Nick Chater is the author of The Mind is Flat–the Remarkable Shallowness of the Improvising Brain, Yale University Press, New Haven, 2019. He is a professor of behavioral science at the Warwick Business School. The book is two parts and overall it is as ambitious as it is simple. The first part is the most convincing. He shows how misguided we are on our perceptions, emotions, and decision making. Our vision seems to provide us with a full fledged model of our environment, when we really only can focus on a very small area with our furtive eye movements providing the impression of a complete detailed picture. Our emotions do not well up from deep inside, but are the results of in-the-moment interpretations based on the situation we are in, and highly ambiguous evidence from our own bodily state. Chater sees our beliefs, desires, and hopes as just as much inventions as our favorite fictional characters. Introspection does not work, because there is nothing to look at. We are imaginative creatures with minds that pretty much do everything on the fly. We improvise so our decision making is inconsistent as are our preferences.
This post is based on the book, Elastic–Flexible Thinking in a Time of Change by Leonard Mlodinow, Pantheon Books, New York, 2018. Mlodinow is a physicist and worked with Stephen Hawking. His previous book Subliminal evidently gave him considerable access to interesting people like Seth MacFarlane. He mentions that Stephen Hawking’s pace of communicating was at best six words a minute with public presentations being done ahead of time. Mlodinow notes that this slowing of the pace of a conversation is actually quite helpful in forcing you to consider the words as opposed to thinking of what you are going to say while the other person is talking so that you can have an instant response.
This post is inspired by the book: Rebooting AI – Building Artificial Intelligence We Can Trust, written by Gary Marcus and Ernest Davis, New York, 2019. Gary Marcus (see post Kluge) is a well known author and artificial intelligence entrepreneur and Ernest Davis is a professor of computer science at Carnegie Mellon. To oversimplify, the authors emphasize that the successes of AI are narrow and tend to be greedy, opaque, and brittle. They provide history of AI seemingly about being ready for prime time decade after decade after decade. Self driving cars are almost there, but they are not. Human frailties in driving result in a death about every 100,000,000 miles driven, but Marcus and Davis indicate that self driving cars require human intervention every 10,000 miles which is 10,000 times in 100,000,000 miles. It may be a very long time before we are ready to sign off on self-driving cars, because the progress thus far has been the easy part.
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 Webb 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 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.
David Brooks seems to be a fascination of mine. The New York Times columnist surprises me both in positive and negative ways. I only mention it when the surprise is negative. Below is an excerpt from his November 25, 2016, column.
And this is my problem with the cognitive sciences and the advice world generally. It’s built on the premise that we are chess masters who make decisions, for good or ill. But when it comes to the really major things we mostly follow our noses. What seems interesting, beautiful, curious and addicting?
Have you ever known anybody to turn away from anything they found compulsively engaging?
We don’t decide about life; we’re captured by life. In the major spheres, decision-making, when it happens at all, is downstream from curiosity and engagement. If we really want to understand and shape behavior, maybe we should look less at decision-making and more at curiosity. Why are you interested in the things you are interested in? Why are some people zealously seized, manically attentive and compulsively engaged?
Now that we know a bit more about decision-making, maybe the next frontier is desire. Maybe the next Kahneman and Tversky will help us understand what explains, fires and orders our loves.
I can imagine his frustration with the advice world and maybe with Kahneman and Tversky (see post Prospect Theory), but it appears that Brooks is only looking at the advice world. Brooks would benefit by looking at the work of Ken Hammond. The post Cognitive Continuum examines some of Hammond’s 1980 work. Hammond has those chess masters to whom Brooks refers as one extreme of the cognitive continuum. The post Intuition in J-DM looks at the work of Tilmann Betsch and Andreas Glockner in what is called Parallel Constraint Satisfaction theory.
Betsch and Glockner believe that information integration and output formation (choice, preference) is intuitive. Analysis involves directed search (looking for valid cues or asking an expert for advice), making sense of information, anticipating future events, etc. Thus, they see a judgment as a collaboration of intuition and analysis. The depth of analysis varies, but intuition is always working so preferences are formed even without intention. Limiting processing time and capacity constrains only input. Thus, once information is in the system, intuition will use that information irrespective of amount and capacity.
Curiosity might be considered the degree of dissonance we encounter in our automatic decision making that in effect tells us to analyze–find more information and examine it. We do mostly follow our noses, because it is adaptive. But it is also adaptive to be able to recognize change that is persistent and must be responded to. A parameter of the parallel constraint satisfaction model is the individual sensitivity to differences between cue validities. This implies that individuals respond differently to changing cue validities. Some change quickly when they perceive differences and others change at a glacial pace.
