Fictitious Animals and Pseudowords for Artificial Intelligence and Human Training

This dataset contains a curated collection of 80 unique visual stimuli depicting fictitious animals (novel visual categories), designed entirely by the author using Adobe Illustrator. These custom-made, original illustrations are specifically engineered to train and test experimental architectures in Artificial Intelligence, Neural Networks, and Large Language Models (LLMs), as well as human training processes, while eliminating pre-existing real-world biases. The stimuli function as controlled visual representations associated with novel lexical labels (pseudowords) to evaluate the following cognitive and computational capacities: - Capacity 1 (Storing a Perceptual Category): Pure non-linguistic stimulus processing where visual features are clustered into a distinct novel category before its integration with linguistic information. - Capacity 2 (Storing a Category-Word Mapping): The direct association of a perceptual category with a novel lexical label (pseudoword) to consolidate the mapping. - Capacity 3 (Storing the Mutual Exclusivity Rule): The operational rule establishing that if a category already has a label, a new incoming lexical label cannot map onto that same category. - Capacity 4 (Inferential Reasoning): The execution of reasoning driven by the visual context and conditional (IF-THEN) rules, along with the mutual exclusivity rule, to resolve ambiguity and map a novel label onto the remaining novel category. The repository includes these visual representations along with a structured index that maps each character to its designated identification label.