Spatial attention shapes task representations.

(a) Schematic of the maze navigation task. Participants fixated at the start of each trial, after which a maze was presented, which they were asked to navigate. Maze stimuli either remained on the screen during navigation (left panel; concurrent planning experiments) or were removed before navigation (right panel; upfront planning experiments). Once participants finished navigating the maze, they were asked to report their awareness of every obstacle presented on a given trial in a random order. (b) Left panel: schematic of the analysis pipeline. An example maze is shown where seven obstacles (plotted in orange) are presented on every trial according to pre-defined mazes. Participants report their awareness of every obstacle at the end of each trial (middle maze). The VGC model predicts which obstacles in a maze will likely be included in participants’ task representation (right maze). We use participants’ awareness reports to test the influence of neighbouring obstacles on the probe obstacle (presented in green). We compute the influence of neighbouring obstacles (in grey) on participants’ awareness of the probed obstacle (in green). Right panel: Results of the ranked regression model for dataset Ho 1. We observed that obstacles closest to the probed item (rank 1 & 2) positively impact awareness reports. In contrast, obstacles furthest from the probed item negatively impact awareness reports (rank 5 & 6). (c) Left panel: The effect of neighbouring obstacles on task representations varied across participants (each represented by a point). Right Panel: Inter-individual differences in the attentional effects correlate with the sparsity of participants’ representations. Participants who showed the greatest influence of neighbouring obstacles (more negative slopes), showed the simplest representations (greatest variance in awareness reports).

Lateralization of task-relevant information affects task representations.

(a) For each maze, we computed a vertical meridian lateralization index. This index reflects whether task-relevant information is lateralized to a hemifield. In the example plotted, there is more task-relevant information presented on the left than on the right of the maze, therefore this would correspond to a moderate level of vertical meridian (i.e., left vs right) lateralization. We similarly computed an attention index for the horizontal meridian (i.e., above vs below). (b) Density plots of the reported awareness of obstacles on the basis of whether the value-guided construal (VGC) model predicted them to be task-relevant (≥0.5; in orange) or task-irrelevant (< 0.5; in grey). Note sVGC model predictions for each obstacle were binarized for visualization purposes only. Participants were more likely to be aware of obstacles predicted as task-relevant. We split maze stimuli based into terciles based on the degree to which task-relevant information was presented preferentially to one hemifield (x-axis). The leftmost plots are mazes where task-relevant information is presented on both hemifields. In contrast, the rightmost plot depicts mazes with the largest lateralization. We observed that the awareness reports of participants become increasingly aligned to the VGC model’s predictions as lateralization increases. (c) Scatter plot of the effect of maze lateralization on the relationship between the value-guided model and participants’ awareness of obstacles. We observed a significant vertical meridian lateralization effect whereby participants’ awareness reports were more strongly aligned with the VGC model’s predictions when task-relevant information was presented unilaterally in all datasets. Each point represents an obstacle in a maze, and each line represents the model fit for that specific maze stimulus.

Inter-individual variation in lateralization of awareness.

(a) For each maze and participant, we computed an awareness lateralization index (ALI). This index reflects the degree to which participants tended to pay attention to obstacles in a single hemifield. In the example plotted, the participant preferentially paid attention to the obstacles presented to the left hemifield regardless of whether they were taskrelevant or task-irrelevant. Note that this lateralization index is based on participants’ selfreports, unlike the lateralization index presented in Figure 2 which is based on the sVGC model predictions. (b) Histogram of the ALI of participants across the Ho 1 & 2 datasets. In these experiments, some participants showed substantial lateralization of awareness (ALI > 0.5), despite the maze stimuli for these experiments being—on average—non-lateralized in their sVGC model predictions. (c) Histogram of the ALI of participants for maze stimuli with non-lateralized VGC model predictions. We plot ALI values separately for the original non-lateralized mazes, and the left-right reversed (flipped) mazes separately. Participants on average did not lateralize their awareness. We note, however, that on some trials participants’ awareness reports were strongly lateralized, which contrasts with the sVGC model predictions. (d) Scatter plot of participants’ tendency to lateralize their attention to either hemifield (i.e., absolute value of ALI). We plot this for mazes with left and right lateralized model predictions (left panel) and for mazes with non-lateralized and lateralized model predictions (right panel). The large linear relationships indicate that participants’ tendency to lateralize their awareness is a stable inter-individual difference.

A VGC model augmented with an attentional spotlight model predicts participants’ task representations.

(a) Schematic of the attentional spotlight model. Inspired by the spotlight of attention analogy, we recompute an obstacle’s probability of being included in a task representation as the weighted average of its neighbours. We first search for all neighbours of obstaclei that are w squares away. We then compute P(Obstaclei) as the weighted average of obstaclei and its neighbours. This generates more graded model predictions (far right panel). (b) Left panel: Each row represents a different example maze stimulus. The left column depicts the original VGC model prediction P(Obstaclei) for every obstacle in the example maze. The middle column shows the attentional-spotlight model prediction for every obstacle. Obstacles that were considered task-relevant (deep orange) in the original model become less important when surrounded by task-irrelevant information (grey obstacles). The right column shows the participants’ average awareness of each obstacle in the example mazes. Right panel: Scatter plot of the linear relationship between participants’ awareness reports of obstacles and model predictions (original-VGC in green and the spotlight-VGC model in orange) for dataset Ho 1. The latter fits participants’ reports better than the original VGC model. (c) Scatter plot of the linear relationship between participants’ awareness reports of obstacles and model predictions (original VGC in green and the spotlight-VGC model in orange) for dataset dSC 1 separately for non-lateralized (left panel) and lateralized mazes (right panel). Although both models fit participants’ awareness reports better for lateralized mazes, the advantage of the spotlight model over the original model (better model fit / lower BIC) was observed only in non-lateralized mazes.