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date: 24 January 2020

(p. 391) Index

(p. 391) Index

Abductive reasoning in clinical cognitive science, 343–344
Absolute identification
absolute and relative judgment, 129–130
intertrial interval and sequential effects, 136–138
learning, 130–133
perfect pitch versus, 133–135
response times, 135
theories of, 124–129
Absorbing barriers, 30
Accumulator models, 321–322, 327–328
Across-trial variability, 37–38, 46, 56–57
Actions, in Markov decision process (MDP), 102–103
ACT-R architectures, 126, 219, 301
Additive factors method, 69–70, 89
ADHD, 49–50
Affine transformation, 28
Aging studies, diffusion models in, 48
Akaike information criterion (AIC), 306–308
Alcohol consumption, 50
Aleatory uncertainty, 210
Algom, D., 63
Allais paradox, 219
ANCHOR-based exemplar model of absolute identification, 126
Anderson’s ACT-R model, 126, 219, 301
Anxiety, diffusion models of, 49
Anxiety-prone individuals, threat sensitivity modeling in, 352–354
Aphasia, 49–50
Ashby, F. G., 13
Assimilation and contrast, in absolute identification, 123–124, 128
Associative learning, 194–196
Associative recognition, 47
Attention allocation differences data, 291–292
descriptive model and parameters, 292–293
overview, 290–291
posterior distribution interpretation, 293–295
Attention-weight parameters, 144
Attraction, as context effect in DFT, 225–226
Austerweil, J. L., 187
Autism spectrum disorders, 354–356
Automaticity, 143, 148–150, 325
Autonomous search models, 178–179
Bandit tasks, 111
Basa ganglia model, 51
Baseball batting example, 282–290
data, 283
descriptive model and parameters, 283–285
overview, 282–283
posterior distribution interpretation, 285–290
shrinkage and multiple comparisons, 290
Basis functions, 201
Bayesian information criterion (BIC), 9, 21, 306–308
Bayesian models. See also Hierarchical models, Bayesian estimation in
of cognition, 187–208
clustering observations, 192–196
conclusions, 203–204
continuous quantities, 200–203
features as perceptual units, 196–200
future directions, 204
mathematical background, 188–192
overview, 187–188
overview, 40, 169
parsimony principle in, 309–314
of shape perception, 258–260
Bayesian parameter estimation, 348–349
Bayes’ rule, 6, 281–282
BEAGLE (Bound Encoding of the Aggregate Language Environment) model, 243–244, 248
Bellman equation, 103
Benchmark model, 74–75
Benchmark phenomena, in perceptual judgment, 122–124
Berlin Institute of Physiology, 64
Bernoulli, Daniel, 210–211
Bessel, F. W., 65
Bias-variance trade-off, 190–191
BIC (Bayesian information criterion), 9, 21, 306–308
Blood sugar reduction, 50
Bootstrapping, 105
Boundary setting across tasks, 48
Bound Encoding of the Aggregate Language Environment (BEAGLE) model, 243–244, 248
Bow effects, in absolute identification, 123, 128
Brown, S. D., 121
Brown and Heathcote’s linear ballistic accumulator model, 301
BUGS modeling specification language, 282
Busemeyer, J. R., 1, 369
Calculus, 3–5
Candidate decision processes, 14
Capacity coefficient, 72–74
Capacity limitations, in absolute identification, 122–123
Capacity reallocation model, 69
Capacity theory, 90–91
Catastrophe theory, 346
Category learning, 30–31, 189
Cattell, James McKeen, 66
Chaos-theoretic modeling, 345–346
(p. 392) Child development, diffusion models in, 48–49
Chinese restaurant process (CRP) metaphor, 193–195
Choice axiom testing, 211–214
Choice behavior, 199–200
Cholesky transformation, 20
“Chunking,” 66
Clinical psychology, mathematical and computational modeling in, 341–368
contributions of, 349–359
cognition in autism spectrum disorders, 354–356
cognitive modeling of routinely used measures, 356–357
multinomial processing tree modeling of memory, 350–352
in pathocognition and functional neuroimaging, 357–359
threat sensitivity modeling of anxiety-prone individuals, 352–354
distinctions in, 343–346
overview, 341–343
parameter estimation in, 346–349
special considerations, 359–361
Clustering observations, 192–196
Coactivation, 73
COALS (Correlated Occurrence Analogue to Lexical Semantics) model, 241, 248
Coexistence model (CXM), 303–304, 306, 308–309, 313
Cognitive control of perceptual decisions, 330, 334
Cognitive modeling, 219–226
of clinical science measures, 356–357
context effects example, 225–226
decision field theory
for multialternative choice problems, 222–225
multi-attribute, 221–222
overview, 220–221
“horse race,” 356
Cognitive-psychological complementarity, 87–90
Cognitive psychometrics, 290
Cohen’s PDP model, 301
Cold cognition, 361
Commutativity, 375
Competing accumulator models, 322
Complication experiment, 66–68
Component power laws model, 301
Compositional semantics, 249
Compromise, as context effect in DFT, 225–226
