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For many years it has been speculated that some learning and attention problems in children are related to underlying problems in neurological functioning. In fact, the IDEA (1997) definition of learning disabilities utilizes terminology that specifically includes neurological processes and conditions: Specific learning disabilities means a disorder in one or more of the basic psychological processes involved in understanding or in using language, spoken or written, which may manifest itself in an imperfect ability to listen, think, speak, read, write, spell, or do mathematical calculations. The term includes such conditions as perceptual handicaps, brain injury, minimal brain dysfunction, dyslexia, and developmental aphasia. The term does not include children who have learning problems which are primarily the result of visual, hearing, or motor handicaps, of mental retardation, or of environmental, cultural, or economic disadvantage.This chapter begins with a review of the role of neuroimaging in advancing an understanding of the basis and nature of learning and attention problems. The ever-increasing sophistication of neurodiagnostic technology has made it possible to obtain more precise information about neuroanatomical and neurophysiological bases of behavior, including learning and attention. Advances in technology have greatly increased the ability to study the functioning of the brain during the performance of relatively complex mental activities. With this advanced technology it is becoming increasingly possible to visualize normal and abnormal brain functioning, including important components of basic academic skills. The chapter includes a discussion of the recent evidence about the neurological basis of learning and attention problems.

Purpose – This experimental economics study using brain imaging techniques investigates the risk-ambiguity distinction in relation to the source preference hypothesis (Fox & Tversky, 1995) in which identically distributed risks arising from different sources of uncertainty may engender distinct preferences for the same decision maker, contrary to classical economic thinking. The use of brain imaging enables sharper testing of the implications of different models of decision-making including Chew and Sagi's (2008) axiomatization of source preference.

Methodology/approach – Using fMRI, brain activations were observed when subjects make 48 sequential binary choices among even-chance lotteries based on whether the trailing digits of a number of stock prices at market closing would be odd or even. Subsequently, subjects rate familiarity of the stock symbols.

Findings – When contrasting brain activation from more familiar sources with those from less familiar ones, regions appearing to be more active include the putamen, medial frontal cortex, and superior temporal gyrus. ROI analysis showed that the activation patterns in the familiar–unfamiliar and unfamiliar–familiar contrasts are similar to those in the risk–ambiguity and ambiguity–risk contrasts reported by Hsu et al. (2005). This supports the conjecture that the risk-ambiguity distinction can be subsumed by the source preference hypothesis.

Research limitations/implications – Our odd–even design has the advantage of inducing the same “unambiguous” probability of half for each subject in each binary comparison. Our finding supports the implications of the Chew–Sagi model and rejects models based on global probabilistic sophistication, including rank-dependent models derived from non-additive probabilities, e.g., Choquet expected utility and cumulative prospect theory, as well as those based on multiple priors, e.g., α-maxmin. The finding in Hsu et al. (2005) that orbitofrontal cortex lesion patients display neither ambiguity aversion nor risk aversion offers further support to the Chew–Sagi model. Our finding also supports the Levy et al. (2007) contention of a single valuation system encompassing risk and ambiguity aversion.

Originality/value of chapter – This is the first neuroimaging study of the source preference hypothesis using a design which can discriminate among decision models ranging from risk-based ones to those relying on multiple priors.

This study is the first to examine consumer’s neural reaction to different merchandising communication strategies at the point-of-sale (PoS) by applying functional near-infrared spectroscopy (fNIRS). By doing so, the purpose of this study is to extend consumer neuroscience to retail and shopper research.

Two experiments were conducted in which 36 shoppers were exposed to a realistic grocery shopping scenario while their brain haemodynamics were measured using mobile fNIRS.

Results revealed that mobile fNIRS appears a valid method to study neural activation of the prefrontal cortex (PFC) in the field of “shopper neuroscience”. More precisely, results demonstrated that the orbitofrontal cortex (OFC) might be crucial for processing and predicting merchandising communication strategy effectiveness.

