In buying a product, people express a preference for that particular product. Therefore, understanding cognitive processes underlying consumers' preferences is an important issue, especially in market field. The Information Integration Theory posits that judgment follows specific mathematical rules, and it considers a weighted Averaging model that allows to split the value of each level of each attribute of product into two components: each level is described by the value assigned to the stimulus on a response scale (scale value) and by its importance (weight) into the global judgment. Moving from this theoretical framework, we applied a method for parameter estimation of averaging models, developed by our research group to marketing research about pasta product. Using a real pasta brand, we manipulated two experimental factors: Packaging (Box with window, Box without window and Plastic bag) and Price (0.89(sic), 0.99(sic), 1.09(sic)). Within the whole sample we identified three clusters (A=19, B=15 and C=10) that represents three different kind of preferences. ANOVA shows a significant interaction between Price and Packaging only in Cluster C and a not significant interaction both in Cluster A and in Cluster B. This indicates that Cluster A and Cluster B integrate the values of the levels of factors in an additive way and show an equal-weight configuration within the levels of each factor. Differently, Cluster C highlights a differential-weight configuration double weighting the highest price. Concluding, our estimation method allows to catch differences within consumers, suggesting which is the most appreciated combination for a specific population.

The anterior cingulate cortex (ACC) is involved in performance monitoring and in learning from performance feedback. Recent research suggests that the feedback-related negativity (FRN), an event-related potentials (ERP) component reflecting neural activity in the ACC, codes the size of a negative prediction error when reward probabilities are varied. There is as yet no clear evidence that the FRN is also sensitive to violations of reward magnitude expectations. In the present study, 20 healthy young subjects engaged in a learning task in which a coin had to be found on each trial. The value of the coin (the potential reward magnitude) was varied from trial to trial and amounted to 5 cent, 20 cent or 50 cent. Analysis of ERPs revealed that FRN amplitude differences between reward and non-reward were significantly modulated by (potential) reward magnitude. This effect was driven by the neural response to non-reward: the larger the potential reward, the larger was the FRN amplitude in response to non-reward. In contrast, the P300 was larger for positive outcomes and showed an effect of (potential) reward magnitude independent of valence. Together with evidence from previous studies, these results show that the FRN codes negative prediction errors in the context of varying reward probabilities and magnitudes. The findings are in line with recent results based on functional neuroimaging and lend further support to the idea of a key role of the ACC in the integration of information on different aspects of performance outcomes.

When making decisions, people show different attitudes in risk-taking. Classically, individual differences have been investigated using personality tests but, recently, neuroscience methods are providing a novel point of view through which this aspect can be better understood. Here, we present a study in which participants play a gambling task by choosing between a first option that constantly yielded a small gain and a second option that provided either a larger gain or a loss. While participants performed the task, event-related potentials (ERPs) were recorded in order to investigate brain activity during the gambling task. Two groups were analysed post hoc: The first group was more risk-prone, while the second group was more risk-averse. The Feedback Related Negativity (FRN) occurring between 250 and 450 millisecond after decision outcomes, was differentially affected in the two groups. In addition, source analyses indicated that distinct brain areas are responsible for such a difference: Anterior Cingulate Cortex (ACC) is more activated in risk-prone group, while Dorso-Lateral Prefrontal Cortex (DLPFC) is more activated in risk-averse group. Our results show that risk-taking behaviour is related to differential activations in the brain. How the brain works may be used to predict the participants risk-attitude.

In the present study, we showed that, in a social gambling task, individuals are influenced more by the type of social interaction than by the pattern of gains and losses. More precisely, the neural responses, as well as the level of pleasantness/unpleasantness following gains and losses, are modulated by social interaction factors. Here we present an Event-Related Potentials (ERPs) study in which three groups of participants were compared. Subjects were engaged in gambling tasks differing with regard to social factors: in a first condition, there was no social context; in a second condition, participants compared their outcomes with those of another individual; in a third condition, participants competed for a limited amount of money with another contender. In all conditions, all participants were revealed the outcome of an unselected alternative (non-obtained outcome) prior to the payoff associated with their selected option (obtained outcome). In addition, affective ratings were measured after the outcomes were presented. In the group without social context, ERPs results replicated previous findings. Interestingly, the P200 was modulated by varying social contexts, suggesting that attentive resources allocated to payoffs in comparison and competitive situations are decreased presumably in favor of social cues. Furthermore, Feedback Related Negativity (FRN) was predictive of the subjective feeling of pleasantness/unpleasantness following monetary outcomes. The present data provide information about neural and cognitive processing underlying economic decision-making when other individuals are involved.

