| Summary: | This dissertation investigates two facts about language processing. The Good Enough Approach claims that language users do not form a fully detailed representation of the input unless the task at hand requires it. On the other hand it has been shown that language users display internal preferences when they are faced with ambiguous input, as to what direction disambiguation should take. It has been proposed that these preferences are based on previous experience with similar inputs. This thesis investigates these two issues using tools from the fields of decision making and reinforcement learning. Specifically feedback and payoffs associated with sentence interpretations are manipulated to explore reading behavior, understood as a process of information seeking, and disambiguation choices. In four eye-tracking-reading experiments, the experimental stimuli are sentences containing a relative clause attachment ambiguity. Experiment 1 investigates whether the combination of the degree of ambiguity of a sentence and the possible payoffs, affect people’s reading times for the potentially ambiguous parts of a sentence, as well as their disambiguation choices. Experiment 2 investigates the role of feedback in such processes, a combination related to expected utility maximization. Experiment 3 studies how participants learn from feedback under risky or non-risky conditions. The last experiment investigates whether participants adjust their responses to evidence provided by feedback even overriding their internal initial bias towards a default response. === text
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