Search

Scholarly Works (57 results)

Sort By:

Show:

Thesis
Peer Reviewed

Prenatal drug exposure

Lewis, Keeta DeStefano

UC San Francisco Electronic Theses and Dissertations (1998)

Article
Peer Reviewed

Use of Complimentary Actions Decreases with Expertise

Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 29 (2007)

Article

Hierarchical Models of Individuals Engaged in Statistical Learning

Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 40 (2018)

Our ability to learn statistically regular patterns present in our environment is central to many cognitive processes. Thereare many competing theories about what kind of mechanisms could explain this ability. While different theories makeslightly different predictions about the kinds of patterns that can be learned, they often make very different predictionsabout the process of learning. One way to constrain the set of possible theories is to measure the shape of learning curvesas people learn new patterns. To do this, we gathered response time data as people learned new patterns. We fit probabilisticmodels to individual-level data using a hierarchical Bayesian nonlinear regression. Our results suggest the learning curvesat the level of individual items tend to have strong inflection points, which is inconsistent with cognitive models that arebased purely on associative and error-driven learning.

Cover page: Hierarchical Models of Individuals Engaged in Statistical Learning

Article
Peer Reviewed

Where Should Researchers Look for Strategy Discoveries during the Acquisition of Complex Task Performance? The Case of Space Fortress

Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 38 (2016)

In complex task domains, such as games, students may ex-ceed their teachers. Such tasks afford diverse means to trade-off one type of performance for another, combining task ele-ments in novel ways to yield method variations and strategydiscoveries that, if mastered, might produce large or smallleaps in performance. For the researcher interested in the de-velopment of extreme expertise in the wild, the problem posedby such tasks is “where to look” to capture the explorations,trials, errors, and successes that eventually lead to the inven-tion of superior performance. In this paper, we present severalsuccessful discoveries of methods for superior performance.For these discoveries we used Symbolic Aggregate Approx-imation as our method of identifying changepoints withinscore progressions in the venerable game of Space Fortress.By decomposing performance at these changepoints, we findpreviously unknown strategies that even the designers of thetask had not anticipated.

Cover page: Where Should Researchers Look for Strategy Discoveries during the Acquisition of Complex Task Performance? The Case of Space Fortress

Article
Peer Reviewed

Predicting Memory Errors with a Bayesian Model of Concept Generalization

Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 43 (2021)

“Similarity” is often thought to dictate memory errors. For example, in visual memory, memory judgements of lures are related to their psychophysical similarity to targets: an approximately exponential function in stimulus space (Schurgin et al. 2020). However, similarity is ill-defined for more complex stimuli, and memory errors seem to depend on all the remembered items, not just pairwise similarity. Such effects can be captured by a model that views similarity as a byproduct of Bayesian generalization (Tenenbaum & Griffiths, 2001). Here we ask whether the propensity of people to generalize from a set to an item predicts memory errors to that item. We use the “number game” generalization task to collect human judgements about set membership for symbolic numbers and show that memory errors for numbers are consistent with these generalization judgements rather than pairwise similarity. These results suggest that generalization propensity, rather than “similarity”, drives memory errors.

Cover page: Predicting Memory Errors with a Bayesian Model of Concept Generalization

Creative Commons 'BY' version 4.0 license

Thesis
Peer Reviewed

Controlling Polymer Conformation and Hydration with Monomer Sequence

UC Santa Barbara Electronic Theses and Dissertations (2023)