Understanding the neurocognitive basis of risk-taking behavior is an important issue, especially in economic decision-making. Classical behavioral studies have shown that risk-attitude changes across different contexts, but little is so far known about the brain correlates of processing of outcomes across such context shifts. In this study, EEG was recorded while subjects performed a gambling task. Participants could choose between a risky and a safer option, within two different contexts: one in which options yielded gains and losses of the same magnitude (Zero Expected Value context) and another in which gains were larger than losses (Positive Expected Value context). Based on their risk-attitude, two groups were compared: subjects who are risk-seekers in the zero Expected Value context (Zero-Oriented group) and subjects who are risk-seekers in the positive Expected Value condition (Positive-Oriented group). The Feedback Related Negativity (FRN) reflects this distinction, with each group being insensitive to magnitude of outcomes in the condition in which they were risk-prone. P300 amplitude mirrored the behavioral results, with larger amplitudes in the condition in which each group showed a higher risk-tendency. Source analyses highlighted the involvement of posterior cingulate cortex in risky decision-making. Taken together, the findings make a contribution to the clarification of the neurocognitive substrates of risky decision-making.

In the Ultimatum Game, participants typically reject monetary offers they consider unfair even if the alternative is to gain no money at all. In the present study, ERPs were recorded while subjects processed different offers of a proposer. In addition to clearly fair and unfair offers, mid-value offers which cannot be easily classified as fair or unfair and therefore involve more elaborate decision making were analyzed. A fast initial distinction between fair and other kinds of offers was reflected by amplitude of the feedback related negativity (FRN). Mid-value offers were associated with longer RTs, and a larger N350 amplitude. In addition, source analyses revealed a specific involvement of the superior temporal gyrus and the inferior parietal lobule during processing of mid-value offers compared to offers categorized clearly as fair or unfair, suggesting a contribution of mentalizing about the intention of the proposer to the decision making process. Taken together, the present findings support the idea that economic decisions are significantly affected by non-rational factors, trying to narrow the gap between formal theory and the real decisional behaviour.

When making decisions, the outcomes of different choices play an important role. Feedback is mainly processed in terms of gains and losses. It is as yet unclear whether this distinction holds for predictable as well as unpredictable outcomes. Using ERPs, the present study aimed to determine whether predictable and unpredictable outcomes are coded differently in the brain. Participants had to choose between one of two options: the certain option was always associated with a gain of 10 euro, while the uncertain option entailed a gain of 30 euro or a loss of 10 euro, with a probability of 50% each. Overall, subjects showed a clear preference for the certain option, a tendency which became more pronounced during the course of the experiment. An early ERP component, the P200, reflected the predictability of outcomes, which was critical for the subsequent decisions. The later feedback related negativity (FRN) reflected the known distinction between gains and losses, while the N500 again reflected differential processing of predictable and unpredictable outcomes. Neither FRN nor the N500 were significantly related to behaviour. Predictability appears to play a central role in outcome evaluation.

