The human brain is equipped with the ability to plan ahead, i.e. to mentally simulate the expected consequences of candidate actions to select the one with the most desirable expected long-term outcome. Insufficient planning can lead to maladaptive behaviour and may even be a contributory cause of important societal problems such as the depletion of natural resources or man-made climate change. Understanding the cognitive and neural mechanisms of forward planning and its regulation are therefore of great importance and could ultimately give us clues on how to better align our behaviour with long-term goals.
Apart from its potential beneficial effects, planning is time-consuming and therefore associated with opportunity costs. It is assumed that the brain regulates the investment into planning based on a cost-benefit analysis, so that planning only takes place when the perceived benefits outweigh the costs. But how can the brain know in advance how beneficial or costly planning will be? One potential solution is that people learn from experience how valuable planning would be in a given situation. It is however largely unknown how the brain implements such learning, especially in environments with large state spaces.
This dissertation tested the hypothesis that humans construct and use so-called control contexts to efficiently adjust the degree of planning to the demands of the current situation. Control contexts can be seen as abstract state representations, that conveniently cluster together situations with a similar demand for planning. Inferring context thus allows to prospectively adjust the control system to the learned demands of the global context. To test the control context hypothesis, two complex sequential decision making tasks were developed. Each of the two tasks had to fulfil two important criteria. First, the tasks should generate both situations in which planning had the potential to improve performance, as well as situations in which a simple strategy was sufficient. Second, the tasks had to feature rich state spaces requiring participants to compress their state representation for efficient regulation of planning. Participants’ planning was modelled using a parametrized dynamic programming solution to a Markov Decision Process, with parameters estimated via hierarchical Bayesian inference.
The first study used a 15-step task in which participants had to make a series of decisions to achieve one or multiple goals. In this task, the computational costs of accurate forward planning increased exponentially with the length of the planning horizon. We therefore hypothesized that participants identify ‘distance from goal’ as the relevant contextual feature to guide their regulation of forward planning. As expected we found that participants predominantly relied on a simple heuristic when still far from the goal but progressively switched towards forward planning when the goal approached.
In the second study participants had to sustainably invest a limited but replenishable energy resource, that was needed to accept offers, in order to accumulate a maximum number of points in the long run. The demand for planning varied across the different situations of the task, but due to the large number of possible situations (n = 448) it would be difficult for the participants to develop an expectation for each individual situation of how beneficial planning would be. We therefore hypothesized, that to regulate their forward planning participants used a compressed tasks representation, clustering together states with similar demands for planning. Consistent with this, reaction times (operationalising planning duration) increased with trial-by-trial value-conflict (operationalising approximate planning demand), but this increase was more pronounced in a context with generally high demand for planning. We further found that fMRI activity in the dorsal anterior cingulate cortex (dACC) increased with conflict, but this increase was more pronounced in a context with generally high demand for planning as well. Taken together, the results suggest that the dACC integrates representations of planning demand on different levels of abstraction to regulate prospective information sampling in an efficient and situation-appropriate way.
This dissertation provides novel insights into the question how humans adapt their planning to the demands of the current situation. The results are consistent with the view that the regulation of planning is based on an integrated signal of the expected costs and benefits of planning. Furthermore, the results of this dissertation provide evidence that the regulation of planning in environments with real-world complexity critically relies on the brain’s powerful ability to construct and use abstract hierarchical representations.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:78618 |
| Date | 29 March 2022 |
| Creators | Ott, Florian |
| Contributors | Kiebel, Stefan, Strobel, Alexander, Eppinger, Ben, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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