Scientific Highlight
Top-down and bottom-up effects on visual perception during data-based scientific reasoning in the context of population dynamics - Sabine Meister, Sandra Nitz and Annette Upmeier zu Belzen
.jpg)
Why managing AI risk presents new challenges
Aliquet morbi justo auctor cursus auctor aliquam. Neque elit blandit et quis tortor vel ut lectus morbi. Amet mus nunc rhoncus sit sagittis pellentesque eleifend lobortis commodo vestibulum hendrerit proin varius lorem ultrices quam velit sed consequat duis. Lectus condimentum maecenas adipiscing massa neque erat porttitor in adipiscing aliquam auctor aliquam eu phasellus egestas lectus hendrerit sit malesuada tincidunt quisque volutpat aliquet vitae lorem odio feugiat lectus sem purus.
- Lorem ipsum dolor sit amet consectetur lobortis pellentesque sit ullamcorpe.
- Mauris aliquet faucibus iaculis vitae ullamco consectetur praesent luctus.
- Posuere enim mi pharetra neque proin condimentum maecenas adipiscing.
- Posuere enim mi pharetra neque proin nibh dolor amet vitae feugiat.
The difficult of using AI to improve risk management
Viverra mi ut nulla eu mattis in purus. Habitant donec mauris id consectetur. Tempus consequat ornare dui tortor feugiat cursus. Pellentesque massa molestie phasellus enim lobortis pellentesque sit ullamcorper purus. Elementum ante nunc quam pulvinar. Volutpat nibh dolor amet vitae feugiat varius augue justo elit. Vitae amet curabitur in sagittis arcu montes tortor. In enim pulvinar pharetra sagittis fermentum. Ultricies non eu faucibus praesent tristique dolor tellus bibendum. Cursus bibendum nunc enim.
How to bring AI into managing risk
Mattis quisque amet pharetra nisl congue nulla orci. Nibh commodo maecenas adipiscing adipiscing. Blandit ut odio urna arcu quam eleifend donec neque. Augue nisl arcu malesuada interdum risus lectus sed. Pulvinar aliquam morbi arcu commodo. Accumsan elementum elit vitae pellentesque sit. Nibh elementum morbi feugiat amet aliquet. Ultrices duis lobortis mauris nibh pellentesque mattis est maecenas. Tellus pellentesque vivamus massa purus arcu sagittis. Viverra consectetur praesent luctus faucibus phasellus integer fermentum mattis donec.
Pros and cons of using AI to manage risks
Commodo velit viverra neque aliquet tincidunt feugiat. Amet proin cras pharetra mauris leo. In vitae mattis sit fermentum. Maecenas nullam egestas lorem tincidunt eleifend est felis tincidunt. Etiam dictum consectetur blandit tortor vitae. Eget integer tortor in mattis velit ante purus ante.
- Vestibulum faucibus semper vitae imperdiet at eget sed diam ullamcorper vulputate.
- Quam mi proin libero morbi viverra ultrices odio sem felis mattis etiam faucibus morbi.
- Tincidunt ac eu aliquet turpis amet morbi at hendrerit donec pharetra tellus vel nec.
- Sollicitudin egestas sit bibendum malesuada pulvinar sit aliquet turpis lacus ultricies.
“Lacus donec arcu amet diam vestibulum nunc nulla malesuada velit curabitur mauris tempus nunc curabitur dignig pharetra metus consequat.”
Benefits and opportunities for risk managers applying AI
Commodo velit viverra neque aliquet tincidunt feugiat. Amet proin cras pharetra mauris leo. In vitae mattis sit fermentum. Maecenas nullam egestas lorem tincidunt eleifend est felis tincidunt. Etiam dictum consectetur blandit tortor vitae. Eget integer tortor in mattis velit ante purus ante.
This study investigates the role of visual perception in data-basedscientific reasoning within population dynamics. Visual perception involves representational characteristics of stimuli and context-based conceptions, influenced by top-down (contextual assumptions) and bottom-up (data representation) effects. In population dynamics, top-down effects relate to assumptions like natural equilibrium, while bottom-up effects concern how data are represented, typically through line graphs. We assessed pre-service biology teachers’ (N = 20) initial conceptions on population dynamics, categorizing them as either the Balance of Nature (BoN) or Flux of Nature (FoN) metaphors. Participants viewed line graphs depicting population dynamics while eye-tracking technology recorded their visual focus and think-aloud protocols provided verbal data. ANOVA results showed significant differences in eye-tracking metrics between AOI groups of BoN and FoN graphs, but no significant effects between initial conceptions and eye-tracking measures. Heat map analyses revealed that participants focused on visually prominent graph features, especially when interpretation required conceptual knowledge and pattern identification.
.jpg)
.jpg)