Measuring Early Knowledge of Object Categories
My postdoctoral research investigates how infants acquire knowledge about objects and how we can measure the concepts they have acquired during early development
I have been exploring this work during my postdoc with the Baby Language & Conceptual Knowledge Study (BLoCKS) team at the University of East Anglia. BLoCKS is funded by an ESRC grant awarded to Teea Gliga (PI), Nadja Althaus, Marie Smith, and Evelyne Mercure.
Across the first years of life, infants accumulate extensive experience with the objects around them. Through everyday interactions, they learn that balls, books, cups, and dogs each belong to meaningful categories. These concepts help infants make sense of new experiences and provide a foundation for later learning and language development.
Despite the importance of these early concepts, measuring what infants know is surprisingly difficult. Researchers have developed several powerful methods for studying infants’ word knowledge. Parents can report which words their children understand, and laboratory measures such as eye-tracking and EEG can reveal whether infants recognise the meanings of familiar words long before they begin speaking. However, these approaches often make it difficult to separate conceptual knowledge from language knowledge.
For example, if an infant recognises a ball, is this because they have developed a concept of ball, because they know the word “ball”, or because these forms of knowledge are tightly intertwined? To understand how concepts emerge, we need methods that can measure what infants know about the world independently of the words that describe it.
Measuring infants’ category knowledge
A major focus of my current research is developing ways to identify and measure the category knowledge infants have already acquired outside the laboratory.
Using electroencephalography (EEG), I examine how infants’ brains respond when viewing objects from categories that are likely to be familiar through everyday experience, compared with objects from categories they are less likely to know. By combining EEG with machine-learning approaches (known as Multivariate Pattern Analysis), I investigate whether patterns of brain activity can reveal differences in how familiar and unfamiliar categories are represented.
This work aims to develop sensitive neural measures of conceptual knowledge in infancy and to better understand how early concepts are represented in the developing brain.
Why does this matter?
Understanding how to measure infants’ knowledge is essential for answering broader questions about cognitive development. Reliable neural measures could help researchers identify what infants know before they can communicate verbally, track conceptual development over time, and better understand how experience shapes learning during the first years of life.
Read more about this work being presented at national and international conferences
LCICD 2025: Decoding infants’ existing category knowledge using EEG Multivariate Pattern Analysis
Last week, the BLoCKS team packed our bags and headed to the Lancaster Conference on Infant and Early Child Development 2025. We spent two days catching up with colleagues from across the globe to hear about their latest work in infant developmental science…
ICIS 2026, Panama: The Emergence of Object Category Knowledge - Indicators and Potential Processes of Concept Formation Symposium
Last week, the BLoCKS team packed our bags and headed to the Lancaster Conference on Infant and Early Child Development 2025. We spent two days catching up with colleagues from across the globe to hear about their latest work in infant developmental science…