Cuteness is a powerful aesthetic, and psychological research shows that cute things such as infants, baby animals, and toys capture and secure our attention, promote nurturing behaviour, and influence our preferences. Therefore, cuteness is a common design outcome in many consumer products, including robotics. However, we suggest that making cute zoomorphic robots may not be without its issues due to the complexities introduced by the analogies they make to various animals. We summarise the impact of cuteness in animals and robotics and analyse the intersection of the two domains by comparing the presence of baby schema features in different canine zoomorphic robots and dog breeds. Finally, we speculate on the benefits and drawbacks to cute zoomorphic robots, and provide suggestions for a new design approach that centres animals’ well-being. The aim of this work is to synthesise research on cuteness from different disciplines and prompt robot designers to be more conscious of cuteness and its potentially detrimental consequences in zoomorphic robots.

Zoomorphic robots are a promising substitute for live animals in situations where their involvement is impractical or impossible. One significant application is in animal welfare education, which plays a key role in shaping children’s relationships with animals and the natural world. In this work we present the evaluation of an educational intervention using a custom zoomorphic robot that communicates dog sentience and welfare needs to 10–11-year-old children (N = 104). The results show that the intervention as a whole is an effective animal welfare education intervention, as it succeeded in improving children’s belief in dog sentience, recognition of dog emotions, knowledge of dog welfare needs, and attachment to pets. While there was no significant effect of the zoomorphic robot over the same intervention delivered using a stuffed toy, children who interacted with the robot reported higher intrinsic motivation to participate in the intervention, suggesting potential future use as a proxy animal, particularly to sustain engagement in longer-term interventions.

Zoomorphic robots are a promising tool for animal welfare education and could be used to teach children that animals have minds and emotions and thereby reduce acceptance of cruelty towards animals. This study investigated the influence of animallike affective non-verbal behavior on children’s perceptions of the attributes and mental abilities of a zoomorphic robot, as well as their acceptance of cruelty towards it. Children who interacted with a robot that displayed animallike affective non-verbal behavior ascribed a significantly higher level of mental abilities. Higher levels of perceived mental abilities were not generally correlated with lower acceptance of cruelty but higher levels of perceived social attributes were. Post-hoc analysis of reasoning given for unacceptability of cruelty found that the group of children who made moral judgments about the cruelty had rated the zoomorphic robot as significantly more animate.

The paradox of choice refers to the observation that numerous choices can have a detrimental effect on the quality of decision making. We study this effect in swarms in the context of a resource foraging task. We simulate the evolution of swarms gathering energy from a number of energy sources distributed in a two-dimensional environment. As the number of sources increases, the evolution of the swarm results in reduced levels of efficiency, despite the sources covering more space. Our results indicate that this effect arises because the simultaneous detection of multiple sources is incompatible with an evolutionary scheme that favours greedy energy consumption. In particular, the communication among the agents tends to reduce their efficiency by precluding the evolution of a clear preference for an increasing number of options. The overabundance of explicit information in the swarm about fitness-related options cannot be exploited by the agents lacking complex planning capabilities. If the sensor ranges evolve in addition to the behaviour of the agents, a preference for reduced choice results, and the average range approaches zero as the number of sources increases. Our study thus presents a minimal model for the paradox of choice, which implies several options of experimental verification.

A local-autoencoding (LAE) method is proposed for the parameter estimation in a Hidden Potts-Markov random field model. Due to sampling cost, Markov chain Monte Carlo methods are rarely used in real-time applications. Like other heuristic methods, LAE is based on a conditional independence assumption. It adapts, however, the parameters in a block-by-block style with a simple Hebbian learning rule. Experiments with given label fields show that the LAE is able to converge in far less time than required for a scan. It is also possible to derive an estimate for LAE based on a Cramer–Rao bound that is similar to the classical maximum pseudolikelihood method. As a general algorithm, LAE can be used to estimate the parameters in anisotropic label fields. Furthermore, LAE is not limited to the classical Potts model and can be applied to other types of Potts models by simple label field transformations and straightforward learning rule extensions. Experimental results on image segmentations demonstrate the efficiency and generality of the LAE algorithm.

