, does ‘brainchild’ share features with ‘childbirth’ or ‘brainstorm’? Does ‘brainstorm’ share features with ‘birdbrain’ or ‘sandstorm’?)

Aerts, D. 1993. Quantum Structures Due to Fluctuations of the Measurement Situations. International Journal of Theoretical Physics 32:2207—2220.

Aerts, D., Aerts, S., Broekaert, J. & Gabora, L. 2000a. The Violation of Bell Inequalities in the Macroworld. Foundations of Physics 30:1387—1414.

Aerts, D., Broekaert, J. & Gabora, L. 1999. Nonclassical Contextuality in Cognition: Borrowing from Quantum Mechanical approaches to Indeterminism and Observer Dependence. In (R. Campbell, Ed.) Dialogues Proceedings of Mind IV Conference, Dublin, Ireland.

Aerts, D., Broekaert, J. & Gabora, L. 2000b. Intrinsic Contextuality as the Crux of Consciousness. In (K. Yasue, Ed.) Fundamental Approaches to Consciousness. John Benjamins Publishing Company.

Aerts, D. & Durt, T. 1994a. Quantum, Classical and Intermediate: A Measurement Model. In Proceedings of the International Symposium on the Foundations of Modern Physics 1994, Helsinki, Finland, eds. C. Montonen et al., Editions Frontieres, Gives Sur Yvettes, France.

Aerts, D., Colebunders, E., Van der Voorde, A. & Van Steirteghem, B. 1999. State Property Systems and Closure Spaces: a Study of Categorical Equivalence. International Journal of Theoretical Physics 38:359-385.

Aerts, D. & Durt, T. 1994b. Quantum, Classical, and Intermediate, an Illustrative Example. Foundations of Physics 24:1353-1368.

Aerts, D., D’Hondt, E. & Gabora, L. 2000c. Why the Disjunction in Quantum Logic is Not Classical. Foundations of Physics 30:1473—1480.

Bell, J. 1964. On the Einstein Podolsky Rosen Paradox, Physics 1:195.

Boden, M. 1991. The Creative Mind: Myths and Mechanisms. Cambridge UK: Cambridge University Press.

Foulis, D., Piron C., & Randall, C. 1983. Realism, Operationalism and Quantum Mechanics. Foundations of Physics 13(813).

Foulis, D. & Randall, C. 1981. What are Quantum Logics and What Ought They to Be?" In Current Issues in Quantum Logic, 35, eds. Beltrametti, E. & van Fraassen, B., New York NY: Plenum Press.

Gabora, L. 2001. Cognitive mechanisms underlying the origin and evolution of culture. PhD. Diss., CLEA, Free University of Brussels.

Gerrig, R. & Murphy, G. 1992. Contextual Influences on the Comprehension of Complex concepts. Language and Cognitive Processes 7:205-230.

Hampton, J. 1987. Inheritance of Attributes in Natural Concept Conjunctions. Memory & Cognition 15:55-71.

Heit, E. & Barsalou, L. 1996. The Instantiation Principle in Natural Language Categories. Memory 4:413-451.

Jauch, J. 1968. Foundations of Quantum Mechanics, Reading Mass: Addison-Wesley.

Mackey, G. 1963. Mathematical Foundations of Quantum Mechanics. Reading Mass: Benjamin.

Medin, D., Altom, M., & Murphy, T. 1984. Given versus Induced Category Representations: Use of Prototype and Exemplar Information in Classification. Journal of Experimental Psychology: Learning, Memory, and Cognition 10:333-352.

Medin, D., & Shoben, E. 1988. Context and Structure in Conceptual Combinations. Cognitive Psychology 20:158-190.

Murphy, G. & Medin, D. 1985. The Role of Theories in Conceptual Coherence. Psychological Review, 92, 289-316.

Nosofsky, R. 1988. Exemplar-based Accounts of Relations between Classification, Recognition, and Typicality. Journal of Experimental Psychology: Learning, Memory, and Cognition 14:700-708.

