Family Violence and Football: The Effect of Unexpected Emotional Cues on Violent Behavior

Family violence is a pervasive and costly problem, yet there is no consensus on how to interpret the phenomenon of violence by one family member against another. Some analysts assume that violence has an instrumental role in intra-family incentives. Others argue that violent episodes represent a loss of control that the offender immediately regrets. In this paper we specify and test a behavioral model of the latter form in which the strength of an emotional cue depends on outcomes relative to expectations and individuals exhibit loss aversion. Our key hypothesis is that negative emotional cues -- benchmarked relative to a rationally expected reference point -- make a breakdown of control more likely. We test this hypothesis using data on police reports of family violence on Sundays during the professional football season. Controlling for location and time fixed effects, weather factors, the pre-game point spread, and the size of the local viewing audience, we find that upset losses by the home team (losses in games that the home team was predicted to win by more than 3 points) lead to an 8 percent increase in police reports of at-home male-on-female intimate partner violence. There is no corresponding effect on female-on-male violence. Consistent with the behavioral prediction that losses matter more than gains, upset victories by the home team have (at most) a small dampening effect on family violence. We also find that unexpected losses in highly salient or frustrating games have a 50% to 100% larger impact on rates of family violence. The evidence that payoff-irrelevant events affect the rate of family violence leads us to conclude that at least some fraction of family violence is better characterized as a breakdown of control than as an intra-family incentive system. More generally, the empirical findings suggest that gain-loss utility with a rational reference point could be a useful approach to modeling other cues and visceral influences.

Hopf bifurcations in time-delay systems with band-limited feedback

We investigate the steady-state solution and its bifurcations in time-delay systems with band-limited feedback. This is a first step in a rigorous study concerning the effects of AC-coupled components in nonlinear devices with time-delayed feedback. We show that the steady state is globally stable for small feedback gain and that local stability is lost, generically, through a Hopf bifurcation for larger feedback gain. We provide simple criteria that determine whether the Hopf bifurcation is supercritical or subcritical based on the knowledge of the first three terms in the Taylor-expansion of the nonlinearity. Furthermore, the presence of double-Hopf bifurcations of the steady state is shown, which indicates possible quasiperiodic and chaotic dynamics in these systems. As a result of this investigation, we find that AC-coupling introduces fundamental differences to systems of Ikeda-type [Ikeda et al., Physica D 29 (1987) 223-235] already at the level of steady-state bifurcations, e.g. bifurcations exist in which limit cycles are created with periods other than the fundamental ``period-2'' mode found in Ikeda-type systems.Comment: 32 pages, 5 figures, accepted for publication in Physica D: Nonlinear Phenomen

Generalized Stochastic Gradient Learning

We study the properties of generalized stochastic gradient (GSG) learning in forwardlooking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both di1er from and are related to E-stability, which governs stability under least squares learning. SG algorithms are sensitive to units of measurement and we show that there is a transformation of variables for which E-stability governs SG stability. GSG algorithms with constant gain have a deeper justification in terms of parameter drift, robustness and risk sensitivity

Is social decision making for close others consistent across domains and within individuals?

Humans make decisions across a variety of social contexts. Though social decision-making research has blossomed in recent decades, surprisingly little is known about whether social decision-making preferences are consistent across different domains. We conducted an exploratory study in which participants made choices about 2 types of close others: parents and friends. To elicit decision making preferences, we pit the interests in parents and friends against one another. To assess the consistency of preferences for close others, decision making was assessed in three domains-risk taking, probabilistic learning, and self-other similarity judgments. We reasoned that if social decision-making preferences are consistent across domains, participants ought to exhibit the same preference in all three domains (i.e., a parent preference, based on prior work), and individual differences in preference magnitude ought to be conserved across domains within individuals. A combination of computational modeling, random coefficient regression, and traditional statistical tests revealed a robust parent-over-friend preference in the risk taking and probabilistic learning domains but not the self-other similarity domain. Preferences for parent-over-friend in the risk-taking domain were strongly associated with similar preferences in the probabilistic learning domain but not the self-other similarity domain. These results suggest that distinct and dissociable value-based and social-cognitive computations underlie social decision making. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

