Adam N. Sanborn & Nick Chater
Trends in Cognitive Science
Published Online: October 26, 2016
Bayesian explanations have swept through cognitive science over the past two decades, from intuitive physics and causal learning, to perception, motor control and language. Yet people flounder with even the simplest probability questions. What explains this apparent paradox? How can a supposedly Bayesian brain reason so poorly with probabilities? In this paper, we propose a direct and perhaps unexpected answer: that Bayesian brains need not represent or calculate probabilities at all and are, indeed, poorly adapted to do so. Instead, the brain is a Bayesian sampler. Only with infinite samples does a Bayesian sampler conform to the laws of probability; with finite samples it systematically generates classic probabilistic reasoning errors, including the unpacking effect, base-rate neglect, and the conjunction fallacy.
The article is here.
Showing posts with label Bayes Theorem. Show all posts
Showing posts with label Bayes Theorem. Show all posts
Friday, November 18, 2016
Monday, October 3, 2016
Moral learning: Why learning? Why moral? And why now?
Peter Railton
Cognition
Abstract
What is distinctive about a bringing a learning perspective to moral psychology? Part of the answer lies in the remarkable transformations that have taken place in learning theory over the past two decades, which have revealed how powerful experience-based learning can be in the acquisition of abstract causal and evaluative representations, including generative models capable of attuning perception, cognition, affect, and action to the physical and social environment. When conjoined with developments in neuroscience, these advances in learning theory permit a rethinking of fundamental questions about the acquisition of moral understanding and its role in the guidance of behavior. For example, recent research indicates that spatial learning and navigation involve the formation of non-perspectival as well as ego-centric models of the physical environment, and that spatial representations are combined with learned information about risk and reward to guide choice and potentiate further learning. Research on infants provides evidence that they form non-perspectival expected-value representations of agents and actions as well, which help them to navigate the human environment. Such representations can be formed by highly-general mental processes such as causal and empathic simulation, and thus afford a foundation for spontaneous moral learning and action that requires no innate moral faculty and can exhibit substantial autonomy with respect to community norms. If moral learning is indeed integral with the acquisition and updating of casual and evaluative models, this affords a new way of understanding well-known but seemingly puzzling patterns in intuitive moral judgment—including the notorious “trolley problems.”
The article is here.
Cognition
Abstract
What is distinctive about a bringing a learning perspective to moral psychology? Part of the answer lies in the remarkable transformations that have taken place in learning theory over the past two decades, which have revealed how powerful experience-based learning can be in the acquisition of abstract causal and evaluative representations, including generative models capable of attuning perception, cognition, affect, and action to the physical and social environment. When conjoined with developments in neuroscience, these advances in learning theory permit a rethinking of fundamental questions about the acquisition of moral understanding and its role in the guidance of behavior. For example, recent research indicates that spatial learning and navigation involve the formation of non-perspectival as well as ego-centric models of the physical environment, and that spatial representations are combined with learned information about risk and reward to guide choice and potentiate further learning. Research on infants provides evidence that they form non-perspectival expected-value representations of agents and actions as well, which help them to navigate the human environment. Such representations can be formed by highly-general mental processes such as causal and empathic simulation, and thus afford a foundation for spontaneous moral learning and action that requires no innate moral faculty and can exhibit substantial autonomy with respect to community norms. If moral learning is indeed integral with the acquisition and updating of casual and evaluative models, this affords a new way of understanding well-known but seemingly puzzling patterns in intuitive moral judgment—including the notorious “trolley problems.”
The article is here.
Saturday, May 14, 2016
On the Source of Human Irrationality
Oaksford, Mike et al.
Trends in Cognitive Sciences , Volume 20 , Issue 5 , 336 - 344
Summary
Reasoning and decision making are error prone. This is often attributed to a fast, phylogenetically old System 1. It is striking, however, that perceptuo-motor decision making in humans and animals is rational. These results are consistent with perceptuo-motor strategies emerging in Bayesian brain theory that also appear in human data selection. People seem to have access, although limited, to unconscious generative models that can generalise to explain other verbal reasoning results. Error does not emerge predominantly from System 1, but rather seems to emerge from the later evolved System 2 that involves working memory and language. However language also sows the seeds of error correction by moving reasoning into the social domain. This reversal of roles suggests key areas of theoretical integration and new empirical directions.
Trends
System 1 is supposedly the main cause of human irrationality. However, recent work on animal decision making, human perceptuo-motor decision making, and logical intuitions shows that this phylogenetically older system is rational.
Bayesian brain theory has recently proposed perceptuo-motor strategies identical to strategies proposed in Bayesian approaches to conscious verbal reasoning, suggesting that similar generative models are available at both levels.
Recent approaches to conditional inference using causal Bayes nets confirm this account, which can also generalise to logical intuitions.
People have only imperfect access to System 1. Errors arise from inadequate interrogation of System 1, working memory limitations, and mis-description of our records of these interrogations. However, there is evidence that such errors may be corrected by moving reasoning to the social domain facilitated by language.
The article is here.
Trends in Cognitive Sciences , Volume 20 , Issue 5 , 336 - 344
Summary
Reasoning and decision making are error prone. This is often attributed to a fast, phylogenetically old System 1. It is striking, however, that perceptuo-motor decision making in humans and animals is rational. These results are consistent with perceptuo-motor strategies emerging in Bayesian brain theory that also appear in human data selection. People seem to have access, although limited, to unconscious generative models that can generalise to explain other verbal reasoning results. Error does not emerge predominantly from System 1, but rather seems to emerge from the later evolved System 2 that involves working memory and language. However language also sows the seeds of error correction by moving reasoning into the social domain. This reversal of roles suggests key areas of theoretical integration and new empirical directions.
Trends
System 1 is supposedly the main cause of human irrationality. However, recent work on animal decision making, human perceptuo-motor decision making, and logical intuitions shows that this phylogenetically older system is rational.
Bayesian brain theory has recently proposed perceptuo-motor strategies identical to strategies proposed in Bayesian approaches to conscious verbal reasoning, suggesting that similar generative models are available at both levels.
Recent approaches to conditional inference using causal Bayes nets confirm this account, which can also generalise to logical intuitions.
People have only imperfect access to System 1. Errors arise from inadequate interrogation of System 1, working memory limitations, and mis-description of our records of these interrogations. However, there is evidence that such errors may be corrected by moving reasoning to the social domain facilitated by language.
The article is here.
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