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A growing body of evidence suggests that emotional states under which individuals perform decision-making tasks modulate performance. Studies have mainly reported that negative emotions can differentially increase or decrease performance by modulating feedback processing. In contrast, differential influences of specific emotions inside positive valence have been poorly investigated. The objective of the present work was to assess specific effect of different types of positive emotions on decision-making and to investigate whether this effect also depends on feedback processing. In our study, after being induced to feel either hope or happiness, participants undertook a risky sequential decision-making task in which feedback was required to obtain a good performance. We found that the more positive was the feedback received, the more happiness led participants to make risky decisions. This tendency was not observed among participants in the hopeful or in the control condition. Our results contribute to the literature showing that the effects of emotions on sequential decision-making performance can be explained by feedback processing and are not solely due to the valence of the emotional state. They also suggest that further research is required to determine which potential specific dimension is involved in the effects of positive emotions on sequential decision-making.

Research has given little attention to the influence of incidental emotions on the Iowa Gambling Task (IGT), in which processing of the emotional cues associated with each decision is necessary to make advantageous decisions. Drawing on cognitive theories of emotions, we tested whether uncertainty-associated emotion can cancel the positive effect of the hunch period, by preventing participants from developing a tendency towards advantageous decisions. Our explanation is that uncertainty appraisals initiate deliberative processing that is irrelevant to process emotional cues, contrary to intuitive processing (Kahneman, 2003; Tiedens & Linton, 2001). As expected, uncertainty-associated emotion cancelled the positive effect of the hunch period in the IGT compared to certainty-associated emotion: disgusted participants (certainty-associated emotion) and sad participants induced to feel certainty developed a stronger tendency towards advantageous decisions than sad participants induced to feel uncertainty. We discuss the importance of the core components that trigger incidental emotions to predict decision making.

According to the Appraisal-Tendency Framework (Han, Lerner, & Keltner, 2007), certainty-associated emotions increase risk taking compared with uncertainty-associated emotions. To date, this general effect has only been shown in static judgement and decision-making paradigms; therefore, the present study tested the effect of certainty on risk taking in a sequential decision-making task. We hypothesised that the effect would be reversed due to the kind of processing involved, as certainty is considered to encourage heuristic processing that takes into account the emotional cues arising from previous decisions, whereas uncertainty leads to more systematic processing. One hundred and one female participants were induced to feel one of three emotions (film clips) before performing a decision-making task involving risk (Game of Dice Task; Brand et al., 2005). As expected, the angry and happy participants (certainty-associated emotions) were more likely than the fearful participants (uncertainty-associated emotion) to make safe decisions (vs. risky decisions).

This article proposes a connectionist model of the social learning theory developed by Bandura (1977). The theory posits that an individual in an interactive situation is capable of learning new behaviours merely by observing them in others. Such learning is acquired through an initial phase in which the individual memorizes what he has observed (observation phase), followed by a second phase where he puts the recorded observations to use as a guide for adjusting his own behaviour (reproduction phase). We shall refer to the two above-mentioned phases to demonstrate that it is conceivable to simulate learning by observation otherwise than through the recording of perceived information using symbolic representation. To this end we shall rely on the formalism of ecological neuron networks (Parisi, Cecconi, & Nolfi, 1990) to implement an agent provided with the major processes identified as essential to learning through observation. The connectionist model so designed shall implement an agent capable of recording perceptive information and producing motor behaviours. The learning situation we selected associates an agent demonstrating goal-achievement behaviour and an observer agent learning the same behaviour by observation. Throughout the acquisition phase, the demonstrator supervises the observer's learning process based on association between spatial information (input) and behavioural information (output). Representation thus constructed then serves as an adjustment guide during the production phase, involving production by the observer of a sequence of actions which he compares to the representation stored in distributed form as constructed through observation. An initial simulation validates model architecture by confirming the requirement for both phases identified in the literature (Bandura, 1977) to simulate learning through observation. The representation constructed over the observation phase evidences acquisition of observed behaviours, although this phase alone is not sufficient to ensure accurate reproduction and must be made functional through the production phase (Deakin & Proteau, 2000). Results obtained through a second simulation replicate those produced by Bandura & Jeffery (1973), who observed that the individual tested following the retention phase recalled recorded information better than he realized in the production phase. The outcome of a third simulation shows that, when performing the transfer task, agents performed the task all the more effectively when they were required to learn a simple path which facilitated knowledge transfer to an adjacent situation. New explanatory assumptions of the mechanics of learning through observation may be produced through OLEANNet. Thus, observed deterioration between memorization and production is caused by successive approximations which occur in the acquisition phase then in the production phase. Further, depending on the type of learning undergone by agents, use of representation as a production guide induces a more or less stringent constraint in the approximation of actual behaviour. This results, during the transfer task, in the ability to effectively generalize acquired knowledge where such knowledge is not specifically related to the task at hand. In conclusion, connectionist model architecture appears valid for modeling learning through observation as defined by Bandura (1977). However, certain limitations appear during implementation, especially in terms of the observed behaviour's availability and the planning of produced behaviours that future developments are liable to counter.