Neil Bramley

The University of Edinburgh | UoE · Department of Psychology

A defining aspect of being human is an ability to reason about the world by generating and adapting ideas and hypotheses. Here we explore how this ability develops by comparing children's and adults' active search and explicit hypothesis generation patterns in a task that mimics the open-ended process of scientific induction. In our experiment, 54...

Computational models are powerful tools for understanding human cognition and behavior. They let us express our theories clearly and precisely, and offer predictions that can be subtle and often counter-intuitive. However, this same richness and ability to surprise means our scientific intuitions and traditional tools are ill-suited to designing ex...

Research on causal cognition has largely focused on learning and reasoning about contingency data aggregated across discrete observations or experiments. However, this setting represents only the tip of the causal cognition iceberg. A more general problem lurking beneath is that of learning the latent causal structure that connects events and actio...

Human cognition is marked by its ability to explain patterns in the world in terms of variables and regularities that are not directly observable, e.g., mental states, natural laws, and causal relationships. Previous research has demonstrated a capacity for inferring hidden causes from covariational evidence, as well as the use of temporal informat...

Over-flexibility in the definition of Friston blankets obscures a key distinction between observational and interventional inference. The latter requires cognizers form not just a causal representation of the world but also of their own boundary and relationship with it, in order to diagnose the consequences of their actions. We suggest this locate...

We explore how children and adults actively experiment within the physical world to achieve different epistemic goals. In our experiment, one hundred one 4- to 10-year-old children and 24 adults either passively observed or used a touchscreen interface to actively interact with objects in a dynamic physical microworld with the goal of inferring one...

We investigate the idea that human concept inference utilizes local adaptive search within a compositional mental theory space. To explore this, we study human judgments in a challenging task that involves actively gathering evidence about a symbolic rule governing the behavior of a simulated environment. Participants learn by performing mini-exper...

Changing one variable at a time while controlling others is a key aspect of scientific experimentation and a central component of STEM curricula. However, children reportedly struggle to learn and implement this strategy. Why do children’s intuitions about how best to intervene on a causal system conflict with scientific practices? Mathematical ana...

Most research into causal learning has focused on atemporal contingency data settings while fewer studies have examined learning and reasoning about systems exhibiting events that unfold in continuous time. Of these, none have yet explored learning about preventative causal influences. How do people use temporal information to infer which component...

Recent work has shown that people use temporal information including order, delay, and variability to infer causality between events. In this study, we build on this work by investigating the role of time in dynamic systems, where causes take continuous values and also continually influence their effects. Recent studies of learning in these systems...

We explore how children and adults actively experiment within the physical world to achieve different epistemic goals. In our experiment, 101 4-10-year-old children and 24 adults either passively observed or used a touchscreen interface to actively interact with objects in a dynamic physical microworld with the goal of inferring one of two latent p...

Human learning and generalization benefit from bootstrapping: we arrive at complex concepts by starting small and building upon past successes. In this paper, we examine a computational account of causal conceptual bootstrapping, and describe a novel experiment in which the sequence of training data results in a dramatic order effect: participants...

Research on causal learning has largely focused on learning and reasoning about contingency data aggregated across discrete observations or experiments. However, this setting represents only the tip of the causal cognition iceberg. A more general problem lurking beneath is that of learning the latent causal structure that connects events and action...

Generalization does not come from repeatedly observing phenomena in numerous settings, but from theories explaining what is general in those phenomena. Expecting future behavior to look like past observations is especially problematic in psychology, where behaviors change when people's knowledge changes. Psychology should thus focus on theories of...

How do people decide how general a causal relationship is, in terms of the entities or situations it applies to? What features do people use to decide whether a new situation is governed by a new causal law or an old one? How can people make these difficult judgments in a fast, efficient way? We address these questions in two experiments that ask p...

We present a novel task that measures how people generalize objects' causal powers based on observing a single (Experiment 1) or a few (Experiment 2) causal interactions between object pairs. We propose a computational modeling framework that can synthesize human-like generalization patterns in our task setting, and sheds light on how people may na...

Bayesian optimal experimental design (BOED) is a methodology to identify experiments that are expected to yield informative data. Recent work in cognitive science considered BOED for computational models of human behavior with tractable and known likelihood functions. However, tractability often comes at the cost of realism; simulator models that c...

We investigate the idea that human concept inference utilizes local incremental search within a compositional mental theory space. To explore this, we study judgments in a challenging task, where participants actively gather evidence about a symbolic rule governing the behavior of a simulated environment. Participants construct mini-experiments bef...

Generalization does not come from repeatedly observing phenomena in numerous settings, but from theories explaining what is general in those phenomena. Expecting future behavior to look like past observations is especially problematic in psychology, where behaviors change when people’s knowledge changes. Psychology should thus focus on theories of...

How do people decide how general a causal relationship is, in terms of the entities or situations it applies to? What features do people use to decide whether a new situation is governed by a new causal law or an old one? How can people make these difficult judgments in a fast, efficient way? We address these questions in two experiments that ask p...

Suspense is a cognitive and affective state that is often experienced in the anticipation of information and contributes to the enjoyment and consumption of entertainment such as movies or sports. Ely et al. proposed a formal definition of suspense which relies upon predictions about future belief updates. In order to empirically evaluate this theo...

A popular explanation of the human ability for physical reasoning is that it depends on a sophisticated ability to perform mental simulations. According to this perspective, physical reasoning problems are approached by repeatedly simulating relevant aspects of a scenario, with noise, and making judgments based on aggregation over these simulations...

