359:, or true actuarial facts about the probability or rate of a phenomenon, and instead give inappropriate amounts of weight to specific observations. In a Bayesian model of inference, this would amount to an underweighting of the prior probability, which has been cited as evidence against the appropriateness of a normative Bayesian framework for modeling cognition. Frequency representations can resolve base rate neglect, and some consider the phenomenon to be an experimental artifact, i.e., a result of probabilities or rates being represented as mathematical abstractions, which are difficult to intuitively think about. Gigerenzer speculates an ecological reason for this, noting that individuals learn frequencies through successive trials in nature. Tversky and Kahneman refute Gigerenzer's claim, pointing to experiments where subjects predicted a disease based on the presence vs. absence of pre-specified symptoms across 250 trials, with feedback after each trial. They note that base rate neglect was still found, despite the frequency formulation of subject trials in the experiment.
661:, the problems given to participants often use base rates of disease prevalence. In these experiments, physicians and non-physicians are similarly susceptible to base rate neglect, or errors in calculating conditional probability. Here is an example from an empirical survey problem given to experienced physicians: Suppose that a hypothetical cancer had a prevalence of 0.3% in the population, and the true positive rate of a screening test was 50% with a false positive rate of 3%. Given a patient with a positive test result, what is the probability that the patient has cancer? When asked this question, physicians with an average of 14 years experience in medical practice ranged in their answers from 1-99%, with most answers being 47% or 50%. (The correct answer is 5%.) This observation of clinical base rate neglect and conditional probability error has been replicated in multiple empirical studies. Physicians' judgments in similar problems, however, improved substantially when the rates were re-formulated as natural frequencies.
461:") in the brain. On this view, infants and children are essentially proto-scientists because they regularly use a kind of scientific method, developing hypotheses, performing experiments via play, and updating models about the world based on their results. For Gopnik, this use of scientific thinking and categorization in development and everyday life can be formalized as models of Bayesian inference. An application of this view is the "sampling hypothesis," or the view that individual variation in children's causal and probabilistic inferences is an artifact of random sampling from a diverse set of hypotheses, and flexible generalizations based on sampling behavior and context. These views, particularly those advocating general Bayesian updating from specialized theories, are considered successors to Piaget's theory rather than wholesale refutations because they maintain its domain-generality, viewing children as randomly and unsystematically considering a range of models before selecting a probable conclusion.
526:. In general, preverbal infants appear to be capable of discriminating between grammars with which they have been trained with experience, and novel grammars. In 7-month-old infant looking-time tasks, infants seemed to pay more attention to unfamiliar grammatical structures than to familiar ones, and in a separate study using 3-syllable strings, infants appeared to similarly have generalized expectations based on abstract syllabic structure previously presented, suggesting that they used surface occurrences, or data, in order to infer deeper abstract structure. This was taken to support the “multiple hypotheses ” view by the researchers involved.
371:, described in an experiment by Tversky and Kahneman known as the "Linda problem." In this experiment, participants are presented with a short description of a person called Linda, who is 31 years old, single, intelligent, outspoken, and went to a university where she majored in philosophy, was concerned about discrimination and social justice, and participated in anti-nuclear protests. When participants were asked if it were more probable that Linda is (1) a bank teller, or (2) a bank teller and a feminist, 85% responded with option 2, even though it option 1 cannot be less probable than option 2. They concluded that this was a product of a
285:. As a result, cognitive psychologists have largely adopted the view that intuitive judgments, generalizations, and numerical or probabilistic calculations are systematically biased. The result is commonly an error in judgment, including (but not limited to) recurrent logical fallacies (e.g., the conjunction fallacy), innumeracy, and emotionally motivated shortcuts in reasoning. Social and cognitive psychologists have thus considered it "paradoxical" that humans can outperform powerful computers at complex tasks, yet be deeply flawed and error-prone in simple, everyday judgments.
627:. Such models serve as a starting point for intuitive generalizations to be made from a small number of cues, resulting in the physician's tradeoff between the "art and science" of medical judgement. This tradeoff was captured in an artificially intelligent (AI) program called MYCIN, which outperformed medical students, but not experienced physicians with extensive practice in symptom recognition. Some researchers argue that despite this, physicians are prone to systematic biases, or cognitive illusions, in their judgment (e.g., satisficing to make premature diagnoses,
316:, but involve some kind of experimenter confusion between subjective probabilities with degrees of confidence and long-run outcome frequencies. Cosmides and Tooby similarly claim that different probabilistic assumptions can be more or less normative and rational in different types of situations, and that there is not general-purpose statistical toolkit for making inferences across all informational domains. In a review of several experiments they conclude, in support of Gigerenzer, that previous heuristics and biases experiments did not represent problems in an
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preferences for the more probable outcomes - on initial evidence of the proportions in their available options. Critics of the effectiveness of looking-time tasks allowed infants to search for preferred objects in single-sample probability tasks, supporting the notion that infants can infer probabilities of single events when given a small or large initial sample size. The researchers involved in these findings have argued that humans possess some statistically structured, inferential system during preverbal stages of development and prior to formal education.
490:, many of which might emerge at different stages of development. Specifically, infant categorization at as early as 4.5 months involves iterative and interdependent processes by which exemplars (data) and their similarities and differences are crucial for drawing boundaries around categories. These abstract rules are statistical by nature, because they can entail common co-occurrences of certain perceived properties in past instances and facilitate inferences about their structure in future instances. This idea has been extrapolated by
404:, for example, asserts that training data would have to be completely exhaustive for generalizations to occur in existing connectionist models, and that as a result, they do not handle novel observations well. He further advocates an integrationist perspective between a language of thought, consisting of symbol representations and operations, and connectionist models than retain the distributed processing that is likely used by neural networks in the brain.
308:. Tversky and Kahneman argue that people are regularly biased in their judgments under uncertainty, because in a speed-accuracy tradeoff they often rely on fast and intuitive heuristics with wide margins of error rather than slow calculations from statistical principles. These errors are called "cognitive illusions" because they involve systematic divergences between judgments and accepted, normative rules in statistical prediction.
108:. More recently, theorists have considered the problem by emphasizing techniques for arriving from data to hypothesis using formal content-independent procedures, or in contrast, by considering informal, content-dependent tools for inductive inference. Searches for formal procedures have led to different developments in statistical inference and probability theory with different assumptions, including
519:. For language, creating generalizations with Bayesian inference and similarity detection has been advocated by researchers as a special case of concept formation. Infants appear to be proficient in inferring abstract and structural rules from streams of linguistic sounds produced in their developmental environments, and to generate wider predictions based on those rules.
