Simple trees in complex forests: Growing Take The Best by Approximate Bayesian Computation

How can heuristic strategies emerge from smaller building blocks? We propose Approximate Bayesian Computation as a computational solution to this problem. As a first proof of concept, we demonstrate how a heuristic decision strategy such as Take The Best (TTB) can be learned from smaller, probabilistically updated building blocks. Based on a self-reinforcing sampling scheme, different building blocks are combined and, over time, tree-like non-compensatory heuristics emerge. This new algorithm, coined Approximately Bayesian Computed Take The Best (ABC-TTB), is able to recover a data set that was generated by TTB, leads to sensible inferences about cue importance and cue directions, can outperform traditional TTB, and allows to trade-off performance and computational effort explicitly

Causal learning through repeated decision making

Abstract Many decisions refer to actions that have a causal impact on other events. Such actions allow for mere learning of expected values, but also for causal learning about the structure of the decision context. Whereas most theories of decision making neglect causal knowledge, causal learning theories emphasize the importance of causal beliefs and assume that people represent decision problems in terms of their causal structure. In three studies we investigated the representations people acquire when repeatedly making decisions to maximize a certain payoff. Our results show that (i) initial causal hypotheses guide the interpretation of decision feedback, (ii) consequences of interventions are used to revise existing causal beliefs, (iii) decision makers use the experienced feedback to induce a causal model of the choice situation, which (iv) enables them to adapt their choices to changes of the decision problem

Informavores: Active information foraging and human cognition

Just as the body survives by ingesting negative entropy, so the mind survives by ingesting information. In a very general sense, all higher organisms are informavores. The study of active information search is in the midst of a renaissance. Psychological research from diverse areas ranging from developmental psychology This symposium aims to bring together leading experts in this area to discuss how active information foraging can be understood from a diverse set of perspectives within cognitive science. Key themes include how prior knowledge influences search (Markant & Gureckis), how information and reward interact to determine choice (Meder & Nelson), developmental patterns in information seeking behavior (Nelson et al.), information foraging in complex sensemaking tasks (Pirolli), and the allocation of attention during statistical word learning (Yu). While each represents a distinct area of research, all discussants in the symposium share a core approach of applying computational models to understand information search in humans. The symposium should appeal to a broad set of attendees including educators, developmental psychologists, cognitive modelers, and computer scientists. The influence of priors on sequential search decisions - Doug Markant and Todd Gureckis Normative models of information acquisition predict that people's search decisions should be strongly influenced by their prior beliefs, which capture the set of alternative hypotheses they are considering. In the present experiments we tested whether people adjusted their information search behavior in response to sequential changes in the prior. Participants played a search game in which they had to identify the shape and location of multiple hidden targets in a display (similar to the board game Battleship). During the task they were told that the set of possible shapes had changed, and the key question was whether they would adjust their search decisions according to the predictions of a normative model. Manipulations of the prior included changes in the frequency of certain classes of targets as well as the introduction of higherorder constraints (e.g., that all targets would have the same shape). The results showed that an individual's prior could be recovered from their sequences of search decisions, but that there were notable differences in their ability to adjust to certain changes in the hypothesis space, an effect that is not predicted by the normative model. We discuss the implications of these findings for how people generate and represent hypotheses during the course of information foraging. Is people's information search behavior sensitive to different reward structures? -Björn Meder and Jonathan Nelson In situations where humans actively acquire information for classification, information search preferentially maximizes accurac

The tight coupling between category and causal learning

The main goal of the present research was to demonstrate the interaction between category and causal induction in causal model learning. We used a two-phase learning procedure in which learners were presented with learning input referring to two interconnected causal relations forming a causal chain (Experiment 1) or a common-cause model (Experiments 2a, b). One of the three events (i.e., the intermediate event of the chain, or the common cause) was presented as a set of uncategorized exemplars. Although participants were not provided with any feedback about category labels, they tended to induce categories in the first phase that maximized the predictability of their causes or effects. In the second causal learning phase, participants had the choice between transferring the newly learned categories from the first phase at the cost of suboptimal predictions, or they could induce a new set of optimally predictive categories for the second causal relation, but at the cost of proliferating different category schemes for the same set of events. It turned out that in all three experiments learners tended to transfer the categories entailed by the first causal relation to the second causal relation

