Boundary extension is constrained by naturalistic image properties

Boundary extension (BE) is a classical memory illusion in which observers remember more of a scene than was presented. According to predictive accounts, BE reflects the integration of visual input and expectations of what is beyond the boundaries of a scene. Alternatively, according to normalization accounts, BE reflects one end of a normalization process towards the typically-experienced viewing distance of a scene, such that BE and boundary contraction (BC) are equally common. Here, we show that BE and BC depend on depth-of-field (DOF), as determined by the aperture settings on a camera. Photographs with naturalistic DOF led to the strongest BE across a large stimulus set, while BC was primarily observed for unnaturalistic DOF. The relationship between DOF and BE was confirmed in three controlled experiments that isolated DOF from co-varying factors. In line with predictive accounts, we propose that BE is strongest for scene images that resemble day-to-day visual experience.

RUBubbles as a novel tool to study categorization learning

Grouping objects into discrete categories affects how we perceive the world and represents a crucial element of cognition. Categorization is a widespread phenomenon that has been thoroughly studied. However, investigating categorization learning poses several requirements on the stimulus set in order to control which stimulus feature is used and to prevent mere stimulus–response associations or rote learning. Previous studies have used a wide variety of both naturalistic and artificial categories, the latter having several advantages such as better control and more direct manipulation of stimulus features. We developed a novel stimulus type to study categorization learning, which allows a high degree of customization at low computational costs and can thus be used to generate large stimulus sets very quickly. ‘RUBubbles’ are designed as visual artificial category stimuli that consist of an arbitrary number of colored spheres arranged in 3D space. They are generated using custom MATLAB code in which several stimulus parameters can be adjusted and controlled separately, such as number of spheres, position in 3D-space, sphere size, and color. Various algorithms for RUBubble generation can be combined with distinct behavioral training protocols to investigate different characteristics and strategies of categorization learning, such as prototype- vs. exemplar-based learning, different abstraction levels, or the categorization of a sensory continuum and category exceptions. All necessary MATLAB code is freely available as open-source code and can be customized or expanded depending on individual needs. RUBubble stimuli can be controlled purely programmatically or via a graphical user interface without MATLAB license or programming experience. Graphical abstract

THINGS-EEG: Human electroencephalography recordings for 1,854 concepts presented in rapid serial visual presentation streams

The neural basis of object recognition and semantic knowledge have been the focus of a large body of research but given the high dimensionality of object space, it is challenging to develop an overarching theory on how brain organises object knowledge. To help understand how the brain allows us to recognise, categorise, and represent objects and object categories, there is a growing interest in using large-scale image databases for neuroimaging experiments. Traditional image databases are based on manually selected object concepts and often single images per concept. In contrast, 'big data' stimulus sets typically consist of images that can vary significantly in quality and may be biased in content. To address this issue, recent work developed THINGS: a large stimulus set of 1,854 object concepts and 26,107 associated images. In the current paper, we present THINGS-EEG, a dataset containing human electroencephalography responses from 50 subjects to all concepts and 22,248 images in the THINGS stimulus set. The THINGS-EEG dataset provides neuroimaging recordings to a systematic collection of objects and concepts and can therefore support a wide array of research to understand visual object processing in the human brain.

Asymmetries in Accessing Vowel Representations Are Driven by Phonological and Acoustic Properties: Neural and Behavioral Evidence From Natural German Minimal Pairs

