This work aims to further the understanding of the trajectory and velocity profile of curved motion. Two competing theories, the two-thirds power law and the minimum jerk velocity profile, were tested. A set of two experiments was run that had the subjects generate curved motion. The first experiment had subjects move along a bounded oval and the second experiment had subjects move is a less constrained manner inducing a curved path. The study shows evidence for the expected effects of distance travel and allowable room for error. The evidence for the two movement profiles explaining the data is minimal.

A desire to advance humanity's presence in space prompts the need for improved technology to send crew to places such as the Moon, Mars, and nearby asteroids. Safely placing a crewed vehicle on and in any landing condition requires a design decision regarding the distribution of responsibilities between the crew and automation. In this thesis, a cognitive process model is used to determine the necessary automated functionality to support astronaut decision making. Current literature lacks sufficient detailed knowledge regarding astronaut decision making during this task and observations of astronauts landing on the Moon are not readily available. Therefore, a series of human-in-the-loop experiments, one of which was conducted with the NASA Astronaut Office at Johnson Space Center, have been conducted to examine the changes in performance due to differing function allocations, trajectory profiles, and scenario operations. The data collected in the human-in-the-loop study has provided empirical data that has informed the cognitive process model, the requirements analysis, and provided insight regarding cockpit display usage and information needs. The proposed system requirements include design guidance for assisting astronauts during both nominal and off-nominal landing scenarios.

130 pages / Progressive aspect has traditionally been linked to notions of speaker viewpoint on conceptual structure - specifically, whether the speaker perceives an event as bounded or unbounded. The following research examines the cognitive structures of the mental representation that might underlay these conceptual notions, recasting viewpoint in cognitive terms. A cognitive model of information processing is presented, explaining processes of information parsing, message formulation and linguistic encoding as carried out by a functional grammar. In particular, we examine how the grammar uses the habituated or dishabituated states of neural models formed from sensory or memorial inputs in determining progressive or non-progressive message encoding. Hypotheses are tested by experiments based upon a paradigm in which speakers describe visual stimuli while simultaneously watching them on a screen. This online paradigm allows us to approximate the speaker's mental representation, providing text-independent measures to compare against linguistic output.

This article summarizes and extends recent results linking deterministic parsing to observed "locality principles" in syntax. It also argues that grammatical theories based on explicit phrase structure rules are unlikely to provide comparable explanations of why natural languages are built the way they are.

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This thesis evaluates several aspects of the cognitive modeling tools, using a questionnaire as the survey method. We try to assess the the suitability for cognitive modeling task of the cognitive modeling tools, from the perspective of international community of cognitive modeling tool users. Part of this assessment is done with respect to general usability of software and the rest is specialized for the cognitive modeling issues. Frequency and correlation analyses reveal that there is a significant relationship between suitability as a software product and suitability as a cognitive modeling tool. Specifically, there are correlations between the features of the tool involving flexibility, presentation of input and output and the process of design, implementation and evaluation of a cognitive modeling tool, while these processes are negatively related to adversely effecting features of the tool, such as having to do extra tasks that are not related to the actual task. Our study confirms that a cognitive modeling tool can also be evaluated from the perspective of a general purpose software product, and also gives clues about directions for improvement to tool developers.

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Distributional Semantic Models are one of the possible answers produced in (computational) semantics to the question of what the meaning of a word is. The distributional semantic answer to this question is a usage-based one, as distributional semantics models (henceforth, DSMs) are employed to produce semantic representations of words from co-occurrence patterns in texts or documents. DSMs have proven to be useful in many applications in the domains of Natural Language Processing. Despite this progress, however, a full understanding of the different parameters governing a DSM and their influence on model performance (which, in fact, is also important for getting a better linguistic understanding of neural word embeddings) has not been achieved yet. This is precisely the goal of this dissertation. Taken together, the experiments presented in this thesis represent (to the best of our knowledge) the largest-scope study in which window and syntax-based DSMs have been tested in all parameter settings. As a further contribution, the thesis proposes a novel methodology for the interpretation of evaluation results: we employ linear regression as a statistical tool to understand the impact of different parameters on model performance. In this way, we achieve a solid understanding of the influence of specific parameters and parameter interactions on DSM performance, which can inform the selection of DSM settings that are robust to overfitting. This thesis has a strong focus on cognitive data, that is, on DSM parameters that lend themselves to a cognitive interpretation and on evaluation tasks in which DSMs are tested in their capability of mirroring speakers’ behavior in psychological tasks (semantic priming and free associations). One of the most important contributions of this thesis is the consistent finding that neighbor rank (i.e., the rank of a word among the distributional neighbors of a target) is a better indicator of semantic similarity/relatedness than the distance in the semantic space, which is commonly used in the literature. The cognitive interpretation of this result is straightforward: neighbor rank, which is evaluated systematically for the first time in this thesis, is able to capture asymmetry in the relation between two words, while distance metrics, commonly employed in distributional semantics, are symmetric.

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XNL-Soar is a specialized implementation of the Soar cognitive architecture. The version of XNL-Soar described in this thesis builds upon and extends prior research (Lewis, 1993; Rytting,2000) using Soar for natural language processing. This thesis describes the updates made to operators creating syntactic structure and the improved coverage of syntactic phenomena. It describes the addition of semantic structure building capability. This thesis also details the implementation of semantic memory and describes two experiments utilizing semantic memory in structural disambiguation. This thesis shows that XNL-Soar, as currently instantiated, resolves ambiguities common in language using strategies and resources including: reanalysis via snip operators, use of data-driven techniques with annotated corpora, and complex part-of-speech and word sense processing based on WordNet.