Hit: A signal is present, and the signal is detected (correct response)
False Alarm: No signal is presented, but a signal is detected (incorrect response)
Miss: A signal is present, but no signal is detected (incorrect response)
Correct Rejection: No signal is presented, and no signal is detected (correct response)
If the signal is clear, like a bright light against a dark background, and the decision maker has good visual acuity and is motivated to watch for the signal, we should see a large number of Hits and Correct Rejections and very few False Alarms and Misses. As these properties change, so does the quality of the decision. It is under ordinary conditions of uncertainty that signal-detection theory yields a powerful way to assess the stimulus and respondent qualities, including the respondent’s idiosyncratic criterion (or cutting score) for decision making.
Signal-detection theory has been applied in areas as diverse as locating objects by sonar, the quality of remembering, the comprehension of language, visual perception, consumer marketing, jury decisions, price predictions in financial markets, and medical diagnoses. The reason signal-detection theory should be in the toolkit of every scientist is because it provides a mathematically rigorous framework for understanding the nature of decision processes. The reason its logic should be in the toolkit of every thinking person is because it forces a completion of the four cells when analyzing the quality of any statement, such as “Good management positions await Sagittarius this week.”
Everyday Apophenia
David Pizarro
Assistant professor, Department of Psychology, Cornell University
The human brain is an amazing pattern-detecting machine. We possess a variety of mechanisms that allow us to uncover hidden relationships between objects, events, and people. Without these, the sea of data hitting our senses would surely appear random and chaotic. But when our pattern-detection systems misfire, they tend to err in the direction of perceiving patterns where none actually exist.
The German neurologist Klaus Conrad coined the term “apophenia” to describe this tendency in patients suffering from certain forms of mental illness. But it is increasingly clear from a variety of findings in the behavioral sciences that this tendency is not limited to ill or uneducated minds; healthy, intelligent people make similar errors on a regular basis. A superstitious athlete sees a connection between victory and a pair of socks; a parent refuses to vaccinate her child because of a perceived causal connection between inoculation and disease; a scientist sees hypothesis-confirming results in random noise; and thousands of people believe the random “shuffle” function on their music software is broken because they mistake spurious coincidence for meaningful connection.
In short, the pattern-detection responsible for so much of our species’ success can just as easily betray us. This tendency to oversee patterns is likely an inevitable by-product of our adaptive pattern-detecting mechanisms. But the ability to acknowledge, track, and guard against this potentially dangerous tendency would be aided if the simple concept of “everyday apophenia” were an easily accessible concept.
A Cognitive Toolkit Full of Garbage
Ernst Pöppel
Neuroscientist; chairman of the Human Science Center, Munich University; author, Mindworks: Time and Conscious Experience
To get rid of garbage is essential. Also mental garbage. Cognitive toolkits are filled with such garbage simply because we are victims of ourselves. We should regularly empty this garbage can or, if we enjoy sitting in garbage, we’d better check how “shorthand abstractions” (SHAs) limit our creativity (certainly itself a SHA). Why is the cognitive toolkit filled with garbage?
Let us look back in history (SHA): Modern science (SHA) can be said to have started in 1620 with Novum Organum (“New Instrument”), by Francis Bacon. It should impress us today that his analysis (SHA) begins with a description (SHA) of four mistakes we run into when we do science. Unfortunately, we usually forget these warnings. Francis Bacon argued that we are, first, victims of evolution (SHA)—that is, that our genes (SHA) define constraints that necessarily limit insight (SHA). Second, we suffer from the constraints of imprinting (SHA); the culture (SHA) we live in provides a frame for epigenetic programs (SHA) that ultimately define the structure (SHA) of neuronal processing (SHA). Third, we are corrupted by language (SHA), because thoughts (SHA) cannot be easily transformed into verbal expressions. Fourth, we are guided, or even controlled, by theories (SHA), be they explicit or implicit.
What are the implications for a cognitive toolkit? We are caught, for instance, in a language trap. On the basis of our evolutionary heritage, we have the power of abstraction (SHA), but this has, in spite of some advantages we brag about (to make us seem superior to other creatures), a disastrous consequence: Abstractions are usually represented in words; apparently we cannot do otherwise. We have to “ontologize”; we invent nouns to extract knowledge (SHA) from processes (SHA). (I do not refer to the powerful pictorial shorthand abstractions.) Abstraction is obviously complexity reduction (SHA). We make it simple. Why do we do this? Evolutionary heritage dictates rapidity. However, speed may be an advantage for a survival toolkit but not for a cognitive toolkit. It is a categorical error (SHA) to confuse speed in action with speed in thinking. The selection pressure for speed invites us to neglect the richness of facts. This pressure allows the invention (SHA) of a simple, clear, easy-to-understand, easy-to-refer-to, easy-to-communicate shorthand abstraction. Thus, because we are a victim of our biological past, and as a consequence a victim of ourselves, we end up with shabby SHAs, having left behind reality. If there is one disease all humans share, it is “monocausalitis,” the motivation (SHA) to explain everything on the basis of just one cause. This may be a nice intellectual exercise, but it is simply misleading.
