When presented with two difficult tasks and limited resources, it is better to focus on one task and complete it successfully than to divide your efforts and fail on both. Although this logic seems obvious, people demonstrate a surprising failure to apply it when faced with prioritizing dilemmas. In previous research, the choice about which task to prioritise was arbitrary, because both tasks were equally difficult and had the same reward for success. In a series of three experiments, we investigated whether the equivalence of two tasks contributes to suboptimal decisions about how to prioritize them. First, we made one task more difficult than the other. Second, we compared conditions in which both tasks had to be attempted to conditions in which participants had to select one. Third, participants chose whether to place an equal or unequal reward value onto the two tasks. Each of these experiments removed or manipulated the arbitrary nature of the decision between options, with the goal of facilitating optimal decisions about whether to focus effort on one goal or divide effort over two. None of these manipulations caused participants to uniformly adopt a more optimal strategy, with the exception of trials where participants voluntarily placed more reward on one task over the other. In these, choices were modified more effectively with task difficulty than in previous experiments. However, participants were more likely to choose to distribute rewards equally than unequally. The results demonstrate that equal rewards across two tasks are preferred over unequal, even though this reward equivalence leads to poorer task strategies and smaller gains.

Here we report persistent choice variability in the presence of a simple decision rule. Two analogous choice problems are presented, both of which involve making decisions about how to prioritize goals. In one version, participants choose a place to stand to throw a beanbag into one of two hoops. In the other, they must choose a place to fixate to detect a target that could appear in one of two boxes. In both cases, participants do not know which of the locations will be the target when they make their choice. The optimal solution to both problems follows the same, simple logic: when targets are close together, standing at/fixating the midpoint is the best choice. When the targets are far apart, accuracy from the midpoint falls, and standing/fixating close to one potential target achieves better accuracy. People do not follow, or even approach, this optimal strategy, despite substantial potential benefits for performance. Two interventions were introduced to try and shift participants from sub-optimal, variable responses to following a fixed, rational rule. First, we put participants into circumstances in which the solution was obvious. After participants correctly solved the problem there, we immediately presented the slightly-less-obvious context. Second, we guided participants to make choices that followed an optimal strategy, and then removed the guidance and let them freely choose. Following both of these interventions, participants immediately returned to a variable, sub-optimal pattern of responding. The results show that while constructing and implementing rational decision rules is possible, making variable responses to choice problems is a strong and persistent default mode. Borrowing concepts from classic animal learning studies, we suggest this default may persist because choice variability can provide opportunities for reinforcement learning.

Experiments on the efficiency of human search sometimes reveal large differences between individual participants. We argue that reward-driven task-specific learning may account for some of this variation. In a computational reinforcement learning model of this process, a wide variety of strategies emerge, despite all simulated participants having the same visual acuity. We conduct a visual search experiment, and replicate previous findings that participant preferences about where to search are highly varied, with a distribution comparable to the simulated results. Thus, task-specific learning is an under-explored mechanism by which large inter-participant differences can arise.

To what extent can our eye movement be thought of as efficient, that is, targeting locations where high resolution is needed to differentiate objects? Previous research reported conflicting conclusions: people are near-optimal, random, or even counter-optimal, according to different lines of evidence. Researchers tend to use the simplest possible environments to measure search efficiency, assuming these will minimize the individual differences in experience that could introduce unnecessary variation in behaviour. Here we measured the efficiency of 30 participants as they searched through simple line segment stimuli and through a set of complex icons. We observed a dramatic shift from highly variable strategies with the line segments, to uniformly efficient search with the icons. These results demonstrate that changing what may initially appear to be irrelevant, surface-level details of the task can lead to large changes in measured behaviour, and that visual primitives are not always representative of more complex objects.

We measured the efficiency of 30 participants as they searched through simple line segment stimuli and through a set of complex icons. We observed a dramatic shift from highly variable, and mostly inefficient, strategies with the line segments, to uniformly efficient search behaviour with the icons. These results demonstrate that changing what may initially appear to be irrelevant, surface-level details of the task can lead to large changes in measured behaviour, and that visual primitives are not always representative of more complex objects.