In this paper we describe a few interrelated issues for validating theories that posit levels of consciousness. First, validating levels of consciousness requires consensus about the ordering of conscious states, which cannot be easily achieved. This problem is particularly severe if we believe conscious states can be irreducibly smeared over time. Second, the relationship between conscious states is probably sometimes intransitive, which means levels of consciousness will not be amenable to a single continuous measure. Finally, even if a multidimensional approach (...) to levels of consciousness is adopted, we argue that there are further problems for its validation. (shrink)

Applying machine learning algorithms to automatically infer relationships between concepts from large-scale collections of documents presents a unique opportunity to investigate at scale how human semantic knowledge is organized, how people use it to make fundamental judgments (“How similar are cats and bears?”), and how these judgments depend on the features that describe concepts (e.g., size, furriness). However, efforts to date have exhibited a substantial discrepancy between algorithm predictions and human empirical judgments. Here, we introduce a novel approach to generating (...) embeddings for this purpose motivated by the idea that semantic context plays a critical role in human judgment. We leverage this idea by constraining the topic or domain from which documents used for generating embeddings are drawn (e.g., referring to the natural world vs. transportation apparatus). Specifically, we trained state-of-the-art machine learning algorithms using contextually-constrained text corpora (domain-specific subsets of Wikipedia articles, 50+ million words each) and showed that this procedure greatly improved predictions of empirical similarity judgments and feature ratings of contextually relevant concepts. Furthermore, we describe a novel, computationally tractable method for improving predictions of contextually-unconstrained embedding models based on dimensionality reduction of their internal representation to a small number of contextually relevant semantic features. By improving the correspondence between predictions derived automatically by machine learning methods using vast amounts of data and more limited, but direct empirical measurements of human judgments, our approach may help leverage the availability of online corpora to better understand the structure of human semantic representations and how people make judgments based on those. (shrink)

Cognitive Science, Volume 46, Issue 2, February 2022.

Mark S. Ferrara’s principle scholarly interests lie within the fields of religious studies and Asian philosophy, as indicated on his State University of New York–Oneonta English faculty page and demonstrated in his other books Between Noble and Humble: Cao Xueqin and the Dream of the Red Chamber and Palace of Ashes: China and the Decline of American Higher Education. However, it is his interests in rhetoric and political discourse, cultural studies, and world literature that make Barack Obama and the Rhetoric (...) of Hope such an insightful and pleasant contribution to the commentary on and criticism of the outgoing president.... (shrink)

Interdisciplinary in scope and bringing together work from around the world, The Individual and Utopia enquires after the nature of the utopian as citizen, demonstrating the inherent value of making the individual central to utopian theorizing and highlighting the methodologies necessary for examining the utopian individual. The various approaches employed reveal what it is to be an individual yoked by the idea of citizenship and challenge the ways that we have traditionally been taught to think of the individual as citizen. (...) As such, it will appeal to scholars with interests in social theory, philosophy, literature, cultural studies, architecture and feminist thought, whose work intersects with political thought, utopian theorizing, or the study of humanity or human nature. (shrink)