This is a collection of essays in honor of Philip Johnson-Laird, one of the founding figures in mental models. These essays represent application of his theory to several particular domains.

George Miller: Contextuality

This essay is about handling words and stituations with multiple meanings. The process of figuring out these meanings is contextualization, described as a basic cognitive process. This is closely related to Goffman’s frame analysis. The goal of contextualization is to resolve ambiguity that is heavily present in interpretation of everyday language and knowledge.

Computational linguistics is a tricky area in cognitive science and computation. It is deeply affected by the issue of context. Miller’s analysis focuses on linguistics exclusively (mirroring Johnson-Laird), as opposed to other sorts of ambiguous circumstances. Computational linguistics involves processing language and attempting to identify and process the correct word meanings from that language. Miller mentions Bar-Hillel (1960), who finds that this sort of language processing can identify correct meanings about 80% of the time. He estimates that this last bit could never be achieved without significant advances in AI.

Expert systems, which are the general approach for working with specialized knowledge, limit the domain of word meanings to a significant degree, but this still does not absolve the “Curse of Bar-Hillel.” Miller theorizes that context identification is the key to unlocking this last bit of meaning.

An aside to note is that Miller is a collaborator on WordNet.

Alan Garnham: The Other Side of Mental Models: Theories of Language Comprehension

This essay looks at language comprehension by examining issues of reference and inference. One key element to inference and communication is instantiation. Where an abstract idea is replaced by a more concrete (or other known) one. However, Garnham is concerned with the communication of abstracts, and notes that we communicate information about abstracts without instantiation.

Propositional relations are a strategy used frequently in AI for world modeling, and relate to information as discrete facts. Garnham gives an example which uses locational prepositions, things of the form: “The lamp is in front of the candle,” etcetera. In terms of these locational structure here, it seems dubious. There is a suggestion that mental models use a more analog depiction and representation of spatial relations.

It is true that we do use abstracts in communication, but models of communication that have emerged from Vygotsky indicate that communication emerges in development when social interaction transforms from something embodied and physical to something symbolic. If we follow Lakoff and Johson, then relations are all metaphorical and based ultimately in the body.

Paolo Legrenzi and Vittorio Girotto: Mental Models in Reasoning and Decision-making Processes

This essay discusses decision making according to psychological studies, and explained in terms of mental models. The interesting thing here is that totally rational decision making is not present, rather, decision making is based on the matter of focusing. This sounds a lot like priming and activation (related to neural networks). Models illustrate the construction of ideas, but neglect to factor how the focusing works intrinsically.

David Green: Models, Arguments, and Decisions

Green builds a theory of decisions (as derived from Craik, 1943) based on argument. Argument is done through warrants, which are bits of relevant information.This work is built from Toulmin’s scheme. Further, the goal here is to analyze argument through mental models. The interplay between observation and model mirrors warrant and argument. Warrants also relate to beliefs, which may be connectable to the belief, desire, and intention scheme in AI. We can also apply warrants to causal models.

The final conclusion in this section is that there is an interplay between argument and simulation, as well as decisions and commitment.

Keith Oatley: Emotions, Rationality, and Informal Reasoning.

Oatley’s focus here is on informal reasoning as it relates to emotions. Informal is opposed to logical or day-to-day. Oatley argues that emotion is critical to this sort of everyday conventional reasoning.

He opens with an analysis of Aristotle’s rhetoric, which discusses two types of reason. There is absolute mathematical reasoning, and also persuaded reasoning, where there is no demonstrable truth. Persuasion instead aims to achieve the best truth possible. The interesting example with this is that Aristotle’s writing is based on the sort of rhetoric used in his day, where law is extremely dependent on performance and dramatic emotional appeal.

Oatley makes some notes on Aristotle’s Rhetoric: The first is that persuasion applies to the imperfectly knowable, and the field of the imperfectly knowable is huge. AI, on the other hand, seeks to only understand that which is perfectly knowable, or that which can be logically concluded or deduced. The second point is that Aristotle explains emotion as a tool of judgement, as opposed to something that is bestial or irrational. Furthermore, there are three impediments to making rational decisions:

1. Limited knowledge and resources. Our mental models are incomplete to fully predict the effects of our actions.
2. Multiple goals. Multiple goals cannot always all be satisfied rationally.
3. Distributed agency. Actions are performed in relation to others, planning must occur among multiple agents, where the problem of limited knowledge becomes especially difficult.

Rationality depends on environment and context. Emotion is used as a form of feedback for goals. Oatley describes emotion as a heuristic function for potential actions.

Oatley makes a connection to Vygotsky and Hutchins. Emotions play a role in the distribution and extension of cognition. There is a connection between Aristotle and the Roman historian Quintillian, who documents the practice of law in the Roman court. This is an argument for the theatricality of reason, relating to the ideas of performance. The performance of law is an enactment and exaggeration of events. The social nature of the audience is essential.

Oatley follows this with the analysis of two experiments, where individuals change behavior based on emotional priming. Emotional induction proved to be immensely relevant in both examples. One of which consisted of examining decision making in judgement of evidence of a trial (after having viewed a happy or a sad film clip), and the other examined forward or reverse reasoning (after reading an angry or sad short story). The experimental corrolation was immensely strong in both examples.

Ciuliano Geminiani, Antonella Carassa, Bruno Bara: Causality by Contact

This essay is about the role of causality in reasoning. Causality is related to the construction of scientific models, but also is relevant from the perspective of narrative. Using causality implies the use of simulation mentally. Causality has an evolutionary basis that is associative (for instance, a rat who smells a type of food on a dead rat will not eat that type of food). This associative logic is also imaginably present in humans, but humans also do use causal reasoning, which comes with the demand for knowing why something occurs. This connects well to Vygotsky and development. The why relates to the narrative/linguistic model of thought.

An interesting note: In developmental study, causality is dependent on contact. Touching is necessary for causality to be interpreted by infants. Gradually, though, causality becomes analogically based. Causal models are a subset of dynamic models. The authors give a funny example of two narrative segments: “Cleopatra was bitten by an asp, Cleopatra died” versus “Cleopatra was bitten by an asp, a tourniquet was applied to her arm, Cleopatra was saved.” This example is a little strange, but is used to understand how people might model what happens to the poison. Mental imagery and metaphors are especially important: poison is a particle, poison is like paint, etc. The important thing to note here is that the example is fundamentally a narrative one.

To understand how models are formed and used, the authors give a three part theory for development of causal models: Construction, comparison, falsification. The construction phase involves taking the components (as a pre-model) and understanding them quantitatively. This is literally formulated as collecting symbols and describing them qualitatively. Next, qualities are quantified, fixing values and times. Finally, the model is simulated dynamically at a sub-cognitive level. The sub-cognitive simulation involves 1) activation of implicit knowledge, 2) generation of instantaneous changes in quantities according to the simulation, and 3) simulation of the temporal evolution of the model.

At the comparison phase, the effects of the mental model with the base model are compared. In this context, the base model is imaginably the observed phenomenon, which is the original story. This comparison intiates revisitations and inferences. Finally, in the falsification phase, plausibility and counterexamples are considered. This sort of analysis derives from Qualitative Process Theory (Forbus 1984), which seems like a good place to check the connection between narrative and models.

This approach is useful in looking at models of fiction as pertains to adaptation, especially in terms of emotional value and responses.