"This is us" - Questions for Stefan Appelhoff
Our institute has over 300 employees. But that is just a number. Who are the people at our institute? What do they do and what drives them? In our "This is us" format, colleagues answer questions about their work and their motivation.
On the occasion of International Women's Day on March 8, 2023, we launched the series "This is us". We have now introduced 13 female scientists. We are following on from this and now also introduce male scientists, starting with Stefan Appelhoff from the Research Group Adaptive Memory and Decision Making. In the last episode, we introduced Lisa Oswald from the Center for Adaptive Rationality.
One of your research topics at the Research Group Adaptive Memory and Decision Making is decision-making in humans. What fascinates you about this topic?
Decisions are something very fundamental. People make thousands of them every day - whether consciously or unconsciously. Decisions are also extremely diverse: from (unconsciously) deciding what color a traffic light is and what it means, to major (often conscious) life decisions, such as whether to pursue a doctorate. I like this wide range of possibilities. In my own research, I am interested in how people weigh and summarize clues that emerge piece by piece in order to make a final decision. Examples of these cues can be found in everyday situations, for example when deciding whether we should take an umbrella with us. Here, clues from the weather forecast, a look outside and the memory of a previous rainy day without an umbrella can be decisive. However, the clues and the "final decisions" in my research are very often of an economic nature ("Which decision can earn you a bigger bonus in this experiment?"). But it is precisely this process of gradually approaching a decision that also fascinates me with all other possible (life) decisions.
Could you explain your results in more detail with an example?
When people are given several numbers step by step in a short space of time and are asked to summarize them, you often notice two different tendencies: Extreme numbers (very low or very high) are either overestimated or underestimated relative to "medium" numbers. This means that the figures are not summarized linearly but using one of the two strategies (overestimating or underestimating the extremes). For example, if the mean is to be calculated in a series of numbers, such as 4, 5, 6, 9, most people would tend to either overestimate the number 9 and weight it more heavily, resulting in a higher than expected average, or underestimate it and weight it less heavily, resulting in a lower average. It is interesting to note that although both strategies deviate from the mathematically optimal path of linear summarization, they can still be optimal in specific decision scenarios. The reason for this is that the neuronal signals that people work with are "noisy". Furthermore, human attention (and other "neural resources") forms a natural bottleneck that makes a mathematically optimal (linear) strategy bumpy and less robust. In our recent studies, we have shown that the exact choice of strategy (overestimating vs. underestimating extremes) depends on the context and difficulty of the task. We have also established a link with neuronal (EEG) signals.
What motivates you in your everyday working life?
I find it very motivating and enriching when my contributions make the work of others easier or even possible in the first place. This is also the reason why, in addition to my work in basic research, I also place a large focus on contributions to free open source software and data standards: for example, one week's work on a code function in a piece of software can ensure that dozens of other researchers save a lot of time and effort for quite some time afterwards because they needed precisely this code function, for example to process, visualize or interpret their data. For 6 years I have been working on MNE-Python, a software for analyzing MEG and EEG signals, and on BIDS, a data standard for neuroscience. It is a great pleasure for me to be a part of these larger movements -- because unlike in basic research, you can see the impact of your work much faster and more concretely. Of course, the same principle applies to me when I can help colleagues with their work.
When did you realize that you wanted to go into science, and what advice would you give your younger self at the beginning of a scientific career?
For me, "going into science" is a process, not a yes/no decision. The process started when I enrolled for a Bachelor's degree in 2012 and then for a Master's degree in 2015, both with a focus on research. At the end of my Master's degree in 2017, I realized that I wanted to dive deeper and more independently into cognitive neuroscience research. Now, after defending my dissertation in 2022, I am continuing to work as a postdoc researcher. Even though my decisions so far have paved the way into science, I am keeping all my options open for the future. It's important for me to stay curious and find a place where I can pursue my interests and use my skills profitably. Whether in science or elsewhere, plays a subordinate role. I would advise my younger self to stick to this principle: Find places, colleagues, and tasks that are fulfilling. Whether in science or elsewhere.
What do you appreciate about the Max Planck Community?
Over the last 6 years at the Max Planck Institute for Human Development, I have come to appreciate my colleagues in particular. I like the fact that we support each other unconditionally and that there is no "elbow society". It is a great enrichment that people from so many different disciplines come together here at the Institute. This regularly leads to very exciting exchanges. I am also aware of the privilege of being able to pursue my research with a lot of resources and without teaching commitments!