Workingmemory load and temporal myopia in dynamic decision making

We examined the role of working memory (WM) in dynamic decision making by having participants perform decision-making tasks under single-task or dual-task conditions. In 2 experiments participants performed dynamic decision-making tasks in which they chose 1 of 2 options on each trial. The decreasing option always gave a larger immediate reward but caused future rewards for both options to decrease. The increasing option always gave a smaller immediate reward but caused future rewards for both options to increase. In each experiment we manipulated the reward structure such that the decreasing option was the optimal choice in 1 condition and the increasing option was the optimal choice in the other condition. Behavioral results indicated that dual-task participants selected the immediately rewarding decreasing option more often, and single-task participants selected the increasing option more often, regardless of which option was optimal. Thus, dual-task participants performed worse on 1 type of task but better on the other type. Modeling results showed that single-task participants ’ data were most often best fit by a win-stay, lose-shift (WSLS) rule-based model that tracked differences across trials, and dual-task participants ’ data were most often best fit by a Softmax reinforcement learning model that tracked recency-weighted average rewards for each option. This suggests that manipulating WM load affects the degree to which participants focus on the immediate versus delayed consequences of thei