Can neuroforecasting predict market behaviour?

For generations, marketers have tried to get into the mind of the consumer. Now, using new brain imaging techniques, we’re tantalizingly close. The emerging science of neuroforecasting is still very young, but bit by bit, researchers are learning more about the connection between thinking – or more specifically, reacting – and doing

Can neuroforecasting predict market behaviour?

For generations, marketers have tried to get into the mind of the consumer. Now, using new brain imaging techniques, we’re tantalizingly close. The emerging science of neuroforecasting is still very young, but bit by bit, researchers are learning more about the connection between thinking – or more specifically, reacting – and doing

Neural affective mechanisms predict market-level microlending

Humans sometimes share with others whom they may never meet or know, in violation of the dictates of pure self-interest. Research has not established which neuropsychological mechanisms support lending decisions, nor whether their influence extends to markets involving significant financial incentives. In two studies, we found that neural affective mechanisms influence the success of requests for microloans. In a large Internet database of microloan requests (N = 13,500), we found that positive affective features of photographs promoted the success of those requests. We then established that neural activity (i.e., in the nucleus accumbens) and self-reported positive arousal in a neuroimaging sample (N = 28) predicted the success of loan requests on the Internet, above and beyond the effects of the neuroimaging sample’s own choices (i.e., to lend or not). These findings suggest that elicitation of positive arousal can promote the success of loan requests, both in the laboratory and on the Internet. They also highlight affective neuroscience’s potential to probe neuropsychological mechanisms that drive microlending, enhance the effectiveness of loan requests, and forecast market-level behavior

Neuroforecasting Aggregate Choice

Advances in brain-imaging design and analysis have allowed investigators to use neural activity to predict individual choice, while emerging Internet markets have opened up new opportunities for forecasting aggregate choice. Here, we review emerging research that bridges these levels of analysis by attempting to use group neural activity to forecast aggregate choice. A survey of initial findings suggests that components of group neural activity might forecast aggregate choice, in some cases even beyond traditional behavioral measures. In addition to demonstrating the plausibility of neuroforecasting, these findings raise the possibility that not all neural processes that predict individual choice forecast aggregate choice to the same degree. We propose that although integrative choice components may confer more consistency within individuals, affective choice components may generalize more broadly across individuals to forecast aggregate choice

Brain activity forecasts video engagement in an internet attention market

The growth of the internet has spawned new “attention markets,” in which people devote increasing amounts of time to consuming online content, but the neurobehavioral mechanisms that drive engagement in these markets have yet to be elucidated. We used functional MRI (FMRI) to examine whether individuals’ neural responses to videos could predict their choices to start and stop watching videos as well as whether group brain activity could forecast aggregate video view frequency and duration out of sample on the internet (i.e., on youtube.com). Brain activity during video onset predicted individual choice in several regions (i.e., increased activity in the nucleus accumbens [NAcc] and medial prefrontal cortex [MPFC] as well as decreased activity in the anterior insula [AIns]). Group activity during video onset in only a subset of these regions, however, forecasted both aggregate view frequency and duration (i.e., increased NAcc and decreased AIns)—and did so above and beyond conventional measures. These findings extend neuroforecasting theory and tools by revealing that activity in brain regions implicated in anticipatory affect at the onset of video

When is giving an impulse? An ERP investigation of intuitive prosocial behavior

Human prosociality is often assumed to emerge from exerting reflective control over initial, selfish impulses. However, recent findings suggest that prosocial actions can also stem from processes that are fast, automatic and intuitive. Here, we attempt to clarify when prosocial behavior may be intuitive by examining prosociality as a form of reward seeking. Using event-related potentials (ERPs), we explored whether a neural signature that rapidly encodes the motivational salience of an event\u2014the P300\u2014can predict intuitive prosocial motivation. Participants allocated varying amounts of money between themselves and charities they initially labelled as high- or low-empathy targets under conditions that promoted intuitive or reflective decision making. Consistent with our predictions, P300 amplitude over centroparietal regions was greater when giving involved high-empathy targets than low-empathy targets, but only when deciding under intuitive conditions. Reflective conditions, alternatively, elicited an earlier frontocentral positivity related to response inhibition, regardless of target. Our findings suggest that during prosocial decision making, larger P300 amplitude could (i) signal intuitive prosocial motivation and (ii) predict subsequent engagement in prosocial behavior. This work offers novel insight into when prosociality may be driven by intuitive processes and the roots of such behaviors

Beyond self-report: a review of physiological and neuroscientific methods to investigate consumer behavior

The current paper investigates the value and application of a range of physiological and neuroscientific techniques in applied marketing research and consumer science, highlighting new insights from research in social psychology and neuroscience. We review measures of sweat secretion, heart rate, facial muscle activity, eye movements, and electrical brain activity, using techniques including skin conductance, pupillometry, eyetracking, and magnetic brain imaging. For each measure, after a brief explanation of the underlying technique, we illustrate concepts and mechanisms that the measure allows researchers in marketing and consumer science to investigate, with a focus on consumer attitudes and behavior. By providing reviews on recent research that applied these methods in consumer science and relevant related fields, we also highlight methodological and theoretical strengths and limitations, with an emphasis on ecological validity. We argue that the inclusion of physiological and neuroscientific techniques can advance consumer research by providing insights into the often unconscious mechanisms underlying consumer behavior. Therefore, such technologies can help researchers and marketing practitioners understand the mechanisms of consumer behavior and improve predictions of consumer behavior

Verbal working memory and functional large-scale networks in schizophrenia

The aim of this study was to test whether bilinear and nonlinear effective connectivity (EC) measures of working memory fMRI data can differentiate between patients with schizophrenia (SZ) and healthy controls (HC). We applied bilinear and nonlinear Dynamic Causal Modeling (DCM) for the analysis of verbal working memory in 16 SZ and 21 HC. The connection strengths with nonlinear modulation between the dorsolateral prefrontal cortex (DLPFC) and the ventral tegmental area/substantia nigra (VTA/SN) were evaluated. We used Bayesian Model Selection at the group and family levels to compare the optimal bilinear and nonlinear models. Bayesian Model Averaging was used to assess the connection strengths with nonlinear modulation. The DCM analyses revealed that SZ and HC used different bilinear networks despite comparable behavioral performance. In addition, the connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area showed differences between SZ and HC. The adoption of different functional networks in SZ and HC indicated neurobiological alterations underlying working memory performance, including different connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area. These novel findings may increase our understanding of connectivity in working memory in schizophrenia