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Pain is one of the most common health problems and has a severe impact on quality of life. Yet, a suitable and efficient treatment is still not available for all patient populations suffering from pain. Interestingly, recent research shows that low threshold mechanosensory C-tactile (CT) fibres have a modulatory influence on pain. CT-fibres are activated by slow gentle stroking of the hairy skin, providing a pleasant sensation. Consequently, slow gentle stroking is known as affective touch. Currently, a clear overview of the way affective touch modulates pain, at a neural level, is missing. This review aims to present such an overview. To explain the interaction between affective touch and pain, first the neural basis of the affective touch system and the neural processing of pain will be described. To clarify these systems, a schematic illustration will be provided in every section. Hereafter, a novel model of interactions between affective touch and pain systems will be introduced. Finally, since affective touch might be suitable as a new treatment for chronic pain, possible clinical implications will be discussed.

In our daily lives, we have to process information about all kinds of quantities such as the set size of a group of items (i.e., numerosity), time and size, among others. We have previously reported specialized neurons in our brain which respond selectively to or ‘prefer’ a specific number of items such as one, two or three. These neurons are organized topographically which means that these neurons are laid out in a shape that allows those most closely related to communicate and interact over the shortest possible distance in the brain. The general research goal of this thesis was to examine the properties of numerosity-tuned neural populations, and numerosity perception as part of a generalized quantity system by investigating the possibility of shared, tuned mechanisms between numerosity and time, and other quantities and sensory modalities. We first examined whether and how the selective response or preference of these numerosity selective neurons can change based on recent sensory experience. We used the method of adaptation and ultra-high-field 7 Tesla fMRI, where participants were repeatedly shown a low or high numerosity so to adapt the numerosity-selective neural populations. Sensory adaptation, makes the appearance of subsequently presented stimuli appear more different from the adapting stimulus than they actually are. This method is a powerful tool which allows us to make inferences about the existence of specialized neurons in the brain which respond selectively to the adapting stimuli. We show the dynamic nature of numerosity selective neural populations, where neural numerosity selectivity was altered systematically in all numerosity selective brain areas. Based on findings showing brain regions which process more than one type of quantity, such as numerosity and time, we proceeded to use cross-adaptation to numerosity and time to study whether neural populations selective for numerosity or time interact. We found an unbalanced interaction between numerosity and time where adaptation to time affected numerosity perception but not the other way around. After finding this interaction between neurons selective for processing numerosity and time, further experiments showed that the neural populations underlying the effect of adaptation to time on numerosity perception are partially distinct from those underlying the effect of the time of adaptation on numerosity perception. Collectively, these results highlight that there are partially overlapping neural mechanisms which are dedicated for processing both numerosity and time. We propose that neurons which are selective or are ‘tuned’ to different quantities such as number, time or size are fundamental to understanding quantity perception. We illustrate how the properties of quantity-tuned neurons can underlie various perceptual phenomena. We further show that quantity-tuned neurons are organized in distinct but overlapping neural networks. We suggest that this overlap in tuning provides the neural basis for perceptual interactions between different quantities.

During visual search, it is important to reduce the interference of distracting objects in the scene. The neuronal responses elicited by the search target stimulus are typically enhanced. However, it is equally important to suppress the representations of distracting stimuli, especially if they are salient and capture attention. We trained monkeys to make an eye movement to a unique “pop-out” shape stimulus among an array of distracting stimuli. One of these distractors had a salient color that varied across trials and differed from the color of the other stimuli, causing it to also pop-out. The monkeys were able to select the pop-out shape target with high accuracy and actively avoided the pop-out color distractor. This behavioral pattern was reflected in the activity of neurons in area V4. Responses to the shape targets were enhanced, while the activity evoked by the pop-out color distractor was only briefly enhanced, directly followed by a sustained period of pronounced suppression. These behavioral and neuronal results demonstrate a cortical selection mechanism that rapidly inverts a pop-out signal to “pop-in” for an entire feature dimension thereby facilitating goal-directed visual search in the presence of salient distractors.

Perception of quantities, such as numerosity, timing, and size, is essential for behavior and cognition. Accumulating evidence demonstrates neurons processing quantities are tuned, that is, have a preferred quantity amount, not only for numerosity, but also other quantity dimensions and sensory modalities. We argue that quantity-tuned neurons are fundamental to understanding quantity perception. We illustrate how the properties of quantity-tuned neurons can underlie a range of perceptual phenomena. Furthermore, quantity-tuned neurons are organized in distinct but overlapping topographic maps. We suggest that this overlap in tuning provides the neural basis for perceptual interactions between different quantities, without the need for a common neural representational code.

