Presentation on theme: "Evolutionary Explanations for ‘Irrationality’ Leeann Breeze."— Presentation transcript:
Evolutionary Explanations for ‘Irrationality’ Leeann Breeze
“In formal logic, a contradiction is the signal of defeat, but in the evolution of real knowledge it marks the first step in progress toward a victory.” - -Alfred North Whitehead
EVOLUTIONARY PSYCHOLOGY & reasoning today All humans today carry cognitive traits that served to help our ancestors survive and reproduce in prehistoric environments. Today’s world is much DIFFERENT than the world in which our ancestors lived and evolved …So the traits we observe today may have been valuable in the past, but some no longer serve any evolutionary advantage, given the nature of modern environments This includes the psychological strategies we have evolved to use…
EVOLUTIONARY PSYCHOLOGY & reasoning today …THUS NON-NORMATIVE REASONING EXISTS TODAY Because we evolved cognitive modules that served for efficiency, reproductive/social success, and environmental safety/typicality in prehistoric contexts So, where Alfred Whitehead is concerned, the contradiction between Normative and Descriptive theory is a failure for formal logic (which is hard to argue against) BUT the fact that we evolved to demonstrate this contradiction because of ecologically sound reasoning marks the success of “the evolution of real knowledge”
HOW IS IRRATIONALITY ADAPTIVE? LET’S BEGIN DEMONSTRATING THE ECOLOGICAL BENEFIT OF IRRATIONAL BEHAVIOR AND COGNITIVE PROCESSES BY OUTLINING THE FORCES THAT GUIDED THE DEVELOPMENT OF HUMAN COGNITIVE TRAITS, ACCORDING TO EVOLUTIONARY THEORY
PRINCIPLES OF EVOLUTION by Natural Selection, from Darwin 1. Traits show variation 2. Some variation is heritable 3. Individuals differ in fitness (the number of offspring they are able to produce) 4. A correlation exists between phenotype and fitness
EVOLUTIONARY INTERPRETATIONS A trait’s adaptiveness is determined by its frequency in the population of interest An adaptive phenotype will have an advantage for personal fitness, those who exhibit it will more frequently survive to reproductive age, and the trait will be inherited by offspring, increasing the trait’s frequency in the population
EVOLUTIONARY TIME Humans are biological creatures programmed by evolution to act, think, feel, and learn in ways that have fostered survival over many past generations. Traits we see today exist because they survived challenges of past environments in which our ancestors lived
EVOLUTIONARY INTERPRETATIONS & REASON Since cognition is not a physical trait, selection acts upon manifested behaviors that result from cognitive ability or task construal. Evolutionary psychology works on the assumption that cognitive traits we observe today developed as responses to problems our ancestors faced over thousands of years of evolution in prehistoric, savanna-style, hunter-gatherer, societies that relied on social interaction to thrive
EVOLUTIONARY PSYCHOLOGY “Evolved psychological mechanisms are functional; they function to solve recurrent adaptive problems that confronted our ancestors.” –David Buss interview in Barker, 2006, pp
RATIONALITY & EEA According to hypotheses that reference the Environment of Evolutionary Adaptiveness(EEA), our mental modules have structures that are better adapted to past environments than the present.
WHERE RATIONALITY IS CONCERNED: Where do these environmental discrepancies apply?
EVOLUTIONARY INTERPRETATIONS & RATIONALITY Because our cognitive modules evolved to serve SURVIVAL AND REPRODUCTION in the highly social, hunter-gatherer, savannah- style EEA, our reasoning abilities today do not appear to fit modern normative theories of logic…resulting in apparent reasoning “errors” defined as mismatching between descriptive and normative models of logic
EVOLUTION and CHANGES The rapidly changing technological environment in which we live makes these previous adaptations seem even more out-of-date in their modern context Because even today, we appear to be designed to more readily respond to tasks with the influence of: 1. Typicality of events & Natural Sampling 2. Social Contexts 3. Time/Effort-Saving Heuristics even when these strategies produce obviously incorrect responses to modern problems
LET’S REVIEW: WHAT IS RATIONAL? BARON: anything that helps us achieve our goals DAWES: rationality is avoidance of self- contradiction Ascribing to formal (normative) rules of logic
Empirical demonstrations of irrationality WASON 4-CARD PROBLEM BAYESIAN INFERENCE PROBABILITY ESTIMATES
PART I: WASON 4 CARD PROBLEM Leda Cosmides and John Tooby The Scenario: GROUP 1: 4 cards are on a table There is ONE RULE: To have a B, there must be 21 or higher on the other side
WASON 4 CARD PROBLEM GROUP 1: What is the maximum number of cards you must check to be SURE this rule is satisfied?
WASON 4 CARD PROBLEM GROUP 2: 4 cards are on a table There is ONE RULE: To have a beer a person must be 21 or older
WASON 4 CARD PROBLEM GROUP 1: What is the minimum number of cards you must check to be SURE this rule is satisfied?