Lies are intentional distortions of event knowledge. No experimental data are available on manipulating lying processes. To address this issue, we stimulated the dorsolateral prefrontal cortex (DLPFC) using transcranial direct current stimulation (tDCS). Fifteen healthy volunteers were tested before and after tDCS (anodal, cathodal, and sham). Two types of truthful (truthful selected: TS; truthful unselected: TU) and deceptive (lie selected: LS; lie unselected: LU) responses were evaluated using a computer-controlled task. Reaction times (RTs) and accuracy were collected and used as dependent variables. In the baseline task, the RT was significantly longer for lie responses than for true responses ([mean +/- standard error] 1153.4 +/- 42.0 ms vs. 1039.6 +/- 36.6 ms; F(1,14) = 27.25, P = 0.00013). At baseline, RT for selected pictures was significantly shorter than RT for unselected pictures (1051.26 +/- 39.0 ms vs. 1141.76 +/- 41.1 ms; F(1,14) = 34.85, P = 0.00004). Whereas after cathodal and sham stimulation, lie responses remained unchanged (cathodal 5.26 +/- 2.7%; sham 5.66 +/- 3.6%), after anodal tDCS, RTs significantly increased but did so only for LS responses (16.86 +/- 5.0%; P = 0.002). These findings show that manipulation of brain function with DLPFC tDCS specifically influences experimental deception and that distinctive neural mechanisms underlie different types of lies.

It is believed that the N400 elicited by concepts belonging to living is larger than N400 to non-living. This is considered as evidence that concepts are organized, in the brain, on the basis of categories. We conducted a feature-verification experiment where living and non-living concepts were matched for relevance of semantic features. Relevance is a measure of the contribution of semantic features to the “core” meaning of a concept. We found that when relevance is low the N400 is large. In addition, we found that when the two categories of living and non-living are equated for relevance the seemingly category effect at behavioral and neural level disappeared. In sum, N400 is sensitive, rather than to categories, to semantic features, thus showing that previously reported effects of semantic categories may arise as a consequence of the differing relevance of concepts belonging to living and non-living categories.

It is believed that the N400 elicited by concepts belonging to Living things is larger than the N400 to Non-living things. This is considered as evidence that concepts are organized, in the brain, on the basis of categories. Similarly, differential N400 to Sensory and Non-sensory semantic features is taken as evidence for a neural organisation of conceptual memory based on semantic features. We conducted a feature-verification experiment where Living and Non-living concepts are described by Sensory and Non-sensory features and were matched for Age-of-Acquisition, typicality and familiarity and finally for relevance of semantic features. Relevance is a measure of the contribution of semantic features to the “core” meaning of a concept. We found that when Relevance is low then the N400 is large. In addition, we found that when the two categories of Living and Non-living concepts are matched for relevance the seemingly category effect at the neural level disappeared. Also no difference between Sensory and Non-sensory descriptions was detected when relevance was matched. In sum, N400 does not differ between categories or feature types. Previously reported effects of semantic categories and feature type may have arisen as a consequence of the differing Relevance of concepts belonging to Living and Non-living categories.

Choosing to accept enough risk, but not too much, is an importan t survival skill. Recently, a model have been developed to predict people behaviou r in dealing with risk. It states that attractiveness of an option depends on: Exp erienced Reward and Risk. Particularly, the riskier is the option the more attractiv e it will be perceived. This model can really well predict monkeys ' behaviour. However, it is still unknown whether it can predict people behaviour. This is the goal of the present study. A group of 28 participants was presented with two options, a safe and a risky option, which lead to different gains. Subjects should try to earn as mu ch as possible. In fact, the safe option gives always the same reward (x), while t he risky option gives in 50% of the cases a higher gain (x+a) and in 50% of the cases a lower gain (x -a). Expected value is exactly the same for both of them. Subjects ha ve to decide 120 times. The percentage of risky choices has been measured in thre e blocks (1 -40, 41 - 80 and 81 -120 decisions). Two conditions have been created: In one conditi on the risky option includes a loss, in the other it does not. Subjects have been divided in two subgroups based on experienced reward. Results show that the experienced reward in the firsts 10 decisions can modulate subjects ' behaviour, especially in the final block. However, the higher is the experienced reward, the lower is the percentage of risky choices. The presence of a loss influences b ehaviour, but only in the high -reward group. Finally, it is important to note that the whole gr oup reveals a clear preference for the safe option, that increases in the fina l block. To conclude, it seems that experienced reward and risk can modulate preferences. However, risk does not seem to rend an option more attractive in humans.