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.
Having the good fortune to be lost in Venice, I was reminded of the nuances of the recognition heuristic. My wife found the perfect antique store which was unsurprisingly closed for lunch. We went on, but a couple of hours later we tried to recreate our steps. For much of the journey, we did well having only to recognize that we had seen a particular store or archway or bridge before. Unfortunately, that broke down when we realized that we were retracing our steps in a five minute period. We still remembered that walk to the restaurant the first night, but it was unfortunately not differentiated in our minds. This was certainly an example of less knowledge being more. Eventually, using a combination of GPS and maps we found our way back to our hotel, but we never did find that antique store. And I was trying.
This post is based on a paper by Benjamin Hilbig, Martha Michalkiewicz, Marta Castela, Rudiger Pohl and Edgar Erdfelder: “Whatever the cost? Information integration in memory-based inferences depends on cognitive effort.” that was scheduled to appear in Memory and Cognition 2014. Fundamental uncertainty is a not uncommon situation for our decision making. There is an ongoing argument with the fast and frugal heuristics toolbox approach and the single tool approaches of evidence accumulation and parallel constraint satisfaction. However that argument depends on the particular task, the type of task, and on and on. I am still waiting for a giant table that puts all those things together.
This post is based on a paper: “What is adaptive about adaptive decision making? A parallel constraint satisfaction account,” that was written by Andreas Glöckner, Benjamin E. Hilbig, and Marc Jekel and appeared in Cognition 133 (2014) 641–666. The paper is quite similar to that discussed in the post Swiss Army Knife or Adaptive Tool Box. However, it reflects an updated model that they call the PCS-DM model (parallel constraint satisfaction-decision making). From what I can tell this model attempts to address past weaknesses by describing the network structure more fully and does this at least partially by setting up a one-free parameter implementation which can accommodate individual differences and differences between tasks.