Predicting Advertising Success Beyond Traditional Measures: New Insights from Neurophysiological Methods and Market Response Modeling

Debate continues over the precise causal contribution made by mesolimbic dopamine systems to reward. There are three competing explanatory categories: 'liking', learning, and 'wanting'. Does dopamine mostly mediate the hedonic impact of reward ('liking')? Does it instead mediate learned predictions of future reward, prediction error teaching signals and stamp in associative links (learning)? Or does dopamine motivate the pursuit of rewards by attributing incentive salience to reward-related stimuli ('wanting')? Each hypothesis is evaluated here, and it is suggested that the incentive salience or 'wanting' hypothesis of dopamine function may be consistent with more evidence than either learning or 'liking'. In brief, recent evidence indicates that dopamine is neither necessary nor sufficient to mediate changes in hedonic 'liking' for sensory pleasures. Other recent evidence indicates that dopamine is not needed for new learning, and not sufficient to directly mediate learning by causing teaching or prediction signals. By contrast, growing evidence indicates that dopamine does contribute causally to incentive salience. Dopamine appears necessary for normal 'wanting', and dopamine activation can be sufficient to enhance cue-triggered incentive salience. Drugs of abuse that promote dopamine signals short circuit and sensitize dynamic mesolimbic mechanisms that evolved to attribute incentive salience to rewards. Such drugs interact with incentive salience integrations of Pavlovian associative information with physiological state signals. That interaction sets the stage to cause compulsive 'wanting' in addiction, but also provides opportunities for experiments to disentangle 'wanting', 'liking', and learning hypotheses. Results from studies that exploited those opportunities are described here. In short, dopamine's contribution appears to be chiefly to cause 'wanting' for hedonic rewards, more than 'liking' or learning for those rewards.

Abstract A successful method for removing artifacts from electroencephalogram (EEG) recordings is Independent Component Analysis (ICA), but its implementation remains largely user-dependent. Here, we propose a completely automatic algorithm (ADJUST) that identifies artifacted independent components by combining stereotyped artifact-specific spatial and temporal features. Features were optimized to capture blinks, eye movements, and generic discontinuities on a feature selection dataset. Validation on a totally different EEG dataset shows that (1) ADJUST's classification of independent components largely matches a manual one by experts (agreement on 95.2% of the data variance), and (2) Removal of the artifacted components detected by ADJUST leads to neat reconstruction of visual and auditory event-related potentials from heavily artifacted data. These results demonstrate that ADJUST provides a fast, efficient, and automatic way to use ICA for artifact removal.

The affective primacy hypothesis (R. B. Zajonc, 1980) asserts that positive and negative affective reactions can be evoked with minimal stimulus input and virtually no cognitive processing. The present work tested this hypothesis by comparing the effects of affective and cognitive priming under extremely brief (suboptimal) and longer (optimal) exposure durations. At suboptimal exposures only affective primes produced significant shifts in Ss' judgments of novel stimuli. These results suggest that when affect is elicited outside of conscious awareness, it is diffuse and nonspecific, and its origin and address are not accessible. Having minimal cognitive participation, such gross and nonspecific affective reactions can therefore be diffused or displaced onto unrelated stimuli. At optimal exposures this pattern of results was reversed such that only cognitive primes produced significant shifts in judgments. Together, these results support the affective primacy hypothesis.