This research gives evidence that certain regions of the PFC, in particular the OFC and the dorsolateral prefrontal cortex (dlPFC), are crucial to process and evaluate merchandising communication strategies.

The current work opens a promising new avenue for studying and understanding shopper’s behaviour. Mobile fNIRS enables marketing management to collect neural data from shoppers and analyse neural activity associated with real-life settings. Furthermore, based on a better understanding of shoppers’ perceptual processes of communication strategies, marketers can design more effective merchandising communication strategies.

The study is the first to implement the innovative, mobile neuroimaging method of fNIRS to a PoS setting. It, therefore, opens up the promising field of “shopper neuroscience”.

The authors explore neuro-enhanced reality (NeR) as a novel approach for enhancing service communication between customers, frontline employees, and service organizations that extends beyond current state-of-the-art approaches based on augmented reality (AR) and virtual reality (VR) technologies.

The authors first take stock of research on reality-enhanced service communication with AR and VR, then complement these insights with emerging neuroscientific research to conceptualize how NeR enables innovative forms of service communication. On this basis, the authors develop a research agenda to guide the future study and managerial exploitation of NeR.

AR and VR already offer unique affordances for digital-to-physical communication, but these can be extended with NeR. Specifically, NeR supports neuro-to-digital and digital-to-neuro communication based on neuroimaging (e.g. controlling digital content through thought) and neurostimulation (e.g. eliciting brain responses based on digital content). This provides a basis for outlining possible applications of NeR across service settings.

The authors advance knowledge on reality-enhanced service communication with AR and VR, whilst also demonstrating how neuroscientific research can be extended from understanding brain activity to generating novel service interactions.

The purpose of this paper is to provide an integrated analysis base to facilitate computational neuroscience experiments, following a user-led approach to provide access to the integrated neuroscience data and to enable the analyses demanded by the biomedical research community.

The design and development of the N4U analysis base and related information services addresses the existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image data sets and algorithms to conduct analyses.

The provision of an integrated e-science environment of computational neuroimaging can enhance the prospects, speed and utility of the data analysis process for neurodegenerative diseases.

The N4U analysis base enables conducting biomedical data analyses by indexing and interlinking the neuroimaging and clinical study data sets stored on the grid infrastructure, algorithms and scientific workflow definitions along with their associated provenance information.

The purpose of this paper is to uncover the neural basis underlying the negative impact of graffiti on evaluations of neighborhood safety.

While in a functional magnetic resonance imaging scanner, participants rated the perceived safety of neighborhood scenes shown in photographic images. Some scenes were systematically altered to include graffiti, surveillance cameras or framed public art.

Consistent with previous research, the average safety rating for each scene including graffiti was significantly lower than for every other non-graffiti scene presented. The powerful salience of graffiti (as demonstrated by increased activation of the left fusiform gyrus) accompanied a decreased salience of other built environment scene elements (as demonstrated by reduced activation in the right parahippocampal gyrus). It appears as though the presence of graffiti causes the cognitive impact of other scene elements to fade. This redirection of cognitive focus may help to explain the dominant impact of graffiti on neighborhood safety evaluations.

Because of these basic perceptual tendencies, graffiti will necessarily dominate neighborhood safety evaluations. In any attempts to improve neighborhood safety evaluations, combating graffiti should be accorded a position of great importance, in keeping with these cognitive realities.

This is the first neuroimaging study of graffiti and perceived neighborhood safety.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is difficult to diagnose accurately due to its heterogeneous clinical manifestations. Comprehensive models combining different big data approaches (e.g. neuroimaging, genetics, eye tracking, etc.) may offer the opportunity to characterize ASD from multiple distinct perspectives. This paper aims to provide an overview of a novel diagnostic approach for ASD classification and stratification based on these big data approaches.

