This book, Nervous States – Democracy and the Decline of Reason, 2019, written by William Davies tries to explain the state we are in. The end of truth or the domination of feelings or the end of expertise all come to mind. People perceive that change is so fast that the slow knowledge developed by reason and learning is devalued, while instant knowledge that will be worthless tomorrow like that used by commodity, bond or stock trading networks is highly valued. Davies builds on Hayek and says many things that ring true. In three posts, I will present the main points of Davies’ book, argue with some of the points, and present what Davies says we can do about it. Devaluing reason is a big deal for decision making.
In Confidence, Part II, the authors conclude that confidence is computed continuously, online, throughout the decision making process, thus lending support to models of the mind as a device that computes with probabilistic estimates and probability distributions.
The Embodied Mind
One such explanation is that of predictive processing/embodied mind. Andy Clark, Jacob Hohwy, and Karl Friston have all helped to weave together this concept. Our minds are blends of top down and bottom up processing where error messages and the effort to fix those errors makes it possible for us to engage the world. According to the embodied mind model, our minds do not just reside in our heads. Our bodies determine how we interact with the world and how we shape our world so that we can predict better. Our evolutionary limitations have much to do with how our minds work. One example provided by Andy Clark and Barbara is a robot without any brain imitating human walking nearly perfectly (video go to 2:40). Now how does this tie into confidence? Confidence at a conscious level is the extent of our belief that our decisions are correct. But the same thing is going on as a fundamental part of perception and action. Estimating the certainty of our own prediction error signals of our own mental states and processes is as Clark notes: “clearly a delicate and tricky business. For it is the prediction error signal that…gets to ‘carry the news’.”
I discovered that I was a celiac a few months ago and accordingly I am on a gluten free diet. Compared to most conditions discovered in one’s late sixties, celiac disease seems almost inconsequential. However, it fits into the idea of prediction error minimization. In effect, the environment has changed and I need to change my predictions. Bread and beer are now bad. My automatic, intuitive prediction machine has not been getting it right. It is disorienting. I can no longer “See food, eat food.” I can change the environment at home, but in the wider world I need to be aware. My brain needs to dedicate perpetual, and at least for now, conscious effort to this cause. It is almost as if I became instantly even dumber. It makes me more self absorbed in social settings that involve food. Not known for my social skills, I have been a good listener, but now not so much. On my Dad’s 94th birthday, I ate a big piece of German chocolate cake, enjoyed it thoroughly, and then remembered that it was not allowed. In my particular case, I do not get sick or nauseated when I make such a mistake so my commitment is always under threat. This demands an even larger share of my brain to be compliant. My main incentive to comply is those photos of my scalloped small intestine. I note that I was diagnosed after years of trying to figure out my low ferritin levels. (It will be extremely disappointing if I find that my ferritin is still low.) Continue reading
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 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.
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 draft dated July 10, 2015, “Learning in Dynamic Probabilistic Environments: A Parallel-constraint Satisfaction Network-model Approach,” written by Marc Jekel, Andreas Glöckner, & Arndt Bröder. The paper includes experiments that contrast Parallel Constraint Satisfaction with the Adaptive Toolbox Approach. I have chosen to look only at the update of the PCS model with learning. The authors develop an integrative model for decision making and learning by extending previous work on parallel constraint satisfaction networks with algorithms of backward error-propagation learning. The Parallel Constraint Satisfaction Theory for Decision Making and Learning (PCS-DM-L) conceptualizes decision making as process of coherence structuring in which learning is achieved by adjusting network weights from one decision to the next. PCS-DM-L predicts that individuals adapt to the environment by gradual changes in cue weighting.
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 based on a paper: “Intuition and analytic processes in probabilistic reasoning: The role of time pressure,” authored by Sarah Furlan, Franca Agnoli, and Valerie F. Reyna. Valerie Reyna is, of course, the primary creator of fuzzy-trace theory. Reyna’s papers tend to do a good job of summing up the state of the decision making art and fitting in her ideas.
The authors note that although there are many points of disagreement, theorists generally agree that there are heuristic processes (Type 1) that are fast, automatic, unconscious, and require low effort. Many adult judgment biases are considered a consequence of these fast heuristic responses, also called default responses, because they are the first responses that come to mind. Type 1 processes are a central feature of intuitive thinking, requiring little cognitive effort or control. In contrast, analytic (Type 2) processes are considered slow, conscious, deliberate, and effortful, and they place demands on central working memory resources. Furlan, Agnoli, and Reyna assert that Type 2 processes are thought to be related to individual differences in cognitive capacity and Type 1 processes are thought to be independent of cognitive ability, a position challenged by the research presented in their paper. I was surprised by the given that intuitive abilities were unrelated to overall intelligence and cognitive abilities as set up by typical dual process theories.