WASON 4 CARD PROBLEM FINDINGS: although the 2 problems have the same logical structure, less than 25 percent of college students can solve the problem for group 1, but roughly 75 percent of college students answer the problem of group 2 correctly After re-designing the problem to eliminate issues of familiarity, Cosmides and Tooby conclude that we seem to be predisposed to more easily solve the problems that involve CHEAT DETECTION
WASON 4 CARD PROBLEM & irrationality Therefore, people seem to violate Dawe’s definition of rationality by failing to be consistent when the 2 problems have the same logical structure People also violate the 3 rd law of rationality when they are placed with problems like group 1 by failing to follow normative models
WASON 4 CARD PROBLEM & evolutionary theory Robert Trivers, evolutionary psychologist, has argued that reciprocal altruism is crucial to the social evolution of our species. Additionally, reciprocity can only be spread if non-reciprocators are punished
WASON 4 CARD PROBLEM & evolutionary theory In light of Triver’s theory of reciprocal altruism, Cosmides and Tooby interpret their findings as being indicative of an evolved mental capacity for recognizing when some one has cheated by violating a SOCIAL CONTRACT
WASON 4 CARD PROBLEM & cheat detection Evolutionary strategy holds that individuals are controlled by behaviors that will serve to maximize the success of their OWN genes THUS THE BEST STRATEGY WOULD BE TO CHEAT (getting all possible gains for oneself & profit from the good nature of others) AND NEVER RECIPROCATE
WASON 4 CARD PROBLEM & reciprocal altruism THEORY: altruism/reciprocity gene and cheater gene are both FREQUENCY DEPENDENT. Because if there were too many cheats, competition would override. But, eventually a random mutation for a set of ‘altruist genes’ in the cheater population would begin to have an advantage. Likewise, in an entirely altruist population, the best strategy is to be a cheat. and in a mixed population the best strategy is to be an altruist with cheater-detection, share with other altruists and punish cheats. So cheaters and alrtuists hold a balance in our population…
WASON 4 CARD PROBLEM & cheat detection …and in a mixed population the best strategy is to be an altruist with cheater-detection, share with other altruists and punish cheats. THUS, WE HAVE EVOLVED TO HAVE PREDISPOSITION TO GIVE THE NORMATIVELY CORRECT ANSWER TO SCENARIOS INVOLVING CHEAT DETECTION. AN APOLOGIST/EVOLUTIONARY PSYCHOLOGIST WOULD SAY THE REASON FOR THE NON-NORMATIVE RESPONSE TO THE ABSTRACT PROBLEM IS BECAUSE WE HAVE NOT EVOLVED IN ENVIRONMENTS THAT PROMOTE THE DEVELOPMENT OF THE RIGHT EQUIPMENT TO INTERPRET THE PROBLEM IN A WAY THAT ALLOWS US TO ANSWER IT CORRECTLY.
PART II: PROBABILITY ESTIMATES
PROBABILITY ESTIMATES THE LINDA PROBLEM Linda is 31, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and participated in anti-nuclear and anti-war demonstrations.. What happened to Linda? Rank order the following possible outcomes: (a) Linda failed to graduate from college (b) Linda works as a bank teller (c) Linda works for Green Peace (d) Linda works as a bank teller and is active in the feminist movement
PROBABILITY ESTIMATES The probability that Linda is a bank teller must be at least as large as the probability that Linda is a bank teller and active in the feminist movement, by shear odds occurrence of ONE event is much more likely than the combined occurrence of TWO events
PROBABILITY ESTIMATES & The Linda Problem WHY THE ERROR? Evolutionarily, people have adapted to assume continuity in the environment. This seems to have had a consequential effect on the human affinity for narrative. We adopt a story of Linda from the snippet of info, and continue it in our estimates of likelihood for her future narrative.
PROBABILITY ESTIMATES & The Linda Problem This is adaptive because it served to give us appropriate responses to social environments. According to Geoffrey Miller, it is adaptive to assume people are generally consistent, because it serves our “cheat detection” and “trustworthy mate” concepts, helping us to better deem who is a safe reproductive partner—improving our reproductive success. So, we may not answer the normative answer to the Linda problem, statistically speaking…but, we are answering with the most ecologically-appropriate response. (Panglossian)
PROBABILITY ESTIMATES & percentages vs. frequencies PROBLEM 1: You are a gynecologist who conducts breast cancer screening in your region using mammography. The probability that a woman in this region has breast cancer is 1%. If a woman has breast cancer, the probability she tests positive is 80% (sensitivity). If she does not have breast cancer, the probability she tests positive is 9.6% (false positive rate). A woman tests positive. What is the probability that she has breast cancer?
PROBABILITY ESTIMATES & percentages vs. frequencies PROBLEM 2: You are an experienced physician in a preliterate society. You have no books or surveys, only your accumulated experience. A severe disease is plaguing your people. You have discovered a symptom that signals the disease, but not with certainty. Over the years you have seen many people & most don’t have the disease. Of those who did have the disease, 8 had the symptom. Of those who did not have the disease, 95 had the symptom. Now you meet a patient who has the symptom. What is the chance he has the disease?