Chaotic Dynamics Enhance the Sensitivity of Inner Ear Hair Cells

Hair cells of the auditory and vestibular systems are capable of detecting sounds that induce sub-nanometer vibrations of the hair bundle, below the stochastic noise levels of the surrounding fluid. Hair bundles of certain species are also known to oscillate without external stimulation, indicating the presence of an underlying active mechanism. We propose that chaotic dynamics enhance the sensitivity and temporal resolution of the hair bundle response, and provide experimental and theoretical evidence for this effect. By varying the viscosity and ionic composition of the surrounding fluid, we are able to modulate the degree of chaos observed in the hair bundle dynamics in vitro. We consistently find that the hair bundle is most sensitive to a stimulus of small amplitude when it is poised in the weakly chaotic regime. Further, we show that the response time to a force step decreases with increasing levels of chaos. These results agree well with our numerical simulations of a chaotic Hopf oscillator and suggest that chaos may be responsible for the sensitivity and temporal resolution of hair cells

Transition to chaos in random neuronal networks

Firing patterns in the central nervous system often exhibit strong temporal irregularity and heterogeneity in their time averaged response properties. Previous studies suggested that these properties are outcome of an intrinsic chaotic dynamics. Indeed, simplified rate-based large neuronal networks with random synaptic connections are known to exhibit sharp transition from fixed point to chaotic dynamics when the synaptic gain is increased. However, the existence of a similar transition in neuronal circuit models with more realistic architectures and firing dynamics has not been established. In this work we investigate rate based dynamics of neuronal circuits composed of several subpopulations and random connectivity. Nonzero connections are either positive-for excitatory neurons, or negative for inhibitory ones, while single neuron output is strictly positive; in line with known constraints in many biological systems. Using Dynamic Mean Field Theory, we find the phase diagram depicting the regimes of stable fixed point, unstable dynamic and chaotic rate fluctuations. We characterize the properties of systems near the chaotic transition and show that dilute excitatory-inhibitory architectures exhibit the same onset to chaos as a network with Gaussian connectivity. Interestingly, the critical properties near transition depend on the shape of the single- neuron input-output transfer function near firing threshold. Finally, we investigate network models with spiking dynamics. When synaptic time constants are slow relative to the mean inverse firing rates, the network undergoes a sharp transition from fast spiking fluctuations and static firing rates to a state with slow chaotic rate fluctuations. When the synaptic time constants are finite, the transition becomes smooth and obeys scaling properties, similar to crossover phenomena in statistical mechanicsComment: 28 Pages, 12 Figures, 5 Appendice

Interaction-induced mode switching in steady-state microlasers

We demonstrate that due to strong modal interactions through cross-gain saturation, the onset of a new lasing mode can switch off an existing mode via a negative power slope. In this process of interaction-induced mode switching (IMS) the two involved modes maintain their identities, i.e. they do not change their spatial field patterns or lasing frequencies. For a fixed pump profile, a simple analytic criterion for the occurrence of IMS is given in terms of their self- and cross-interaction coefficients and non-interacting thresholds, which is verified for the example of a two-dimensional microdisk laser. When the spatial pump profile is varied as the pump power is increased, IMS can be induced even when it would not occur with a fixed pump profile, as we show for two coupled laser cavities. Our findings apply to steady-state lasing and are hence different from dynamical mode switching or hopping. IMS may have potential applications in robust and flexible all-optical switching.Comment: 14 pages, 5 figure

Are risk preferences dynamic? : Within-subject variation in risk-taking as a function of background music

This paper investigates whether preference interactions can explain why risk preferences change over time and across contexts. We conduct an experiment in which subjects accept or reject gambles involving real money gains and losses. We introduce within-subject variation by alternating subjectively liked music and disliked music in the background. We find that favourite music increases risk-taking, and disliked music suppresses risk-taking, compared to a baseline of no music. Several theories in psychology propose mechanisms by which mood affects risktaking, but none of them fully explain our results. The results are, however, consistent with preference complementarities that extend to risk preference

Self-consistent method for density estimation

The estimation of a density profile from experimental data points is a challenging problem, usually tackled by plotting a histogram. Prior assumptions on the nature of the density, from its smoothness to the specification of its form, allow the design of more accurate estimation procedures, such as Maximum Likelihood. Our aim is to construct a procedure that makes no explicit assumptions, but still providing an accurate estimate of the density. We introduce the self-consistent estimate: the power spectrum of a candidate density is given, and an estimation procedure is constructed on the assumption, to be released \emph{a posteriori}, that the candidate is correct. The self-consistent estimate is defined as a prior candidate density that precisely reproduces itself. Our main result is to derive the exact expression of the self-consistent estimate for any given dataset, and to study its properties. Applications of the method require neither priors on the form of the density nor the subjective choice of parameters. A cutoff frequency, akin to a bin size or a kernel bandwidth, emerges naturally from the derivation. We apply the self-consistent estimate to artificial data generated from various distributions and show that it reaches the theoretical limit for the scaling of the square error with the dataset size.Comment: 21 pages, 5 figure