Computational reinforcement learning (CRL), 99–117
decision environment, 102
exploration and exploitation balance, 106
goal of, 101
good decision making, 103–104
historical perspective, 100–101
neural correlates of, 106–108
Q-learning, 105–106
research issues, 108–114
human exploration varieties, 110–113
model-based versus model-free learning, 108–109
reward varieties, 113–114
state representation influence, 109–110
temporal difference learning, 104–105
values for states and actions, 102–103
Conditioning, 101–103, 111, 195
Confidence judgments, 52–53
Conjunction probability judgment errors, 376–379
Connectionist models
decision field theory as, 223, 225
of semantic memory, 234–239
Constancy, in shape perception, 256–257
Constructed semantics model (CSM), 247
Context, 175–178
Context-noise models, 172
Contingency table, 6f
Continuous quantities, relationships of, 200–203
Contrast and assimilation, in absolute identification, 123–124, 128
Correlated Occurrence Analogue to Lexical Semantics (COALS) model, 241, 248
COVIS theory of category learning, 30–31
Credit assignment problem, in reinforcement learning, 100–101, 103
Criss, A. H., 165
CRL (Computational reinforcement learning). See Computational reinforcement learning (CRL)
CrossCat model, 195
CRP (Chinese restaurant process) metaphor, 193–195
Crude two-part code, in MDL, 307–308
CSM (constructed semantics model), 247
Cued recall models of episodic memory, 173–174
Cumulative prospect theory, 217–219
CXM (coexistence model), 303–304, 306, 308–309, 313
Deadline tasks, 41–42
Decisional separability, 15–16, 22f, 23
Decision-boundary models, 30, 142
Decision field theory (DFT), 220–225
Decision-making models, 209–231. See also Computational reinforcement learning; Perceptual decision making, neurocognitive modeling of; Quantum models of cognition and decision
choice axiom testing, 211–214
cognitive models
context effects example, 225–226
decision field theory, 220–221
decision field theory for multialternative choice problems, 222–225
multi-attribute decision field theory, 221–222
overview, 219–220
historical development of, 210–211
overview, 209–210
rational choice models, 214–219
Decision rules for Bayesian posterior distribution, 287
Dennis, S., 232
(p. 393) Density estimation, in Bayesian models, 188–190
Depression, diffusion models of, 49
Derivatives and integrals, 3–5
Destructive updating model (DUM), 303–304, 306, 308–309, 313
Deterministic processes, 72
Diederich, A., 1, 209
Differential-deficit, psychometric-artifact problem, 360
Differential equations, 4–5
Diffusion models, 35–62
in aging studies, 48
in child development, 48–49
in clinical applications, 49–50, 352
competing two-choice models, 51–56
failure of, 50–51
in homeostatic state manipulations, 49–50
in individual differences studies, 48
in lexical decision, 46–47
optimality, 44–45
in perceptual tasks, 45–46
for practice effects, 301
for rapid decisions, 35–44
accuracy and RT distribution expressions, 38–41
drift rate, 36–38
overview, 35–36
standard two-choice task, 41–44
in recognition memory, 46
in semantic and recognition priming effects, 47
in value-based judgments, 47–48
Diffusion process, 30
Díríchlet-process mixture model, 192–194, 244
Disjunction probability judgment errors, 376–379
Dissociations, in categorization and recognition, 158–159
Distributional models of semantic memory, 239–247
latent semantic analysis, 239–240
moving window models, 240–241
probabilistic topic models, 243–246
random vector models, 241–243
retrieval-based semantics, 246–247
Domain of the function, 1
Donders, Franciscus, 65–66
Donkin, C., 121
Double factorial paradigm, 83
Drift rates
accumulator model assumptions about, 321–322
across-trial variability in, 56–57
in perceptual decision making, 36–38, 45, 325–327
Dual process models of recognition, 166
DUM (destructive updating model), 303–304, 306, 308–309, 313
Dynamic attractor networks, 237–239
Dynamic decision models, 219–220. See also Decision-making models
Dynamic programming, 104
Dyslexia, 50
Effective sample size (ESS) statistic, 282
EGCM (extended generalized context model) of absolute identification, 126, 143
Eidels, A., 1, 63
Emotional bias, 49
Episodic memory, 165–183
cued recall models of, 173–174
free recall models of, 174–179
future directions, 179
overview, 165–166
recognition memory models, 166–172
context-noise models, 172
global matching models,167–168
retrieving effectively from memory (REM) model, 168–171
updating consequences, 171–172
Epistemic uncertainty, 210
Error signal, 4–5
ESS (effective sample size) statistic, 282
EUT (expected utility theory), 209, 211
EVL (Expectancy Valence Learning Model), 356–357
Exemplar-based random walk (EBRW) model
of absolute identification, 125–126
