Following the development of advanced neuroimaging techniques, the growing interest in studying the brain's response to marketing stimuli resulted in the birth of consumer neuroscience within the field of neuroeconomics. However, marketing scholars have remained reluctant to adopt the techniques of neuroscience and there is still uncertainty about the capacity of neuroimaging data to provide useful findings about consumer psychology and behaviour. In order to clarify the current scope and contribution of consumer neuroscience, we first develop a semantic cluster analysis of the boundaries of the field, followed by a comprehensive empirical review from 34 selected studies. We propose a novel approach to classify findings and facilitate the assessment of evidence around the topics of decision-making, rewards, memory and emotions. Finally, we discuss the possible role of several brain mechanisms in the processing of marketing stimuli as well as obstacles to the integration of these findings with classical consumer behaviour theories. We conclude that the contribution of neuroimaging remains too limited to replace existing consumer research techniques and provide recommendations for future research.

Recent advances in the reliability of the eye-tracking methodology as well as the increasing availability of affordable non-intrusive technology have opened the door to new research opportunities in a variety of areas and applications. This has raised an increasing interest within disciplines such as medicine, business and education for analysing human perceptual and psychological processes based on eye-tracking data. However, most of the currently available software requires programming skills and focuses on the analysis of a limited set of eyemovement measures (e.g. saccades and fixations), thus excluding other measures of interest to the classification of a determined state or condition. This paper describes 'EALab', a MATLAB analytics toolbox aimed at easing the extraction, multivariate analysis and classification stages of eye-activity data collected from commercial and independent eye trackers. The processing implemented in this toolbox enables to evaluate variables extracted from a wide range of measures including saccades, fixations, blinks, pupil diameter and glissades. Using EALab does not require any programming and the analysis can be performed through a user-friendly graphical user interface (GUI) consisting of three processing modules: 1) eye-activity measure extraction interface, 2) variable analysis and selection interface, and 3) classification interface.