Probabilistic models of cognition: exploring representations and inductive biases

Griffiths, Thomas L.; Chater, Nick; Kemp, Charles; Perfors, Amy; Tenenbaum, Joshua B.

doi:10.1016/j.tics.2010.05.004

Cited by 472 publications

(417 citation statements)

References 34 publications

Supporting

Mentioning

414

Contrasting

Unclassified

Order By: Relevance

“…Griffiths, Chater, Kemp, Perfors, & Tenenbaum, 2010). Quantum models are about what is computed (e.g., a…”

Section: A Quantum Probability Framework For Human Probabilistic Infementioning

confidence: 99%

“…The predominant framework regarding levels of explanation is still that of Marr (1982), though the ideas from Griffiths et al (2010) have been increasingly influential too. To understand the placement of the quantum framework for probabilistic inference, we briefly review the corresponding literature.…”

Section: Explanatory Scope Of the Quantum Frameworkmentioning

confidence: 99%

“…Griffiths et al (2010) further explain the intention of a probabilistic cognitive model with a range of questions, such as in relation to the information needed, the necessary representations, and the constraints on the computation. While most of these questions are computational level explanations, it is also clear that some of them are algorithmic level ones.…”

Section: Explanatory Scope Of the Quantum Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

A quantum probability framework for human probabilistic inference.

Trueblood

Yearsley

Pothos

2017

Journal of Experimental Psychology: General

View full text Add to dashboard Cite

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThere is considerable variety in human inference (e.g., a doctor inferring the presence of a disease, a juror inferring the guilt of a defendant, or someone inferring future weight loss based on diet and exercise). As such, people display a wide range of behaviors when making inference judgments.Sometimes, people's judgments appear Bayesian (i.e., normative), but in other cases, judgments deviate from the normative prescription of classical probability theory. How can we combine bothBayesian and non-Bayesian influences in a principled way? We propose a unified explanation of human inference using quantum probability theory. In our approach, we postulate a hierarchy of mental representations, from 'fully' quantum to 'fully' classical, which could be adopted in different situations. In our hierarchy of models, moving from the lowest level to the highest involves changing assumptions about compatibility (i.e., how joint events are represented). Using results from three experiments, we show that our modeling approach explains five key phenomena in human inference including order effects, reciprocity (i.e., the inverse fallacy), memorylessness, violations of the Markov condition, and anti-discounting. As far as we are aware, no existing theory or model can explain all five phenomena. We also explore transitions in our hierarchy, examining how representations change from more quantum to more classical. We show that classical representations provide a better account of data as individuals gain familiarity with a task.We also show that representations vary between individuals, in a way that relates to a simple measure of cognitive style, the Cognitive Reflection Test.Keywords: Human judgment, quantum probability theory, Bayes' rule, order effects, Markov condition A Quantum Framework for Probabilistic Inference 3 A Quantum Probability Framework for Human Probabilistic InferenceEveryday we face situations where we must make inferences about the world around us. For example, a doctor must determine the likelihood that a patient has a disease based on a set of symptoms. A juror must decide the probability that a defendant is guilty after hearing the cases made by the prosecution and defense. Or, maybe you want to judge the likelihood that you will weigh less next month if you start exercising more regularly and you improve your diet. In general, the inference problem involves judging the likelihood of some hypothesis (e.g., presence of a disease, guilt of a defendant, future weight loss) based on a series of evidence (e.g., medicalsymptoms, prosecution and defense cases, changes in your diet and exercise).Bayesian inference is widely accepted as the normative approach to inference. However, decades of research in human judgment and decision-making have suggested that people's judgments often violate the rules of Bayesian inference and classical probability theory (Tversky...

show abstract

“…Griffiths, Chater, Kemp, Perfors, & Tenenbaum, 2010). Quantum models are about what is computed (e.g., a…”

Section: A Quantum Probability Framework For Human Probabilistic Infementioning

confidence: 99%

Section: Explanatory Scope Of the Quantum Frameworkmentioning

confidence: 99%

Section: Explanatory Scope Of the Quantum Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

A quantum probability framework for human probabilistic inference.

