Petra Buljat

This study investigated how congruence between pictogram complexity and typographic complexity influences visual processing and perceived appropriateness in multimodal communication. Drawing on theories of visual communication, legibility and aesthetic perception, the study examined whether simple or complex pictograms harmonise more effectively with sans-serif or serif typefaces. Ninety participants viewed stimuli from three thematic categories (cobbler, herbal pharmacy and gluten-free restaurant), while their eye movements were recorded using a Tobii Pro Fusion eye-tracking device. Measures included reading time, fixation count and saccade count, together with subjective evaluations of pictogram–typeface suitability. The results show that reading time was the most sensitive indicator of formal congruence. In the cobbler and gluten-free restaurant categories, simple pictograms increased reading time with sans-serif typography but decreased it with serif typography. In the herbal pharmacy category, simple pictograms and sans-serif typography independently supported faster reading performance. Subjective evaluations showed no significant differences between combinations, indicating that participants perceived all pairings as similarly appropriate despite measurable differences in processing efficiency. The findings suggest that the effectiveness of pictogram–typeface combinations depends on both formal complexity and thematic context. Eye-tracking proved valuable for revealing subtle cognitive processing differences not reflected in subjective judgements.

This study explores the impact of different color hue combinations and medium types (print and digital) on reading performance and comprehension. The purpose is to understand how these factors may influence text legibility and to offer practical insights for designers on optimizing visual design in presenting promotional content. Fifty-two participants read texts with various color palettes presented on both print and digital formats. The following color combinations were used in the experiment: achromatic (black on gray), achromatic–chromatic (black on green), and chromatic (purple on green). Reading time and comprehension score were measured to identify any significant differences between these conditions. The results show that the color combinations significantly affected reading time, regardless of the medium type. However, neither color nor medium had a significant effect on text comprehension. The main outcome is that the combination of achromatic text on a chromatic background appeared to be the most effective combination for enhancing the legibility of promotional text. Our findings suggest that graphic designers can choose particular color combinations to optimize reading speed without worrying about negative effects on the comprehension of the texts used in promotional materials across various mediums.

This paper explores how effectively Artificial Intelligence (AI) can translate text descriptions into accurate and functional 3D models of mechanical components, highlighting both the potential and the current shortcomings of this technology. A functional 3D model implies that it is suitable for 3D printing. Three different AI 3D model generators were selected for this analysis. Text prompts involving gear mechanisms were used as input. Each generated model was assessed on key parameters such as the accurate depiction of gear teeth, the correct alignment and meshing of gears, and the overall structural integrity required for functional gear mechanisms. Additionally, the generated models were verified for any deviations from the expected design standards of mechanical gears. The analysis revealed that none of the selected AI 3D model generators could consistently produce fully functional and technically accurate gear models based on simple text prompts. While the AI generators could produce visually correct models that generally resembled gears, significant shortcomings were observed in their functional and technical accuracy. The generated models often failed to meet the precise geometric and structural requirements necessary for practical application in mechanical systems. Automating the 3D modelling process for mechanical components using AI technology would reduce time and cost. However, further advancements in AI and machine learning are required to enhance the accuracy and reliability of AI-generated 3D models for use in real-world mechanical design and manufacturing