Modeling Deductive Inference: A Historico-Philosophical Introduction to First-Order Logic

This book is a companion text for lectures on first-order logic and its elementary metatheory (used in Intermediate Logic at Northern Illinois University). It covers the basic concepts of set theory necessary for a mathematical development of first-order logic; develops a formal language of first-order logic; presents a classical Tarskian semantics for the language and the “semantic” conception of logical consequence; presents a Gentzenian proof system and the “syntactic” conception of logical consequence; develops a partial decision procedure for logical consequence in the language; demonstrates applications of the formal system to modeling deductive inference expressed in natural language; and extends the system to include identity. Unlike a typical logic textbook, however, these topics are embedded within an overarching historico-philosophical narrative that illuminates (some of) the interaction of rational argument and historical context that led to their development and exhibits their engagement with broader philosophical issues.https://huskiecommons.lib.niu.edu/allfacultyother-bookschapters/1025/thumbnail.jp

Cultural Learning of Metacognition: Evidence from Blind Individuals

The cultural origins hypothesis posits conscious metacognitive abilities in humans are acquired through cultural selection facilitated by cultural learning. Interpersonal broadcasting, much of which is nonverbal, is hypothesized to be one conduit of cultural learning. Thus, individuals with no or limited access to nonverbal information, such as blind persons, should have metacognitive abilities different from those of the larger culture. To test this prediction, we asked blind (n = 23) and sighted (n = 20) adults to recall the modality (Spoken or Written) of words whose definitions they had learned, and to indicate their degree of confidence in their response. We found blind participants had increased metacognitive confidence compared to sighted participants, despite being no more accurate in their judgments. Neither group showed metacognitive calibration, i.e., a relationship between confidence and accuracy. The findings are consistent with the cultural origins hypothesis and support broadcasting as a vehicle for cultural learning

The Emerging Danger of AI-Generated Child Sexual Abuse Material and an Unprepared Criminal Code

The development and dissemination of child sexual abuse material (“CSAM”) using artificial intelligence (“AI”) is becoming easier and easier with technological advancements. This Article argues that traditional child pornography statutes should be extended to encompass fully AI-generated images. The Article outlines the history of the nation’s current federal CSAM laws and details the results of a nationwide survey of statutes which determined that few states have effective laws on the books regarding AI-generated CSAM. This Article also offers recommendations for constructing effective laws against AI-generated CSAM and concludes that harm comes from the very existence of CSAM, no matter how that CSAM is made, and that as a result, we must reanalyze the current state of the law in order to better protect our children