The consequences of AI training on human decision-making.

AI is now an integral part of everyday decision-making, assisting us in both routine and high-stakes choices. These AI models often learn from human behavior, assuming this training data is unbiased. However, we report five studies that show that people change their behavior to instill desired routines into AI, indicating this assumption is invalid. To show this behavioral shift, we recruited participants to play the ultimatum game, where they were asked to decide whether to accept proposals of monetary splits made by either other human participants or AI. Some participants were informed their choices would be used to train an AI proposer, while others did not receive this information. Across five experiments, we found that people modified their behavior to train AI to make fair proposals, regardless of whether they could directly benefit from the AI training. After completing this task once, participants were invited to complete this task again but were told their responses would not be used for AI training. People who had previously trained AI persisted with this behavioral shift, indicating that the new behavioral routine had become habitual. This work demonstrates that using human behavior as training data has more consequences than previously thought since it can engender AI to perpetuate human biases and cause people to form habits that deviate from how they would normally act. Therefore, this work underscores a problem for AI algorithms that aim to learn unbiased representations of human preferences.

Humans do not avoid reactively implementing cognitive control.

The ability to exert cognitive control allows us to achieve goals in the face of distraction and competing actions. However, control is costly-people generally aim to minimize its demands. Because control takes many forms, it is important to understand whether such costs apply universally. Specifically, reactive control, which is recruited in response to stimulus or contextual features, is theorized to be deployed automatically, and not depend on attentional resources. Here, we investigated whether people avoided implementing reactive control in three experiments. In all, participants performed a Stroop task in which certain items were mostly incongruent (MI), that is, associated with a high likelihood of conflict (triggering a focused control setting). Other items were mostly congruent, that is, associated with a low likelihood of conflict (triggering a relaxed control setting). Experiment 1 demonstrated that these control settings transfer to a subsequent unbiased transfer phase. In Experiments 2-3, we used a demand selection task to investigate whether people would avoid choice options that yielded items that were previously MI. In all, participants continued to retrieve focused control settings for previously MI items, but they did not avoid them in the demand selection task. Critically, we only found demand avoidance when there was an objective difference in demand between options. These findings are consistent with the idea that implementing reactive control does not register as costly. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Evidence for shallow cognitive maps in schizophrenia.

Individuals with schizophrenia can have marked deficits in goal-directed decision making. Prominent theories differ in whether schizophrenia (SZ) affects the ability to exert cognitive control, or the motivation to exert control. An alternative explanation is that schizophrenia negatively impacts the formation of cognitive maps, the internal representations of the way the world is structured, necessary for the formation of effective action plans. That is, deficits in decision-making could also arise when goal-directed control and motivation are intact, but used to plan over ill-formed maps. Here, we test the hypothesis that individuals with SZ are impaired in the construction of cognitive maps. We combine a behavioral representational similarity analysis technique with a sequential decision-making task. This enables us to examine how relationships between choice options change when individuals with SZ and healthy age-matched controls build a cognitive map of the task structure. Our results indicate that SZ affects how people represent the structure of the task, focusing more on simpler visual features and less on abstract, higher-order, planning-relevant features. At the same time, we find that SZ were able to display similar performance on this task compared to controls, emphasizing the need for a distinction between cognitive map formation and changes in goal-directed control in understanding cognitive deficits in schizophrenia.