Our research

Our research programme on infant cognition explores the journey of learning and development in infancy, a phase where rapid acquisition of knowledge occurs. Newborns enter a world full of unknown elements, necessitating the quick formation of a framework for perceiving and understanding the world around them. This process is essential for infants to make accurate predictions and responses in their environment. Central to this learning process is the infant's ability to recognize, classify, and link new encounters to prior experiences.

• In this research, we aim to gain a deeper understanding of a critical component of human social interactions: how infants develop the ability to categorize other individuals' identities and emotional expressions by integrating visual (face) and auditory (voice) cues. Our objectives are to pinpoint the developmental timeline for the emergence of face and voice categorization skills, determine if these skills form a cohesive multimodal representation, and observe the development of these categorization abilities as infants increasingly interact with others.

• Our methodology includes both cognitive testing of infants in laboratory settings and the use of computational modeling to investigate the neural and computational foundations driving the developmental patterns of face and voice categorization.

Dog cognition is a fascinating and evolving field that explores the mental and behavioural processes of dogs. Dogs have co-evolved with humans for about 15’000 years, a process that affected their brains and behaviour. We focus on the following aspects in canine cognition.

• Social Cognition: Dogs derive from wolves and, hence, form strong bonds with each other. Dogs additionally form bonds with humans. Understanding how dogs perceive and interpret social signals from conspecifics and heterospecifics, such as body language and vocalizations, is a fundamental aspect of our research. 

• Communication and Language: Exploring the extent to which dogs can understand and communicate with humans through gestures, vocal commands, and other forms of non-verbal and verbal communication. 

• Inter-breed variability in cognition: Dogs vary in morphology, giving raise to assume that dogs are not comparable. Most modern attempts on dog research, therefore accounts for inter-breed variability. We here focus on olfactory capabilities of dogs that varies largely according to their skull shape (from brachycephalic to dolichocephalic) and, as a consequence, the size and shape of neural correlate responsible for olfaction. This research is at the interface of applied ethology, addressing welfare issues due to selective breeding practices.

In the field of neuroscience, an emphasis shift has occurred towards computational methodologies, advanced mathematical frameworks, and computer simulations. This shift has gained traction not only by the need to interpret and integrate the immense volumes of data produced by state-of-the-art experimental methods but also by the recognition that computational techniques in isolation can generate substantial amounts of data. This dual-source of data underscores the complexity and the multi-dimensional nature of modern neuroscience research, where both experimental and computational methodologies independently contribute to the needs of modern science demanding advanced analytical strategies. In this integrative framework, we tackle a range of computational challenges, such as complex phenomena like face and object recognition, perceptual narrowing, and the broader aspects of learning.

• Face and Object Recognition: Computational methods allow us to mimic the human brain's ability to recognize faces and objects. Using neural network models and machine learning algorithms, we can simulate and understand the neural mechanisms underlying these recognition processes. These models can be trained and tested against the vast datasets generated from both experimental observations and computational simulations, providing insights into how the brain processes visual information and distinguishes between different stimuli.

• Perceptual Narrowing: Perceptual narrowing is the process by which infants gradually lose their ability to recognize differences in stimuli (like faces or sounds) that they are not exposed to regularly. Computational models can simulate how neural networks evolve over time in response to varying environmental stimuli. These computational models can then be compared to experimental findings from infant neuroscience.

• Learning Processes: The intersection of computational and experimental neuroscience is particularly useful to address learning processes. By applying computational models to data derived from beahvioural and neural recordings during learning tasks, we can identify patterns and algorithms that mimic the brain's learning mechanisms. 

Research project funding: General Research Project: Identification number 112-2410-H-038-027; National Science and Technology Council (NSTC), formerly known as MOST; Title: Computational modeling of adaptation in the visual system

We use an approach referred to as computational ethology in this research programme. Computational ethology is the interdisciplinary research domain that addresses the lack of instrumental methods in ethology by introducing tools originating in mathematics and computer sciences, in particular in artificial intelligence and machine learning. The defined goals of computational ethology are to (a) detect predefined behaviours in freely moving animals and (b) discover novel behavioural patterns and new behaviours (Wiltschko et al., 2015; Dahl, Wyss, Zuberbühler, & Bachmann, 2018).

Higher-order cognition in small-brained animals

• The goal in this research programme, is to (a) describe similarities (and dissimilarities) of small-brained animal sensory systems (Borst & Helmstaedter, 2015; Wilson, 2013), particularly in combination to higher-cognitive processes, to (b) understand the neuronal circuitries of internal representation and to (c) pursue these objectives in a virtual and actual environment; the latter providing free animal behaviour partly in interaction with conspecifics. The study of small-brained animal cognition and cognitive computation is in very early stages, however, providing a unique window into the function of neuronal mechanisms and circuitries underlying cognition due to a ”simpler” brain.

Social interaction and group dynamics in zebrafish

• A useful computationally and quantitative valid description of the behavioural dynamics in groups of zebra fish during learning tasks is essential for the subsequent establishment of a link between social behaviour and neural activations using modern neuroscience methods. This data-driven research approach for characterizing social interaction behaviour in groups of animals is only just beginning, but already led to very promising results. For instance, by tracking social interaction in groups of mice a new high-order structure was found [Shemesh et al., 2013]. In particular, it was found by tracking the exact movement trajectories of a group of mice in a semi-natural environment over a long time that the statistics of the spatial configuration of the mice over time could not explain by a model only relying on pairwise correlations. Instead higher order terms had to be taken into account to explain the data hinting at a more complicated social interaction structure as previously thought. The goal of this research is to establish a similar data-driven approach to characterize social interactions in zebrafish. Moreover, we are interested in the changes of the dynamics of the interaction structure and the characterization of the flow of knowledge (e.g. information about a reward source) from one individual to others while the group performs a social learning task.

Research project funding: Research Project for Newly-recruited Personnel Identification number 110-2311-B-038-002; Ministry of Science and Technology (MOST); Title: Quantifying the effect of multiple neurotransmitter systems on group-level animal behaviour through machine learning