44:
of cognition. Others appeal to a variety of statistical and probabilistic mechanisms behind theory construction and category structuring. Research in this domain commonly focuses on generalizations relating to number, relative frequency, risk, and any systematic signatures in inferential capacity that an organism (e.g.,
264:: In a system that is invariantly structured with a relatively low cost of false positives and high cost of false negatives, naturally selected defenses are expected to err on the side of hyperactivity in response to potential threat cues. This general idea has been applied to hypotheses about the apparent
378:
Gigerenzer argued that the conjunction fallacy is based on a single-event probability, and would dissolve under a frequentist approach. He and other researchers demonstrate that conclusions from the conjunction fallacy result from ambiguous language, rather than robust statistical errors or cognitive
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This is sometimes contrasted to a growing preponderance of empirical evidence suggesting that humans are capable generalizers in infancy. For example, looking-time experiments using expected outcomes of red and white ping pong ball proportions found that 8-month-old infants appear to make inferences
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are efficient rules, or computational shortcuts, for producing a judgment or decision. The intuitive statistician metaphor of cognition led to a shift in focus for many psychologists, away from emotional or motivational principles and toward computational or inferential principles. Empirical studies
206:, but not all the way to, a conclusion implied by previous observations. This pattern of behavior is similar to the pattern of posterior probability distributions when a Bayesian model is conditioned on data, though critics argued that this evidence had been overstated and lacked mathematical rigor.
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calculations, the use of conditional probability calculations are central to parsing speech sounds into comprehensible syllables, a relatively straightforward and intuitive skill emerging as early as 8 months. Infants also appear to be good at tracking not only spatiotemporal states of objects, but
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illusions. In an alternative version of the Linda problem, participants are told that 100 people fit Linda's description and are asked how many are (1) bank tellers and (2) bank tellers and feminists. Experimentally, this version of the task appears to eliminate or mitigate the conjunction fallacy.
323:
Tversky and
Kahneman refuted this claim, arguing that making illusions disappear by manipulating them, whether they are cognitive or visual, does not undermine the initially discovered illusion. They also note that Gigerenzer ignores cognitive illusions resulting from frequency data, e.g., illusory
43:
Because this capacity can accommodate a broad range of informational domains, the subject matter is similarly broad and overlaps substantially with other cognitive phenomena. Indeed, some have argued that "cognition as an intuitive statistician" is an apt companion metaphor to the computer metaphor
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single statistics (e.g., 97% survival rate vs. 3% death rate), and/or fail to sufficiently communicate "reference classes" of probability statements to patients. The reference class is the object of a probability statement: If a psychiatrist says, for example, “this medication can lead to a 30-50%
311:
Gigerenzer has been critical of this view, arguing that it builds from a flawed assumption that a unified "normative theory" of statistical prediction and probability exists. His contention is that cognitive psychologists neglect the diversity of ideas and assumptions in probability theory, and in
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an exceptionally apt prediction machine, and that action-oriented processes underlying this phenomenon, whatever they might entail, are at the core of cognition. Probabilistic inferences and generalization play central roles in concepts and categories and language learning, and infant studies are
449:
It is less clear, however, how and why generalization is observed in infants: It might extend directly from detection and storage of similarities and differences in incoming data, or frequency representations. Conversely, it might be produced by something like general-purpose
Bayesian inference,
220:
representations of conditional dependencies. In a
Bayesian network, edge weights are conditional dependency strengths that are updated in light of new data, and nodes are observed variables. The graphical representation itself constitutes a model, or hypothesis, about the world and is subject to
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and David Murray argue that twentieth century psychology as a discipline adopted probabilistic inference as a unified set of ideas and ignored the controversies among probability theorists. They claim that a normative but incorrect view of how humans "ought to think rationally" follows from this
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Infants use form similarities and differences to develop concepts relating to objects, and this relies on multiple trials with multiple patterns, exhibiting some kind of common property between trials. Infants appear to become proficient at this ability in particular by 12 months, but different
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were similarly capable of using probabilistic and sequentially sampled data to make inferences about rewarding outcomes, and neural activity in the parietal cortex appeared to be involved in the decision-making process when they made inferences. In a series of 7 experiments using a variety of
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The colored ball paradigm in these experiments did not distinguish the possibilities of infants' inferences based on quantity vs. proportion, which was addressed in follow-up research where 12-month-old infants seemed to understand proportions, basing probabilistic judgments - motivated by
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Bayesian models have been quite popular among psychologists, particularly learning theorists, because they appear to emulate the iterative, predictive process by which people learn and develop expectations from new observations, while giving appropriate weight to previous observations.
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starting with a knowledge base that is iteratively conditioned on data to update subjective probabilities, or beliefs. This ties together questions about the statistical toolkit(s) that might be involved in learning, and how they apply to infant and childhood learning specifically.
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to non-agents based on uncertain or agent-like cues. In particular, some claim that it is adaptive for potential prey to assume agency by default if it is even slightly suspected, because potential predator threats typically involve cheap false positives and lethal false negatives.
412:
In practice, humans routinely make conceptual, linguistic, and probabilistic generalizations from small amounts of data. There is some debate about the utility of various tools of statistical inference in understanding the mind, but it is commonly accepted that the human mind is
131:
as specialized for different problem domains, rather than a content- or context-free toolkit. Signal detection theorists and object detection models, for example, often use a Neyman-Pearson approach, whereas
Fisherian frequentist statistics might aid cause-effect inferences.
27:
about either population-level properties, future data, or both. Inferences can involve revising hypotheses, or beliefs, in light of probabilistic data that inform and motivate future predictions. The informal tendency for cognitive animals to intuitively generate
255:
This is closely related to a concept called the "smoke detector principle" in evolutionary theory. It is defined by the tendency for immune, affective, and behavioral defenses to be hypersensitive and overreactive, rather than insensitive or weakly expressed.
387:
There has been some question about how concept structuring and generalization can be understood in terms of brain architecture and processes. This question is impacted by a neighboring debate among theorists about the nature of thought, specifically between
643:
Statistical literacy and risk judgments have been described as problematic for physician-patient communication. For example, physicians frequently inflate the perceived risk of non-treatment, alter patients' risk perceptions by positively or negatively
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at tracking properties of objects, and these cognitive systems appear to be developmentally distinct. This has been interpreted as domain specific toolkits of inference, each of which corresponds to separate types of information and has applications to
442:
about population characteristics from which the sample came, and vice versa when given population-level data. Other experiments have similarly supported a capacity for probabilistic inference with 6- and 11-month-old infants, but not in 4.5-month-olds.
159:: Single-event probabilities, they claim, are not observable because organisms evolved to intuitively understand and make statistical inferences from frequencies of prior events, rather than to "see" probability as an intrinsic property of an event.
