One argument in favor of current PDP models has been that the availability of "hidden units" allows the system to create an internal representation of the input domain, and to use this representation in producing output weights. The "microfeatures" learned by sets of hidden units, it has been argued, provide an alternative to symbols for certain reasoning tasks. In this paper we try to further this argument by demonstrating several results that indicate that such representations are formed. We show that by using a spreading activation model over the weights learned by networks trained via backpropagation, we can model certain cognitive effects. In particular, we show some results in the areas of modeling phoneme confusions and handling word-sense disambiguation, and some preliminary results demonstrating that priming effects can be modeled by this activation spreading approach.

Loaded language is an umbrella term for words, phrases, and overall rhetorical strategies that have strong emotional implications and intent to sway others. Belief in conspiracy theories is tied to a range of strong emotions (van Prooijen and Douglas, 2018). Accordingly, language with strong emotional and persuasive content may be expressed by people experiencing the strong emotions associated with conspiracy theorizing. In this research, we examine multiple types of loaded language in two online parenting forums: one historically against vaccination, and another historically accepting of vaccination. It is well-established that conspiracy theories are the most influential contributor to anti-vaccination views (Hornsey et al., 2018) and anti-vaccination beliefs are strongly correlated with belief in unrelated conspiracy theories (Goldberg & Richey, 2020). Results indicate that users of an anti-vaccination forum use a greater frequency of loaded language to express themselves than users of a vaccination-neutral forum.

Previous research found that loaded language (i.e., words, phrases, and overall rhetorical strategies that have strong emotional implications and intent to sway others) is used with significantly greater frequency by users of an online anti-vaccination forum compared to users of a vaccination-neutral forum. Our present research expands on these findings by examining the presence of loaded language on forums dedicated to discussing a broader range of conspiratorial topics. We compare loaded language usage on the subreddit r/conspiracy with usage on r/science and r/wallstreetbets. Results corroborate the previous literature. We additionally examined the presence of loaded language in a dataset from the alt-tech social networking platform Parler on January 6, 2021, when the U.S. Capitol was attacked because of conspiracy theories regarding the 2020 Presidential Election. Results show that loaded language, whose usage is linked to the cognitive motivations underlying belief in conspiracy theories, is a linguistic marker of conspiracy theorizing.