A normally developing child, Charlie (16 months old at the beginning and 27 months old at the end of this study), was tested several times for the derivation of relations over a period of 8 months. In a series of studies Charlie was: (1) taught to match names to pictures or pictures to names and was tested for derived relations of mutual entailment, (2) tested for retention of trained and derived relations after a 2 week delay and for the derivation of mutual entailment relations after a 1 week delay from training, (3) taught to match sounds to pictures and names to pictures and tested for mutual entailment relations and name-sound and sound-name combinatorial entailment relations, and (4) tested for the matching of a novel picture to a novel name ("nonverbal" exclusion) and for subsequent naming of the novel excluded picture ("verbal" exclusion). The results show that Charlie derived mutual entailment relations and showed nonverbal exclusion as early as 17 months. Combinatorial entailment relations and verbal exclusion emerged later. These findings lend support to the view that derivation of relations is not dependent upon sophisticated verbal abilities, and that such performances can be viewed as historically and contextually situated actions that develop over time.

Download full-text PDF

Source
http://dx.doi.org/10.1006/jecp.1993.1032DOI Listing

Publication Analysis

Top Keywords

entailment relations
20
mutual entailment
16
derived relations
12
relations
10
derivation relations
8
taught match
8
names pictures
8
pictures names
8
relations week
8
week delay
8

Similar Publications

Manner implicatures in large language models.

Sci Rep

November 2024

School of Languages and Cultures, Purdue University, West Lafayette, 47907, USA.

In human speakers' daily conversations, what we do not say matters. We not only compute the literal semantics but also go beyond and draw inferences from what we could have said but chose not to. How well is this pragmatic reasoning process represented in pre-trained large language models (LLM)? In this study, we attempt to address this question through the lens of manner implicature, a pragmatic inference triggered by a violation of the Grice manner maxim.

View Article and Find Full Text PDF
Article Synopsis
  • The goal of relationship classification (RC) is to identify the semantic relationship between entities in sentences, but current approaches mostly rely on predefined relationships, making it hard to recognize new ones, a challenge known as zero-shot relationship classification (ZSRC).
  • Existing ZSRC methods struggle with autonomy and often require manual definitions, so researchers propose a new framework called inference on category attributes (ICA) to improve how models understand unseen relationships.
  • The ICA framework uses hypothesis templates based on relationship descriptions to convert RC data into a textual entailment format, enhancing a model's ability to generalize knowledge to new classes, and has shown strong performance on benchmark datasets like FewRel and Wiki-ZSL.
View Article and Find Full Text PDF

The purpose of the current study was to extend the research on the possible role of verbal mediation in the establishment of comparative relations. We conducted four experiments in which 14 participants received conditional discrimination training with nonarbitrary and arbitrary stimuli, followed by derived comparative and transformation of function tests. Participants learned to select the smallest or biggest comparison across multiple exemplars in the presence of abstract samples.

View Article and Find Full Text PDF

Overview and Discussion of the Competition on Legal Information, Extraction/Entailment (COLIEE) 2023.

Rev Socionetwork Strateg

January 2024

Faculty of Information Science and Technology, Hokkaido University, Sapporo-shi, Hokkaido Japan.

We summarize the 10th Competition on Legal Information Extraction and Entailment. In this tenth edition, the competition included four tasks on case law and statute law. The case law component includes an information retrieval task (Task 1), and the confirmation of an entailment relation between an existing case and a selected unseen case (Task 2).

View Article and Find Full Text PDF

Emotion-cause pair extraction (ECPE) is a challenging task that aims to automatically identify pairs of emotions and their causes from documents. The difficulty of ECPE lies in distinguishing valid emotion-cause pairs from many irrelevant ones. Most previous methods have primarily focused on utilizing multi-task learning to extract semantic information solely from documents without explicitly encoding the relations between clauses.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!