433:
have traditionally argued that children do not develop the general cognitive capacities for probabilistic inference and hypothesis testing until concrete operational (age 7–11 years) and formal operational (age 12 years-adulthood)
598:
Research on reasoning in medicine, or clinical reasoning, usually focuses on cognitive processes and/or decision-making outcomes among physicians and patients. Considerations include assessments of risk, patient preferences, and
555:
of numerals. Recent experiments also indicated that, given some language training and capacity for referencing recognized objects, they also have some ability to make inferences about probabilities and hidden object type ratios.
856:
400:, specifically in domains like language learning and categorization. Some emphasize the limitations of pure connectionist models when they are expected to generalize future instances after training on previous instances.
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chance of a sexual problem,” it is ambiguous whether this means that 30-50% of patients will develop a sexual problem at some point, or if all patients will have problems in 30-50% of their sexual encounters.
147:. This entails a view that "probability" is nonsensical in the absence of pre-existing data, because it is understood as a relative frequency that long-run samples would approach given large amounts of data.
498:
is a process of inference relying on similarities and differences between concept properties, analogy and categorization are fundamentally the same process used for organizing concepts from incoming data.
23:, is the cognitive phenomenon where organisms use data to make generalizations and predictions about the world. This can be a small amount of sample data or training instances, which in turn contribute to
64:
structuring. Data are typically probabilistic and uncertainty is the rule, rather than the exception, in learning, perception, language, and thought. Recently, researchers have drawn from ideas in
202:, rather than passive or reactive. More classic lines of evidence cited among supporters of Bayesian inference include conservatism, or the phenomenon where people modify previous beliefs
849:
522:
For example, 9-month-old infants are capable of more quickly and dramatically updating their expectations when repeated syllable strings contain surprising features, such as rare
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Gigerenzer, G. (1994). Why the distinction between single-event probabilities and frequencies is important for psychology (and vice versa). In G. Wright & P. Ayton (Eds.),
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Gestalt grouping principle in infancy".
343:. Researchers advocating this adaptive rationality view argue that evolutionary theory casts heuristics and biases in a new light, namely, as computationally efficient and
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Cosmides, L; Tooby, J (January 1996). "Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty".
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Statisticians and probability theorists have long debated about the use of various tools, assumptions, and problems relating to inductive inference in particular.
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Gerken, L.; Bollt, A. (2008). "Three
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178:. It requires a "starting point" called a prior probability, which has been contentious for some frequentists who claim that frequency data are required to
435:
127:
acceptance. They also maintain, however, that the intuitive statistician metaphor of cognition is promising, and should consider different formal tools or
84:, allowing information structures to support multiple inferences in a variety of contexts and combinations. This approach has been called a probabilistic
4554:"Can facts trump unconditional trust? Evidence-based information halves the influence of physicians' non-evidence-based cancer screening recommendations"
328:
in basketball. This, they note, is an example of an illusory positive autocorrelation that cannot be corrected by converted data to natural frequencies.
277:
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Clements, Katherine A.; Gray, Suzanne L.; Gross, Brya; Pepperberg, Irene M. (May 2018). "Initial evidence for probabilistic reasoning in a grey parrot (
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Milofsky, Carl; Elstein, Arthur S.; Shulman, Lee S.; Sprafka, Sarah A. (November 1979). "Medical
Problem Solving: An Analysis of Clinical Reasoning".
104:, questioning the logical foundations of how and why people can arrive at conclusions that extend beyond past experiences - both spatiotemporally and
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way, and that re-representing problems in terms of frequencies rather than single-event probabilities can make cognitive illusions largely vanish.
143:
focuses on the relative proportions or frequencies of occurrences to draw probabilistic conclusions. It is defined by its closely related concept,
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some cases, their mutual incompatibility. Consequently, Gigerenzer argues that many cognitive illusions are not violations of probability theory
2742:
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Thinking in Young Children: Theoretical Advances, Empirical Research, and Policy Implications".
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also appeared to guide their decisions based on the ratios favoring the banana pellets after this was established as their preferred food item.
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investigating these principles have led some to conclude that human cognition, for example, has built-in and systematic errors in inference, or
3779:
Chambers, Kyle E.; Onishi, Kristine H.; Fisher, Cynthia (March 2003). "Infants learn phonotactic regularities from brief auditory experience".
194:, a cognitive scientist and philosopher, recently wrote a detailed argument in support of understanding the brain as a constructive Bayesian
507:
Infants and small children are not only capable generalizers of trait quantity and proportion, but of abstract rule-based systems such as
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have argued that it is not possible to derive a probability without reference to some frequency of previous outcomes, and this likely has
88:
because it constructs representations probabilistically, from pre-existing concepts to predict a possible and likely state of the world.
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Gomez, Rebecca L.; Gerken, LouAnn (March 1999). "Artificial grammar learning by 1-year-olds leads to specific and abstract knowledge".
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Haselton, Martie; Bryant, Gregory A.; Wilke, Andreas; Frederick, David; Galperin, Andrew; Frankenhuis, Willem E.; Moore, Tyler (2009).
515:. These rules can be referred to as “algebraic rules” of abstract informational structure, and are representations of rule systems, or
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Gilovich, Thomas; Vallone, Robert; Tversky, Amos (July 1985). "The hot hand in basketball: On the misperception of random sequences".
335:, EMT can be applied to inference under any informational domain, where risk or uncertainty are present, such as predator avoidance,
252:, with initial research focusing on its possible role in sexual overperception bias in men and sexual underperception bias in women.
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models. Concept generalization and classification have been modeled in a variety of connectionist models, or
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Concepts and conceptual development : ecological and intellectual factors in categorization
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Denison, Stephanie; Bonawitz, Elizabeth; Gopnik, Alison; Griffiths, Thomas L. (February 2013).
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Intuitive inferences can involve generating hypotheses from incoming sense data, such as
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knowledge. On a cognitive level, clinical inference relies heavily on interplay between
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4553:
3948:
3856:
3831:
3713:
3688:
3353:
3328:
3035:
3008:
2884:
2850:"Infants consider both the sample and the sampling process in inductive generalization"
2849:
2610:
2585:
2374:
2347:
2097:
1630:
1322:
1079:
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282:
57:
4648:
4615:
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568:
were able to make inferences about proportions inferred by sequentially sampled data.
4804:
4764:
4521:
Better doctors, better patients, better decisions : envisioning health care 2020
4467:
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4432:
4393:
4376:
4361:
3389:
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2708:
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2544:
2419:
2402:
2144:
2062:
1981:
1913:
1599:
1470:
1152:
785:
742:
458:
389:
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2235:
Goodman, Noah; Tenenbaum, Joshua; Feldman, Jacob; Griffiths, Thomas (January 2008).