The post Rationality Defined Again: RUN & JUMP looks at the work of Tilmann Betsch and Carsten Held. Brooks in his opinion piece seems to be suggesting that analytic processing is pretty worthless. Betsch and Held have seen this before. They note that research on non-analytic processing has led some authors to conclude that intuition is superior to analysis or to at least promote it as such with the obvious example being Malcolm Gladwell in Blink. Such a notion, however, neglects the important role of decision context. The advantages and disadvantages of the different types of thought depend on the nature of the task. Moreover, the plea for a general superiority of intuition neglects the fact that analysis is capable of things that intuition is not. Consider, for example, the case of routine maintenance and deviation decisions. Routine decisions will lead to good results if prior experiences are representative for the task at hand. In a changing world, however, routines can become obsolete.
In the absence of analytic thought, adapting to changing contexts requires slow, repetitive learning. Upon encountering repeated failure, the individual’s behavioral tendencies will change. The virtue of deliberate analysis, Brooks’ chess mastering, lies in its power to quickly adapt to new situations without necessitating slow reinforcement learning. Whereas intuition is fast and holistic due to parallel processing, it is a slave to the pre-formed structure of knowledge as well as the representation of the decision problem. The relations among goals, situations, options and outcomes that result from prior knowledge provide the structural constraints under which intuitive processes operate. They can work very efficiently but, nevertheless, cannot change these constraint. The potential of analytic thought dwells in the power to change the structure of the representation of a decision problem.
I believe that Brooks realizes that analytic thought is one thing that distinguishes us from other creatures even though it does not seem to inform much of our decision making. The post Embodied(Grounded) prediction(cognition might also open a window for Brooks.
This post starts from the conclusion of the previous post that the evidence supports a single strategy framework, looks at Julian Marewski’s criticism, and then piles on with ideas on how weights can be changed in a single strategy framework.
Marewski provided a paper for the special issue of the Journal of Applied Research in Memory and Cognition (2015) on “Modeling and Aiding Intuition in Organizational Decision Making”: “Unveiling the Lady in Black: Modeling and Aiding Intuition,” authored by Ulrich Hoffrage and Julian N. Marewski. The paper gives the parallel constraint satisfaction model a not so subtle knock:
By exaggerating and simplifying features or traits, caricatures can aid perceiving the real thing. In reality, both magic costumes and chastity belts are degrees on a continuum. In fact, many theories are neither solely formal or verbal. Glöckner and Betsch’s connectionist model of intuitive decision making, for instance, explicitly rests on both math and verbal assumptions. Indeed, on its own, theorizing at formal or informal levels is neither “good” nor “bad”. Clearly, both levels of description have their own merits and, actually, also their own problems. Both can be interesting, informative, and insightful – like the work presented in the first three papers of this special issue, which we hope you enjoy as much as we do. And both can border re-description and tautology. This can happen when a theory does not attempt to model processes. Examples are mathematical equations with free parameters that carry no explanatory value, but that are given quasi-psychological, marketable labels (e.g., “risk aversion”).
This post tries to do a little tying together on a familiar subject. I look at a couple of papers that provide more perspective than typical research papers provide. First is the preliminary dissertation of Anke Söllner. She provides some educated synthesis which my posts need, but rarely get. Two of her papers which are also part of her dissertation are discussed in the posts Automatic Decision Making and Tool Box or Swiss Army Knife? I also look at a planned special issue of the Journal of Behavioral Decision Making to address “Strategy Selection: A Theoretical and Methodological Challenge.”
Söllner’s work is concerned with the question: which framework–multiple strategy or single strategy– describes multi-attribute decision making best? In multi-attribute decision making we have to choose among two or more options. Cues can be consulted and each cue has some validity in reference to the decision criterion. If the criterion is an objective one (e.g., the quantity of oil), the task is referred to as probabilistic inference, whereas a subjective criterion (e.g., preference for a day trip) characterizes a preferential choice task. The multiple strategy framework is most notably the adaptive toolbox that includes fast and frugal heuristics as individual strategies. Single strategy frameworks assume that instead of selecting one from several distinct decision strategies, decision makers employ the same uniform decision making mechanism in every situation. The single strategy frameworks include the evidence accumulation model and the connectionist parallel constraint satisfaction model.
This post starts with the paper “Brains striving for coherence: Long-term cumulative plot formation in the default mode network,” authored by K. Tylén, P. Christensen, A. Roepstorff, T. Lund, S. Østergaard, and M. Donald. The paper appeared in NeuroImage 121 (2015) 106–114.
People are capable of navigating and keeping track of all the parallel social activities of everyday life even when confronted with interruptions or changes in the environment. Tylen et al suggest that even though these situations present themselves in series of interrupted segments often scattered over huge time periods, they tend to constitute perfectly well-formed and coherent experiences in conscious memory. However, the underlying mechanisms of such long-term integration is not well understood. While brain activity is generally traceable within the short time frame of working memory, these integrative processes last for minutes, hours or even days.