# Syllable-, Bigram-, and Morphology-Driven Pseudoword Generation in Greek
## 2 Related Work  
### Exploring Current Methods and Systems for Pseudoword Generation  
In the construction of pseudowords, numerous studies have followed a well-established tradition in which pseudowords are generated by modifying one or more letters or segments of an existing word (e.g., _CLEAM_ derived from _CLEAN;_[22]). [PERSON] et al. [23] developed the _English Lexicon Project,_ which consisted of 40,481 words and was intended for a lexical decision task. An equivalent number of pseudowords was also generated by modifying one or two letters from the original words, such as _addomen_ from _abdomen_. However, visual similarity between the pseudoword and the original word poses a risk because participants can occasionally recognize the original word within specific pseudowords. [PERSON] et al. [24] highlight that this unintended recognition of the original word may introduce an experimental priming bias that researchers are unable to control. In recent years, researchers have developed more evolved methods for pseudoword construction. [PERSON] et al. [25] identified three basic strategies, that is, stimulus manipulation, high-frequency bigrams, and a combination of sub-syllabic elements. The first method, i.e., stimulus manipulation, involves taking a word present in the lexicon and modifying it in various ways, e.g., by changing one or two characters through insertion, deletion, transposition, or replacement, to produce a pseudoword. Alternatively, a pseudoword can be created by adding an affix to the stimulus, provided that the outcome does not match an existing word in the lexicon [26]. The second method creates pseudowords using high-frequency bigrams. This approach employs software applications like _WordGen_[27] that take into account neighborhood size and orthographic similarity factors. WordGen generates pseudowords in English, Dutch, German, and French using the _CELEX_[28] and _Lexique_[29] databases. The primary steps in the pseudoword generation process involve creating random letter sequences and then determining, based on several predetermined characteristics, whether these sequences constitute existing words or not. Any deviation from these qualities leads to the rejection of the letter sequence. However, strict WordGen parameters can prolong candidate search times. Furthermore, its use may be challenging for researchers unfamiliar with sub-lexical statistics, especially when configuring effective pseudoword parameters for the experimental material's design [24]. The _character-gram chaining algorithm_ (CGCA, [25]) is another tool that processes wordlists or words from a corpus. It begins by forming a wordlist by extracting all unique tokens from the input (i.e., the original wordlist or corpus). It then identifies character-grams (bigrams, trigrams, four-grams, etc.) and assigns them to three potential positions, namely, the word's start, middle, or end. Subsequently, it generates and validates chains of character-grams. The outcome is a list of pseudowords linked explicitly to the words used for their construction. CGCA is a script executed via the terminal, which is a feature that makes it less user-friendly for researchers unfamiliar with such interfaces. _UniPseudo_ also generates pseudowords from words given by the user (in the form of a wordlist or a corpus) and is accessible through a web interface [24; 30]. Currently,it can generate pseudowords for 64 languages. The algorithm is based on Markov chains, like CGCA, and extracts all trigrams (or bigrams, depending on the length of the requested pseudoword) at a given position from the input set of words. It begins by randomly choosing a trigram from words that have the trigram sequence in word-initial position, e.g., _gla_ from _glared_, while at the same time respecting their frequencies in the original corpus.