Toward a Deeper Understanding of the Mechanisms of Animal-Assisted Interventions: How Important is the Animal?

There is an ever-increasing interest in animal-assisted interventions, and while its effects seem promising, little is known about the underlying mechanisms. The literature on animal-assisted interventions generally assumes that the animal itself is responsible for the effects of the interventions. However, evidence from placebo research suggests that a significant portion of treatment effects can be explained by contextual factors that are not specific to a treatment itself. Regarding animal-assisted interventions, this would suggest that the effects are not due to the animal but to contextual factors. In order to better understand the role of the animal and contextual factors in animal-assisted interventions, this thesis pursued two aims. First, it investigated to what extent the effects of animal-assisted interventions on pain can be attributed to the presence of an animal or to how the animal is embedded in the treatment rationale. Second, it identified the hypotheses previous studies have pursued regarding the underlying mechanisms of animal-assisted interventions and what factors have been considered as specific and nonspecific. Two different approaches were applied to address these two aims. For the first aim, we conducted two randomized controlled trials with healthy participants in a heat-pain placebo paradigm (Study I and Study II). For the second aim, a systematic review was conducted to assess factor hypotheses that researchers have presented in previous studies on animal-assisted interventions and to identify what specific and nonspecific factors have been considered in animal- assisted interventions (Study III). In the two experimental heat-pain studies, we did not find any analgesic effects in healthy participants compared to the control group when the dog was not part of the treatment rationale (Study I). Instead, participants experienced heat-pain to be more intense at the limit of their tolerance in the presence of the dog compared to the control group (i.e., self-reported pain intensity at the limit of pain tolerance, p = 0.041). When the dog was part of the treatment rationale (Study II), it did have a positive effect on pain perception in healthy participants compared to the control group (i.e., self-reported ratings of pain unpleasantness at the limit of pain tolerance, p = 0.010). The systematic review (Study III) found that a majority of studies did not define specific hypotheses regarding potential mechanisms of animal-assisted interventions. Further, most studies controlled for the animal or the interaction with the animal as specific factors. Based on the findings of this thesis, it is urgent to reconsider the explanatory model for the effectiveness of animal-assisted interventions. More precisely, instead of only focusing on the animal in animal-assisted interventions, researchers and practitioners should start to include contextual factors in their explanatory models. A better understanding of the relevant factors in animal-assisted interventions might also reveal how important the animal is and whether these effects can be facilitated through other factors

Probability of graphs with large spectral gap by multicanonical Monte Carlo

Graphs with large spectral gap are important in various fields such as biology, sociology and computer science. In designing such graphs, an important question is how the probability of graphs with large spectral gap behaves. A method based on multicanonical Monte Carlo is introduced to quantify the behavior of this probability, which enables us to calculate extreme tails of the distribution. The proposed method is successfully applied to random 3-regular graphs and large deviation probability is estimated.Comment: 3pages 4figure

Web Science emerges

The relentless rise in Web pages and links is creating emergent properties, from social networks to virtual identity theft, that are transforming society. A new discipline, Web Science, aims to discover how Web traits arise and how they can be harnessed or held in check to benefit society. Important advances are beginning to be made; more work can solve major issues such as securing privacy and conveying trust

A generative spike train model with time-structured higher order correlations

Emerging technologies are revealing the spiking activity in ever larger neural ensembles. Frequently, this spiking is far from independent, with correlations in the spike times of different cells. Understanding how such correlations impact the dynamics and function of neural ensembles remains an important open problem. Here we describe a new, generative model for correlated spike trains that can exhibit many of the features observed in data. Extending prior work in mathematical finance, this generalized thinning and shift (GTaS) model creates marginally Poisson spike trains with diverse temporal correlation structures. We give several examples which highlight the model's flexibility and utility. For instance, we use it to examine how a neural network responds to highly structured patterns of inputs. We then show that the GTaS model is analytically tractable, and derive cumulant densities of all orders in terms of model parameters. The GTaS framework can therefore be an important tool in the experimental and theoretical exploration of neural dynamics