"Blue Sky Effect": Contextual Influences on Pupil Size During Naturalistic Visual Search.

Pupil size is influenced by cognitive and non-cognitive factors. One of the strongest modulators of pupil size is scene luminance, which complicates studies of cognitive pupillometry in environments with complex patterns of visual stimulation. To help understand how dynamic visual scene statistics influence pupil size during an active visual search task in a visually rich 3D virtual environment (VE), we analyzed the correlation between pupil size and intensity changes of image pixels in the red, green, and blue (RGB) channels within a large window (~14 degrees) surrounding the gaze position over time. Overall, blue and green channels had a stronger influence on pupil size than the red channel. The correlation maps were not consistent with the hypothesis of a foveal bias for luminance, instead revealing a significant contextual effect, whereby pixels above the gaze point in the green/blue channels had a disproportionate impact on pupil size. We hypothesized this differential sensitivity of pupil responsiveness to blue light from above as a "blue sky effect," and confirmed this finding with a follow-on experiment with a controlled laboratory task. Pupillary constrictions were significantly stronger when blue was presented above fixation (paired with luminance-matched gray on bottom) compared to below fixation. This effect was specific for the blue color channel and this stimulus orientation. These results highlight the differential sensitivity of pupillary responses to scene statistics in studies or applications that involve complex visual environments and suggest blue light as a predominant factor influencing pupil size.

Dissociable mappings of tonic and phasic pupillary features onto cognitive processes involved in mental arithmetic.

Pupil size modulations have been used for decades as a window into the mind, and several pupillary features have been implicated in a variety of cognitive processes. Thus, a general challenge facing the field of pupillometry has been understanding which pupil features should be most relevant for explaining behavior in a given task domain. In the present study, a longitudinal design was employed where participants completed 8 biweekly sessions of a classic mental arithmetic task for the purposes of teasing apart the relationships between tonic/phasic pupil features (baseline, peak amplitude, peak latency) and two task-related cognitive processes including mental processing load (indexed by math question difficulty) and decision making (indexed by response times). We used multi-level modeling to account for individual variation while identifying pupil-to-behavior relationships at the single-trial and between-session levels. We show a dissociation between phasic and tonic features with peak amplitude and latency (but not baseline) driven by ongoing task-related processing, whereas baseline was driven by state-level effects that changed over a longer time period (i.e. weeks). Finally, we report a dissociation between peak amplitude and latency whereby amplitude reflected surprise and processing load, and latency reflected decision making times.

The Influence of Feedback on Task-Switching Performance: A Drift Diffusion Modeling Account.

Task-switching is an important cognitive skill that facilitates our ability to choose appropriate behavior in a varied and changing environment. Task-switching training studies have sought to improve this ability by practicing switching between multiple tasks. However, an efficacious training paradigm has been difficult to develop in part due to findings that small differences in task parameters influence switching behavior in a non-trivial manner. Here, for the first time we employ the Drift Diffusion Model (DDM) to understand the influence of feedback on task-switching and investigate how drift diffusion parameters change over the course of task switch training. We trained 316 participants on a simple task where they alternated sorting stimuli by color or by shape. Feedback differed in six different ways between subjects groups, ranging from No Feedback (NFB) to a variety of manipulations addressing trial-wise vs. Block Feedback (BFB), rewards vs. punishments, payment bonuses and different payouts depending upon the trial type (switch/non-switch). While overall performance was found to be affected by feedback, no effect of feedback was found on task-switching learning. Drift Diffusion Modeling revealed that the reductions in reaction time (RT) switch cost over the course of training were driven by a continually decreasing decision boundary. Furthermore, feedback effects on RT switch cost were also driven by differences in decision boundary, but not in drift rate. These results reveal that participants systematically modified their task-switching performance without yielding an overall gain in performance.

Uncertainty in fast task-irrelevant perceptual learning boosts learning of images in women but not men.

A key tenet of models of reinforcement learning is that learning is most desirable in the times of maximum uncertainty. Here we examine the role of uncertainty in the paradigm of fast task-irrelevant perceptual learning (fast-TIPL), where stimuli that are consistently presented at relevant points in times (e.g., with task targets or rewards) are better encoded than when presented at other times. We manipulated two forms of uncertainty, expected uncertainty and unexpected uncertainty (Yu & Dayan, 2005), and compared fast-TIPL under uncertainty with fast-TIPL under no uncertainty. Results indicate a larger fast-TIPL effect under uncertainty than under no uncertainty without a difference between expected and unexpected uncertainty. However, interestingly, this effect of uncertainty on fast-TIPL was found in women but not in men. In men, equivalent fast-TIPL was observed under no uncertainty and uncertainty, whereas in women, confirming previous results (Leclercq & Seitz, 2012b), no fast-TIPL was observed in the no-uncertainty condition, but fast-TIPL was observed in the uncertainty conditions. We discuss how these results imply differences in attention or neuromodulatory processes between men and women.