Researchers in the Faculty of Psychology and Educational Science have proposed doctoral projects for Chinese candidates who apply through the joint LMU–China Scholarship Council (CSC) program. Each project is specific, so please apply only for a position that matches your research interests and qualifications.
The China Scholarship Council (CSC) funds highly qualified Chinese candidates to complete doctoral studies or research stays abroad. In 2005, LMU became the first German university to sign a cooperation agreement with the CSC. Since then, the program has brought around 650 doctoral researchers to LMU, and it remains one of the pillars of LMU's strategic cooperation with China.
The size-weight illusion is an old but still poorly understood illusion. People who grasp a small object that has the same weight as a bigger object experience the small object as much heavier than the big object. At the same time, when people lift those objects, they use identical grip forces for both. It appears that there are two representations of the physical attribute "weight" in the brain. One representation, the perceptual representation that is the basis of the verbal report, is fooled by the difference in size. The other, the motor representation that is used to compute the forces required to lift the object, is unaffected by the size difference. But what is the nature of this motor representation, and is the difference between the perceptual report and the motor performance really evidence of distinct representations of the same physical parameter? To address these questions, we will introduce a series of novel behavioural paradigms that exploit our unique technical setups. These setups allow us to measure grip, load and torque forces in interactions with real objects, and also give us virtual objects with computer-defined haptic qualities and weight profiles, so that we can measure the movements and forces used when interacting with them. Furthermore, to understand the contribution of different brain regions to this process, we will examine grip-force control in patients with specific neuropsychological deficits following focal brain lesions.
Our sense of time is not a stopwatch. How long a moment seems depends on what came just before: an interval judged right after a longer one tends to feel longer, and after a shorter one, shorter. For years this "serial dependence" was viewed as a passive by-product of perception that keeps our view of a noisy world stable. Our recent work tells a different story. In time perception, the bias appears only when people act on what they perceive. When observers merely watch, or switch from one kind of response to another, it disappears, even though the stimulus was seen in exactly the same way. Action, it turns out, is not simply the output of perception; it is a gate that decides whether past experience enters the present.
This PhD project asks how that gate works. Building on theories of action control, in which every response binds what we perceived to how we acted, you will test three ideas: that the depth of this action–feature binding determines how strongly a past experience is stored; that merely expecting to act is enough to reopen that memory; and that performance feedback recalibrates how strongly past errors steer the next judgement. You will combine elegant psychophysical experiments with EEG, to track anticipatory motor preparation as it unfolds millisecond by millisecond, and/or fMRI, to locate binding and retrieval in striatal, premotor and medial-parietal circuits. The results feed into a Bayesian computational model.
The project sits where perception, action, memory and computational modelling meet, and its exact focus can be shaped around the candidate's interests and strengths. You will join the Multisensory Perception Lab (MSense Lab) at LMU Munich, an international and collaborative team working on time perception, multisensory integration and attention, with access to EEG, eye-tracking, virtual reality and a 3T research MRI scanner, and embedded in Munich's vibrant cognitive neuroscience community. Training covers experimental design, advanced statistics (mixed-effects and Bayesian models), computational modelling in Python/R, and open-science practice.
A full list of required documents is available on the LMU-CSC Scholarship Program page.