Intuitions and perceptual constraints on causal learning from dynamics

Many of the real world phenomena that cognizers must grapple with are continuous, not only in the values they can take, but also in how these values change over time. The mind must somehow abstract from these inputs to extract useful discrete concepts such as objects, events and causal relationships. We investigate several factors that affect basic inferences about causal relationships between continuous variables based on observations in continuous time. In a novel experiment, we explore the ways in which causal judgments are sensitive to factors that relate to causal inductive biases (e.g. causal lags, the direction of variation) and causal perception (e.g. the range and rapidity of variation). We argue standard statistical time-series models have limited utility in accounting for human sensitivity to these factors. We suggest further work is needed to fully understand the cognitive processes that underlie causal induction from time-series information

Evidence from the future

The outcome of any scientific experiment or intervention will naturally unfold over time. How then should individuals make causal inferences from measurements over time? Across three experiments, we had participants observe experimental and control groups over several days post-treatment in a fictional biological research setting. We identify competing perspectives in the literature: Contingency-driven accounts predict no effect of outcome timing while the contiguity principle suggests people will view a treatment as more harmful to the extent that bad treatment outcomes occur earlier rather than later. In contrast, inference to the functional form of a treatment effect can license extrapolation beyond the measurements and lead to different causal inferences. We find participants’ causal strength and direction judgments in temporal settings vary with minimal manipulations of instruction framing. When it is implied that the observations are made over a pre-planned number of days, causal judgments depend strongly on contiguity. When it is implied that the observation may be ongoing, participants extrapolate current trends into the future and adapt their causal judgments accordingly. When data are revealed sequentially, participants rely on extrapolation regardless of instruction framing. Our results demonstrate human flexibility in interpreting temporal evidence for causal reasoning and emphasize human tendency to generalize from evidence in ways that are acutely sensitive to task framing

Learning preventative and generative causal structures from point events in continuous time

Many previous accounts of causal structure induction have focused on atemporalcontingency data while fewer have described learning on the basis of observations of events unfolding over time. How do people use temporal information to infer causal structures? Here we develop a computational-level framework and propose several algorithmic-level approximations to explain how people impute causal structures from continuous-time event sequences. We compare both normative and process accounts to participant behavior across two experiments. We consider structures combining both generative and preventative causal relationships in the presence of either regular or irregular background noise in the form of spontaneous activations. We find that 1) humans are robustly capable learners in this setting, successfully identifying a variety of ground truth structures but 2) diverging from our computational-level account in ways we can explain with a more tractable simulation and summary statistics approximation scheme. We thus argue that human structure induction from temporal information relies on comparisons between observed patterns and expectations established via mental simulation

Inferring epistemic intention in simulated physical microworlds

We explore whether people can recognise the epistemic goal of active learners. In a novel online experiment, 110 adults watched screen recordings of other adults (``players'') manipulating objects in a 2D simulated physical microworld. Players had the goal of either identifying the magnet-like force connecting two of the objects, or their relative masses. Observers were asked to identify the learning goal of the player. By drawing from a previously collected dataset of active physical learning interactions and an ideal observer analysis, we manipulated how informative the players' actions are about the target property, and observers' level of access to the players' micro-control actions. We found observers were better at identifying the goals of successful players and of players trying to identify force, while the micro-dynamic evidence improved accuracy on identifying the mass goal. We use mixed methods to explore what cues observers used to make these judgments

What you didn't see:Prevention and generation in continuous time causal induction

How do people use temporal information to make causal judg-ments? A number of studies have investigated the role of timein inferring generative causal structure, while few have exam-ined prevention. Here, we focus on a challenging task in whichparticipants learn the structure of several causal “devices” bywatching the devices’ patterns of activation over time. Eachdevice potentially includes both generative (producing an acti-vation of its effect) and preventative (blocking any effect acti-vations within a short time window) causal relationships. Weexamine judgment patterns through the lens of a normativemodel which incorporates actual causation with considerationsof prevention. We contrast this with a more computationallytractable feature-based approximation. Participants’ perfor-mance was substantially above chance in all conditions. Themajority of participants’ causal judgments were best fit by thefeature-based approximation based on delay and count heuris-tic cues