In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

The organization of behavior: A neuropsychological theory

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

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Building machines that learn and think like people.

Foundations Of Statistics

No scientific conclusion follows automatically from a statistically non-significant result, yet people routinely use non-significant results to guide conclusions about the status of theories (or the effectiveness of practices). To know whether a non-significant result counts against a theory, or if it just indicates data insensitivity, researchers must use one of: power, intervals (such as confidence or credibility intervals), or else an indicator of the relative evidence for one theory over another, such as a Bayes factor. I argue Bayes factors allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches. Specifically, Bayes factors use the data themselves to determine their sensitivity in distinguishing theories (unlike power), and they make use of those aspects of a theory’s predictions that are often easiest to specify (unlike power and intervals, which require specifying the minimal interesting value in order to address theory). Bayes factors provide a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive. They allow accepting and rejecting the null hypothesis to be put on an equal footing. Concrete examples are provided to indicate the range of application of a simple online Bayes calculator, which reveal both the strengths and weaknesses of Bayes factors.

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Using Bayes to get the most out of non-significant results

Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models is thus explaining how optimal solutions can be approximated by psychological processes. We outline a general strategy for answering this question, namely to explore the psychological plausibility of approximation algorithms developed in computer science and statistics. In particular, we argue that Monte Carlo methods provide a source of rational process models that connect optimal solutions to psychological processes. We support this argument through a detailed example, applying this approach to Anderson's (1990, 1991) rational model of categorization (RMC), which involves a particularly challenging computational problem. Drawing on a connection between the RMC and ideas from nonparametric Bayesian statistics, we propose 2 alternative algorithms for approximate inference in this model. The algorithms we consider include Gibbs sampling, a procedure appropriate when all stimuli are presented simultaneously, and particle filters, which sequentially approximate the posterior distribution with a small number of samples that are updated as new data become available. Applying these algorithms to several existing datasets shows that a particle filter with a single particle provides a good description of human inferences.

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Rational approximations to rational models: Alternative algorithms for category learning

Bayesian Brains without Probabilities

Objectives: Because insomnia is a common comorbidity of chronic pain, scientific and clinical interest in the relationship of pain and sleep has surged in recent years. Although experimental studies suggest a sleep-interfering property of pain and a pain-enhancing effect of sleep deprivation/ fragmentation, the temporal association between pain and sleep as experienced by patients is less understood. The current study was conducted to examine the influence of presleep pain on subsequent sleep and sleep on pain reports the next day, taking into consideration other related psychophysiologic variables such as mood and arousal. Design: A daily process study, involving participants to monitor their pain, sleep, mood, and presleep arousal for 1 wk. Multilevel modeling was used to analyze the data. Setting: In the patients' natural living and sleeping environment. Patients: One hundred nineteen patients (73.9% female, mean age = 46 years) with chronic pain and concomitant insomnia. Measurement: An electronic diary was used to record patients' self-reported sleep quality/efficiency and ratings of pain, mood, and arousal at different times of the day; actigraphy was also used to provide estimates of sleep efficiency. Results: Results indicated that presleep pain was not a reliable predictor of subsequent sleep. Instead, sleep was better predicted by presleep cognitive arousal. Although sleep quality was a consistent predictor of pain the next day, the pain-relieving effect of sleep was only evident during the first half of the day. Conclusions: These findings challenge the often-assumed reciprocal relationship between pain and sleep and call for a diversification in thinking of the daily interaction of these 2 processes.

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Deciphering the temporal link between pain and sleep in a heterogeneous chronic pain patient sample: a multilevel daily process study

People have strong intuitions about the influence objects exert upon one another when they collide. Because people's judgments appear to deviate from Newtonian mechanics, psychologists have suggested that people depend on a variety of task-specific heuristics. This leaves open the question of how these heuristics could be chosen, and how to integrate them into a unified model that can explain human judgments across a wide range of physical reasoning tasks. We propose an alternative framework, in which people's judgments are based on optimal statistical inference over a Newtonian physical model that incorporates sensory noise and intrinsic uncertainty about the physical properties of the objects being viewed. This noisy Newton framework can be applied to a multitude of judgments, with people's answers determined by the uncertainty they have for physical variables and the constraints of Newtonian mechanics. We investigate a range of effects in mass judgments that have been taken as strong evidence for heuristic use and show that they are well explained by the interplay between Newtonian constraints and sensory uncertainty. We also consider an extended model that handles causality judgments, and obtain good quantitative agreement with human judgments across tasks that involve different judgment types with a single consistent set of parameters.

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Reconciling intuitive physics and Newtonian mechanics for colliding objects.

Probabilistic models of cognition characterize the abstract computational problems underlying inductive inferences and identify their ideal solutions. This approach differs from traditional methods of investigating human cognition, which focus on identifying the cognitive or neural processes that underlie behavior and therefore concern alternative levels of analysis. To evaluate the theoretical implications of probabilistic models and increase their predictive power, we must understand the relationships between theories at these different levels of analysis. One strategy for bridging levels of analysis is to explore cognitive processes that have a direct link to probabilistic inference. Recent research employing this strategy has focused on the possibility that the Monte Carlo principle—which concerns sampling from probability distributions in order to perform computations—provides a way to link probabilistic models of cognition to more concrete cognitive and neural processes.

https://cocosci.berkeley.edu/tom/papers/LabPublications/BridgingLevelsAnalysis.pdf

Adam N. Sanborn

Papers

Rational approximations to rational models: Alternative algorithms for category learning

Bayesian Brains without Probabilities

Deciphering the temporal link between pain and sleep in a heterogeneous chronic pain patient sample: a multilevel daily process study

Reconciling intuitive physics and Newtonian mechanics for colliding objects.

Bridging Levels of Analysis for Probabilistic Models of Cognition