The identification of the second of two targets presented in close succession is often impaired-a phenomenon referred to as the attentional blink. Extending earlier work (Di Lollo, Kawahara, Ghorashi, and Enns, in Psychological Research 69:191-200, 2005), the present study shows that increasing the number of targets in the stream can lead to remarkable improvements as long as there are no intervening distractors. In addition, items may even recover from an already induced blink whenever they are preceded by another target. It is shown that limited memory resources contribute to overall performance, but independent of the attentional blink. The findings argue against a limited-capacity account of the blink and suggest a strong role for attentional control processes that may be overzealously applied.

Measurement of pupil size (pupillometry) has recently gained renewed interest from psychologists, but there is little agreement on how pupil-size data is best analyzed. Here we focus on one aspect of pupillometric analyses: baseline correction, i.e., analyzing changes in pupil size relative to a baseline period. Baseline correction is useful in experiments that investigate the effect of some experimental manipulation on pupil size. In such experiments, baseline correction improves statistical power by taking into account random fluctuations in pupil size over time. However, we show that baseline correction can also distort data if unrealistically small pupil sizes are recorded during the baseline period, which can easily occur due to eye blinks, data loss, or other distortions. Divisive baseline correction (corrected pupil size = pupil size/baseline) is affected more strongly by such distortions than subtractive baseline correction (corrected pupil size = pupil size − baseline). We discuss the role of baseline correction as a part of preprocessing of pupillometric data, and make five recommendations: (1) before baseline correction, perform data preprocessing to mark missing and invalid data, but assume that some distortions will remain in the data; (2) use subtractive baseline correction; (3) visually compare your corrected and uncorrected data; (4) be wary of pupil-size effects that emerge faster than the latency of the pupillary response allows (within ±220 ms after the manipulation that induces the effect); and (5) remove trials on which baseline pupil size is unrealistically small (indicative of blinks and other distortions).

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The PyGaze toolbox is an open-source software package for Python, a high-level programming language. It is designed for creating eyetracking experiments in Python syntax with the least possible effort, and it offers programming ease and script readability without constraining functionality and flexibility. PyGaze can be used for visual and auditory stimulus presentation; for response collection via keyboard, mouse, joystick, and other external hardware; and for the online detection of eye movements using a custom algorithm. A wide range of eyetrackers of different brands (EyeLink, SMI, and Tobii systems) are supported. The novelty of PyGaze lies in providing an easy-to-use layer on top of the many different software libraries that are required for implementing eyetracking experiments. Essentially, PyGaze is a software bridge for eyetracking research.

Traditionally, interocular suppression is believed to disrupt high-level (i.e., semantic or conceptual) processing of the suppressed visual input. The development of a new experimental paradigm, breaking continuous flash suppression (b-CFS), has caused a resurgence of studies demonstrating high-level processing of visual information in the absence of visual awareness. In this method the time it takes for interocularly suppressed stimuli to breach the threshold of visibility, is regarded as a measure of access to awareness. The aim of the current review is twofold. First, we provide an overview of the literature using this b-CFS method, while making a distinction between two types of studies: those in which suppression durations are compared between different stimulus classes (such as upright faces versus inverted faces), and those in which suppression durations are compared for stimuli that either match or mismatch concurrently available information (such as a colored target that either matches or mismatches a color retained in working memory). Second, we aim at dissociating high-level processing from low-level (i.e., crude visual) processing of the suppressed stimuli. For this purpose, we include a thorough review of the control conditions that are used in these experiments. Additionally, we provide recommendations for proper control conditions that we deem crucial for disentangling high-level from low-level effects. Based on this review, we argue that crude visual processing suffices for explaining differences in breakthrough times reported using b-CFS. As such, we conclude that there is as yet no reason to assume that interocularly suppressed stimuli receive full semantic analysis.