2168:
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In contrast to the general-purpose mechanistic view, some researchers advocate both
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3765:
3673:
3638:
3502:
3405:
3313:
3262:
3219:
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2586:"Infants use rational decision criteria for choosing among models of their input"
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1849:
1747:
1569:
1382:
4285:
4121:
3665:
3529:
3432:
2253:
2236:
2099:
Women, fire, and dangerous things : what categories reveal about the mind
1461:
1287:
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1211:
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249:
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97:
77:
37:
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2728:
2710:
The scientist in the crib : what early learning tells us about the mind
2693:
2364:
2117:
2082:
2047:
2014:
1504:
1364:"The Role of Control in Attributing Intentional Agency to Inanimate Objects"
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937:
904:
574:
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2818:
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2619:
2570:
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costs and benefits of type I (false positive) and type II (false negative)
4782:
4402:
3494:
1818:
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to model cognition as a predictive and generative system of probabilistic
4733:
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1973:
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582:
523:
516:
495:
61:
260:
maintains that this is a consequence of a typical payoff structure in
232:(EMT) is an application of Neyman-Pearson statistics to cognitive and
3559:
3058:
2540:
2493:
2467:
2451:
2398:
2343:
1876:
1834:"Adaptive rationality: An evolutionary perspective on cognitive bias"
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Surfaces and essences : Analogy as the fuel and fire of thinking
457:
for generalization, acting upon specialized information structures ("
1531:
174:
of a hypothesis, which is computed as a posterior probability using
4494:
573:
relative frequency differences between banana pellets and carrots,
4616:"Simple tools for understanding risks: from innumeracy to insight"
4377:"Mental representations of medical diagnostic knowledge: a review"
512:
45:
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Heuristics and biases : the psychology of intuitive judgment
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concepts and properties employ different relevant principles of
367:
Another popular example of a supposed cognitive illusion is the
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3007:Tataru, Paula; Hobolth, Asger (5 December 2011).
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850:"Concepts in a Probabilistic Language of Thought"
812:Hofstadter, Douglas R.; Sander, Emmanuel (2012).
4301:The Cambridge handbook of thinking and reasoning
1895:"The base-rate fallacy in probability judgments"
2854:Proceedings of the National Academy of Sciences
2352:Proceedings of the National Academy of Sciences
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4675:. Oxford; New York: Oxford University Press.
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1276:Journal of Personality and Social Psychology
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693:Gigerenzer, Gerd; Murray, David J. (2015).
2977:: CS1 maint: location missing publisher (
2186:. Frankfurt am Main, Germany: MIND Group.
1311:Annals of the New York Academy of Sciences
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198:that is fundamentally action-oriented and
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4647:
4587:
4577:
4392:
3947:
3855:
3712:
3352:
3034:
3024:
2883:
2873:
2609:
2560:
2418:
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2363:
2252:
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1469:
1330:
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288:Much of this research was carried out by
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4016:"Number Comprehension by a Grey Parrot (
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1398:Personality and Social Psychology Review
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623:in concept spaces, or alternatively, as
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1629:Nisbett, Richard E.; Ross, Lee (1980).
1261:Formal representation of human judgment
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670:
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4412:
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3458:
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2940:
2938:
2217:
2207:
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1579:
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1549:
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1203:
1094:
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1015:. New York, N.Y.: Barnes & Noble.
992:
991:
980:
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4257:
3564:"Word learning as Bayesian inference"
3562:; Tenenbaum, Joshua B. (April 2007).
2487:
2485:
2338:
2336:
1955:
1953:
1443:
1441:
1439:
1437:
1435:
1134:
1132:
1130:
1128:
843:
841:
724:
722:
48:, or non-human primates) might have.
7:
3327:Thiessen, Erik D. (5 January 2017).
2450:Denison, Stephanie; Reed, Christie;
1450:European Review of Social Psychology
951:
949:
947:
880:
878:
876:
807:
805:
803:
688:
686:
684:
682:
680:
678:
676:
674:
4706:The New England Journal of Medicine
4456:Artificial Intelligence in Medicine
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266:tendency for humans to apply agency
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1101:Probability, statistics, and truth
543:and her colleagues suggested that
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4063:Journal of Comparative Psychology
4024:Journal of Comparative Psychology
4020:), Including a Zero-Like Concept"
3654:Language Learning and Development
3518:The Behavioral and Brain Sciences
959:Probabilistic Models of Cognition
695:Cognition as intuitive statistics
236:. It maintains that the possible
4765:10.1097/00001888-199805000-00024
4394:10.1097/00001888-199610000-00044
4270:. Cham: Springer International.
3390:10.1111/j.1467-9280.2007.01910.x
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1371:Journal of Cognition and Culture
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110:Fisherian frequentist statistics
4224:10.1016/j.cognition.2013.12.011
3940:10.1016/j.cognition.2012.07.003
3893:10.1016/j.cognition.2005.03.003
3848:10.1016/j.cognition.2010.01.006
3615:10.1016/j.cognition.2003.11.005
3247:10.1016/j.cognition.2004.02.002
2811:10.1016/j.cognition.2012.10.010
2602:10.1016/j.cognition.2010.01.006
2510:10.1016/j.cognition.2013.12.001
2067:. Cambridge, Mass.: MIT Press.
2032:. Cambridge, Mass.: MIT Press.
1999:. Cambridge, Mass.: MIT Press.
966:from the original on 2018-05-11
862:from the original on 2017-04-27
653:Base rates in clinical judgment
32:, when formalized with certain
4774:11858/00-001M-0000-0025-A092-2
1765:. Cambridge University Press.
1763:Logic of Statistical Inference
885:Nickerson, Raymond S. (2004).
774:Child Development Perspectives
1:
4483:American Journal of Sociology
3793:10.1016/s0010-0277(02)00233-0
3750:10.1016/s0010-0277(99)00003-7
3075:10.1016/s0001-6918(99)00029-3
2996:. Cambridge University Press.
2635:Words, thoughts, and theories
1216:Behavioral and Brain Sciences
1081:The Foundations of Statistics
639:Communication of patient risk
631:when diagnoses are suspected
530:Evidence in non-human animals
4579:10.1371/journal.pone.0183024
4468:10.1016/0933-3657(92)90036-o
4433:10.1016/0025-5564(75)90047-4
4346:10.3109/0142159X.2014.956052
3985:10.1016/j.beproc.2012.09.016
2637:. Cambridge, MA: MIT Press.
2319:Handbook of Child Psychology
2182:Clark, Andy (January 2015).