In vowel discrimination, commonly found discrimination patterns are directional asymmetries where discrimination is faster (or easier) if differing vowels are presented in a certain sequence compared to the reversed sequence. Different models of speech sound processing try to account for these asymmetries based on either phonetic or phonological properties. In this study, we tested and compared two of those often-discussed models, namely the Featurally Underspecified Lexicon (FUL) model (Lahiri and Reetz, 2002) and the Natural Referent Vowel (NRV) framework (Polka and Bohn, 2011). While most studies presented isolated vowels, we investigated a large stimulus set of German vowels in a more naturalistic setting within minimal pairs. We conducted an mismatch negativity (MMN) study in a passive and a reaction time study in an active oddball paradigm. In both data sets, we found directional asymmetries that can be explained by either phonological or phonetic theories. While behaviorally, the vowel discrimination was based on phonological properties, both tested models failed to explain the found neural patterns comprehensively. Therefore, we additionally examined the influence of a variety of articulatory, acoustical, and lexical factors (e.g., formant structure, intensity, duration, and frequency of occurrence) but also the influence of factors beyond the well-known (perceived loudness of vowels, degree of openness) in depth via multiple regression analyses. The analyses revealed that the perceptual factor of perceived loudness has a greater impact than considered in the literature and should be taken stronger into consideration when analyzing preattentive natural vowel processing.

Two electrical engineers, one problem, and evolution produced the same solution: A historical note

This note adds historical context into solving the problem of improving the speed of the step response of a low-order plant in two different types of control systems, a chemical mixing system and the human saccadic system. Two electrical engineers studied the above problem: one to understand and model how nature and evolution solved it and the other to design a control system to solve it in a man-made commercial system. David A. Robinson discovered that fast and accurate saccades were produced by a pulse-step of neural innervation applied to the extraocular plant. Leonidas M. Mantgiaris invented a method to achieve rapid and accurate chemical mixing by applying a large stimulus for a short period of time and then replacing it with the desired steady-state value (i.e., a “pulse-step” input). Thus, two humans used their brains to: 1) determine how the human brain produced human saccades; and 2) invent a control-system method to produce fast and accurate chemical mixing. That the second person came up with the same method by which his own brain was making saccades may shed light on the question of whether the human brain can fully understand itself.

An EZ-Diffusion Model Analysis of Attentional Ability in Patients With Retinal Pigmentosa

Retinitis pigmentosa (RP) is characterized by visual acuity decrease and visual field loss. However, the impact of visual field loss on the cognitive performance of RP patients remains unknown. In the present study, in order to understand whether and how RP affects spatial processing and attentional function, one spatial processing task and three attentional tasks were conducted on RP patients and healthy controls. In addition, an EZ-diffusion model was performed for further data analysis with four parameters, mean decision time, non-decision time, drift rate, and boundary separation. It was found that in the spatial processing task, compared with the control group, the RP group exhibited a slower response speed in large and medium visual eccentricities, and slower drift rate for the large stimulus, which is strongly verified by the significant linear correlation between the visual field eccentricity with both reaction time (p = 0.047) and non-decision time (p = 0.043) in RP patients. In the attentional orienting task and the attentional switching task, RP exerted a reduction of speed and an increase of non-decision time on every condition, with a decrease of drift rate in the orienting task and boundary separation in the switching task. In addition, the switching cost for large stimulus was observed in the control group but not in the RP group. The stop-signal task demonstrated similar inhibition function between the two groups. These findings implied that RP exerted the impairment of spatial cognition correlated with the visual field eccentricity, mainly in the peripheral visual field. Moreover, specific to the peripheral visual field, RP patients had deficits in the attentional orienting and flexibility but not in the attentional inhibition.

Q-SpAM: How to Efficiently Measure Similarity in Online Research

Measuring the similarity of stimuli is of great interest to a variety of social scientists. Spatial arrangement by dragging and dropping “more similar” targets closer together on the computer screen is a precise and efficient method to measure stimulus similarity. We present Qualtrics-spatial arrangement method (Q-SpAM), a feature-rich and user-friendly online version of spatial arrangement. Combined with crowdsourcing platforms, Q-SpAM provides fast and affordable access to similarity data even for large stimulus sets. Participants may spatially arrange up to 100 words or images, randomly selected targets, self-selected targets, self-generated targets, and targets self-marked in different colors. These and other Q-SpAM features can be combined. We exemplify how to collect, process, and visualize similarity data with Q-SpAM and provide R and Excel scripts to do so. We then illustrate Q-SpAM’s versatility for social science, concluding that Q-SpAM is a reliable and valid method to measure the similarity of lots of stimuli with little effort.