Nosofsky, R. 1992. Exemplars, Prototypes, and Similarity Rules. In From Learning Theory to Connectionist Theory: Essays in Honor of William K. Estes 1:149-167. A. Healy, S. Kosslyn, & R. Shiffrin, eds. Hillsdale NJ: Lawrence Erlbaum.

Osherson, D. & Smith, E. 1981. On the Adequacy of Prototype Theory as a Theory of Concepts. Cognition 9:35-58.

Piron, C. 1976. Foundations of Quantum Physics, Reading Mass.: W. A. Benjamin.

Piron, C. 1989. Recent Developments in Quantum Mechanics. Helvetica Physica Acta, 62(82).

Piron, C. 1990. Mécanique Quantique: Bases et Applications, Press Polytechnique de Lausanne, Lausanne, Suisse.

Pitowsky, I. 1989. Quantum Probability - Quantum Logic, Lecture Notes in Physics 321, Berlin: Springer.

Randall, C. & Foulis, D. 1976. A Mathematical Setting for Inductive Reasoning, in Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science III, 169, ed. Hooker, C. Dordrecht: Reidel.

Randall, C. and Foulis, D. 1978. The Operational Approach to Quantum Mechanics, in Physical Theories as Logico-Operational Structures, C. Hooker, ed. Dordrecht, Holland Reidel, 167.

Reed, S. 1972. Pattern Recognition and Categorization. Cognitive Psychology 3:382-407.

Riegler, A. Peschl, M. & von Stein, A. 1999. Understanding Representation in the Cognitive Sciences. Dortrecht Holland: Kluwer.

Rosch, E. 1975. Cognitive Reference Points. Cognitive Psychology 7:532-547.

Rosch, E. 1978. Principles of Categorization. In Cognition and Categorization, E. Rosch & B. Lloyd, Eds., 27-48. Hillsdale, NJ: Erlbaum.

Rosch, E. 1983. Prototype Classification and Logical Classification: The two systems. In New Trends in Conceptual Representation: Challenges to Piaget’s Theory? E. K. Scholnick, Ed., 73-86. Hillsdale NJ: Erlbaum.

Rosch, E., & Mervis, C. 1975. Family Resemblances: Studies in the Internal Structure of Categories. Cognitive Psychology 7:573-605.

Rosch, E. 1999. Reclaiming Concepts. Journal of Consciousness Studies 6(11).

Smith, E., & Medin, D. 1981. Categories and Concepts. Cambridge MA: Harvard University Press.

Storms, G., De Boeck, P., Hampton, J., & Van Mechelen, I. 1999. Predicting conjunction typicalities by component typicalities. Psychonomic Bulletin and Review 4:677-684.

Storms, G., De Boeck, P., & Ruts, W. 2000. Prototype and Exemplar Based Information in Natural Language Categories. Journal of Memory and Language 42:51-73.

Storms, G., De Boeck, P., Van Mechelen, I. & Ruts, W. 1996. The Dominance Effect in Concept Conjunctions: Generality and Interaction Aspects. Journal of Experimental Psychology: Learning, Memory & Cognition 22:1-15.

Storms, G., De Boeck, P., Van Mechelen, I. & Ruts, W. 1998. Not Guppies, nor Goldfish, but Tumble dryers, Noriega, Jesse Jackson, Panties, Car crashes, Bird books, and Stevie Wonder. Memory and Cognition 26:143-145.

Sutcliffe, J. 1993. Concepts, Class, and Category in the Tradition of Aristotle. In I. Van Mechelen, J. Hampton, R. Michalski, & P. Theuns (Eds.), Categories and Concepts: Theoretical Views and Inductive Data Analysis, 35-65. London: Academic Press.

Wisniewski, E. 1997. When Concepts Combine. Psychonomic Bulletin & Review 4:167-183.

Wisniewski, E. & Gentner, D. 1991. On the combinatorial semantics of noun pairs: Minor and major adjustments. In G. B. Simpson (Ed.), Understanding Word and Sentence, 241-284. Amsterdam: Elsevier.