PROBABILITY ESTIMATES & percentages vs. frequencies Which was easier to solve? IN PROBLEM 2, the solution is simple. Total people=8+95=103. of that 103, only 8 had the disease—thus the likelihood of a person coming in and having the disease is 8 out of 103=VERY LOW(7.8 percent) PROBLEM 1: I will go into detail on HOW to solve problem 1 in the next section. For now, know the answer here too is 7.8%, and more math is required. Physicians who typically solve problem 2 correctly give estimates of problem 1 of roughly 70-80% -- nearly 10 times too high!
PROBABILITY ESTIMATES & percentages vs. frequencies Why is the normatively correct answer only intuitive in problem version 2? The difference between the problems is the use of percentages in version 1 and frequencies in version 2 In fact, Cosmides and Tooby (1996) found that when they converted relevant info from probability to frequency formats in an experiment, their subjects’ performance improved in parallel SO WHY DID WE ADAPT TO PREFER FLAT RATES INSTEAD OF PERCENTAGES?
PROBABILITY ESTIMATES & percentages Why frequency preference adaptive? 1. Probabilities and percentages were not an everyday encounter until the 20th century 2. Formal percentages began as scientific notation during the 19th century 3. Mathematical probability arose in the mid-17th century Thus, the Environment of Evolutionary Adaptiveness didn’t have selection pressures involving these mathematical structures BECAUSE THEY DID NOT EXIST IN THE EEA.
PROBABILITY ESTIMATES & percentages Instead, the EEA built our probability estimates on naturally occurring phenomena. Therefore, we base our conclusions on NATURAL SAMPLING– taking census of events in the environment and judging likelihoods based on past encounters
PROBABILITY ESTIMATES & percentages Using natural sampling, we set an event counter each time some event occurs. Humans seem to spontaneously count events, and because this is so automatic to us, our cognitive processing is more conducive to problems that suit this type of format. So, as the apologists say, we are doing the best we can with the equipment we have evolved.
PART III: SOLVING PROBLEM # 1: BAYESIAN INFERENCE To find the normative answer to problem 1, we need to use BAYESIAN INFERENCE. This is a formula for using base rates, likelihoods/probabilities of 2 events to come to a final likelihood estimate for one occurrence, given some evidence.
BAYESIAN INFERENCE Suppose we have two events, C and T, with probabilities P(c) and P(t) There are two conditional probabilities, P(U|K) and P(c|t) We define P(c|t) = P(c)*P(t/c) / P(c)*P(t/c)+ P( not c)*P(t/ not c) This tells us how to go from one conditional probability to the other If we know P(c|t), P(c), and P(t), we can calculate P(t|c) ** assume c is the unknown state (a hypothesis that the patient has cancer) t is the known information (i.e., evidence a positive on the mammogram ) I.e., we use t to update our probability of c
BAYESIAN INFERENCE By solving the formula with the given values, we can reason that the probability of cancer is.078=7.8% BUT THIS IS NOT HOW WE REASON… WHY? - fast and fruegal heuristics are more evolutionarily adaptive than normative reasoning skills - We haven’t evolved the capacity to be sensitive to base rates, in the way that we would need to be to if we had intuitive guide towards using bayes’ theorem
BAYES THEOREM why fast and frugal heuristics? Gigerenzer points out that we use our impression of what is representative or what is more familiar to us in order to solve problems, even when that is not going to produce normatively correct responses. This is because ‘FAST AND FRUGAL HEURISTICS’ are much more effective at dolving real-world problems quickly with minimum information
BAYES’ THEOREM heuristics/evolution when you look at it from the point of view of evolution, this makes sense. The adaptive value of saving in the EEa time was very high. Using utility theories, Bayes theorem, and doing the math to find the probability of attack from a wild animal given the evidence that you see him approaching quickly, but you are unsure of how long it has been since he has eaten could cost you your life. It is better to use a HEURISTIC—and err on the side of saftey: an over- generalized false positive is much less detrimental in these circumstances than a false negative. So, it is suggested that we have adapted to have less sensitivity to the occurrence of false-negative rates and we hone in on false positives.
BAYES’ THEOREM & base rate neglect TVERSKY AND KAHNEMAN consider the breast cancer example to include a type of cognitive bias called BASE RATE NEGLECT In this specific example, the overall rarity of breast cancer is being ignored. Gigerenzer references back to our bias for frequency data as to why we may neglect base rates: because in the EEA, a concept such as base rates would not have existed. Our minds have evolved algorithms that can only work on the sort of input that would’ve been available in the EEA, so input such as base rates are commonly ignored.
EVOLUTION AND RATIONALITY Our cognitive processes were designed by selection to solve problems our ancestors faced in the EEA Cognitive errors arise from rules made based on typicality & natural sampling that do not fit probability scenarios and contemporary mathematics Other cognitive errors arise from our predisposition to favor reasoning that promotes sociality, rather than normative logic Additional problems occur because of our tendency to conserve effort and time by using heuristics EVOLUTIONARY THEORISTS ACT AS APOLOGISTS CLAIMING THAT ALL OF THESE ERRORS ARE THE RESULT OF MAKING THE BEST USE OF OUR COGNITIVE CAPACITIES GIVEN OUR LIMITATIONS