1914:10.1016/0001-6918(80)90046-3
1600:10.1016/0010-0285(85)90010-6
1153:10.1016/0010-0277(95)00664-8
697:. London: Psychology Press.
373:representativeness heuristic
4718:10.1056/NEJM197811022991808
4036:10.1037/0735-7036.119.2.197
3830:Gerken, LouAnn (May 2010).
3580:10.1037/0033-295X.114.2.245
3487:10.1126/science.283.5398.77
2584:Gerken, LouAnn (May 2010).
1811:10.1037/0033-295x.103.3.582
1099:Von Mises, Richard (1981).
1077:Savage, Leonard J. (1954).
427:Developmental psychologists
296:as an expansion of work by
40:as an academic discipline.
4832:
2945:Barrett, H. Clark (2015).
1995:Elman, Jeffrey L. (1996).
1850:10.1521/soco.2009.27.5.733
1748:10.1037/0022-3514.35.5.303
1570:10.1037/0033-295x.90.4.293
1383:10.1163/156853703322336634
1013:A treatise of human nature
4671:Gigerenzer, Gerd (2008).
4122:10.1007/s10071-016-1043-9
3666:10.1080/15475440802143117
3530:10.1017/s0140525x01000061
3433:10.1017/S0305000904006786
3421:Journal of Child Language
2254:10.1080/03640210701802071
1471:21.11116/0000-0000-BD7A-3
1462:10.1080/14792779143000033
1288:10.1037/0022-3514.78.1.81
1229:10.1017/s0140525x12000477
1210:Clark, Andy (June 2013).
816:. New York: Basic Books.
455:general-purpose mechanism
324:correlations such as the
170:generally emphasizes the
118:Neyman-Pearson statistics
4816:Philosophy of statistics
4632:10.1136/bmj.327.7417.741
4421:Mathematical Biosciences
3026:10.1186/1471-2105-12-465
2456:Developmental Psychology
2061:Marcus, Gary F. (2001).
1410:10.1177/1088868309350299
221:change, given new data.
100:famously considered the
3290:10.1111/1467-9280.00476
3196:10.1111/1467-9280.00459
2921:10.1111/1467-8624.00115
2875:10.1073/pnas.1003095107
2764:10.1126/science.1223416
2365:10.1073/pnas.0704450105
2096:Lakoff, George (1987).
470:conditional probability
234:evolutionary psychology
230:Error management theory
225:Error management theory
145:frequentist probability
3345:10.1098/rstb.2016.0056
3333:Phil. Trans. R. Soc. B
2192:10.15502/9783958570115
1962:Psychological Research
1936:Subjective probability
601:evidence-based medical
172:subjective probability
30:statistical inferences
4387:(10 Suppl): S55–561.
3973:Behavioural Processes
3693:Developmental Science
3378:Psychological Science
3278:Psychological Science
3184:Psychological Science
3149:Developmental Science
3098:Psychological Science
2549:Developmental Science
2133:Psychological Science
1174:Pearl, Judea (2000).
1044:Hacking, Ian (2001).
975:https://probmods.org/
731:Developmental Science
594:Reasoning in medicine
436:stages of development
355:People often neglect
345:ecologically rational
273:Heuristics and biases
141:Frequentist inference
136:Frequentist inference
52:Background and theory
34:axioms of probability
3568:Psychological Review
2842:Tenenbaum, Joshua B.
2539:Denison, Stephanie;
2492:Denison, Stephanie;
2397:Denison, Stephanie;
1865:Psychological Review
1799:Psychological Review
1709:Psychological Review
1666:Psychological Review
1588:Cognitive Psychology
1558:Psychological Review
539:Multiple studies by
383:Computational models
157:evolutionary origins
102:problem of induction
36:theory, constitutes
25:inductive inferences
17:Intuitive statistics
4811:Inductive reasoning
4570:2017PLoSO..1283024W
4173:10.1038/nature05852
4165:2007Natur.447.1075Y
4159:(7148): 1075–1080.
4059:Psittacus erithacus
4018:Psittacus erithacus
3479:1999Sci...283...77M
2866:2010PNAS..107.9066G
2756:2012Sci...337.1623G
2750:(6102): 1623–1627.
2184:Embodied Prediction
549:Psittacus erithacus
394:language of thought
369:conjunction fallacy
363:Conjunction fallacy
302:bounded rationality
86:language of thought
4075:10.1037/com0000106
3705:10.1111/desc.12183
3339:(1711): 20160056.
3013:BMC Bioinformatics
2543:(September 2010).
1974:10.1007/bf00309212
1085:. New York: Wiley.
560:Non-human primates
492:Douglas Hofstadter
488:Gestalt psychology
408:Evidence in humans
318:ecologically valid
168:Bayesian inference
163:Bayesian inference
114:Bayesian inference
70:philosophy of mind
66:probability theory
4753:Academic Medicine
4626:(7417): 741–744.
4381:Academic Medicine
3063:Acta Psychologica
2909:Child Development
2860:(20): 9066–9071.
2407:Cognitive Science
2358:(13): 5012–5015.
2241:Cognitive Science
1902:Acta Psychologica
990:External link in
659:base rate neglect
629:confirmation bias
625:semantic networks
503:Language learning
481:Concept formation
351:Base rate neglect
246:natural selection
214:Bayesian networks
106:epistemologically
4823:
4795:
4794:
4776:
4744:
4738:
4737:
4712:(18): 999–1001.
4701:
4695:
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4611:
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4542:
4516:
4507:
4506:
4478:
4472:
4471:
4451:
4445:
4444:
4427:(3–4): 351–379.
4416:
4407:
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4296:
4290:
4289:
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4207:
4201:
4200:
4148:
4142:
4141:
4110:Animal Cognition
4101:
4095:
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4047:
4011:
4005:
4004:
3968:
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3223:
3179:
3173:
3172:
3144:
3138:
3137:
3093:
3087:
3086:
3069:(2–3): 113–136.
3055:
3049:
3048:
3038:
3028:
3004:
2998:
2997:
2994:Mapping the Mind
2989:
2983:
2982:
2976:
2968:
2942:
2933:
2932:
2904:
2898:
2897:
2887:
2877:
2846:Schulz, Laura E.
2837:
2831:
2830:
2790:
2784:
2783:
2739:
2733:
2732:
2704:
2698:
2697:
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2530:
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2489:
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2468:10.1037/a0028278
2447:
2441:
2440:
2422:
2394:
2388:
2387:
2377:
2367:
2340:
2331:
2330:
2314:
2308:
2307:
2281:
2275:
2274:
2256:
2232:
2226:
2225:
2219:
2215:
2213:
2205:
2179:
2173:
2172:
2128:
2122:
2121:
2093:
2087:
2086:
2058:
2052:
2051:
2025:
2019:
2018:
1992:
1986:
1985:
1957:
1948:
1945:
1939:
1932:
1926:
1925:
1899:
1891:Bar-Hillel, Maya
1887:
1881:
1880:
1877:10.1037/h0034747
1860:
1854:
1853:
1838:Social Cognition
1829:
1823:
1822:
1794:
1785:
1784:
1761:Hacking (1965).
