Although there has been research regarding the horses´ responses to human behavior, there is still a gap concerning the knowledge about the interaction of horses and humans in showing individual responses to different human behavior in the same situation. In this work, the horses´ individual responses to different humans were examined to close this research gap and to identify whether horses do really respond differently to different people. To this end, 29 horse and human interactions (including two identical exercises in each situation) were videoed and then transcribed in the style of HANOS. The qualitative content analysis was appropriated on the basis of Mayring. Both of the methods were adjusted to the special study conditions as the nonverbal interactions between each person and one horse were focused but no verbal expressions. In total, just under 600 interactions were analyzed (quantitative analyses). Based on these analyses, it can be assumed that each human individual received an individual, different feedback from the horses.

Human non-conscious reasoning is one of the most successful procedures developed to solve everyday problems in an efficient way. This is why the field of artificial intelligence should analyze, formalize and emulate the multiple ways of non-conscious reasoning with the purpose of applying them in knowledge based systems, neurocomputers and similar devices for aiding people in the problem-solving process. In this paper, a framework for those non-conscious ways of reasoning is presented based on object-oriented representations, fuzzy sets and multivalued logic.

Achieving a long-lasting behavioral change is a major challenge that can have a large impact on the quality of life. Recently, it has been shown that using the cue-approach task (CAT), preferences for snack food items could be modified without external reinforcement. In the task, the mere association of images of snack food items with a neutral auditory cue and a speeded button press resulted in increased preferences for the associated items. We examined the generality and long term endurance of the mechanism underlying this non-reinforced behavioral change. In a series of nine independent experiments with 236 participants we used multiple cues of different modalities and a variety of conceptually different stimuli including snacks, affective images, faces and fractals. Our results show a consistent preference modification for all stimuli of neutral and positive (but not negative) valence, regardless of cue modality. More importantly, in five follow-up sessions performed one to six months after training, behavioral change was maintained and durable. Our results provide evidence of a replicable general mechanism for non-reinforced long lasting behavioral change, extending beyond cue modality and stimulus type. This mechanism can be utilized to improve our understanding of non-reinforced learning processes as well as for development of novel clinical behavioral change applications