Most AI simulations have modeled memory retrieval separately from language understanding, even though both activities seem to use many of the same processes. This paper describes REMIND, a structured reading-activation model of integrated text comprehension and episodic reminding. In REMIND , activation is spread through a semantic network that performs dynamic inferencing and disambiguation to infer a conceptual representation of an input cue. Because stored episodes are associated with the concepts used to understand them. the spreading-activation process also activates any memory episodes that share features (X knowledge structures with the cue. After a conceptual representation is formed of the cue, the episode in the network with the highest activation is recalled from memory. Since the inferences made from a cue often include actors' plans and goals only implied in its text. REMIND is able to get abstract remindings that would not be possible without an integrated understanding and revival model.

Frame selection is a fundamental problem in high-level reasoning. Connectionist models have been unable to approach this problem because of their inability to represent multiple dynamic variable bindings and use them by applying general knowledge rules. These deficits have barred them from performing the high-level inferencing necessary for planning, reasoning, and natural language understanding. This paper describes a localist spreading-activation model, ROBIN, which solves a significant subset of these problems. ROBIN incorporates the normal semantic network su^ucture of previous localist networks, but has additional stfucture to handle variables and dynamic role-binding. Each concept in the network has a uniquely-identifying activation value, called its signature. A dynamic binding is created when a binding node receives the activation of a concept's signature. Signatures propagates across paths of binding nodes to dynamically instantiate candidate inference paths, which are selected by the evidential activation on the network's semantic structure. R O B I N is thus able to approach many of the high-level inferencing and frame selection tasks not handled by previous connectionist models.

An unexplored means by which analogical transfer might take place is through indirect priming through the interaction of text comprehension and memory retrieval processes. Remind is a structured spreading-activation model of language understanding and reminding in which simple transfer can result from indirect priming from previously processed source analogs. This paper describes two experiments based on Remind's priming-based transfer framework. In Experiment 1, subjects (1) summarized analogous source stories' common plot; (2) rated the comprehensibility of targets related to sources by similar themes, contexts, or themes and contexts; then (3) described any sources incidentally recalled during target rating. Source/target similarity influenced comprehensibility and reminding without any explicit mapping or problem-solving. In Experiment 2, subjects (1) rated each story's comprehensibility in source/target pairs having similar relationships to each other as in Experiment 1; then (2) rated source/target similarity. Analogous targets were rated as more comprehensible than non-analogous targets. Both experiments imply that transfer can be caused by activation of abstract knowledge representations without explicit mapping.

AI models of reminding (ARCS, MAC/FAC) that predict that memory access is influenced by analogical similarity are tested. In Experiment 1, subjects initially studied a set of 12 target stories. Later, subjects read 10 other cue stories and were asked to write down the stories they were reminded of from the first set. Cue stories were associated with either an analogous and disanalogous target (competition condition), an analogous target (singleton condition), or a disanalogous target (singleton condition). An effect of analogical similarity was found only in the competition condition. Experiment 2 used the same design but targets and cues were simple subject-verb-object sentences. Cue sentences shared similar nouns and verbs with target sentences. Materials were constructed such that associated nouns either consistently mapped or cross-mapped between cues and targets. Consistent-mapped sentences were recalled more than cross-mapped sentences in both conditions. Issues for future research are addressed.

An important question for cognitive models of human memory is the question of how analogical similarity affects memory retrieval. While the importance of surface lexical and semantic similarities between reminding cues and memory targets has been well-documented, clear empirical evidence that human memory retrieval is influenced by analogy has proven difficult to demonstrate. W e report two experiments in which subjects used a series of single sentences as reminding cues for previously-seen mini-texts. Some cue sentences contained noims and verbs that were hyponyms (i.e., words subordinate to the same category) of those in corresponding target sentences presented in one or two earlier passages. The role of analogical similarity in reminding was examined by varying the correspondence of noun case-role assignments of cue/target homonyms. Results indicate that retrieval competition and analogical similarity influence reminding. Recall of semanticallyrelated passages was significantly greater for structurally consistent (i.e., analogical) cues. Retrieval access was impaired when two semantically related passages were present in memory. Access to the passage with analogical resemblance to the cue was decreased by retrieval competition to an extent consistent with a ratio rule.

The symbolic and subsymbolic paradigms each offer advantages and disadvantages in constructing models for understanding the processes of cognition. A number of research programs at U C L A utilize connectionist modeling strategies, ranging from distributed and localist spreading-activation networks to semantic networks with symbolic marker passing. As a way of combining and optimizing the advantages offered by different paradigms, we have started to explore hybrid networks, i.e. multiple processing mechanisms operating on a single network, or multiple networks operating in parallel under different paradigms. Unfortunately, existing tools do not allow the simulation of these types of hybrid connectionist architectures. To address this problem, we have developed a tool which enables us to create and operate these types of networks in a flexible and general way. W e present and describe the architecture and use of DESCARTES, a simulation environment developed to accomplish this type of integration.