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.
Although I have had much respect for Dan Kahan’s work, I have had a little trouble with the Identity protective Cognition Thesis (ICT). The portion in bold in the quote below from “Motivated Numeracy and Enlightened Self-Government” has never rung true.
On matters like climate change, nuclear waste disposal, the financing of economic stimulus programs, and the like, an ordinary citizen pays no price for forming a perception of fact that is contrary to the best available empirical evidence: That individual’s personal beliefs and related actions—as consumer, voter, or public discussant—are too inconsequential to affect the level of risk he or anyone else faces or the outcome of any public policy debate. However, if he gets the ‘wrong answer” in relation to the one that is expected of members of his affinity group, the impact could be devastating: the loss of trust among peers, stigmatization within his community, and even the loss of economic opportunities.
Why should Thanksgiving be so painful if it were true? I do not even know what my friends think of these things. Now at some point issues like climate change become so politically tainted that you may avoid talking about them to not antagonize your friends, but that does not change my view. But now Kahan has a better explanation.
This post is based on a comment paper: “Honest People Tend to Use Less–Not More—Profanity: Comment on Feldman et al.’s (2017) Study,” that appeared in Social Psychological and Personality Science 1-5 and was written by R. E. de Vries, B. E. Hilbig, Ingo Zettler, P. D. Dunlop, D. Holtrop, K. Lee, and M. C. Ashton. Why would honesty suddenly be important with respect to decision making when I have largely ignored it in the past? You will have to figure that out for yourself. It reminded me that most of our decision making machinery is based on relative differences. We compare, but we are not so good at absolutes. Thus, when you get a relentless fearless liar, the relative differences are widened and this is likely to spread out what seems to be a reasonable decision.
This post is based on a paper: “Learning from experience in nonlinear environments: Evidence from a competition scenario,” authored by Emre Soyer and Robin M. Hogarth, Cognitive Psychology 81 (2015) 48-73. It is not a new topic, but adds to the evidence of our nonlinear shortcomings.
In 1980, Brehmer questioned whether people can learn from experience – more specifically, whether they can learn to make appropriate inferential judgments in probabilistic environments outside the psychological laboratory. His assessment was quite pessimistic. Other scholars have also highlighted difficulties in learning from experience. Klayman, for example, pointed out that in naturally occurring environments, feedback can be scarce, subject to distortion, and biased by lack of appropriate comparative data. Hogarth asked when experience-based judgments are accurate and introduced the concepts of kind and wicked learning environments (see post Learning, Feedback, and Intuition). In kind learning environments, people receive plentiful, accurate feedback on their judgments; but in wicked learning environments they don’t. Thus, Hogarth argued, a kind learning environment is a necessary condition for learning from experience whereas wicked learning environments lead to error. This paper explores the boundary conditions of learning to make inferential judgments from experience in kind environments. Such learning depends on both identifying relevant information and aggregating information appropriately. Moreover, for many tasks in the naturally occurring environment, people have prior beliefs about cues and how they should be aggregated.
This post is based on the paper: “Priors in perception: Top-down modulation, Bayesian
perceptual learning rate, and prediction error
minimization,” authored by Jakob Hohwy (see post Explaining Away) that appeared (or is scheduled to appear) in Consciousness and Cognition, 2017. Hohwy writes in an understandable manner and is so open that he posts papers even before they are complete of which this is an example. Hohwy pursues the idea of cognitive penetration – the notion that beliefs can determine perception.
Can ‘high level’ or ‘cognitive’ beliefs modulate perception? Hohwy methodically examines this question by trying to create the conditions under which it might work and not be trivial. For under standard Bayesian inference, the learning rate declines gradually as evidence is accumulated, and the prior updated to be ever more accurate. The more you already know the less you will learn from the world. In a changing world this is not optimal since when things in the environment change we should vary the learning rate. Hohwy provides this example. As the ambient light conditions improve, the learning rate for detecting a visible target should increase (since the samples and therefore the prediction error has better precision in better light). This means Bayesian perceptual inference needs a tool for regulating the learning rate. The inferential system should build expectations for the variability in lighting conditions throughout the day, so that the learning rate in visual detection tasks can be regulated up and down accordingly.
The human brain is thus hypothesized to build up a vast hierarchy of expectations that overall help regulate the learning rate and thereby optimize perceptual inference for a world that delivers changeable sensory input. Hohwy suggests that this makes the brain a hierarchical filter that takes the non-linear time series of sensory input and seeks to filter out regularities at different time scales. Considering the distributions in question to be normal or Gaussian, the brain is considered a hierarchical Gaussian filter or HGF .
This post is from Judgment and Decision Making, Vol. 11, No. 6, November 2016, pp. 601–610, and is based on the paper: “The irrational hungry judge effect revisited: Simulations reveal that the magnitude of the effect is overestimated,” written by Andreas Glöckner. Danziger, Levav and Avnaim-Pesso analyzed legal rulings of Israeli parole boards concerning the effect of serial order in which cases are presented within ruling sessions. DLA analyzed 1,112 legal rulings of Israeli parole boards that cover about 40% of the parole requests of the country. They assessed the effect of the serial order in which cases are presented within a ruling session and took advantage of the fact that the ruling boards work on the cases in three sessions per day, separated by a late morning snack and a lunch break. They found that the probability of a favorable decision drops from about 65% to 5% from the first ruling to the last ruling within each session. This is equivalent to an odds ratio of 35. The authors argue that these findings provide support for extraneous factors influencing judicial decisions and speculate that the effect might be driven by mental depletion. Glockner notes that the article has attracted attention and the supposed order effect is considerably cited in psychology.