A popular explanation of the human ability for physical reasoning is that it depends on a sophisticated ability to perform mental simulations. According to this perspective, physical reasoning problems are approached by repeatedly simulating relevant aspects of a scenario, with noise, and making judgments based on aggregation over these simulations...

One remarkable aspect of human cognition is our ability to reason about physical events. This article provides novel evidence that intuitive physics is subject to a peculiar error, the classic conjunction fallacy, in which people rate the probability of a conjunction of two events as more likely than one constituent (a logical impossibility). Parti...

Suspense is a cognitive and affective state that is often experienced in the anticipation of information and contributes to the enjoyment and consumption of entertainment such as movies or sports. \cite{ely_suspense_2015} proposed a formal definition of suspense which relies upon predictions about future belief updates. In order to empirically eval...

Changing one variable at a time while controlling others is a key aspect of scientific experimentation and is a central component of STEM curricula. However, children struggle to learn and implement this strategy. Why do children's intuitions about how best to intervene on a causal system conflict with accepted scientific practices? Interestingly,...

Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e., those that have been mapped to binary variables and abstracted from time. For example, participants may be asked to assess the efficacy of a headache-relief pill on the...

What is the best way of discovering the underlying structure of a causal system composed of multiple variables? One prominent idea is that learners should manipulate each candidate variable in isolation to avoid confounds (sometimes known as the control of variables [CV] strategy). We demonstrate that CV is not always the most efficient method for...

In this paper, we describe three experiments involving simple physical judgments and predictions, and argue their results are generally inconsistent with three core commitments of probabilistic mental simulation theory (PMST). The first experiment shows that people routinely fail to track the spatio-temporal identity of objects. The second experime...

In this paper, we describe three experiments involving simple physical judgments and predictions, and argue their results are generally inconsistent with three core commitments of probabilistic mental simulation theory (PMST). The first experiment shows that people routinely fail to track the spatio-temporal identity of objects. The second experime...

When encountering unfamiliar physical objects, children and adults often perform structured interrogatory actions such as grasping and prodding, so revealing latent physical properties such as masses and textures. However, the processes driving and supporting these curious behaviors are still largely mysterious. In this paper, we develop and train...

A large body of research has explored how the time between two events affects judgments of causal strength between them. In this paper, we extend this work in 4 experiments that explore the role of temporal information in causal structure induction with multiple variables. We distinguish two qualitatively different types of information: The order i...

Many aspects of our physical environment are hidden. For example, it is hard to estimate how heavy an object is from visual observation alone. In this paper we examine how people actively "experiment" within the physical world to discover such latent properties. In the first part of the paper, we develop a novel framework for the quantitative analy...

The notion that the mind approximates rational (Bayesian) inference has had a strong influence on thinking in psychology since the 1950s. In constrained scenarios, typical of psychology experiments, people often behave in ways that approximate the dictates of probability theory. However, natural learning contexts are typically much more open-ended...

A large body of research has explored how the time between two events affects judgments of causal strength between them. In this article, we extend this work in 4 experiments that explore the role of temporal information in causal structure induction with multiple variables. We distinguish two qualitatively different types of information: The order...

What is the best way of discovering the underlying structure of a causal system composed of multiple variables? One prominent idea is that learners should manipulate each candidate variable in isolation to avoid confounds (sometimes known as the “Control of Variables” or CV strategy). We demonstrate that CV is not always the most efficient method f...

Many aspects of our physical environment are hidden. For example, it is hard to estimate how heavy an object is from visual observation alone. In this paper we examine how people actively "experiment" within the physical world to discover such latent properties. In the first part of the paper, we develop a novel framework for the quantitative analy...

Humans are adept at constructing causal models of the world that can support prediction, explanation, simulation-based reasoning, planning and control. In this thesis I explore how people learn about the causal world interacting with it, and how they represent and modify their causal knowledge as they gather evidence. Over 10 experiments and modell...

Event timing and interventions are important and intertwined cues to causal structure, yet they have typically been studied separately. We bring them together for the first time in an experiment where participants learn causal structure by performing interventions in continuous time. We contrast learning in acyclic and cyclic devices, with reliable...

Higher-level cognition depends on the ability to learn models of the world. We can characterize this at the computational level as a structure-learning problem with the goal of best identifying the prevailing causal relationships among a set of relata. However, the computational cost of performing exact Bayesian inference over causal models grows r...

In this paper, we bring together research on active learning and intuitive physics to explore how people learn about “microworlds” with continuous spatiotemporal dynamics. Participants interacted with objects in simple two-dimensional worlds governed by a physics simulator, with the goal of identifying latent physical properties such as mass, and f...

Children between 5 and 8years of age freely intervened on a three-variable causal system, with their task being to discover whether it was a common cause structure or one of two causal chains. From 6 or 7years of age, children were able to use information from their interventions to correctly disambiguate the structure of a causal chain. We used a...

Causal models are key to flexible and efficient exploitation of the environment. However, learning causal structure is hard, with massive spaces of possible models, hard-to-compute marginals and the need to integrate diverse evidence over many instances. We report on two experiments in which participants learnt about probabilistic causal systems in...

Interacting with a system is key to uncovering its causal structure. A computational framework for interventional causal learning has been developed over the last decade, but how real causal learners might achieve or approximate the computations entailed by this framework is still poorly understood. Here we describe an interactive computer task in...

The timing and order in which a set of events occur strongly in-fluences whether people judge them to be causally related. But what do people think particular temporal patterns of events tell them about causal structure? And how do they integrate multi-ple pieces of temporal evidence? We present a behavioral ex-periment that explores human causal s...