While most synthetic polymers are best described by their statistical nature, many biological polymers derive their highly tailored functions from precisely defined monomeric sequences. Sequence-controlled polymers, such as polypeptoids, present a unique tool for controlling material properties by harnessing both the robustness of synthetic polymers and the ability to tailor the inter- and intra-molecular interactions so crucial to many biological materials. Significant progress has been made towards predicting how protein folding is controlled by monomer sequence and interactions with water, but a thorough understanding of how to use polymer sequence to similarly control chain conformation and resulting functionality in synthetic polymer assemblies is lacking. Similarly, advances in understanding the effect of polymer surface chemistry and topology on hydration water dynamics and resulting polymer-solute interactions lags behind analogous protein hydration studies. Together, the ability to control polymer structure and hydration water behavior via sequence-control offers an avenue to pursue polymeric materials with protein-like functionality. This work focuses on how polypeptoid sequence can be utilized to control polymer conformation and nearby water behavior. First, monomer sequence effects on polymer conformation are probed, primarily making use double electron-electron resonance (DEER) spectroscopy. Second, the sequence specificity of polypeptoids enables observation of multiple hydration environments within multi-chain assemblies via Overhauser dynamic nuclear polarization (ODNP) spectroscopy. Finally, these differences in water behavior are utilized to tune catalytic activity within micelles, much like proteins.

Cover page: Controlling Polymer Conformation and Hydration with Monomer Sequence

Article
Peer Reviewed

Influences of both prior knowledge and recent historyon visual working memory

Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 42 (2020)

Existing knowledge shapes and distorts our memories, serv-ing as a prior for newly encoded information. Here, we in-vestigate the role of stable long-term priors (e.g. categoricalknowledge) in conjunction with priors arising from recentlyencountered information (e.g. ’serial dependence’) in visualworking memory for color. We use an iterated reproductionparadigm to allow a model-free assessment of the role of suchpriors. In Experiment 1, we find that participants’ reports re-liably converge to certain areas of color space, but that thisconvergence is largely distinct for different individuals, sug-gesting responses are biased by more than just shared categoryknowledge. In Experiment 2, we explicitly manipulate trialn-1 and find recent history plays a major role in participants’reports. Thus, we find that both global prior knowledge and re-cent trial information have biasing influences on visual work-ing memory, demonstrating an important role for both short-and long-term priors in actively maintained information.

Cover page: Influences of both prior knowledge and recent historyon visual working memory

Article
Peer Reviewed

Use of Complementary Actions Decreases with Expertise

Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 33 (2011)

Article
Peer Reviewed

Chunks are not “Content-Free”: Hierarchical Representations Preserve Perceptual Detail within Chunks

Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 43 (2021)

Chunks allow us to use long-term knowledge to efficiently represent the world in working memory. Most views of chunking assume that when we use chunks, this results in the loss of specific perceptual details, since it is presumed the contents of chunks are decoded from long-term memory rather than reflecting the exact details of the item that was presented. However, in two experiments, we find that in situations where participants make use of chunks to improve visual working memory, access to instance-specific perceptual detail (that cannot be retrieved from long-term memory) increased, rather than decreased. This supports an alternative view: that chunks facilitate the encoding and retention into memory of perceptual details as part of structured, hierarchical memories, rather than serving as mere “content-free” pointers. It also provides a strong contrast to accounts in which working memory capacity is assumed to be exhaustively described by the number of chunks remembered.

Cover page: Chunks are not “Content-Free”: Hierarchical Representations Preserve Perceptual Detail within Chunks

Article
Peer Reviewed

Robustness of graph theoretic representations of semantic networks

Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 44 (2022)

Recent network-based approaches leverage graph theoretic analyses to study individual differences in semantic networks and how they relate to other cognitive processes. However, it remains ambiguous whether individual differences captured via semantic network analyses reflect true differences in latent knowledge representations, or strategic differences in how people approach semantic relatedness tasks. To determine the robustness of content- and structure- based metrics of individual semantic networks we test their reliability across different tasks. We find both weighted and unweighted graph theoretic representations can predict individual differences in connections between semantic units across tasks. Furthermore, node centrality, a content-based metric which captures relative ‘importance’ of units within a network, is reliable across tasks, but metrics of structural properties of semantic networks, i.e. average clustering coefficient and shortest path length, are less robust. These results highlight the importance of validating graph-theoretic measures in the study of individual differences in semantic memory.

Cover page: Robustness of graph theoretic representations of semantic networks