We present a system of virtual particles that interact using simple kinetic rules. It is known that heterogeneous mixtures of particles can produce particularly interesting behaviors. Here we present a two-species three-dimensional swarm in which a behavior emerges that resembles cell division. We show that the dividing behavior exists across a narrow but finite band of parameters and for a wide range of population sizes. When executed in a two-dimensional environment the swarm's characteristics and dynamism manifest differently. In further experiments we show that repeated divisions can occur if the system is extended by a biased equilibrium process to control the split of populations. We propose that this repeated division behavior provides a simple model for cell-division mechanisms and is of interest for the formation of morphological structure and to swarm robotics.

We compare classifiers for the classification of myoelectric signals and show that the performance can be improved by using spatial features that are extracted by independent component analysis. The obtained filters can be interpreted as reflecting the spatial structure of the data source. We find that the performance improves for several preprocessing algorithms, but it affects the relative performance for various classifiers in different ways. A critical performance difference is especially seen when non-stationary signal regimes during the onset of static contractions are included. Although a practically utilizable performance appears to be reached for the present data set by a certain combination of classification and preprocessing algorithms, it remains to be further optimized in order to keep this level for more realistic data sets.

A prominent finding in psychophysical experiments on time perception is Weber's law, the linear scaling of timing errors with duration. The ability to reproduce this scaling has been taken as a criterion for the validity of neurocomputational models of time perception. However, the origin of Weber's law remains unknown, and currently only a few models generically reproduce it. Here, we use an information-theoretical framework that considers the neuronal mechanisms of time perception as stochastic processes to investigate the statistical origin of Weber's law in time perception and also its frequently observed deviations. Under the assumption that the brain is able to compute optimal estimates of time, we find that Weber's law only holds exactly if the estimate is based on temporal changes in the variance of the process. In contrast, the timing errors scale sublinearly with time if the systematic changes in the mean of a process are used for estimation, as is the case in the majority of time perception models, while estimates based on temporal correlations result in a superlinear scaling. This hierarchy of temporal information is preserved if several sources of temporal information are available. Furthermore, we consider the case of multiple stochastic processes and study the examples of a covariance-based model and a model based on synfire chains. This approach reveals that existing neurocomputational models of time perception can be classified as mean-, variance- and correlation-based processes and allows predictions about the scaling of the resulting timing errors.

We introduce an approach to compensate for temporal distortions of repeated measurements in event-related potential research. The algorithm uses a combination of methods from nonlinear time-series analysis and is based on the construction of pairwise registration functions from cross-recurrence plots of the phase-space representations of ERP signals. The globally optimal multiple-alignment path is approximated by hierarchical cluster analysis, i.e. by iteratively combining pairs of trials according to similarity. By the inclusion of context information in form of externally acquired time markers (e.g. reaction time) into a regularization scheme, the extracted warping functions can be guided near paths that are implied by the experimental procedure. All parameters occurring in the algorithm can be optimized based on the properties of the data and there is a broad regime of parameter configurations where the algorithm produces good results. Simulations on artificial data and the analysis of ERPs from a psychophysical study demonstrate the robustness and applicability of the algorithm.

Self-organized control of myoelectric prostheses aims at an automatic selection of communication channels between a prosthetic device and its user. During training, the patient is instructed to generate control signals that follow the observed autonomous movements of the prosthesis. At the same time, the prosthetic controller maximizes both the diversity of movements and the coincidences of prosthetic movements and human control signals by varying the sensory features and control actions. This dual control algorithm is derived from the homeokinetic principle for robot control and is tested in a proportional control task for a hand prostheses.

We present an approach to the control of myoelectric prostheses that is based on a collaborative interaction of a prosthesis and the patient. During training, the patient is instructed to generate control signals that follow the observed autonomous movements of the prosthesis. At the same time, the prosthetic controller maximizes both the diversity of movements and the coincidences of prosthetic movements and human control signals by varying the features and control actions. This dual control principle which is derived from the homeokinetic robot control is demonstrated to be efficient for the control of a hand prostheses with two degrees of freedom.

