Why are the batteries in the microwave?: Use of semantic information under uncertainty in a search task


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Description: A major problem in human cognition is to understand how newly-acquired information combines with long-standing beliefs about the environment to make decisions and plan behaviors. While considerable attention has been given to studying how beliefs derived from past experience are updated with new evidence, less is known about how new evidence can modify expectations derived from long-standing beliefs about the environment. Over-dependence on such beliefs may be a significant source of suboptimal decision-making in unusual circumstances. The present study examined the role of long-standing semantic beliefs during active search through visual scenes. Target locations were either congruent with scene semantics, incongruent with what would be expected from scene semantics, or random. Target locations in the incongruent condition could either be learned or predicted from unlikely target locations in the congruent condition. The ability to adjust expectations to the task environment was predicted using a Bayesian ideal searcher, and the performance of subjects was compared to ideal searchers. Half of the subjects were able to learn to search for the target in incongruent target locations over repeated experimental sessions when it was optimal to do so. These results suggest that searchers can learn to prioritize recent experience over knowledge of scenes when it is beneficial to do so, but there are also limits on our ability to trade-off between use of recent experience and long-established knowledge to accomplish task goals.


Search Task Demo You can view a demo of the search task (Experiment 2) here: dr-gwen.github.io/sassi-search Data All data files are tab-delimited text files. SASSI_ratings_data.txt Contains behavioral data from Experiment 1 in which we empirically derived object location probabilities for the two room scenes (manuscript Figure 2). SASSI_congruent_probabilities.txt Probabilities for object loca...


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