Multiple types of data were collected and recorded for three consecutive years, including clinical assessment, neuroimaging, gene mutation and expression and response signal data. The authors propose to establish a classification model for predicting ASD clinical diagnostic status by integrating the various data types. Furthermore, the authors suggest a data-driven approach to stratify ASD into subtypes based on genetic and genomic data.

By utilizing complementary information from different types of ASD patient data, the proposed integration model has the potential to achieve better prediction performance than models focusing on only one data type. The use of unsupervised clustering for the gene-based data-driven stratification will enable identification of more homogeneous subtypes. The authors anticipate that such stratification will facilitate a more consistent and personalized ASD diagnostic tool.

This study aims to utilize a more comprehensive investigation of ASD-related data types than prior investigations, including proposing longitudinal data collection and a storage scheme covering diverse populations. Furthermore, this study offers two novel diagnostic models that focus on case-control status prediction and ASD subtype stratification, which have been under-explored in the prior literature.

The main goal of this paper is to systematically evaluate the bibliographic production on neuromarketing from 1945 to 2018, with an emphasis on consumer behavior, to identify possible convergence of interests among health and marketing fields.

This article was based on a systematic literature review (SLR) with the intention of investigating research related to neuromarketing by means of network analysis of citations.

The research reveals that there are formed networks in the health and management areas, but they are insufficient to enhance the neuromarketing results. Ethics has been mentioned in some studies, but the maps do not suggest that it is a developing field. Similar results can be seen within the sales area that can be capitalized on by research of gains related to consumer behavior.

One limitation of this study was the use of only one database (Web of Science®) that published several works from 1945 to the present time, but certainly has not exhausted the possibilities of research in the area.

This article helped to highlight the importance of ethics in clinical and business processes using neuroimaging, which could be an easy way to understand the behavioral and physiological mechanisms.

The purpose of this paper is to explore the prospect of using neurophenomenology to understand, design and test phygital consumer experiences. It aims to clarify interpretivist approaches to consumer neuroscience, wherein theoretical models of individual phenomenology can be combined with modern neuroimaging techniques to detect and interpret the first-person accounts of phygital experiences.

The argument is conceptual in nature, building its position through synthesizing insights from phenomenology, phygital marketing, theoretical neuroscience and other related fields.

Ultimately, the paper presents the argument that interpretivist neuroscience in general, and neurophenomenology specifically, provides a valuable new perspective on phygital marketing experiences. In particular, we argue that the approach to studying first-personal experiences within the phygital domain can be significantly refined by adopting this perspective.

One of the primary goals of this paper is to stimulate a novel approach to interpretivist phygital research, and in doing so, provide a foundation by which the impact of phygital interventions can be empirically tested through neuroscience, and through which future research into this topic can be developed. As such, the success of such an approach is yet untested.

Phygital marketing is distinguished by its focus on the quality of subjective first-personal consumer experiences, but few papers to date have explored how neuroscience can be used as a tool for exploring these inner landscapes. This paper addresses this lacuna by providing a novel perspective on “interpretivist neuroscience” and proposes ways that current neuroscientific models can be used as a practical methodology for addressing these questions.

Purpose – This chapter provides an overview of the policy implications of neuroscience and argues that research initiatives, individual use, and aggregate social consequences of unfolding knowledge about the brain and the accompanying applications require particularly close scrutiny because of the centrality of the brain to human behavior and thoughts.

Design/methodology/approach – The chapter summarizes the technological context of interventions in the brain and discusses their policy implications. It then examines research findings, principally from neuroimaging studies, that relate to decision making and emotions and looks at their potential impact on frameworks of political decision making.

Findings – Research on brain structure and functioning raises difficult policy issues and necessitates a reevaluation of our assumptions concerning the policy process, itself.

Practical implications – Given the inevitability of expanded strategies for exploration and therapy of the brain and the concerns they raise, it is important that these issues surrounding their application be clarified and debated before such techniques fall into routine use.

Originality/value – The chapter provides original analysis of the policy ramifications of interventions in the brain and neuroscience in general and makes some observations about the brain and society.