Search

Real world search behaviour often involves limb movements, either during search or following search. Here we investigated whether movement-related costs influence search behaviour in two kinds of search tasks. In our visual search tasks, participants made saccades to find a target object among distractors and then moved a cursor, controlled by the handle of a robotic manipulandum, to the target. In our manual search tasks, participants moved the cursor to perform the search, placing it onto objects to reveal their identity as either a target or a distractor. In all tasks, there were multiple targets. Across experiments, we manipulated either the effort or time costs associated with movement such that these costs varied across the search space. We varied effort by applying different resistive forces to the handle and we varied time costs by altering the speed of the cursor. Our analysis of cursor and eye movements during manual and visual search, respectively, showed that effort influenced manual search but did not influence visual search. In contrast, time costs influenced both visual and manual search. Our results demonstrate that, in addition to perceptual and cognitive factors, movement-related costs can also influence search behaviour.

It is well-established that people can factor into account the distribution of their errors in motor performance so as to optimize reward. Here we asked whether, in the context of motor learning where errors decrease across trials, people take into account their future, improved performance so as to make optimal decisions to maximize reward. One group of participants performed a virtual throwing task in which, periodically, they were given the opportunity to select from a set of smaller targets of increasing value. A second group of participants performed a reaching task under a visuomotor rotation in which, after performing a initial set of trials, they selected a reward structure (ratio of points for target hits and misses) for different exploitation horizons (i.e., numbers of trials they might be asked to perform). Because movement errors decreased exponentially across trials in both learning tasks, optimal target selection (task 1) and optimal reward structure selection (task 2) required taking into account future performance. The results from both tasks indicate that people anticipate their future motor performance so as to make decisions that will improve their expected future reward., Author summary A hallmark of motor learning is the reduction of performance errors with practice, which can have important ramifications for decision making. For example, when purchasing a new surfboard, our choice should anticipate our improvement with practice so that we select an appropriate board. In this paper we asked whether, in the context of two different motor learning tasks, people take into account their future, improved performance so as to make optimal decisions to maximize reward. In the throwing task, which involved learning the dynamics of the thrown object, participants were periodically given the opportunity to select from a set of smaller targets of increasing value. In the reaching task, which involved adapting to a visuomotor rotation, participants—after performing an initial block of trials—selected a reward structure (ratio of points for target hits and misses) for different exploitation horizons (i.e., numbers of trials they might be asked to perform). Performance errors decreased across trials in both tasks and therefore optimal target selection (task 1) and optimal reward structure selection (task 2) required taking into account future performance. In both tasks, participants anticipated their future motor performance so as to make decisions that improved their expected future reward.

Expecting a particular stimulus can facilitate processing of that stimulus over others, but what is the fate of other stimuli that are known to co-occur with the expected stimulus? This study examined the impact of learned association on feature-based attention. The findings show that the effectiveness of an uninformative color transient in orienting attention can change by learned associations between colors and the expected target shape. In an initial acquisition phase, participants learned two distinct sequences of stimulus–response–outcome, where stimuli were defined by shape (‘S’ vs. ‘H’), responses were localized key-presses (left vs. right), and outcomes were colors (red vs. green). Next, in a test phase, while expecting a target shape (80% probable), participants showed reliable attentional orienting to the color transient associated with the target shape, and showed no attentional orienting with the color associated with the alternative target shape. This bias seemed to be driven by learned association between shapes and colors, and not modulated by the response. In addition, the bias seemed to depend on observing target–color conjunctions, since encountering the two features disjunctively (without spatiotemporal overlap) did not replicate the findings. We conclude that associative learning – likely mediated by mechanisms underlying visual object representation – can extend the impact of goal-driven attention to features associated with a target stimulus.

The ideomotor theory of action posits that the cognitive representation of an action includes the learned perceptual effects of the action. Support for this theory has come from studies demonstrating how perceptual features that match the outcome of a response can facilitate selection of that response. We investigated another, complementary implication of ideomotor theory: would a bias toward selecting a response result in a perceptual bias toward the known effect of the response? In other words, would an action tendency direct attention to the anticipated perceptual features? Through an initial acquisition phase, participants learned that two possible responses (left/right keypress) consistently produced two distinct colors. Next, in a test phase, we manipulated response bias at the beginning of each trial, using an uninformative spatial prime presented at the left or right periphery. We then examined the extent to which color transients that either matched or mismatched the induced response bias can orient participants’ visual attention. Results revealed a perceptual bias toward the color effect of the primed response, manifested in a stronger visual orienting toward this color. Thus, biasing response selection can bias perception. These findings extend the scope of the ideomotor theory to visual perceptual processes.