Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Erik Eriksen is a remarkable individual. He has no college degrees yet is Professor of Human Development at Harvard University. He came to psychology via art, which explains why the reader will find him painting contexts and backgrounds rather than stating dull facts and concepts. He has been a training psychoanalyst for many years as well as a perceptive observer of cultural and social settings and their effect on growing up. This is not just a book on childhood. It is a panorama of our society. Anxiety in young children, apathy in American Indians, confusion in veterans of war, and arrogance in young Nazis are scrutinized under the psychoanalytic magnifying glass. The material is well written and devoid of technical jargon. The theme of the volume is that it is human to have a long childhood which will leave a lifelong residue of emotional immaturity in man. Primitive groups and

Childhood and Society.

Corruption and Growth

The ability to recognize one's own successful cognitive processing, in e.g. perceptual or memory tasks, is often referred to as metacognition. How should we quantitatively measure such ability? Here we focus on a class of measures that assess the correspondence between trial-by-trial accuracy and one's own confidence. In general, for healthy subjects endowed with metacognitive sensitivity, when one is confident, one is more likely to be correct. Thus the degree of association between accuracy and confidence can be taken as a quantitative measure of metacognition. However, many studies use a statistical correlation coefficient (e.g. Pearson's r) or its variant to assess this degree of association, and such measures are susceptible to undesirable influences from factors such as response biases. Here we review other measures based on signal detection theory and receiver operating characteristics (ROC) analysis that are “bias free”, and relate these quantities to the calibration and discrimination measures developed in the probability estimation literature. We go on to distinguish between the related concepts of metacognitive bias (a difference in subjective confidence despite basic task performance remaining constant), metacognitive sensitivity (how good one is at distinguishing between one’s own correct and incorrect judgments) and metacognitive efficiency (a subject’s level of metacognitive sensitivity given a certain level of task performance). Finally, we discuss how these three concepts pose interesting questions for the study of metacognition and conscious awareness.

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How to measure metacognition

We offer a formal treatment of choice behavior based on the premise that agents minimize the expected free energy of future outcomes. Crucially, the negative free energy or quality of a policy can be decomposed into extrinsic and epistemic (or intrinsic) value. Minimizing expected free energy is therefore equivalent to maximizing extrinsic value or expected utility (defined in terms of prior preferences or goals), while maximizing information gain or intrinsic value (or reducing uncertainty about the causes of valuable outcomes). The resulting scheme resolves the exploration-exploitation dilemma: Epistemic value is maximized until there is no further information gain, after which exploitation is assured through maximization of extrinsic value. This is formally consistent with the Infomax principle, generalizing formulations of active vision based upon salience (Bayesian surprise) and optimal decisions based on expected utility and risk-sensitive (Kullback-Leibler) control. Furthermore, as with previous active inference formulations of discrete (Markovian) problems, ad hoc softmax parameters become the expected (Bayes-optimal) precision of beliefs about, or confidence in, policies. This article focuses on the basic theory, illustrating the ideas with simulations. A key aspect of these simulations is the similarity between precision updates and dopaminergic discharges observed in conditioning paradigms.

/pdf/active-inference-and-epistemic-value-2cz139s5ty.pdf

Active inference and epistemic value

Decisions are never perfect, with confidence in one's choices fluctuating over time. How subjective confidence and valuation of choice options interact at the level of brain and behavior is unknown. Using a dynamic model of the decision process, we show that confidence reflects the evolution of a decision variable over time, explaining the observed relation between confidence, value, accuracy and reaction time. As predicted by our dynamic model, we show that a functional magnetic resonance imaging signal in human ventromedial prefrontal cortex (vmPFC) reflects both value comparison and confidence in the value comparison process. Crucially, individuals varied in how they related confidence to accuracy, allowing us to show that this introspective ability is predicted by a measure of functional connectivity between vmPFC and rostrolateral prefrontal cortex. Our findings provide a mechanistic link between noise in value comparison and metacognitive awareness of choice, enabling us both to want and to express knowledge of what we want.

/pdf/confidence-in-value-based-choice-3fpsa4ddbs.pdf

Confidence in value-based choice

https://www.cell.com/trends/cognitive-sciences/pdf/S1364-6613(15)00278-8.pdf

Forming Beliefs: Why Valence Matters

Recent evidence suggests that a state of good mental health is associated with biased processing of information that supports a positively skewed view of the future. Depression, on the other hand, is associated with unbiased processing of such information. Here, we use brain imaging in conjunction with a belief update task administered to clinically depressed patients and healthy controls to characterize brain activity that supports unbiased belief updating in clinically depressed individuals. Our results reveal that unbiased belief updating in depression is mediated by strong neural coding of estimation errors in response to both good news (in left inferior frontal gyrus and bilateral superior frontal gyrus) and bad news (in right inferior parietal lobule and right inferior frontal gyrus) regarding the future. In contrast, intact mental health was linked to a relatively attenuated neural coding of bad news about the future. These findings identify a neural substrate mediating the breakdown of biased updating in major depression disorder, which may be essential for mental health.

/pdf/losing-the-rose-tinted-glasses-neural-substrates-of-unbiased-1gc2hvd2fw.pdf

Losing the rose tinted glasses: Neural substrates of unbiased belief updating in depression

Dishonesty is an integral part of our social world, influencing domains ranging from finance and politics to personal relationships. Anecdotally, digressions from a moral code are often described as a series of small breaches that grow over time. Here we provide empirical evidence for a gradual escalation of self-serving dishonesty and reveal a neural mechanism supporting it. Behaviorally, we show that the extent to which participants engage in self-serving dishonesty increases with repetition. Using functional MRI, we show that signal reduction in the amygdala is sensitive to the history of dishonest behavior, consistent with adaptation. Critically, the extent of reduced amygdala sensitivity to dishonesty on a present decision relative to the previous one predicts the magnitude of escalation of self-serving dishonesty on the next decision. The findings uncover a biological mechanism that supports a 'slippery slope': what begins as small acts of dishonesty can escalate into larger transgressions.

/pdf/the-brain-adapts-to-dishonesty-5z7prlz8st.pdf

The brain adapts to dishonesty

Humans show a natural tendency to discount bad news while incorporating good news into beliefs (the "good news-bad news effect"), an effect that may help explain seemingly irrational risk taking. Understanding how this bias develops with age is important because adolescents are prone to engage in risky behavior; thus, educating them about danger is crucial. We reveal a striking valence-dependent asymmetry in how belief updating develops with age. In the ages tested (9-26 y), younger age was associated with inaccurate updating of beliefs in response to undesirable information regarding vulnerability. In contrast, the ability to update beliefs accurately in response to desirable information remained relatively stable with age. This asymmetry was mediated by adequate computational use of positive but not negative estimation errors to alter beliefs. The results are important for understanding how belief formation develops and might help explain why adolescents do not respond adequately to warnings.

https://www.pnas.org/content/pnas/110/41/16396.full.pdf

Neil Garrett

Papers

Confidence in value-based choice

Forming Beliefs: Why Valence Matters

Losing the rose tinted glasses: Neural substrates of unbiased belief updating in depression

The brain adapts to dishonesty

Human development of the ability to learn from bad news