Similarities and differences of emotions in human-machine and human-human interactions: what kind of emotions are relevant for future companion systems?

Cognitive-technical intelligence is envisioned to be constantly available and capable of adapting to the user's emotions. However, the question is: what specific emotions should be reliably recognised by intelligent systems? Hence, in this study, we have attempted to identify similarities and differences of emotions between human-human (HHI) and human-machine interactions (HMI). We focused on what emotions in the experienced scenarios of HMI are retroactively reflected as compared with HHI. The sample consisted of N = 145 participants, who were divided into two groups. Positive and negative scenario descriptions of HMI and HHI were given by the first and second groups, respectively. Subsequently, the participants evaluated their respective scenarios with the help of 94 adjectives relating to emotions. The correlations between the occurrences of emotions in the HMI versus HHI were very high. The results do not support the statement that only a few emotions in HMI are relevant.

The neural implementation of multi-attribute decision making: a parametric fMRI study with human subjects.

Decision making is not a unitary entity but involves rather a series of interdependent processes. Decisions entail a choice between two or more alternatives. Within the complex series of decisional processes, at least two levels can be differentiated: a first level of information integration (process level) and a second level of information interpretation (control level), leading to a subsequent motor response or cognitive process. The aim of this study was to investigate the neural network of these decisional processes. In a single trial fMRI study, we implemented a simple decision-making task, where subjects had to decide between two alternatives represented on five attributes. The similarity between the two alternatives was varied systematically in order to achieve a parametric variation of decisional effort. For easy trials, the two alternatives differed significantly in several attributes, whereas for difficult trials, the two alternatives differed only in small details. The results show a distributed neural network related to decisional effort. By means of time course analysis different subprocesses within this network could be differentiated: regions subserving the integration of the presented information (premotor areas and superior parietal lobe) and regions subserving the interpretation of this information (frontolateral and frontomedial cortex, anterior insula, and caudate) as well as a region in the inferior frontal junction updating task rules.