Successful structure learning from observational data

Rothe, Anselm; Deverett, Ben; Mayrhofer, Ralf; Kemp, Charles

doi:10.1016/j.cognition.2018.06.003

Cited by 29 publications

(10 citation statements)

References 45 publications

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“…This definition implicitly operates over particular representations: discrete states, such as events or facts that have some probability of occurring or being true. Because of this, experimental work in causal cognition has primarily focused on causal relationships between discrete valued (often binary) variables (e.g., Sloman, 2005;Krynski and Tenenbaum, 2007;Ali et al, 2011;Fernbach and Erb, 2013;Hayes et al, 2014;Rehder, 2014;Rothe et al, 2018). These are typically presented in contexts in which temporal information is either unavailable or abstracted away so that cases can be summarized in a contingency table.…”

Section: Probabilistic Causation Over Discrete Eventsmentioning

confidence: 99%

“…It follows that learning successfully in natural settings depends on accommodating these factors. Cognitive psychologists have explored many of these dimensions of complexity in isolation (e.g., stochasticity: Waldmann and Holyoak, 1992;Bramley et al, 2017a;Rothe et al, 2018;interventions: Sloman and Lagnado, 2005;Waldmann and Hagmayer, 2005;Bramley et al, 2015;Coenen et al, 2015;time: Buehner and May, 2003;Lagnado and Sloman, 2006;Rottman and Keil, 2012;Bramley et al, 2018; and continuous variables: Pacer and Griffiths, 2011). However, we argue these components generally can not be isolated in realistic learning settings, meaning a deeper understanding of human causal cognition will require a new framework that naturally accommodates inference from interventions in continuous dynamic settings.…”

Section: Introductionmentioning

confidence: 99%

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Causal Structure Learning in Continuous Systems

2020

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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 basis of multiple patients who take the pill (or not) and find their headache relieved (or not). In contrast, the current study examines learning via interactions with continuous dynamic systems, systems that include continuous variables that interact over time (and that can be continuously observed in real time by the learner). To explore such systems, we develop a new framework that represents a causal system as a network of stationary Gauss-Markov ("Ornstein-Uhlenbeck") processes and show how such OU networks can express complex dynamic phenomena, such as feedback loops and oscillations. To assess adult's abilities to learn such systems, we conducted an experiment in which participants were asked to identify the causal relationships of a number of OU networks, potentially carrying out multiple, temporally-extended interventions. We compared their judgments to a normative model for learning OU networks as well as a range of alternative and heuristic learning models from the literature. We found that, although participants exhibited substantial learning of such systems, they committed certain systematic errors. These successes and failures were best accounted for by a model that describes people as focusing on pairs of variables, rather than evaluating the evidence with respect to the full space of possible structural models. We argue that our approach provides both a principled framework for exploring the space of dynamic learning environments as well as new algorithmic insights into how people interact successfully with a continuous causal world.

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Section: Probabilistic Causation Over Discrete Eventsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Causal Structure Learning in Continuous Systems

2020

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“…Currently, we have only studied how people learn functions on spatial representations of graph structures, where all nodes and edges are visible simultaneously. However, people can perform inferences over discrete structures that are more conceptual such as social hierarchies (Lau, Pouncy, Gershman, & Cikara, 2018) or causal connections (Rothe, Deverett, Mayrhofer, & Kemp, 2018). Given that the GP framework can be used to compare how people learn functions over different (i.e., spatial and conceptual) domains (Wu, Schulz, Garvert, Meder, & Schuck, 2018), comparing functional inference over conceptual and spatial graphs seems like promising extension for future studies.…”

Section: Future Work and Limitationsmentioning

confidence: 99%

Generalization as diffusion: human function learning on graphs

Schulz

Gershman

2019

Preprint

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From social networks to public transportation, graph structures are a ubiquitous feature of life. How do humans learn functions on graphs, where relationships are defined by the connectivity structure? We adapt a Bayesian framework for function learning to graph structures, and propose that people perform generalization by assuming that the observed function values diffuse across the graph. We evaluate this model by asking participants to make predictions about passenger volume in a virtual subway network. The model captures both generalization and confidence judgments, and provides a quantitatively superior account relative to several heuristic models. Our work suggests that people exploit graph structure to make generalizations about functions in complex discrete spaces.

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“…This work fits well with the current study, where humans spontaneously form task-sets comprised of category-response associations, perhaps as a result of inferring structure from the environment (cf. Wilson and Niv, 2012;Rothe et al, 2018). Moreover, this proposal has been expanded to account for differences in memory (cf.…”

Section: Relationship To Reinforcement Learningmentioning

confidence: 99%

Spontaneous Task Structure Formation Results in a Cost to Incidental Memory of Task Stimuli

Bejjani

Egner

2019

Front. Psychol.

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Humans are characterized by their ability to leverage rules for classifying and linking stimuli to context-appropriate actions. Previous studies have shown that when humans learn stimulus-response associations for two-dimensional stimuli, they implicitly form and generalize hierarchical rule structures (task-sets). However, the cognitive processes underlying structure formation are poorly understood. Across four experiments, we manipulated how trial-unique images mapped onto responses to bias spontaneous task-set formation and investigated structure learning through the lens of incidental stimulus encoding. Participants performed a learning task designed to either promote task-set formation (by "motor-clustering" possible stimulus-action rules), or to discourage it (by using arbitrary category-response mappings). We adjudicated between two hypotheses: Structure learning may promote attention to task stimuli, thus resulting in better subsequent memory. Alternatively, building tasksets might impose cognitive demands (for instance, on working memory) that divert attention away from stimulus encoding. While the clustering manipulation affected task-set formation, there were also substantial individual differences. Importantly, structure learning incurred a cost: spontaneous task-set formation was associated with diminished stimulus encoding. Thus, spontaneous hierarchical task-set formation appears to involve cognitive demands that divert attention away from encoding of task stimuli during structure learning.

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Successful structure learning from observational data

Cited by 29 publications

References 45 publications

Causal Structure Learning in Continuous Systems

Causal Structure Learning in Continuous Systems

Generalization as diffusion: human function learning on graphs

Spontaneous Task Structure Formation Results in a Cost to Incidental Memory of Task Stimuli

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