1758:
1752:
1751:
1731:
1725:
1724:
1721:10.1037/h0034747
1704:
1698:
1697:
1678:10.1037/h0042769
1661:
1655:
1654:
1626:
1620:
1619:
1583:
1574:
1573:
1553:
1544:
1543:
1515:
1509:
1508:
1482:
1476:
1475:
1473:
1445:
1430:
1429:
1393:
1387:
1386:
1368:
1359:
1353:
1352:
1334:
1306:
1300:
1299:
1271:
1265:
1264:
1256:
1250:
1249:
1231:
1207:
1198:
1197:
1171:
1165:
1164:
1136:
1123:
1122:
1096:
1087:
1086:
1084:
1074:
1068:
1067:
1041:
1035:
1034:
1008:
1002:
1001:
995:
994:
988:
986:
978:
972:
971:
962:(2nd ed.).
953:
942:
941:
915:
909:
908:
882:
871:
870:
868:
867:
861:
854:
845:
836:
835:
809:
798:
797:
769:
763:
762:
726:
717:
716:
690:
566:capuchin monkeys
541:Irene Pepperberg
475:concept learning
438:, respectively.
337:agency detection
283:cognitive biases
262:signal detection
74:computer science
4831:
4830:
4826:
4825:
4824:
4822:
4821:
4820:
4801:
4800:
4799:
4798:
4746:
4745:
4741:
4703:
4702:
4698:
4683:
4670:
4669:
4665:
4613:
4612:
4605:
4564:(8): e0183024.
4551:
4550:
4546:
4531:
4518:
4517:
4510:
4480:
4479:
4475:
4453:
4452:
4448:
4418:
4417:
4410:
4374:
4373:
4369:
4334:Medical Teacher
4331:
4330:
4326:
4311:
4298:
4297:
4293:
4278:
4265:
4264:
4255:
4209:
4208:
4204:
4150:
4149:
4145:
4103:
4102:
4098:
4056:
4055:
4051:
4013:
4012:
4008:
3970:
3969:
3965:
3921:
3920:
3916:
3878:
3877:
3873:
3829:
3828:
3824:
3778:
3777:
3773:
3735:
3734:
3730:
3686:
3685:
3681:
3651:
3650:
3646:
3600:
3599:
3595:
3558:
3557:
3553:
3515:
3514:
3510:
3473:(5398): 77–80.
3464:
3463:
3456:
3418:
3417:
3413:
3375:
3374:
3370:
3326:
3325:
3321:
3275:
3274:
3270:
3232:
3231:
3227:
3181:
3180:
3176:
3146:
3145:
3141:
3095:
3094:
3090:
3057:
3056:
3052:
3006:
3005:
3001:
2991:
2990:
2986:
2969:
2957:
2944:
2943:
2936:
2906:
2905:
2901:
2840:Gweon, Hyowon;
2839:
2838:
2834:
2792:
2791:
2787:
2741:
2740:
2736:
2721:
2706:
2705:
2701:
2686:
2665:
2664:
2660:
2645:
2632:
2631:
2627:
2583:
2582:
2578:
2538:
2537:
2533:
2491:
2490:
2483:
2449:
2448:
2444:
2396:
2395:
2391:
2342:
2341:
2334:
2316:
2315:
2311:
2296:
2283:
2282:
2278:
2234:
2233:
2229:
2216:
2206:
2202:
2181:
2180:
2176:
2130:
2129:
2125:
2110:
2095:
2094:
2090:
2075:
2060:
2059:
2055:
2040:
2027:
2026:
2022:
2007:
1994:
1993:
1989:
1959:
1958:
1951:
1946:
1942:
1933:
1929:
1897:
1889:
1888:
1884:
1862:
1861:
1857:
1831:
1830:
1826:
1796:
1795:
1788:
1773:
1760:
1759:
1755:
1733:
1732:
1728:
1706:
1705:
1701:
1663:
1662:
1658:
1643:
1628:
1627:
1623:
1585:
1584:
1577:
1555:
1554:
1547:
1532:10.2307/1914185
1517:
1516:
1512:
1497:
1484:
1483:
1479:
1447:
1446:
1433:
1395:
1394:
1390:
1366:
1361:
1360:
1356:
1308:
1307:
1303:
1273:
1272:
1268:
1258:
1257:
1253:
1209:
1208:
1201:
1186:
1173:
1172:
1168:
1138:
1137:
1126:
1111:
1098:
1097:
1090:
1076:
1075:
1071:
1056:
1043:
1042:
1038:
1023:
1010:
1009:
1005:
989:
979:
969:
967:
955:
954:
945:
930:
917:
916:
912:
897:
884:
883:
874:
865:
863:
859:
852:
847:
846:
839:
824:
811:
810:
801:
771:
770:
766:
728:
727:
720:
705:
692:
691:
672:
667:
655:
641:
596:
591:
562:
537:
532:
505:
483:
466:domain-specific
424:
410:
398:neural networks
385:
365:
353:
294:Daniel Kahneman
275:
227:
165:
138:
124:Gerd Gigerenzer
94:
82:representations
54:
21:folk statistics
12:
11:
5:
4829:
4827:
4819:
4818:
4813:
4803:
4802:
4797:
4796:
4759:(5): 538–540.
4739:
4696:
4681:
4663:
4603:
4544:
4529:
4508:
4495:10.1086/227074
4489:(3): 703–705.
4473:
4446:
4408:
4367:
4340:(5): 457–462.
4324:
4309:
4291:
4276:
4253:
4202:
4143:
4116:(2): 243–256.
4096:
4069:(2): 166–177.
4049:
4030:(2): 197–209.
4006:
3963:
3934:(2): 219–232.
3914:
3887:(3): B67–B74.
3871:
3842:(2): 362–366.
3822:
3787:(2): B69–B77.
3771:
3744:(2): 109–135.
3728:
3679:
3660:(3): 228–248.
3644:
3609:(3): B89–B96.
3593:
3574:(2): 245–272.
3551:
3508:
3454:
3427:(2): 249–268.
3411:
3384:(5): 387–391.
3368:
3319:
3284:(5): 431–436.
3268:
3241:(3): 215–240.
3225:
3190:(4): 320–328.
