Temporal predictability enhances judgements of causality in elemental causal induction from both observation and intervention

When the temporal interval or delay separating cause and effect is consistent over repeated instances, it becomes possible to predict when the effect will follow from the cause, hence temporal predictability serves as an appropriate term for describing consistent cause-effect delays. It has been demonstrated that in instrumental action-outcome learning tasks, enhancing temporal predictability by holding the cause-effect interval constant elicits higher judgements of causality compared to conditions involving variable temporal intervals. Here, we examine whether temporal predictability exerts a similar influence when causal learning takes place through observation rather than intervention through instrumental action. Four experiments demonstrated that judgements of causality were higher when the temporal interval was constant than when it was variable, and that judgements declined with increasing variability. We further found that this beneficial effect of predictability was stronger in situations where the effect base-rate was zero (Experiments 1 and 3). The results therefore clearly indicate that temporal predictability enhances impressions of causality, and that this effect is robust and general. Factors that could mediate this effect are discussed.     

Consistent Treatment of Variables and Causation Poses a Challenge for Behavioral Research Methods: A Commentary on Nesselroade and Molenaar (2016)

Nesselroade and Molenaar presented the ideographic filter as a proposal for analyzing lawful regularities in behavioral research. The proposal highlights an inconsistency that poses a challenge for behavioral research more generally. One can distinguish a broadly Humean approach from a broadly non-Humean approach as they relate to variables and to causation. Nesselroade and Molenaar rejected a Humean approach to latent variables that characterizes them as nothing more than summaries of their manifest indicators. By contrast, they tacitly accepted a Humean approach to causes characterized as nothing more than summaries of their manifest causal effects. A non-Humean treatment of variables coupled with a Humean treatment of causation creates a theoretical tension within their proposal. For example, one can interpret the same model elements as simultaneously representing both variables and causes. Future refinement of the ideographic filter proposal to address this tension could follow any of a number of strategies.     

Searching for Control: Priming Randomness Increases the Evaluation of Ritual Efficacy

Reestablishing feelings of control after experiencing uncertainty has long been considered a fundamental motive for human behavior. We propose that rituals (i.e., socially stipulated, causally opaque practices) provide a means for coping with the aversive feelings associated with randomness due to the perception of a connection between ritual action and a desired outcome. Two experiments were conducted (one in Brazil [n = 40] and another in the United States [n = 94]) to evaluate how the perceived efficacy of rituals is affected by feelings of randomness. In a between‐subjects design, the Scramble Sentence Task was used as a priming procedure in three conditions (i.e., randomness, negativity, and neutral) and participants were then asked to rate the efficacy of rituals used for problem‐solving purposes. The results demonstrate that priming randomness increased participants' perception of ritual efficacy relative to negativity and neutral conditions. Implications for increasing our understanding of the relationship between perceived control and ritualistic behavior are discussed.     

Learning the form of causal relationships using hierarchical bayesian models

People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.     

The unfolding argument: Why IIT and other causal structure theories cannot explain consciousness

How can we explain consciousness? This question has become a vibrant topic of neuroscience research in recent decades. A large body of empirical results has been accumulated, and many theories have been proposed. Certain theories suggest that consciousness should be explained in terms of brain functions, such as accessing information in a global workspace, applying higher order to lower order representations, or predictive coding. These functions could be realized by a variety of patterns of brain connectivity. Other theories, such as Information Integration Theory (IIT) and Recurrent Processing Theory (RPT), identify causal structure with consciousness. For example, according to these theories, feedforward systems are never conscious, and feedback systems always are. Here, using theorems from the theory of computation, we show that causal structure theories are either false or outside the realm of science.     

The larger the cause,the larger the effect: evidence of speed judgment biases in causal scenarios

ABSTRACT

When two motions appear to be causally related, the spatiotemporal features of motions are sometimes distorted in order to increase the consistency with causal impressions. Here, in four experiments, we tested if varying the speed of an object A could affect the judged speed of an object B that appeared to be causally related to A. Participants were presented with classic launching stimuli (Experiment 1), a variant of launching stimuli in which A could move with uniformly accelerated or decelerated motion (Experiment 2), non-launching stimuli that elicited a causal impression (Experiment 3), and stimuli showing a three-object launching event (Experiment 4). Main results showed that the judged speed of B was systematically biased towards the speed of A, and moreover that the judged speed of B depended on the average speed of A, rather than on the speed of A at the moment of collision as it would be predicted by Newtonian mechanics. The results are consistent with the hypothesis that internal representations of causal events based on property transmission (for instance, impetus) can affect judgments of the low-level properties of causal scenarios.     

Visual perception of complex shape-transforming processes

Morphogenesis—or the origin of complex natural form—has long fascinated researchers from practically every branch of science. However, we know practically nothing about how we perceive and understand such processes. Here, we measured how observers visually infer shape-transforming processes. Participants viewed pairs of objects (‘before’ and ‘after’ a transformation) and identified points that corresponded across the transformation. This allowed us to map out in spatial detail how perceived shape and space were affected by the transformations. Participants’ responses were strikingly accurate and mutually consistent for a wide range of non-rigid transformations including complex growth-like processes. A zero-free-parameter model based on matching and interpolating/extrapolating the positions of high-salience contour features predicts the data surprisingly well, suggesting observers infer spatial correspondences relative to key landmarks. Together, our findings reveal the operation of specific perceptual organization processes that make us remarkably adept at identifying correspondences across complex shape-transforming processes by using salient object features. We suggest that these abilities, which allow us to parse and interpret the causally significant features of shapes, are invaluable for many tasks that involve ‘making sense’ of shape.