This post is based on a paper written by Andy Clark, author of Surfing Uncertainty (See Paper Predictive Processing for a fuller treatment.), “A nice surprise? Predictive processing and the active pursuit of novelty,” that appeared in Phenomenology and the Cognitive Sciences, pp. 1-14. DOI: 10.1007/s11097-017-9525-z. For me this is a chance to learn how Andy Clark has polished up his arguments since his book. It also strikes me as connected to my recent posts on Curiosity and Creativity.
Clark and Friston (See post The Prediction Machine) depict human brains as devices that minimize prediction error signals: signals that encode the difference between actual and expected sensory simulations. But we know that we are attracted to the unexpected. We humans often seem to actively seek out surprising events, deliberately seeking novel and exciting streams of sensory stimulation. So how does that square with the idea of minimizing prediction error.
This post is based on the paper: “The role of interoceptive inference in theory of mind,” by
Sasha Ondobaka, James Kilner, and Karl Friston, Brain Cognition, 2017 Mar; 112: 64–68.
Understanding or inferring the intentions, feelings and beliefs of others is a hallmark of human social cognition often referred to as having a Theory of Mind. ToM has been described as a cognitive ability to infer the intentions and beliefs of others, through processing of their physical appearance, clothes, bodily and facial expressions. Of course, the repertoire of hypotheses of our ToM is borrowed from the hypotheses that cause our own behavior.
But how can processing of internal visceral/autonomic information (interoception) contribute to the understanding of others’ intentions? The authors consider interoceptive inference as a special case of active inference. Friston (see post Prediction Error Minimization) has theorized that the goal of the brain is to minimize prediction error and that this can be achieved both by changing predictions to match the observed data and, via action, changing the sensory input to match predictions. When you drop the knife and then catch it with the other hand, you are using active inference.
This post is derived from a review article: “The Role of Intuition in the Generation and Evaluation Stages of Creativity,” authored by Judit Pétervári, Magda Osman and Joydeep Bhattacharya that appeared in Frontiers of Psychology, September 2016 doi: 10.3389/fpsyg.2016.01420. It struck me that in all this blog’s posts, creativity had almost never come up. Then I threw it together with Edward O Wilson’s 2017 book: The Origins of Creativity, Liveright Publishing, New York. (See posts Evolution for Everyone and Cultural Evolution for more from Edward O. Wilson. He is the ant guy. He is interesting, understandable, and forthright.)
Creativity is notoriously difficult to capture by a single definition. Petervari et al suggest that creativity is a process that is broadly similar to problem solving, in which, for both, information is coordinated toward reaching a specific goal, and the information is organized in a novel, unexpected way. Problems which require creative solutions are ill-defined, primarily because there are multiple hypothetical solutions that would satisfy the goals. Wilson sees creativity beyond typical problem solving.
This post is largely a continuation of the Kenneth R Hammond post, but one prompted by recent current events. My opinion on gun control is probably readily apparent. But if it is not, let me say that I go crazy when mental health is bandied about as the reason for our school shootings or when we hear that arming teachers is a solution to anything. However, going crazy or questioning the sincerity of people with whom you are arguing is not a good idea. Dan Kahan (See my posts Cultural Cognition or Curiosity or his blog Cultural Cognition) has some great ideas on this, but Ken Hammond actually had accomplishments and they could help guide all of us today. I should note also that I was unable to quickly find the original sources so I am relying completely on: “Kenneth R. Hammond’s contributions to the study of judgment and decision making,” written by Mandeep K. Dhami and Jeryl L. Mumpower that appeared in Judgment and Decision Making, Vol. 13, No. 1, January 2018, pp. 1–22.
Why do almost all people tell the truth in ordinary everyday
life? […] The reason is, firstly because it is easier; for
lying demands invention, dissimulation, and a good memory
(Friedrich Nietzsche, page 54, Human, All Too Human: A Book for Free Spirits, 1878)
“I just fired the head of the F.B.I. He was crazy, a real nut job,” Mr. Trump said, according to the document, which was read to The New York Times by an American official. “I faced great pressure because of Russia. That’s taken off.”