Trueblood

Yearsley

Pothos

2017

Journal of Experimental Psychology: General

View full text Add to dashboard Cite

show abstract

“…The Bayesian approach to cognitive development, explored in this special issue, and the cognitive sciences more generally, e.g., Griffiths, Chater, Kemp, Perfors, and Tenenbaum (2010), suggest a different perspective on learning: generative models learn by making inferences about the probability distribution that produces the language input. Thus, from a generative perspective, language acquisition is not a matter of discriminating ''good'' from ''bad'' linguistic forms; instead the aim is to model the underlying regularities that give rise to the language.…”

Section: Introductionmentioning

confidence: 99%

The probabilistic analysis of language acquisition: Theoretical, computational, and experimental analysis

2011

Self Cite

View full text Add to dashboard Cite

Keywords:Child language acquisition Poverty of the stimulus No negative evidence Bayesian models Minimum description length Simplicity principle Natural language Probabilistic models Identification in the limit a b s t r a c t There is much debate over the degree to which language learning is governed by innate language-specific biases, or acquired through cognition-general principles. Here we examine the probabilistic language acquisition hypothesis on three levels: We outline a novel theoretical result showing that it is possible to learn the exact generative model underlying a wide class of languages, purely from observing samples of the language. We then describe a recently proposed practical framework, which quantifies natural language learnability, allowing specific learnability predictions to be made for the first time. In previous work, this framework was used to make learnability predictions for a wide variety of linguistic constructions, for which learnability has been much debated. Here, we present a new experiment which tests these learnability predictions. We find that our experimental results support the possibility that these linguistic constructions are acquired probabilistically from cognition-general principles.

show abstract

“…We can so conclude that decisions consistent with CPT should be considered rational (Oaksford & Chater, 2009; see also Griffiths et al, 2010;.…”

Section: The Dutch Book Theorem and The Rational Status Of The Conjunmentioning

confidence: 85%

The rational status of quantum cognition.

Pothos¹,

Busemeyer²,

Shiffrin³

et al. 2017

Journal of Experimental Psychology: General

View full text Add to dashboard Cite

This is the draft version of the paper.This version of the publication may differ from the final published version. Abstract Classic probability theory (CPT) is generally considered the rational way to make inferences, but there have been some empirical findings showing a divergence between reasoning and the principles of classical probability theory (CPT), inviting the conclusion that humans are irrational. Perhaps the most famous of these findings is the conjunction fallacy (CF). Recently, the CF has been shown consistent with the principles of an alternative probabilistic framework, quantum probability theory (QPT). Does this imply that QPT is irrational or does QPT provide an alternative interpretation of rationality? Our presentation consists of three parts. First, we examine the putative rational status of QPT using the same argument as used to establish the rationality of CPT, the Dutch Book (DB) argument, according to which reasoners should not commit to bets guaranteeing a loss. We prove the rational status of QPT by formulating it as a particular case of an extended form of CPT, with separate probability spaces produced by changing context. Second, we empirically examine the key requirement for whether a CF can be rational or not; the results show that participants indeed behave rationally, at least relative to the representations they employ. Finally, we consider whether the conditions for the CF to be rational are applicable in the outside (non-mental) world. Our discussion provides a general and alternative perspective for rational probabilistic inference, based on the idea that contextuality requires either reasoning in separate CPT probability spaces or reasoning with QPT principles. Permanent repository link

show abstract

Probabilistic models of cognition: exploring representations and inductive biases

Cited by 472 publications

References 34 publications

A quantum probability framework for human probabilistic inference.

A quantum probability framework for human probabilistic inference.

The probabilistic analysis of language acquisition: Theoretical, computational, and experimental analysis

The rational status of quantum cognition.

Contact Info

Product

Resources

About