Academic interests
- Nervous system scaling and emergence
- Neurocognitive organization
- Attention and expectation
- Perceptual inference, cognitive control, and motor execution
- Self-similarity, self-reference, and time
Current research
What?
I study how the brain infers the unfolding of sound sequences and the extent to which this is influenced by how we consciously attend.
When we go about our lives, networks of nerve cells extract recurrent and ordered aspects of our experiences to infer, or guess, what is happening now, what happened in the past, and what is going to happen in the future. How the brain achieves this feat is debated but an interesting idea is that it involves encoding of statistical patterns, such as conditional and transitional probabilities, from sequences of neuronal action potentials.
Now, sequences occur along scales of time and levels of abstraction. But we don’t know which time-scales the brain attends to effortlessly nor the extent to which regularities at specific time-scales or abstraction-levels are better learnt if we focus our attention consciously. For example, are recurrent sequence elements registered when we are distracted if recurrency is defined over a relatively long time-span? Or, are abstract transition-rules such as featural scaling registered when our focus is elsewhere?
Another interesting question concerns the degree to which brain networks differentiate unexpected absence vis-à-vis presence in a sequence – because in terms of statistical surprisal, these are comparable. That is, an unexpectedly absent sound is more or less as surprising as its unexpected presence - at least in statistical terms.
This is interesting since many philosophers and neuroscientists now believe that it's a schema or model in the brain, not inflowing sensory information per se, that determines our experience. By this idea, sensations are only attended to if deemed useful for quenching uncertainty about how the world behaves or for guiding our behavior in it (to develop intuition for how this might work, I recommend this TED talk by neuroscientist Anil Seth: https://www.youtube.com/watch?v=lyu7v7nWzfo).
Finally, I’m currently interested in how the brain can learn to expect unexpected transitions in a sequence – as when we learn that certain changes happen regularly but not their exact timing. How does the brain prepare for these changes when we are instructed to detect them? Studying this can help us understand how inferential mechanisms in neural networks intersect and facilitate our ability to consciously attend and act at the right time.
How?
To explore these questions, we record and analyze neural activity from electrodes implanted in the brains of patients with medication-resistant epilepsy. These patients can be relieved of debilitating seizures if the epileptic neural network is focal and excisable.
Excising an epileptic network requires careful mapping of the brain beforehand. Recordings of neuroelectric patterns are used to identify both the epileptogenic network as well as areas crucial for essential functions such as language and movement which should remain unscathed during a potential neurosurgical intervention.
Patients typically remain under observation for 7-14 days while their brain activity is recorded. During this time, they are mostly restricted to the hospital room and we can therefore ask them if they are willing to do cognitive tasks such as detecting irregular events in otherwise regular sound sequences while their brain activity is being recorded.
We primarily work with stereo-electroencephalography (SEEG) field potential data, which are direct recordings of brain activity obtained from electrode discs embedded in thin, cylindrical shafts that are useful for targeting deep brain structures. SEEG also gives expansive 3D-coverage relative to other clinically comparable techniques. We also analyze electrocorticograms (ECoG; from electrode grids or strips placed atop the exposed cortical surface, typically implanted underneath the outermost brain meninge), and scalp EEG.
To test ideas about cognitive neurophysiology, we use analysis techniques to extract periodic (rhythmical) and aperiodic (arrhythmical) neural activity patterns from the field potential signals obtained during different experimental conditions.
Why?
Describing and explaining the neurodynamic of cognition will help us abstract the essence of the brain. This is a theoretical but nevertheless imperative issue because we can still not explain how the dynamics of neuroelectromagnetic fields relate to awareness and behavior.
This is a pressing issue due to the potential for illuminating avenues to reduced mental suffering. Novel perspectives on brain function will cast new light on debilitating cognitive phenomena such as problems inferring the causes of sensory impressions and attending/disattending to/from relevant/irrelevant impressions. When we infer what is going on, we rely on inference to conclude - either by perceiving relatively automatically based on prior knowledge, or by reasoning effortfully on the basis of relevant knowledge.
Hallucination is a form of mis-perception whereby we come to experience events that cannot be confirmed by others, and which can be highly challenging or distressing. Delusion is a related phenomenon of forming and maintaining beliefs that are not shared by others and which are often strange and/or bizarre. Hallucination and delusion probably arise from comparable neurodynamics - the main difference being one of time: to hallucinate is to misperceive that which happens now, whereas to be deluded is to have maladaptive beliefs about what is happening, has happened, or is going to happen on the basis of information accumulated over time. In both cases we are talking about inferences: either effortless/automatic (hallucination) or effortful/controlled (delusion).
Studying the neurodynamic of auditory expectation and how such expectations are influenced by how we attend, will hopefully help us understand a small piece of this very complicated puzzle.
Background
Master of Philosophy in Psychology, University of Oslo
Bachelor of Science in Psychology, Inland Norway University of Applied Sciences (formerly Lillehammer University College)