The empirical literature on phenomenal causality (i.e., the notion that causality can be perceived) is reviewed. In Part I of this two-part series, different potential types of phenomenal causality (launching, triggering, reaction, tool, entraining, traction, braking, enforced disintegration and bursting, coordinated movement, penetration, expulsion) are described. Stimulus variables (temporal gap, spatial gap, spatial overlap, direction, absolute velocity, velocity ratio, trajectory length, radius of action, size, motion type, modality, animacy) and observer variables (attention, eye movements and fixation, prior experience, intelligence, age, (...) culture, psychopathology) that influence phenomenal causality are reviewed. This provides the necessary background for consideration in Part II (Hubbard, in press) of broader questions regarding properties of phenomenal causality, empirical and theoretical connections of phenomenal causality to other perceptual or cognitive phenomena or processes, and potential mechanisms and models of phenomenal causality. (shrink)

Recent work suggests that people predict how objects interact in a manner consistent with Newtonian physics, but with additional uncertainty. However, the sources of uncertainty have not been examined. In this study, we measure perceptual noise in initial conditions and stochasticity in the physical model used to make predictions. Participants predicted the trajectory of a moving object through occluded motion and bounces, and we compared their behavior to an ideal observer model. We found that human judgments cannot be captured by (...) simple heuristics and must incorporate noisy dynamics. Moreover, these judgments are biased consistently with a prior expectation on object destinations, suggesting that people use simple expectations about outcomes to compensate for uncertainty about their physical models. (shrink)

The empirical literature on phenomenal causality (the notion that causality can be perceived) is reviewed. Different potential types of phenomenal causality and variables that influence phenomenal causality were considered in Part I (Hubbard 2012b) of this two-part series. In Part II, broader questions regarding properties of phenomenal causality and connections of phenomenal causality to other perceptual or cognitive phenomena (different types of phenomenal causality, effects of spatial and temporal variance, phenomenal causality in infancy, effects of object properties, naïve physics, spatial (...) localization, other illusions, amodal completion, Gestalt principles of perceptual grouping, effects of context, differences between physical and social causality, effects of learning and experience, individual differences, effects of predictability, asymmetry in phenomenal causality, differences between perceived causality and perceived force, phenomenal causality in nonhuman animals) are considered. Potential mechanisms of phenomenal causality (inference from contiguity, a priori understanding, ampliation, perceptual learning, stimulus activity, beliefs regarding kinematics, haptic experience, beliefs regarding impetus, postdiction, innateness, modularity, specific neural structures) are also considered. (shrink)

In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sampling-based approximations are a common way to implement Bayesian (...) inference, the very limited numbers of samples often used by humans seem insufficient to approximate the required probability distributions very accurately. Here, we consider this discrepancy in the broader framework of statistical decision theory, and ask: If people are making decisions based on samples—but as samples are costly—how many samples should people use to optimize their total expected or worst-case reward over a large number of decisions? We find that under reasonable assumptions about the time costs of sampling, making many quick but locally suboptimal decisions based on very few samples may be the globally optimal strategy over long periods. These results help to reconcile a large body of work showing sampling-based or probability matching behavior with the hypothesis that human cognition can be understood in Bayesian terms, and they suggest promising future directions for studies of resource-constrained cognition. (shrink)

Displacements in the remembered location of stimuli in displays based on Michotte’s (1946/1963) launching effect and entraining effect were examined. A moving object contacted an initially stationary target, and the target began moving. After contacting the target, the mover became stationary (launching trials) or continued moving in the same direction and remained adjacent to the target (entraining trials). In launching trials, forward displacement was smaller for targets than for movers; in entraining trials, forward displacement was smaller for movers than for (...) targets. Also, forward displacement was smaller for targets in launching trials than for targets in entraining trials. Data are consistent with a hypothesis that the launching effect involves an attribution that the mover imparted to the target a dissipating impetus that was responsible for target motion. Introspective experience of a perception of physical causality in the launching effect might result because behavior of movers and targets is consistent with that predicted by an impetus heuristic. (shrink)