Beobachten versus Handeln: Kausale Bayes-Netze als psychologische Modelle kausalen Denkens

Diese Dissertation geht der Frage nach, wie Menschen Vorhersagen über die Folgen von aktiven Interventionen in kausalen Systemen zu treffen, wenn sie diese Systeme zuvor nur passiv beobachtet haben. Die Theorie der kausalen Bayes-Netze (Spirtes, Glymour & Scheines, 1993; Pearl, 2000) stellt einen rationalen Ansatz zur Repräsentation von Kausalwissen dar und formalisiert den Unterschied zwischen passiv beobachteten Ereignissen ( seeing ) und identischen Ereignissen, die durch Interventionen aktiv erzeugt wurde ( doing ). Dadurch ermöglicht es der Formalismus, die Folgen von hypothetischen und kontrafaktischen Interventionen aus Beobachtungswissen abzuleiten. Alternative Theorien kausalen Denkens hingegen, die den Unterschied zwischen passiv beobachteten und aktiv erzeugten Ereignissen nicht berücksichtigen, generieren fehlerhafte Vorhersagen, wenn Beobachtungen und Interventionen unterschiedliche Implikationen haben. Die grundlegende Forschungsfrage der acht Experimente dieser Arbeit ist, ob Menschen die Folgen von hypothetischen und kontrafaktischen Interventionen aus Beobachtungswissen ableiten können, das in einem passiven Trial-by-Trial Lernverfahren erworben wurde. In Übereinstimmung mit der Theorie kausaler Bayes-Netze zeigte sich, dass die Versuchsteilnehmer überraschend gut darin waren, die Folgen von Interventionen aus Beobachtungswissen abzuleiten, und dass sie dabei auch die Struktur und die Parameter des beobachteten Kausalmodells einbeziehen. Zudem zeigen die Befunde, dass konfundierende Variablen bei den jeweiligen Vorhersagen adäquat berücksichtigt werden. Obwohl die Schlussfolgerungen der Versuchsteilnehmer insgesamt den Vorhersagen der Theorie kausaler Bayes-Netze entsprachen, zeigen die Befunde auch einige Randbedingungen auf. So hatten die Probanden zum Beispiel Probleme, zwischen den Implikationen von hypothetischen und kontrafaktischen Interventionen zu differenzieren. Insgesamt stützen die Ergebnisse klar die Theorie der kausalen Bayes-Netze als psychologisches Modell kausalen Denkens. Alternative Theorien kausaler Kognitionen, die die Unterschiede zwischen beobachteten und durch Interventionen erzeugten Ereignissen nicht repräsentieren, können die Ergebnisse der Experimente nicht erklären

Diagnostic causal reasoning with verbal information

Supplemental files (behavioral data) for Meder, B. & Mayrhofer, R. (2017). Diagnostic causal reasoning with verbal information. Cognitive Psychology, 96, 54-84. Corresponding authors: [email protected] or [email protected]

Causal learning through repeated decision making

Many decisions refer to actions that have a causal impact on other events. Such actions allow for mere learning of expected values, but also for causal learning about the structure of the decision context. Whereas most theories of decision making neglect causal knowledge, causal learning theories emphasize the importance of causal beliefs and assume that people represent decision problems in terms of their causal structure. In three studies we investigated the representations people acquire when repeatedly making decisions to maximize a certain payoff. Our results show that (i) initial causal hypotheses guide the interpretation of decision feedback, (ii) consequences of interventions are used to revise existing causal beliefs, (iii) decision makers use the experienced feedback to induce a causal model of the choice situation, which (iv) enables them to adapt their choices to changes of the decision problem