Causal Induction from Continuous Event Streams: Evidence for Delay-Induced Attribution Shifts

Contemporary theories of Human Causal Induction assume that causal knowledge is inferred from observable contingencies. While this assumption is well supported by empirical results, it fails to consider an important problem-solving aspect of causal induction in real time: In the absence of well structured learning trials, it is not clear whether the effect of interest occurred because of the cause under investigation, or on its own accord. Attributing the effect to either the cause of interest or alternative background causes is an important precursor to induction. We present a new paradigm based on the presentation of continuous event streams, and use it to test the Attribution-Shift Hypothesis (Shanks & Dickinson, 1987), according to which temporal delays sever the attributional link between cause and effect. Delays generally impaired attribution to the candidate, and increased attribution to the constant background of alternative causes. In line with earlier research (Buehner & May, 2002, 2003, 2004) prior knowledge and experience mediated this effect. Pre-exposure to a causally ineffective background context was found to facilitate the discovery of delayed causal relationships by reducing the tendency for attributional shifts to occur. However, longer exposure to a delayed causal relationship did not improve discovery. This complex pattern of results is problematic for associative learning theories, but supports the Attribution-Shift Hypothesi

Modeling Socially Desirable Responding and Its Effects

The impact of socially desirable responding or faking on noncognitive assessments remains an issue of strong debate. One of the main reasons for the controversy is the lack of a statistical method to model such response sets. This article introduces a new way to model faking based on the assumption that faking occurs due to an interaction between person and situation. The technique combines a control group design with structural equation modeling and allows a separation of trait and faking variance. The model is introduced and tested in an example. The results confirm a causal nfluence of faking on means and covariance structure of a Big 5 questionnaire. Both effects can be reversed by the proposed model. Finally, a real-life criterion was implemented and predicted by both variance sources. In this example, it was the trait but not the faking variance that was predictive. Implications for research and practice are discussed

Ensemble prediction for nowcasting with a convection-permitting model - II: forecast error statistics

A 24-member ensemble of 1-h high-resolution forecasts over the Southern United Kingdom is used to study short-range forecast error statistics. The initial conditions are found from perturbations from an ensemble transform Kalman filter. Forecasts from this system are assumed to lie within the bounds of forecast error of an operational forecast system. Although noisy, this system is capable of producing physically reasonable statistics which are analysed and compared to statistics implied from a variational assimilation system. The variances for temperature errors for instance show structures that reflect convective activity. Some variables, notably potential temperature and specific humidity perturbations, have autocorrelation functions that deviate from 3-D isotropy at the convective-scale (horizontal scales less than 10 km). Other variables, notably the velocity potential for horizontal divergence perturbations, maintain 3-D isotropy at all scales. Geostrophic and hydrostatic balances are studied by examining correlations between terms in the divergence and vertical momentum equations respectively. Both balances are found to decay as the horizontal scale decreases. It is estimated that geostrophic balance becomes less important at scales smaller than 75 km, and hydrostatic balance becomes less important at scales smaller than 35 km, although more work is required to validate these findings. The implications of these results for high-resolution data assimilation are discussed

When causality shapes the experience of time: evidence for temporal binding in young children

It is well-established that the temporal proximity of two events is a fundamental cue to causality. Recent research with adults has shown that this relation is bidirectional: events that are believed to be causally related are perceived as occurring closer together in time—the so-called temporal binding effect. Here we examined the developmental origins of temporal binding. Participants predicted when an event that was either caused by a button press, or preceded by a non-causal signal, would occur. We demonstrate for the first time that children as young as four years are susceptible to temporal binding. Binding occurred both when the button press was executed via intentional action, and when a machine caused it. These results suggest binding is a fundamental, early developing property of perception and grounded in causal knowledge

Causality influences children’s and adults’ experience of temporal order

Although it has long been known that time is a cue to causation, recent work with adults has demonstrated that causality can also influence the experience of time. In causal reordering (Bechlivanidis & Lagnado, 2013, 2016) adults tend to report the causally consistent order of events rather than the correct temporal order. However, the effect has yet to be demonstrated in children. Across four preregistered experiments, 4- to 10-year-old children (N = 813) and adults (N = 178) watched a 3-object Michotte-style “pseudocollision.” While in the canonical version of the clip, object A collided with B, which then collided with object C (order: ABC), the pseudocollision involved the same spatial array of objects but featured object C moving before object B (order: ACB), with no collision between B and C. Participants were asked to judge the temporal order of events and whether object B collided with C. Across all age groups, participants were significantly more likely to judge that B collided with C in the 3-object pseudocollision than in a 2-object control clip (where clear causal direction was lacking), despite the spatiotemporal relations between B and C being identical in the two clips (Experiments 1-3). Collision judgments and temporal order judgments were not entirely consistent, with some participants—particularly in the younger age range—basing their temporal order judgments on spatial rather than temporal information (Experiment 4). We conclude that in both children and adults, rather than causal impressions being determined only by the basic spatial–temporal properties of object movement, schemata are used in a top-down manner when interpreting perceptual displays

The equivalent-weights particle filter in a high-dimensional system

In general, particle filters need large numbers of model runs in order to avoid filter degeneracy in high-dimensional systems. The recently proposed, fully nonlinear equivalent-weights particle filter overcomes this requirement by replacing the standard model transition density with two different proposal transition densities. The first proposal density is used to relax all particles towards the high-probability regions of state space as defined by the observations. The crucial second proposal density is then used to ensure that the majority of particles have equivalent weights at observation time. Here, the performance of the scheme in a high, 65 500 dimensional, simplified ocean model is explored. The success of the equivalent-weights particle filter in matching the true model state is shown using the mean of just 32 particles in twin experiments. It is of particular significance that this remains true even as the number and spatial variability of the observations are changed. The results from rank histograms are less easy to interpret and can be influenced considerably by the parameter values used. This article also explores the sensitivity of the performance of the scheme to the chosen parameter values and the effect of using different model error parameters in the truth compared with the ensemble model runs

Exploring the effects of replicating shape, weight and recoil effects on VR shooting controllers

Commercial Virtual Reality (VR) controllers with realistic force feedback are becoming available, to increase the realism and immersion of first-person shooting (FPS) games in VR. These controllers attempt to mimic not only the shape and weight of real guns but also their recoil effects (linear force feedback parallel to the barrel, when the gun is shot). As these controllers become more popular and affordable, this paper investigates the actual effects that these properties (shape, weight, and especially directional force feedback) have on performance for general VR users (e.g. users with no marksmanship experience), drawing conclusions for both consumers and device manufacturers. We created a prototype replicating the properties exploited by commercial VR controllers (i.e. shape, weight and adjustable force feedback) and used it to assess the effect of these parameters in user performance, across a series of user studies. We first analysed the benefits on user performance of adding weight and shape vs a conventional controller (e.g. Vive controller). We then explore the implications of adding linear force feedback (LFF), as well as replicating the shape and weight. Our studies show negligible effects on the immediate shooting performance with some improvements in subjective appreciation, which are already present with low levels of LFF. While higher levels of LFF do not increase subjective appreciations any further, they lead users to reach their maximum distance skillset more quickly. This indicates that while adding low levels of LFF can be enough to influence user’s immersion/engagement for gaming contexts, controllers with higher levels of LFF might be better suited for training environments and/or when dealing with particularly demanding aiming tasks