3174:
3155:(5): 567–580.
3139:
3104:(7): 511–515.
3088:
3050:
2999:
2984:
2955:
2934:
2899:
2832:
2805:(2): 285–300.
2785:
2734:
2719:
2699:
2684:
2658:
2643:
2625:
2596:(2): 362–366.
2576:
2555:(5): 798–803.
2531:
2504:(3): 335–347.
2481:
2462:(2): 243–249.
2442:
2413:(5): 885–908.
2389:
2332:
2309:
2294:
2276:
2247:(1): 108–154.
2227:
2218:|journal=
2200:
2174:
2139:(9): 767–773.
2123:
2108:
2088:
2073:
2053:
2038:
2020:
2005:
1987:
1968:(2): 123–129.
1949:
1940:
1927:
1908:(3): 211–233.
1882:
1871:(4): 237–251.
1855:
1844:(5): 733–763.
1824:
1805:(3): 582–591.
1786:
1771:
1753:
1742:(5): 303–314.
1726:
1715:(4): 237–251.
1699:
1672:(2): 129–138.
1656:
1641:
1621:
1594:(3): 295–314.
1575:
1564:(4): 293–315.
1545:
1526:(2): 263–291.
1510:
1495:
1477:
1431:
1388:
1377:(3): 208–217.
1354:
1301:
1266:
1251:
1222:(3): 181–204.
1199:
1184:
1166:
1124:
1109:
1088:
1069:
1054:
1036:
1021:
1003:
943:
928:
910:
895:
872:
837:
822:
799:
780:(3): 161–163.
764:
737:(3): 281–287.
718:
703:
669:
668:
666:
663:
657:In studies of
654:
651:
640:
637:
595:
592:
590:
587:
570:Rhesus monkeys
561:
558:
536:
533:
531:
528:
504:
501:
482:
479:
423:
422:Infant studies
420:
409:
406:
384:
381:
364:
361:
352:
349:
333:adaptationists
274:
271:
258:Randolph Nesse
226:
223:
216:, or directed
176:Bayes' Theorem
164:
161:
137:
134:
93:
90:
58:categorization
53:
50:
13:
10:
9:
6:
4:
3:
2:
4828:
4817:
4814:
4812:
4809:
4808:
4806:
4792:
4788:
4784:
4780:
4775:
4770:
4766:
4762:
4758:
4754:
4750:
4743:
4740:
4735:
4731:
4727:
4723:
4719:
4715:
4711:
4707:
4700:
4697:
4692:
4688:
4684:
4682:9780199747092
4678:
4674:
4667:
4664:
4659:
4655:
4650:
4645:
4641:
4637:
4633:
4629:
4625:
4621:
4617:
4610:
4608:
4604:
4599:
4595:
4590:
4585:
4580:
4575:
4571:
4567:
4563:
4559:
4555:
4548:
4545:
4540:
4536:
4532:
4530:9780262298957
4526:
4522:
4515:
4513:
4509:
4504:
4500:
4496:
4492:
4488:
4484:
4477:
4474:
4469:
4465:
4461:
4457:
4450:
4447:
4442:
4438:
4434:
4430:
4426:
4422:
4415:
4413:
4409:
4404:
4400:
4395:
4390:
4386:
4382:
4378:
4371:
4368:
4363:
4359:
4355:
4351:
4347:
4343:
4339:
4335:
4328:
4325:
4320:
4316:
4312:
4310:9780521824170
4306:
4302:
4295:
4292:
4287:
4283:
4279:
4277:9783319648286
4273:
4269:
4262:
4260:
4258:
4254:
4249:
4245:
4241:
4237:
4233:
4229:
4225:
4221:
4217:
4213:
4206:
4203:
4198:
4194:
4190:
4186:
4182:
4178:
4174:
4170:
4166:
4162:
4158:
4154:
4147:
4144:
4139:
4135:
4131:
4127:
4123:
4119:
4115:
4111:
4107:
4100:
4097:
4092:
4088:
4084:
4080:
4076:
4072:
4068:
4064:
4060:
4053:
4050:
4045:
4041:
4037:
4033:
4029:
4025:
4021:
4019:
4010:
4007:
4002:
3998:
3994:
3990:
3986:
3982:
3978:
3974:
3967:
3964:
3959:
3955:
3950:
3945:
3941:
3937:
3933:
3929:
3925:
3918:
3915:
3910:
3906:
3902:
3898:
3894:
3890:
3886:
3882:
3875:
3872:
3867:
3863:
3858:
3853:
3849:
3845:
3841:
3837:
3833:
3826:
3823:
3818:
3814:
3810:
3806:
3802:
3798:
3794:
3790:
3786:
3782:
3775:
3772:
3767:
3763:
3759:
3755:
3751:
3747:
3743:
3739:
3732:
3729:
3724:
3720:
3715:
3710:
3706:
3702:
3698:
3694:
3690:
3683:
3680:
3675:
3671:
3667:
3663:
3659:
3655:
3648:
3645:
3640:
3636:
3632:
3628:
3624:
3620:
3616:
3612:
3608:
3604:
3597:
3594:
3589:
3585:
3581:
3577:
3573:
3569:
3565:
3561:
3555:
3552:
3547:
3543:
3539:
3535:
3531:
3527:
3523:
3519:
3512:
3509:
3504:
3500:
3496:
3492:
3488:
3484:
3480:
3476:
3472:
3468:
3461:
3459:
3455:
3450:
3446:
3442:
3438:
3434:
3430:
3426:
3422:
3415:
3412:
3407:
3403:
3399:
3395:
3391:
3387:
3383:
3379:
3372:
3369:
3364:
3360:
3355:
3350:
3346:
3342:
3338:
3334:
3330:
3323:
3320:
3315:
3311:
3307:
3303:
3299:
3295:
3291:
3287:
3283:
3279:
3272:
3269:
3264:
3260:
3256:
3252:
3248:
3244:
3240:
3236:
3229:
3226:
3221:
3217:
3213:
3209:
3205:
3201:
3197:
3193:
3189:
3185:
3178:
3175:
3170:
3166:
3162:
3158:
3154:
3150:
3143:
3140:
3135:
3131:
3127:
3123:
3119:
3115:
3111:
3107:
3103:
3099:
3092:
3089:
3084:
3080:
3076:
3072:
3068:
3064:
3060:
3054:
3051:
3046:
3042:
3037:
3032:
3027:
3022:
3018:
3014:
3010:
3003:
3000:
2995:
2988:
2985:
2980:
2974:
2966:
2962:
2958:
2956:9780199348312
2952:
2948:
2941:
2939:
2935:
2930:
2926:
2922:
2918:
2914:
2910:
2903:
2900:
2895:
2891:
2886:
2881:
2876:
2871:
2867:
2863:
2859:
2855:
2851:
2847:
2843:
2836:
2833:
2828:
2824:
2820:
2816:
2812:
2808:
2804:
2800:
2796:
2789:
2786:
2781:
2777:
2773:
2769:
2765:
2761:
2757:
2753:
2749:
2745:
2738:
2735:
2730:
2726:
2722:
2720:9780688177881
2716:
2712:
2711:
2703:
2700:
2695:
2691:
2687:
2681:
2677:
2672:
2671:
2662:
2659:
2654:
2650:
2646:
2644:9780262571265
2640:
2636:
2629:
2626:
2621:
2617:
2612:
2607:
2603:
2599:
2595:
2591:
2587:
2580:
2577:
2572:
2568:
2563:
2558:
2554:
2550:
2546:
2542:
2535:
2532:
2527:
2523:
2519:
2515:
2511:
2507:
2503:
2499:
2495:
2488:
2486:
2482:
2477:
2473:
2469:
2465:
2461:
2457:
2453:
2446:
2443:
2438:
2434:
2430:
2426:
2421:
2416:
2412:
2408:
2404:
2401:(July 2010).
