M.S. Candidate: Burak Büyükyaprak
Program: Cognitive Science
Date: 10.01.2025 / 13:00
Place: B-116

Abstract: Episodic memory is a type of long-term memory that encodes and retrieves personal experiences associated with their context. Previous episodic memory studies showed that the context or preexisting knowledge about retrieved information may influence the performance of memory tasks. So, it becomes crucial to study the semantic proximity effect by comparing memory task performance with different levels of semantic relatedness. In natural language processing studies, semantic relations can be successfully represented by learning word vectors in a large text corpus using neural networks. The study aimed to investigate the impact of semantic factors on delayed free recall tasks by creating word lists that include semantically related and unrelated word lists obtained through neural networks and show how semantic and temporal proximity effects influence recall performance.

fastText and word2vec were used to obtain Turkish word representations and organize words according to their semantic relatedness, and human raters validated words in the word lists. Later, how semantic relatedness affects recall dynamics, four conditions were compared (fastText-related, fastText-unrelated, word2vec-related, word2vec-unrelated).  Results showed that a significant positive correlation between cosine similarity values and human judgment and semantic and temporal proximity effects influenced the probability of recall. In addition, different levels of semantic relatedness and choice of word embeddings played a role in the likelihood of recall and accuracy. Therefore, this thesis suggests that neural networks can represent and manipulate semantic relations in memory studies and that semantic and temporal proximity effects influence different levels of semantic relatedness recall dynamics.