Of course we depend on communication (SHA), and this requires verbal references usually tagged with language. But if we do not understand, within the communicative frame or reference system (SHA), that we are a victim of ourselves by “ontologizing” and continually creating “practical” SHAs, we simply use a cognitive toolkit of mental garbage.
Is there a pragmatic way out, other than to radically get rid of mental garbage? Yes, perhaps: Simply not using the key SHAs explicitly in one’s toolkit. Working on consciousness, don’t use (at least for one year) the SHA “consciousness.” If you work on the “self,” never refer explicitly to self. Going through one’s own garbage, one discovers many misleading SHAs, like just a few in my focus of attention (SHA): the brain as a net, localization of function, representation, inhibition, threshold, decision, the present. An easy way out is, of course, to refer to some of these SHAs as metaphors (SHA), but this, again, is evading the problem (SHA). I am aware of the fact (SHA) that I am also a victim of evolution, and to suggest “garbage” as a SHA also suffers from the same problem; even the concept of garbage required a discovery (SHA). But we cannot do otherwise than simply be aware of this challenge (SHA), that the content of the cognitive toolkit is characterized by self-referentiality (SHA)—that is, by the fact that the SHAs define themselves by their unreflected use.
Acknowledgments
Thanks to Steven Pinker for suggesting this year’s Edge Question and to Daniel Kahneman for advice on its presentation. Thanks to Peter Hubbard of HarperCollins for his continued support. And thanks to Sara Lippincott for her thoughtful and meticulous editing.
Index
The pagination of this electronic edition does not match the edition from which it was created. To locate a specific passage, please use the search feature of your e-book reader.
absence and evidence, 281, 282–84
abstractions, shorthand, see SHAs
Adaptation and Natural Selection (Williams), 196
adoptions, 194
Aether, 338–39
Afghanistan, 19
agreeableness, 232–33
Aguirre, Anthony, 301–2
Alexander, Richard, 321
Alexander, Stephon H., xxvii, 296–98
algebra, 6, 24
Alter, Adam, 150–53
altruism, 194, 196–97
aluminum refining, 110
Amazon, 25
Anasazi, 361
Anderson, Alun, 209–10
Anderson, Ross, 262–63
anecdotalism, 278–80
anomalies, 242–45
Anthropocene thinking, 206–8
anthropologists, 361
anthropophilia, 386–88
anyons, 191
apophenia, 394
Arbesman, Samuel, 11–12
archaeology, 282–84, 361
architecture, 246–49
ARISE (Adaptive Regression In the Service of the Ego), 235–36
Aristotle, 9, 28–29, 35
art:
bricolage in, 271–72
parallelism in commerce and, 307–9
recursive structures in, 146–49
Arthur, Brian, 223
Ascent of Man, The, 340
Asimov, Isaac, 324–25
assertions, 267
assumptions, 218–19
atoms, 128
attention, 130, 211
focusing illusion an, 49–50
spotlight of, 46–48
attractiveness, 136, 137
authority and experts, 18, 20, 34
Avery, Oswald, 244
Avicenna, 9
Aztecs, 361
Bacon, Francis, 395
bacteria, 15–16, 89, 97, 166, 290–91, 292–93, 338
transformation of, 243, 244, 245
Baldwin, Mark, 152
Banaji, Mahzarin R., 389–93
banking crisis, 259, 261, 307, 309, 322, 386
Barondes, Samuel, 32
Barton, Robert, 150–51
base rate, 264–65
Bass, Thomas A., 86–87
Bayesian inference, 70
behavior, ignorance of causes of, 349–52
behavioral sciences, 365–66
belief, 336–37
proof, 355–57
Bell, Alexander Graham, 110
bell curve (Gaussian distribution), 199, 200
benchmarks, 186
bias, 18, 43–45
confirmation, 40, 134
self-serving, 37–38, 40
in technologies, 41–42
biochemical cycles, 170–71
bioengineering, 16
biological ecosystems, 312–14
biological teleology, 4
biology, 234, 312
biophilia, 386
Bird, Sheila, 274
birds, 155, 359
chickens, 62–63, 155
herring gulls, 160
songbirds, 154–55
black box, 303
Blackmore, Sue, 215–17
Black Swan, The (Taleb), 315
black-swan technologies, 314–17
Blake, William, 44
blame, 35–36, 106, 386
blindness, 144