We investigate two-dimensional neural fields as a model of the dynamics of macroscopic activations in a cortex-like neural system. While the one-dimensional case was treated comprehensively by Amari 30 years ago, two-dimensional neural fields are much less understood. We derive conditions for the stability for the main classes of localized solutions of the neural field equation and study their behavior beyond parameter-controlled destabilization. We show that a slight modification of the original model yields an equation whose stationary states are guaranteed to satisfy the original problem and numerically demonstrate that it admits localized noncircular solutions. Typically, however, only periodic spatial tessellations emerge on destabilization of rotationally invariant solutions.

We describe a learning algorithm that generates behaviors by self-organization of sensorimotor loops in an autonomous robot. The behavior of the robot is analyzed by a multi-expert architecture, where a number of controllers compete for the data from the physical robot. Each expert stabilizes the representation of the acquired sensorimotor mapping in dependence of the achieved prediction error and forms eventually a behavioral primitive. The experts provide a discrete representation of the behavioral manifold of the robot and are suited to form building blocks for complex behaviors.

Priming is characterized by a sensitivity of reaction times to the sequence of stimuli in psychophysical experiments. The reduction of the reaction time observed in positive priming is well-known and experimentally understood (Scarborough et al., J. Exp. Psycholol: Hum. Percept. Perform., 3, pp. 1–17, 1977). Negative priming—the opposite effect—is experimentally less tangible (Fox, Psychonom. Bull. Rev., 2, pp. 145–173, 1995). The dependence on subtle parameter changes (such as response-stimulus interval) usually varies. The sensitivity of the negative priming effect bears great potential for applications in research in fields such as memory, selective attention, and ageing effects. We develop and analyse a computational realization, CISAM, of a recent psychological model for action decision making, the ISAM (Kabisch, PhD thesis, Friedrich-Schiller-Universität, 2003), which is sensitive to priming conditions. With the dynamical systems approach of the CISAM, we show that a single adaptive threshold mechanism is sufficient to explain both positive and negative priming effects. This is achieved by comparing results obtained by the computational modelling with experimental data from our laboratory. The implementation provides a rich base from which testable predictions can be derived, e.g. with respect to hitherto untested stimulus combinations (e.g. single-object trials).

There is experimental evidence that cortical neurons show avalanche activity with the intensity of firing events being distributed as a power-law. We present a biologically plausible extension of a neural network which exhibits a power-law avalanche distribution for a wide range of connectivity parameters.

Clusters of correlated activity in functional magnetic resonance imaging data can identify regions of interest and indicate interacting brain areas. Because the extraction of clusters is computationally complex, we apply an approximative method which is based on artificial neural networks. It allows one to find clusters of various degrees of connectivity ranging between the two extreme cases of cliques and connectivity components. We propose a criterion which allows to evaluate the relevance of such structures based on the robustness with respect to parameter variations. Exploiting the intracluster correlations, we can show that regions of substantial correlation with an external stimulus can be unambiguously separated from other activity.

We analyze the effects of synaptic depression on the stability of patterns stored in neural networks with low activity level. Applying mean-field theory we show that the stationary states remain unaffected by the synaptic depression. However the stability of memory patterns changes drastically causing a reduction of memory capacity. Further, it is demonstrated and confirmed by numerical calculations that the sensitivity of the network to input changes is enhanced.

High-dimensional models of pattern formation in visual cortex can be replaced by low-dimensional feature models provided that relations among the features reflect the high-dimensional structure. We consider orientation columns in a simplified flat high-dimensional setting and show that an exact derivation of a Riemannian-curved low-dimensional model is possible. Further evidence to the curved model is provided by the fact that the number of pinwheels is shown to stay non-zero in coincidence with finding in animals though in contrast to other models.

The neighborhood preservation of self-organizing feature maps like the Kohonen map is an important property which is exploited in many applications. However, if a dimensional conflict arises this property is lost. Various qualitative and quantitative approaches are known for measuring the degree of topology preservation. They are based on using the locations of the synaptic weight vectors. These approaches, however, may fail in case of nonlinear data manifolds. To overcome this problem, in this paper we present an approach which uses what we call the induced receptive fields for determining the degree of topology preservation. We first introduce a precise definition of topology preservation and then propose a tool for measuring it, the topographic function. The topographic function vanishes if and only if the map is topology preserving. We demonstrate the power of this tool for various examples of data manifolds.