Creating Common Ground: Architecture For Tactical Learning and Creative Convergence

Certain environments can inhibit learning and stifle enthusiasm, while others enhance learning or stimulate curiosity. Furthermore, in a world where technological change is accelerating we could ask how might architecture connect resource abundant and resource scarce innovation environments? Innovation environments developed out of necessity within urban villages and those developed with high intention and expectation within more institutionalized settings share a framework of opportunity for addressing change through learning and education. This thesis investigates formal and informal learning environments and how architecture can stimulate curiosity, enrich learning, create common ground, and expand access to education. The reason for this thesis exploration is to better understand how architects might design inclusive environments that bring people together to build sustainable infrastructure encouraging innovation and adaptation to change for years to come. The context of this thesis is largely based on Colin McFarlane’s theory that the “city is an assemblage for learning” The socio-spatial perspective in urbanism, considers how built infrastructure and society interact. Through the urban realm, inhabitants learn to negotiate people, space, politics, and resources affecting their daily lives. The city is therefore a dynamic field of emergent possibility. This thesis uses the city as a lens through which the boundaries between informal and formal logics as well as the public and private might be blurred. Through analytical processes I have examined the environmental devices and assemblage of factors that consistently provide conditions through which learning may thrive. These parameters that make a creative space significant can help suggest the design of common ground environments through which innovation is catalyzed.

SES-RELATED DIFFERENCES IN WORD LEARNING: EFFECTS OF COGNITIVE INHIBITION AND WORD LEARNING

Socioeconomic status (SES) influences language and cognitive development, with discrepancies particularly noticeable in vocabulary development. This study examines how SES-related differences impact the development of syntactic processing, cognitive inhibition, and word learning. 38 4-5-year-olds from higher- and lower-SES backgrounds completed a word-learning task, in which novel words were embedded in active and passive sentences. Critically, unlike the active sentences, all passive sentences required a syntactic revision. Measures of cognitive inhibition were obtained through a modified Stroop task. Results indicate that lower-SES participants had more difficulty using inhibitory functions to resolve conflict compared to their higher-SES counterparts. However, SES did not impact language processing, as the language outcomes were similar across SES background. Additionally, stronger inhibitory processes were related to better language outcomes in the passive sentence condition. These results suggest that cognitive inhibition impact language processing, but this function may vary across children from different SES backgrounds

Learning Binary Code Representations for Effective and Efficient Image Retrieval

The size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.

The Impact of Motor Learning on Motor Behavior and Cortical Dynamics in a Complex Stressful Social Environment

An economy of effort is a core characteristic of highly skilled motor performance often described as being effortless or automatic. Electroencephalographic (EEG) evaluation of cortical activity in elite performers has consistently revealed a reduction in extraneous associative cortical activity and an enhancement of task-relevant cortical processes. However, this has only been demonstrated under what are essentially practice-like conditions. Recently it has been shown that cerebral cortical activity becomes less efficient when performance occurs in a stressful, complex social environment. This dissertation examines the impact of motor skill training or practice on the EEG cortical dynamics that underlie performance in a stressful, complex social environment. Sixteen ROTC cadets participated in head-to-head pistol shooting competitions before and after completing nine sessions of skill training over three weeks. Spectral power increased in the theta frequency band and decreased in the low alpha frequency band after skill training. EEG Coherence increased in the left frontal region and decreased in the left temporal region after the practice intervention. These suggest a refinement of cerebral cortical dynamics with a reduction of task extraneous processing in the left frontal region and an enhancement of task related processing in the left temporal region consistent with the skill level reached by participants. Partitioning performance into ‘best’ and ‘worst’ based on shot score revealed that deliberate practice appears to optimize cerebral cortical activity of ‘best’ performances which are accompanied by a reduction in task-specific processes reflected by increased high-alpha power, while ‘worst’ performances are characterized by an inappropriate reduction in task-specific processing resulting in a loss of focus reflected by higher high-alpha power after training when compared to ‘best’ performances. Together, these studies demonstrate the power of experience afforded by practice, as a controllable factor, to promote resilience of cerebral cortical efficiency in complex environments.