Bloch waves, 297
Boccaletti, Giulio, 184–87
body, life-forms in, 13, 290–91, 292
Boeri, Stefano, 78
Bohr, Niels, 28
Bolyai, János, 109
Bony, Jean, 247–48
Bostrom, Nick, 275–77
bottom-up thinking, 157–59
Boyer, Pascal, 182–83
bradykinesia, 63
brain, 48, 129–30, 148, 149, 150, 158, 172, 346, 347, 389, 394
consciousness and, 217
evolution of, 10, 207, 257
mind and, 364, 366
neurons in, see neurons
plasticity of, 250–51
predictive coding and, 132–34
self and, 212
size of, 257
of split-brain patients, 349–50
synapses in, 164
temperament traits and, 229–30
white and gray matter in, 162–63
Bramante, Donato, 248–49
Brand, Stewart, 15–16
Bray, Dennis, 171–72
bricolage, 271–72
Brin, Sergey, xxv
Bronowski, Jacob, 340, 341–42
Brooks, David, xxv–xxviii
Brown, Louise, 165
Bryson, Bill, 387
Buddha, 373
business planning, 186
Buss, David M., 353–54
Byars, James Lee, xxix–xxx
Cabot, John, 90
calculus, 34, 109
Calvin, William, 201–2
cancer, 390
body scans and, 69, 259–60, 264, 265
tests for, 264–65
cannibalism, 361–62
carbon, 81, 82
carbon dioxide (CO2) emissions, 202, 207, 217, 262
car insurance, 66–67
Carr, Nicholas, 116–17
Carroll, Sean, 9–10
Cartesian science, 82–83
Caspi, Avshalom, 279
cats, 286
causality, 34–36, 58–61, 396
blame, 35–36, 106, 386
confabulation, 349–52
correlation and, 215–17, 219
of diseases, 59, 303–4
entanglement and, 331
information flow and, 218–20
nexus, 34–35
root-cause analysis, 303–4
in universe, 9–10
web of causation, 59–60, 61
central-limit theorem, 107–8
certainty, 73, 260
proof, 355–57
uselessness of, 51–52
see also uncertainty
Challenger, 236
chance, 7, 18
change, 127–28, 290
fixation on, 373
chaos theory, 103, 202
character traits, 229
charitable activities, 194
cheating, 351
chess, 343
chickens, 62–63, 155
children, 148, 155, 252
chocolate, 140
cholera, 338
Chomsky, Noam, xxv
Christakis, Nicholas A., xxvii, 81–83, 306
Church, George, 88–89
CINAC (“correlation is not a cause”), 215–17
civil rights movement, 370
Clark, Andy, 132–34
Clarke, Arthur C., 61
climate change, 51, 53, 99, 178, 201–2, 204, 268, 309, 315, 335, 386, 390
CO2 levels and, 202, 207, 217, 262
cultural differences in view of, 387–88
global economy and, 238–39
procrastination in dealing with, 209, 210
clinical trials, 26, 44, 56
cloning, 56, 165
coastlines, xxvi, 246
Cochran, Gregory, 360–62
coffee, 140, 152, 351
cognition, 172
perception and, 133–34
cognitive humility, 39–40
cognitive load, 116–17
cognitive toolkit, 333
Cohen, Daniel, 254
Cohen, Joel, 65
Cohen, Steven, 307–8
cold fusion, 243, 244
Coleman, Ornette, 254, 255
collective intelligence, 257–58
Colombia, 345
color, 150–51
color-blindness, 144
Coltrane, John, 254–55
com
munication, 250, 358, 372
depth in, 227
temperament and, 231
companionship, 328–29
comparative advantage, law of, 100
comparison, 201
competition, 98
complexity, 184–85, 226–27, 326, 327
emergent, 275
computation, 227, 372
computers, 74, 103–4, 146–47, 172
cloud and, 74
graphical desktops on, 135
memory in, 39–40
open standards and, 86–87
computer software, 80, 246
concept formation, 276
conduction, 297
confabulation, 349–52
confirmation bias, 40, 134
Conner, Alana, 367–70
Conrad, Klaus, 394
conscientiousness, 232
consciousness, 217
conservatism, 347, 351
consistency, 128
conspicuous consumption, 228, 308
constraint satisfaction, 167–69
consumers, keystone, 174–76
context, sensitivity to, 40
continental drift, 244–45
conversation, 268
Conway, John Horton, 275, 277
cooperation, 98–99
Copernicanism, 3
Copernican Principle, 11–12, 25
Copernicus, Nicolaus, 11, 294
correlation, and causation, 215–17, 219
creationism, 268–69
creativity, 152, 395
constraint satisfaction and, 167–69
failure and, 79, 225
negative capability and, 225
serendipity and, 101–2
Crick, Francis, 165, 244
criminal justice, 26, 274
Croak, James, 271–72
crude look at the whole (CLAW), 388
Crutzen, Paul, 208
CT scans, 259–60
cultural anthropologists, 361
cultural attractors, 180–83
culture, 154, 156, 395