Latent Semantic Analysis is used as a technique for measuring the coherence of texts. By comparing the vectors for two adjoining segments of text in a highdimensional semantic space, the method provides a characterization of the degree of semantic relatedness between the segments. We illustrate the approach for predicting coherence through re-analyzing sets of texts from two studies that manipulated the coherence of texts and assessed readers' comprehension. The results indicate that the method is able to predict the effect of text coherence on comprehension and is more effective than simple term-term overlap measures. In this manner, LSA can be applied as an automated method that produces coherence predictions similar to propositional modeling. We describe additional studies investigating the application of LSA to analyzing discourse structure and examine the potential of LSA as a psychological model of coherence effects in text comprehension.

This article reviews research on the use of situation models in lnguage comprehension and memory retrieval over the past 15 years. Situation models are integrated mental representations of a described state of affairs. Significant progress has been made in the scientific understanding of how situation models are involved in language comprehension and memory retrieval. Much of this research focuses on establishing the existence of situation models, often by using tasks that assess one dimension of a situation model. However, the authors argue that the time has now come for researchers to begin to take the multidimensionality of situation models seriously. The authors offer a theoretical framework and some methodological observations that may help researchers to tackle this issue.

This article explores how students ’ epistemological ideas about the nature of science interact with their conceptual understanding of a particular domain, as reflected in written explanations for an event of natural selection constructed by groups of high school students through a technology-supported curriculum about evolution. Analyses intended to disentangle conceptual and epistemic aspects of explanation reveal that groups sought plausible causal accounts of observed data, and were sensitive to the need for causal coherence, while articulating explanations consistent with the theory of natural selection. Groups often failed to explicitly cite data to support key claims, however, both because of difficulty in interpreting data and because they did not seem to see explicit evidence as crucial to an explanation. These findings reveal that students have productive epistemic resources to bring to bear during inquiry, but highlight the need for an epistemic discourse around student-generated artifacts to deepen both the conceptual and epistemological understanding students may develop through inquiry. Inquiry-based approaches to science education emphasize processes of inquiry, such as asking questions, generating and interpreting data, and forming conclusions

The question that this volume poses—What can social psychology tell us about the Holocaust?—is a difficult and complex one to answer. Perhaps it is fair to begin by saying that the Holocaust has influenced our understanding of social psychology more than the other way around. Early work in the field was directly motivated by the devastation and tragedies that took place between 1933-1945 (e.g., on the Holocaust, see Hilberg, 1973; on Jewish persecution from 1933-39, see Friedländer, 1997; on the Third Reich, see Shirer, 1998). Central topics in social psychology such as attribution, social influence, and intergroup processes all have their roots in the works of thinkers who had the events of the 1930s and 40s seared in their minds, many of whom had to flee their homelands to escape the specter of Nazism. In the 1960s and early 70s, seminal work in the field, such as Milgram's (1974) research on obedience to authority and the Stanford Prison experiment by Zimbardo and his colleagues (Zimbardo, Banks, Haney, & Jaffe, 1973), continued to be motivated by a need to understand the perpetrators of the Holocaust and other acts of collective violence. To this day, these studies represent social psychology's most salient demonstrations of situationism—a core tenet of the field that emphasizes the power of the situational forces over human behavior (see Ross &

Stories can encapsulate complexity, subtlety, and nuance: all of which are implicitly contained in narrative and reasoned about automatically through the mental processes that come naturally to humans. For example, humans can package complicated plots into a relatively small set of well-recognized and meaningful linguistic terms. This summarization ability though has not been available to systems that deal with narrative and would be important in creating higher quality systems. In this paper, we describe preliminary work towards a machine learning model of plot summarization using conditional random fields and describe our own feature functions inspired by cognitive theories of narrative reasoning. Our approach allows us to learn summarization models of single character event driven narratives and automatically summarize new narratives later on.

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A knowledge digraph defines a set of semantic (or syntactic) associative relationships among propositions in a text (e.g., Graesser and Clark (1985) conceptual graph structures and the causal network analysis of Trabasso & van den Broek, 1985). This paper introduces the Knowledge Digraph Contribution (KDC) data analysis methodology for quantitatively measuring the degree to which a given knowledge digraph can account for the occurrence of specific sequences of propositions in recall, summarization, talkaloud, and question-answering protocol data. KDC data analysis provides statistical tests for selecting the knowledge digraph which "best-fits" a given data set. KDC data analysis also allows one to test hypotheses about the relative contributions of each member in a set of knowledge digraphs. The validity of specific knowledge digraph representational assumptions may be evaluated by comparing human protocol data with protocol data generated by sampling from the KDC distribution. Specifi...

Research in open-domain commonsense reasoning has been hindered by the lack of evaluation metrics for judging progress and comparing alternative approaches. Taking inspiration from large-scale question sets used in natural language processing research, we authored one thousand English-language questions that directly assess commonsense causal reasoning, called the Choice Of Plausible Alternatives (COPA) evaluation. Using a forcedchoice format, each question gives a premise and two plausible causes or effects, where the correct choice is the alternative that is more plausible than the other. This paper describes the authoring methodology that we used to develop a validated question set with sufficient breadth to advance open-domain commonsense reasoning research. We discuss the design decisions made during the authoring process, and explain how these decisions will affect the design of high-scoring systems. We also present the performance of multiple baseline approaches that use statistical natural language processing techniques, establishing initial benchmarks for future systems.

This article examines what happens when students are explicitly supported in understanding the form of the artifacts that their science inquiry should produce. I explore how students' epistemological ideas about the nature of science interact with their conceptual understanding of a particular domain, as reflected in written explanations for an event of natural selection groups of high school students constructed through a technology-supported curriculum about natural selection and evolution. Students' inquiry was supported by a software program designed to structure their explanation. Analyses intended to disentangle conceptual and epistemic aspects of explanation reveal that groups were sensitive to the need for causal coherence, while articulating explanations consistent with the theory of natural selection. Groups often failed to explicitly cite data to support key claims, however, both because of difficulty in interpreting data and because they did not seem to see explicit evidence as crucial to an explanation. These findings suggest that focusing students explicitly on the epistemic aspects of inquiry can be helpful, while highlighting the need for an epistemic discourse around studentgenerated artifacts as a means to deepen both the conceptual and epistemological understanding students may develop through inquiry. Conceptuall l l l l

Close counterfactuals are alternatives to reality that 'almost happened'. A psychological analysis of close counterfactuals offers insights into the underlying representation of causal episodes and the inherent uncertainty attributed to many causal systems. The perception and representation of causal episodes is organized around possible focal outcomes of the episode, evoking a schema of causal forces competing over time. We introduce a distinction between two kinds of assessments of outcome probability: dispositions, based on causal information available prior to the episode; and propensities, based on event cues obtained from the episode itself. The distinction is critical to the use of 'almost', which requires the attribution of a strong propensity to the counterfactual outcome. The final discussion focuses on characteristic differences between psychological and philosophical approaches to the analysis of counterfactuals, causation and probability.