Mr. Trump added, “I’m not under investigation.” (Pres. Donald Trump, discussion with Russian diplomats, May 10, 2017).
This post is based on the paper: ” ‘ I can see it in your eyes’: Biased Processing and Increased Arousal in Dishonest Responses,” authored by Guy Hochman, Andreas Glockner, Susan Fiedler, and Shahar Ayal, that appeared in the Journal of Behavioral Decision Making, December 2015.
This post is based on “Providing information for decision making: Contrasting description and simulation,” Journal of Applied Research in Memory and Cognition 4 (2015) 221–228, written by
Robin M. Hogarth and Emre Soyer. Hogarth and Soyer propose that providing information to help people make decisions can be likened to telling stories. First, the provider – or story teller – needs to know what he or she wants to say. Second, it is important to understand characteristics of the audience as this affects how information is interpreted. And third, the provider must match what is said to the needs of the audience. Finally, when it comes to decision making, the provider should not tell the audience what to do. Although Hogarth and Soyer do not mention it, good storytelling draws us into the descriptions so that we can “experience” the story. (see post 2009 Review of Judgment and Decision Making Research)
Hogarth and Soyer state that their interest in this issue was stimulated by a survey they conducted of how economists interpret the results of regression analysis. The economists were given the outcomes of the regression analysis in a typical, tabular format and the questions involved interpreting the probabilistic implications of specific actions given the estimation results. The participants had available all the information necessary to provide correct answers, but in general they failed to do so. They tended to ignore the uncertainty involved in predicting the dependent variable conditional on values of the independent variable. As such they vastly overestimated the predictive ability of the model. Another group of similar economists who only saw a bivariate scatterplot of the data were accurate in answering the same questions. These economists were not generally blinded by numbers as some in the population, but they still needed the visually presented frequency information.
This post is the first of two that look at a book review written by Karl Friston. Friston is the primary idea man behind embodied cognition (see post Embodied (grounded) Prediction (cognition) so far as I can tell. A book review is a chance to read his ideas in a little less formal and easier to understand environment. He reviews The Age of Insight: the Quest to Understand the Unconscious in Art, Mind, and Brain, from Vienna 1900 to the Present by Eric R. Kandel 2012.
This post is a look at the book by Philip E Tetlock and Dan Gardner, Superforecasting– the Art and Science of Prediction. Phil Tetlock is also the author of Expert Political Judgment: How Good Is It? How Can We Know? In Superforecasting Tetlock blends discussion of the largely popular literature on decision making and his long duration scientific work on the ability of experts and others to predict future events.
In Expert Political Judgment: How Good Is It? How Can We Know? Tetlock found that the average expert did little better than guessing. He also found that some did better. In Superforecasting he discusses the study of those who did better and how they did it.
This post is based on the paper: “Free-energy minimization and the dark-room problem,” written by Karl Friston, Christopher Thornton and Andy Clark that appeared in Frontiers in Psychology in May 2012. Recent years have seen the emergence of an important new fundamental theory of brain function (Posts Embodied Prediction and Prediction Error Minimization). This theory brings information-theoretic, Bayesian, neuroscientific, and machine learning approaches into a single framework whose over arching principle is the minimization of surprise (or, equivalently, the maximization of expectation). A puzzle raised by critics of these models is that biological systems do not seem to avoid surprises. People do not simply seek a dark, unchanging chamber, and stay there. This is the “Dark-Room Problem.”
This post is based on the paper: “The role of cognitive abilities in decisions from experience: Age differences emerge as a function of choice set size,” by Renato Frey, Rui Mata, and Ralph Hertwig that appeared in Cognition 142 (2015) 60–80.
People seldom enjoy access to summarized information about risky options before making
a decision except for things like weather forecasts that explicitly state a probability. Instead, they may search for information and learn from the environment—thus making decisions from experience. Many consequential decisions—including health care choices, finances, and everyday risks (e.g., driving in bad weather; crossing a busy street)—are made without full knowledge of the possible outcomes and their probabilities so we must make decisions from experience. According to the authors, the mind’s most notable transformation across the life span is a substantial decline in processing speed, working memory and short-term memory capacity —all components potentially involved in search and learning processes.