Signal Detection Theory (SDT) is rarely used in higher education, yet has much potential in informing decision-making. In this methodological paper, we describe the potential of SDT for different higher education contexts and demonstrate its practical application. Both the commonly used regression analyses and SDT analyses provide information on the accuracy of a predictor, and thus which instrument(s) to use. SDT analyses, in addition, provide information on the effects of setting specific cut-off scores on outcomes of interest. SDT provides the sensitivity and specificity information for the chosen instrument(s) at specific cut-off scores (criteria in SDT). This allows for evidence-informed, deliberate choice of cut-off scores to steer toward desired outcomes. Depending on how undesirable false positives and false negatives are considered in a specific situation, a lower or higher cut-off score can be deemed adequate. Using SDT analyses in our example, we demonstrate how to use the results to optimize “real-life” student selection. However, selection is only one of many decision-making practices where SDT is applicable and valuable. We outline some of the areas within higher education decision-making and quality assurance, where SDT can be applied to answer specific questions and optimize decision-making.

Perceiving numerosity, i.e. the set size of a group of items, is an evolutionarily preserved ability found in humans and animals. A useful method to infer the neural underpinnings of a given perceptual property is sensory adaptation. Like other primary perceptual attributes, numerosity is susceptible to adaptation. Recently, we have shown numerosity-selective neural populations with a topographic organization in the human brain. Here, we investigated whether numerosity adaptation can affect the numerosity selectivity of these populations using ultra-high field (7 Tesla) functional magnetic resonance imaging (fMRI). Participants viewed stimuli of changing numerosity (1 to 7 dots), which allowed the mapping of numerosity selectivity. We interleaved a low or high numerosity adapter stimulus with these mapping stimuli, repeatedly presenting 1 or 20 dots respectively to adapt the numerosity-selective neural populations. We analyzed the responses using custom-build population receptive field neural models of numerosity encoding and compared estimated numerosity preferences between adaptation conditions. We replicated our previous studies where we found several topographic maps of numerosity-selective responses. We found that overall, numerosity adaptation altered the preferred numerosities within the numerosity maps, resulting in predominantly attractive biases towards the numerosity of the adapter. The differential biases could be explained by the difference between the unadapted preferred numerosity and the numerosity of the adapter, with attractive biases being observed with higher difference. The results could link perceptual numerosity adaptation effects to changes in neural numerosity selectivity.

Emotional facial expressions are important visual communication signals that indicate a sender’s intent and emotional state to an observer. As such, it is not surprising that reactions to different expressions are thought to be automatic and independent of awareness. What is surprising, is that studies show inconsistent results concerning such automatic reactions, particularly when using different face stimuli. We argue that automatic reactions to facial expressions can be better explained, and better understood, in terms of quantitative descriptions of their low-level image features rather than in terms of the emotional content (e.g. angry) of the expressions. Here, we focused on overall spatial frequency (SF) and localized Histograms of Oriented Gradients (HOG) features. We used machine learning classification to reveal the SF and HOG features that are sufficient for classification of the initial eye movement towards one out of two simultaneously presented faces. Interestingly, the identified features serve as better predictors than the emotional content of the expressions. We therefore propose that our modelling approach can further specify which visual features drive these and other behavioural effects related to emotional expressions, which can help solve the inconsistencies found in this line of research.

Emotional facial expressions are important visual communication signals that indicate a sender’s intent and emotional state to an observer. As such, it is not surprising that reactions to different expressions are thought to be automatic and independent of awareness. What is surprising, is that studies show inconsistent results concerning such automatic reactions, particularly when using different face stimuli. We argue that automatic reactions to facial expressions can be better explained, and better understood, in terms of quantitative descriptions of their low-level image features rather than in terms of the emotional content (e.g. angry) of the expressions. Here, we focused on overall spatial frequency (SF) and localized Histograms of Oriented Gradients (HOG) features. We used machine learning classification to reveal the SF and HOG features that are sufficient for classification of the initial eye movement towards one out of two simultaneously presented faces. Interestingly, the identified features serve as better predictors than the emotional content of the expressions. We therefore propose that our modelling approach can further specify which visual features drive these and other behavioural effects related to emotional expressions, which can help solve the inconsistencies found in this line of research.