External details revisited – A new taxonomy for coding ‘non-episodic’ content during autobiographical memory retrieval

Autobiographical memory (ABM) is typically held to comprise episodic and semantic elements, with the vast majority of studies to date focusing on profiles of episodic details in health and disease. In this context, ‘non-episodic’ elements are often considered to reflect semantic processing or are discounted from analyses entirely. Mounting evidence suggests that rather than reflecting one unitary entity, semantic autobiographical information may contain discrete subcomponents, which vary in their relative degree of semantic or episodic content. This study aimed to (1) review the existing literature to formally characterize the variability in analysis of ‘non-episodic’ content (i.e., external details) on the Autobiographical Interview and (2) use these findings to create a theoretically grounded framework for coding external details. Our review exposed discrepancies in the reporting and interpretation of external details across studies, reinforcing the need for a new, consistent approach. We validated our new external details scoring protocol (the ‘NExt’ taxonomy) in patients with Alzheimer's disease (n = 18) and semantic dementia (n = 13), and 20 healthy older Control participants and compared profiles of the NExt subcategories across groups and time periods. Our results revealed increased sensitivity of the NExt taxonomy in discriminating between ABM profiles of patient groups, when compared to traditionally used internal and external detail metrics. Further, remote and recent autobiographical memories displayed distinct compositions of the NExt detail types. This study is the first to provide a fine-grained and comprehensive taxonomy to parse external details into intuitive subcategories and to validate this protocol in neurodegenerative disorders.     

Adjacent and Non-Adjacent Word Contexts Both Predict Age of Acquisition of English Words: A Distributional Corpus Analysis of Child-Directed Speech

Children show a remarkable degree of consistency in learning some words earlier than others. What patterns of word usage predict variations among words in age of acquisition? We use distributional analysis of a naturalistic corpus of child-directed speech to create quantitative features representing natural variability in word contexts. We evaluate two sets of features: One set is generated from the distribution of words into frames defined by the two adjacent words. These features primarily encode syntactic aspects of word usage. The other set is generated from non-adjacent co-occurrences between words. These features encode complementary thematic aspects of word usage. Regression models using these distributional features to predict age of acquisition of 656 early-acquired English words indicate that both types of features improve predictions over simpler models based on frequency and appearance in salient or simple utterance contexts. Syntactic features were stronger predictors of children's production than comprehension, whereas thematic features were stronger predictors of comprehension. Overall, earlier acquisition was predicted by features representing frames that select for nouns and verbs, and by thematic content related to food and face-to-face play topics; later acquisition was predicted by features representing frames that select for pronouns and question words, and by content related to narratives and object play.     

Epistemic Possibility and the Necessity of Origin

The necessity of origin suggests that a person’s identity is determined by the particular pair of gametes from which the person originated. An implication is that speculative scenarios concerning how we might otherwise have been had our gametic origins been different are dismissed as being metaphysically impossible. Given, however, that many of these speculations are intelligible and commonplace in the discourses of competent speakers, it is overhasty to dismiss them as mistakes. This paper offers a way of understanding these speculations that does not commit them to incoherence but aims to make the best sense of what they are expressing. Using the philosophical framework of two-dimensional semantics, it proposes that the speculative scenarios are best analysed as epistemic possibilities, rather than as metaphysical possibilities. It then explores some implications of this analysis for the ethical challenges associated with the non-identity problem.     

Semantic Similarity of Alternatives Fostered by Conversational Negation

Conversational negation often behaves differently from negation as a logical operator: when rejecting a state of affairs, it does not present all members of the complement set as equally plausible alternatives, but it rather suggests some of them as more plausible than others (e.g., “This is not a dog, it is a wolf/*screwdriver”). Entities that are semantically similar to a negated entity tend to be judged as better alternatives (Kruszewski et al., 2016). In fact, Kruszewski et al. (2016) show that the cosine similarity scores between the distributional semantics representations of a negated noun and its potential alternatives are highly correlated with the negated noun-alternatives human plausibility ratings. In a series of cloze tasks, we show that negation likewise restricts the production of plausible alternatives to similar entities. Furthermore, completions to negative sentences appear to be even more restricted than completions to an affirmative conjunctive context, hinting at a peculiarity of negation.     

Conditionals and inferential connections: toward a new semantics*

Abstract

In previous published research (“Conditionals and Inferential Connections: A Hypothetical Inferential Theory,” Cognitive Psychology, 2018), we investigated experimentally what role the presence and strength of an inferential connection between a conditional’s antecedent and consequent plays in how people process that conditional. Our analysis showed the strength of that connection to be strongly predictive of whether participants evaluated the conditional as true, false, or neither true nor false. In this article, we re-analyse the data from our previous research, now focussing on the semantics of conditionals rather than on how they are processed. Specifically, we use those data to compare the main extant semantics with each other and with inferentialism, a semantics according to which the truth of a conditional requires the presence of an inferential connection between the conditional’s component parts.     

Pinning down polysemy: A formalisation for a Brazilian Portuguese preposition

This paper presents a formal definition for a number of distinct cases where the preposition “em” (in) can be applied in Brazilian Portuguese. The aim of this work is to establish a mathematical model using qualitative spatial reasoning formalisms within the idea of precisification from supervaluation semantics. Our long term goal is to implement this model into artificial intelligent systems to allow for seamless communication with humans in a common speech.     

No frills: Simple regularities in language can go a long way in the development of word knowledge

Recent years have seen a flourishing of Natural Language Processing models that can mimic many aspects of human language fluency. These models harness a simple, decades-old idea: It is possible to learn a lot about word meanings just from exposure to language, because words similar in meaning are used in language in similar ways. The successes of these models raise the intriguing possibility that exposure to word use in language also shapes the word knowledge that children amass during development. However, this possibility is strongly challenged by the fact that models use language input and learning mechanisms that may be unavailable to children. Across three studies, we found that unrealistically complex input and learning mechanisms are unnecessary. Instead, simple regularities of word use in children's language input that they have the capacity to learn can foster knowledge about word meanings. Thus, exposure to language may play a simple but powerful role in children's growing word knowledge. A video abstract of this article can be viewed at https://youtu.be/dT83dmMffnM .

Research Highlights

• Natural Language Processing (NLP) models can learn that words are similar in meaning from higher-order statistical regularities of word use.
• Unlike NLP models, infants and children may primarily learn only simple co-occurrences between words.
• We show that infants' and children's language input is rich in simple co-occurrence that can support learning similarities in meaning between words.
• We find that simple co-occurrences can explain infants' and children's knowledge that words are similar in meaning.