No relevant differences in conditioned pain modulation effects between parallel and sequential test design. A cross-sectional observational study

Background Conditioned pain modulation (CPM) is measured by comparing pain induced by a test stimulus with pain induced by the same test stimulus, either during (parallel design) or after (sequential design) the conditioning stimulus. Whether design, conditioning stimulus intensity and test stimulus selection affect CPM remains unclear. Methods CPM effects were evaluated in healthy participants (N = 89) at the neck, forearm and lower leg using the cold pressor test as the conditioning stimulus. In three separate experiments, we compared the impact of (1) design (sequential versus parallel), (2) conditioning stimulus intensity (VAS 40/100 versus VAS 60/100), and (3) test stimulus selection (single versus dual, i.e., mechanical and thermal). Statistical analyses of the main effect of design (adjusted for order) and experiment were conducted using linear mixed models with random intercepts. Results No significant differences were identified in absolute CPM data. In relative CPM data, a sequential design resulted in a slightly lower CPM effect compared to a parallel design, and only with a mechanical test stimulus at the neck (−6.1%; 95% CI [−10.1 to −2.1]) and lower leg (−5.9%; 95% CI [−11.7 to −0.1]) but not forearm (−4.5%; 95% CI [−9.0 to 0.1]). Conditioning stimulus intensity and test stimulus selection did not influence the CPM effect nor the difference in CPM effects derived from parallel versus sequential designs. Conclusions Differences in CPM effects between protocols were minimal or absent. A parallel design may lead to a minimally higher relative CPM effect when using a mechanical test stimulus. The conditioning stimulus intensities assessed in this study and performing two test stimuli did not substantially influence the differences between designs nor the magnitude of the CPM effect.

Personalised Prediction of Self-Reported Emotion Responses to Music Stimuli

<p>In this thesis I develop a robust system and method for predicting individuals’ emotional responses to musical stimuli. Music has a powerful effect on human emotion, however the factors that create this emotional experience are poorly understood. Some of these factors are characteristics of the music itself, for example musical tempo, mode, harmony, and timbre are known to affect people's emotional responses. However, the same piece of music can produce different emotional responses in different people, so the ability to use music to induce emotion also depends on predicting the effect of individual differences. These individual differences might include factors such as people's moods, personalities, culture, and musical background amongst others. While many of the factors that contribute to emotional experience have been examined, it is understood that the research in this domain is far from both a) identifying and understanding the many factors that affect an individual’s emotional response to music, and b) using this understanding of factors to inform the selection of stimuli for emotion induction. This unfortunately results in wide variance in emotion induction results, inability to replicate emotional studies, and the inability to control for variables in research. The approach of this thesis is to therefore model the latent variable contributions to an individual’s emotional experience of music through the application of deep learning and modern recommender system techniques. With each study in this work, I iteratively develop a more reliable and effective system for predicting personalised emotion responses to music, while simultaneously adopting and developing strong and standardised methodology for stimulus selection. The work sees the introduction and validation of a) electronic and loop-based music as reliable stimuli for inducing emotional responses, b) modern recommender systems and deep learning as methods of more reliably predicting individuals' emotion responses, and c) novel understandings of how musical features map to individuals' emotional responses. The culmination of this research is the development of a personalised emotion prediction system that can better predict individuals emotional responses to music, and can select musical stimuli that are better catered to individual difference. This will allow researchers and practitioners to both more reliably and effectively a) select music stimuli for emotion induction, and b) induce and manipulate target emotional responses in individuals.</p>

Personalised Prediction of Self-Reported Emotion Responses to Music Stimuli

<p>In this thesis I develop a robust system and method for predicting individuals’ emotional responses to musical stimuli. Music has a powerful effect on human emotion, however the factors that create this emotional experience are poorly understood. Some of these factors are characteristics of the music itself, for example musical tempo, mode, harmony, and timbre are known to affect people's emotional responses. However, the same piece of music can produce different emotional responses in different people, so the ability to use music to induce emotion also depends on predicting the effect of individual differences. These individual differences might include factors such as people's moods, personalities, culture, and musical background amongst others. While many of the factors that contribute to emotional experience have been examined, it is understood that the research in this domain is far from both a) identifying and understanding the many factors that affect an individual’s emotional response to music, and b) using this understanding of factors to inform the selection of stimuli for emotion induction. This unfortunately results in wide variance in emotion induction results, inability to replicate emotional studies, and the inability to control for variables in research. The approach of this thesis is to therefore model the latent variable contributions to an individual’s emotional experience of music through the application of deep learning and modern recommender system techniques. With each study in this work, I iteratively develop a more reliable and effective system for predicting personalised emotion responses to music, while simultaneously adopting and developing strong and standardised methodology for stimulus selection. The work sees the introduction and validation of a) electronic and loop-based music as reliable stimuli for inducing emotional responses, b) modern recommender systems and deep learning as methods of more reliably predicting individuals' emotion responses, and c) novel understandings of how musical features map to individuals' emotional responses. The culmination of this research is the development of a personalised emotion prediction system that can better predict individuals emotional responses to music, and can select musical stimuli that are better catered to individual difference. This will allow researchers and practitioners to both more reliably and effectively a) select music stimuli for emotion induction, and b) induce and manipulate target emotional responses in individuals.</p>

