One of the Psychonomic Society’s journals, Attention, Perception, & Psychophysics, offers a class of articles known as “tutorial reviews”. The tutorial review mechanism is intended to serve as a high-level introductory review of relatively broad topics that fall within the domain of the journal.
Tutorial reviews may be an attractive mechanism for authors looking to integrate existing fields or extend new ideas based on previous literature and we would like to encourage submissions along these lines.
The length tends to be “moderate”, but the bibliography extensive. Though these reviews are sometimes invited, self-nominations for tutorial review topics is encouraged. Send a brief email to me, the Editor of Attention, Perception, & Psychophysics (Michael Dodd: mdodd2@unl.edu), describing the proposed review and providing a bare outline.
To enhance the profile of tutorial reviews, our most recently published review, by Caitlin Sisk, Roger Remington, and Yuhong Jiang which discusses “mechanisms of contextual cueing”, has been made freely available to the public for the next 8 weeks. You can find the temporarily open-access article here:
Sisk, Remington, & Jiang: Mechanisms of contextual cueing: A tutorial review.
Abstract: Repeated contexts yield faster response time in visual search, compared with novel contexts. This effect is known as contextual cueing. Despite extensive study over the past two decades, there remains a spirited debate over whether repeated displays expedite search before the target is found (early locus) or facilitate response after the target is found (late locus). Here, we provide a tutorial review of contextual cueing, with a focus on assessing the locus of the effect. We evaluate the evidence from psychophysics, EEG, and eye tracking. Existing studies support an early locus of contextual cueing, consistent with attentional guidance accounts. Evidence for a late locus exists, though it is less conclusive. Existing literature also highlights a distinction between habit-guided attention learned through experience and changes in spatial priority driven by task goals and stimulus salience.
Other recent reviews have covered topics as diverse as conditional control in visual selection, using transcranial direct-current stimulation (tDCS) to understand cognitive processing, visual working-memory and visual long-term memory, detecting outliers in statistical data, and layer and framework theories of lightness. Links and abstracts for a selection of these appear below:
Van Zoest, Van der Stigchel, & Donk: Conditional control in visual selection.
Abstract: Attention and eye movements provide a window into the selective processing of visual information. Evidence suggests that selection is influenced by various factors and is not always under the strategic control of the observer. The aims of this tutorial review are to give a brief introduction to eye movements and attention and to outline the conditions that help determine control. Evidence suggests that the ability to establish control depends on the complexity of the display as well as the point in time at which selection occurs. Stimulus-driven selection is more probable in simple displays than in complex natural scenes, but it critically depends on the timing of the response: Salience determines selection only when responses are triggered quickly following display presentation, and plays no role in longer-latency responses. The time course of selection is also important for the relationship between attention and eye movements. Specifically, attention and eye movements appear to act independently when oculomotor selection is quick, whereas attentional processes are able to influence oculomotor control when saccades are triggered only later in time. This relationship may also be modulated by whether the eye movement is controlled in a voluntary or an involuntary manner. To conclude, we present evidence that shows that visual control is limited in flexibility and that the mechanisms of selection are constrained by context and time. The outcome of visual selection changes with the situational context, and knowing the constraints of control is necessary to understanding when and how visual selection is truly controlled by the observer.
Reinhart, Cosman, Fukuda, & Woodman: Using transcranial direct-current stimulation (tDCS) to understand cognitive processing.
Abstract: Noninvasive brain stimulation methods are becoming increasingly common tools in the kit of the cognitive scientist. In particular, transcranial direct-current stimulation (tDCS) is showing great promise as a tool to causally manipulate the brain and understand how information is processed. The popularity of this method of brain stimulation is based on the fact that it is safe, inexpensive, its effects are long lasting, and you can increase the likelihood that neurons will fire near one electrode and decrease the likelihood that neurons will fire near another. However, this method of manipulating the brain to draw causal inferences is not without complication. Because tDCS methods continue to be refined and are not yet standardized, there are reports in the literature that show some striking inconsistencies. Primary among the complications of the technique is that the tDCS method uses two or more electrodes to pass current and all of these electrodes will have effects on the tissue underneath them. In this tutorial, we will share what we have learned about using tDCS to manipulate how the brain perceives, attends, remembers, and responds to information from our environment. Our goal is to provide a starting point for new users of tDCS and spur discussion of the standardization of methods to enhance replicability.
Jones: A note on detecting statistical outliers in psychophysical data.
Abstract: This paper considers how to identify statistical outliers in psychophysical datasets where the underlying sampling distributions are unknown. Eight methods are described, and each is evaluated using Monte Carlo simulations of a typical psychophysical experiment. The best method is shown to be one based on a measure of spread known as Sn. This is shown to be more sensitive than popular heuristics based on standard deviations from the mean, and more robust than non-parametric methods based on percentiles or interquartile range. Matlab code for computing Sn is included.