2400:
2393:
2390:
2385:
2381:
2376:
2371:
2366:
2361:
2357:
2353:
2349:
2345:
2339:
2337:
2333:
2328:
2324:
2320:
2313:
2310:
2305:
2301:
2297:
2295:9780393008036
2291:
2287:
2280:
2277:
2272:
2268:
2264:
2260:
2255:
2250:
2246:
2242:
2238:
2231:
2228:
2223:
2211:
2203:
2201:9783958570115
2197:
2193:
2189:
2185:
2178:
2175:
2170:
2166:
2162:
2158:
2154:
2150:
2146:
2142:
2138:
2134:
2127:
2124:
2119:
2115:
2111:
2109:9780226468044
2105:
2101:
2100:
2092:
2089:
2084:
2080:
2076:
2070:
2066:
2065:
2057:
2054:
2049:
2045:
2041:
2035:
2031:
2024:
2021:
2016:
2012:
2008:
2002:
1998:
1991:
1988:
1983:
1979:
1975:
1971:
1967:
1963:
1956:
1954:
1950:
1944:
1941:
1937:
1931:
1928:
1923:
1919:
1915:
1911:
1907:
1903:
1896:
1892:
1886:
1883:
1878:
1874:
1870:
1866:
1859:
1856:
1851:
1847:
1843:
1839:
1835:
1828:
1825:
1820:
1816:
1812:
1808:
1804:
1800:
1793:
1791:
1787:
1782:
1778:
1774:
1772:9780521051651
1768:
1764:
1757:
1754:
1749:
1745:
1741:
1737:
1730:
1727:
1722:
1718:
1714:
1710:
1703:
1700:
1695:
1691:
1687:
1683:
1679:
1675:
1671:
1667:
1660:
1657:
1652:
1648:
1644:
1638:
1634:
1633:
1625:
1622:
1617:
1613:
1609:
1605:
1601:
1597:
1593:
1589:
1582:
1580:
1576:
1571:
1567:
1563:
1559:
1552:
1550:
1546:
1541:
1537:
1533:
1529:
1525:
1521:
1514:
1511:
1506:
1502:
1498:
1496:9780521796798
1492:
1488:
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3365:. 20160056.
1404:(1): 7–16.
1147:(1): 1–73.
605:abstraction
579:chimpanzees
553:cardinality
431:Jean Piaget
402:Gary Marcus
306:satisficing
92:Probability
4805:Categories
4286:1017833633
2685:0521419662
2074:0262133792
2039:0262650541
2006:0585020345
1642:0134451309
1110:0486242145
1055:0521772877
1022:0760771723
993:|via=
970:2018-04-24
929:0521322197
866:2018-07-24
665:References
621:prototypes
575:orangutans
357:base rates
278:Heuristics
250:David Buss
200:predictive
192:Andy Clark
153:John Tooby
129:heuristics
98:David Hume
78:psychology
38:statistics
4726:0028-4793
4691:156902311
4640:0959-8138
4539:733263173
4503:0002-9602
4441:118063112
4362:207432268
4232:0010-0277
4212:Cognition
4181:0028-0836
3979:: 82–90.
3928:Cognition
3881:Cognition
3836:Cognition
3801:0010-0277
3781:Cognition
3738:Cognition
3623:0010-0277
3603:Cognition
3538:0140-525X
3298:0956-7976
3235:Cognition
3204:0956-7976
3118:0956-7976
2973:cite book
2965:881386175
2799:Cognition
2653:438680292
2590:Cognition
2498:Cognition
2429:1551-6709
2327:863228206
2304:753479728
2263:0364-0213
2220:ignored (
2210:cite book
2153:0956-7976
1982:144369350
1922:0001-6918
1781:704034945
1608:0010-0285
1317:: 75–85.
1238:0140-525X
1194:834142635
1141:Cognition
1031:404025944
983:cite book
832:841172513
794:1750-8592
751:1363-755X
713:918941179
617:induction
613:deduction
609:abduction
4791:15957015
4658:14512488
4598:28832633
4558:PLOS ONE
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4319:56011371
4248:10393347
4240:24440657
4189:17546027
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2161:16984293
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2048:43083232
2015:42854159
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1418:19926831
1349:20128143
1341:11411177
1296:10653507
1246:23663408
1161:18631755
1064:44969631
964:Archived
938:14905937
905:56115142
857:Archived
759:17444969
633:a priori
583:gorillas
524:phonemes
517:grammars
509:language
459:theories
429:such as
341:foraging
326:hot hand
184:a priori
4783:9609869
4589:5568103
4566:Bibcode
4403:8940935
4197:4343931
4161:Bibcode
3949:3434310
3857:2835817
3766:7447597
3714:4188806
3674:8068917
3639:5939461
3560:Xu, Fei
3503:6261323
3495:9872745
3475:Bibcode
3467:Science
3406:8261527
3354:5124081
3314:9058661
3263:2674246
3220:8068752
3059:Xu, Fei
3036:3329461
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2862:Bibcode
2752:Bibcode
2744:Science
2611:2835817
2541:Xu, Fei
2494:Xu, Fei
2452:Xu, Fei
2399:Xu, Fei
2375:2278207
2344:Xu, Fei
1819:8759048
1694:8503301
1651:5411525
1540:1914185
1119:8320292
646:framing
496:analogy
415:somehow
238:fitness
180:develop
62:concept
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314:per se
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116:, and
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4244:S2CID
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1978:S2CID
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1690:S2CID
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