Adaptive Rewiring in Non-Uniform Coupled Oscillators

Structural plasticity of the brain can be represented in a highly simplified form as adaptive rewiring, the relay of connections according to the spontaneous dynamic synchronization in network activity. Adaptive rewiring, over time, leads from initial random networks to brain-like complex networks, i.e., networks with modular small-world structures and a rich-club effect. Adaptive rewiring has only been studied, however, in networks of identical oscillators with uniform or random coupling strengths. To implement information processing functions (e.g., stimulus selection or memory storage), it is necessary to consider symmetry-breaking perturbations of oscillator amplitudes and coupling strengths. We studied whether non-uniformities in amplitude or connection strength could operate in tandem with adaptive rewiring. Throughout network evolution, either amplitude or connection strength of a subset of oscillators was kept different from the rest. In these extreme conditions, subsets might become isolated from the rest of the network or otherwise interfere with the development of network complexity. However, whereas these subsets form distinctive structural and functional communities, they generally maintain connectivity with the rest of the network and allow the development of network complexity. Pathological development was observed only in a small proportion of the models. These results suggest that adaptive rewiring can robustly operate alongside information processing in biological and artificial neural networks.

The ‘naturalistic’ fallacy: Leveraging the power of media to drive cognition

So-called “naturalistic” stimuli have risen in popularity in cognitive, social, and affective psychology and neuroscience over the last 15 years. However, a critical property of these stimuli is frequently overlooked: Media—like film, television, books, and podcasts—are fundamentally not natural. They are deliberately crafted products meant to elicit particular human thought, emotion, and behavior. Given the rich history of scholarship on media as an art and science, subsuming media stimuli under the term “naturalistic” in psychological and brain sciences is inaccurate and obfuscates the advantages that media stimuli offer because they are artificial. Here, we argue for a more informed approach to adopting media stimuli in naturalistic paradigms. We empirically review how researchers currently describe and justify their choice of stimuli for a given experiment and present strategies to improve rigor in the stimulus selection process. We assert that experiencing media should be considered a task akin to any other experimental task(s), and explain how this shift in perspective will compel more nuanced and generalizable research using these stimuli. Throughout, we offer theoretical and practical knowledge from multidisciplinary media research to raise the standard for the treatment of media stimuli in psychological and neuroscientific research.

Snakes vs. Guns: A Systematic Review of Comparisons Between Phylogenetic and Ontogenetic Threats

The potential differences between phylogenetic threats (e.g., snakes) and ontogenetic threats (e.g., guns) can have a wide-ranging impact on a variety of theoretical and practical issues, from etiology of specific phobias to stimulus selection in psychophysiological studies, yet this line of research has not been systematically reviewed. Here, we summarize and synthesize findings from fear conditioning, illusory correlation, attention bias and neuroimaging studies that have compared these two types of threats to human survival. While a few brain imaging studies reveals preliminary evidence for different brain networks involved in the processing of phylogenetic and ontogenetic threats, attention bias studies tentatively show faster reaction time for modern threats, illusory correlation bias is evident for both types of threats, and fear conditioning studies are far from conclusive. The results of behavioral experiments, especially attention bias research, pose a challenge to established theories like biological preparedness and fear module. We discuss the findings in terms of other theories that might explain the same results and conclude with potential future directions.

APPLYING CONSUMER BEHAVIOR MODEL TO THE STUFF LABOR MOTIVATION

The article appeals to the problem of designing motivation model for the labor behavior of company stuff. Human behavior is one of the key areas of research in many fields of knowledge. The main forms of human behavior in economics are consumer and labor behavior. For each of them, extensive theoretical and practical material has been developed, a significant variety of behaviors has been proposed. Moreover, in the absence of general models of human behavior that would be applicable in any field of his activity, each science develops its own methodological apparatus and builds models based on its own approaches. Models of consumer behavior describe a clearly defined object (purchasing act), are specific and practically oriented. Patterns of labour behaviour are more general and relate to conduct in general, but not to a specific act of activity. The article attempts to apply the principles of building models of consumer behavior to modeling labor behavior. The model of type “Definition of target actions — Stimulus selection — Information and desire — Choice and location — Check and preference — Confirmation and relation” is proposed. The content of these stages for the task of changing labor behavior is shown. A methodological feature of the model is the isolation of rational and emotional aspects in some elements of labor behavior.