of categorization and recognition, 142–164
automaticity and perceptual expertise, 148–150
old-new recognition RTs predicted by, 152–157
overview, 142–144
probabilistic feedback to contrast predictions, 150–152
research goals, 157–159
in response times, 144–146
similarity and practice effects, 146–148
in perceptual decision making, 325
Exemplar models of absolute identification, 125–126, 129
Exhaustive processing, 71–72
Expectancy Valence Learning Model (EVL), 356–357
Expectations, 7–8
Expected utility theory (EUT), 209, 211
Experience-based decision making, 215–216
Experimental Psychology (Woodworth), 83
Exploration/exploitation balance
experiments in, 100
human varieties of, 110–113
in reinforcement learning, 106
Exponential functions, 2
Extended generalized context model (EGCM) of absolute identification, 126, 143
Eye movements, saccadic, 323–325
False alarm rates, 23
Feature inference, 196–199
Feature integration theory, 87
Feature-list models, 233–234
Fechnerian paradigm, 257–258
Fechner’s law of psychophysics, 307
Feed-forward networks, 235
FEF (frontal eye field), 321, 323
Fermat, Pierre de, 210
Flexibility-to-fit data, of models, 93
fMRI
category-relevant dimensions shown by, 158
in clinical psychology, 357–359
context word approach and, 241
diffusion models and, 57
model-based analysis of, 107–108
Free recall models of episodic memory, 174–179
Frequentist methods, 281
Frontal eye field (FEF), 321, 323
Functions, mathematical, 1–3
(p. 394) Gabor patch orientation discrimination, 50
Galen, 64
Gate accumulator model, 327
Gaussian distribution, 189, 192
Generalizability, measures of, 303
Generalized context model (GCM), 126, 143, 152, 325
General recognition theory (GRT)
application of, 14
applied to data, 17–21
empirical example, 24–28
multivariate normal distributions assumed by, 16
neural implementations of, 30–31
overview, 15–16
response accuracy and response time accounted for, 28–30
summary statistics approach, 22–24
GenSim software for semantic memory modeling, 246
Gershman, S. J., 187
Global matching models, 167–168
Go/No-Go Discrimination Task, 44, 349, 356
Goodness of fit evaluation, 20–21, 302
Grice inequality, 74–75, 92
Griffiths, T. L., 187
Grouping, power of, 66
GRT (general recognition theory). See General recognition theory (GRT)
Guided search, 89
Gureckis, T. M., 99
HA-LA (higher anxiety-prone-lower anxiety-prone) group differences, 352–353
HAL (Hyperspace Analogue to Language) model, 240–241, 245, 248
Hamilton, Sir William, 66
Hawkins, R. X. D., 63
HDI (highest density interval), 285
Heathcote, A., 121
Hebbian learning, 235
HiDEx software for semantic memory modeling, 246
Hierarchical models, Bayesian estimation in, 279–299
attention allocation differences example, 290–295
data, 291–292
descriptive model and parameters, 292–293
overview, 290–291
posterior distribution interpretation, 293–295
baseball batting example, 282–290
data, 283
descriptive model and parameters, 283–285
overview, 282–283
posterior distribution interpretation, 285–290
shrinkage and multiple comparisons, 290
comparison of, 295–297
ideas in, 279–282
Higher anxiety-prone-lower anxiety-prone (HA-LA) group differences, 352–353
Highest density interval (HDI), 285
Hilbert space, in quantum theory, 371–372, 374–375
Histograms, 9
Homeostatic state manipulations, 49–50
“Horse race” model of cognitive processes, 356
Hot cognition, 361
Howard, M. W., 165
Human function learning, 202–203
Human information processing, 63–70
Donder’s complication experiment, 66–68
Sternberg’s work in, 68–70
von Helmholtz’s measurement of nerve impulse speed, 64–65
Wundt’s reaction time studies, 65–66
Human neuroscience, diffusion models for, 56–58
Hyperspace Analogue to Language (HAL) model, 240–241, 245, 248
IBP (Indian buffet process) metaphor, 194–195, 197–200, 203
Identification data, fitting GRT to, 18–21
Identification hit rate, 23
Importance sampling for Bayes factor, 312–313
Independence, axioms of, 211–212
Independent parallel, limited-capacity (IPLC) processing system, 353
Independent race model, 74–75
Indian buffet process (IBD) metaphor, 194–195, 197–200, 203
Individual differences studies, diffusion models in, 48
Infinite Relational Model (IRM), 195
Information criteria, in model comparison, 306–307
Instance theory, 301, 325
Institute for Collaborative Biotechnologies, 31
Instrumental conditioning, 101, 111
Integrals and derivatives, 3–5
Integrate-and-fire neurons, 39
Intercompletion time equivalence, 77–78
Intertrial interval, sequential effects and, 136–138
Inverse problem, shape perception as, 256–263
Iowa Gambling Task, 349, 356
IPLC (independent parallel, limited-capacity) processing system, 353
IRM (Infinite Relational Model), 195
James, William, 64
Jefferson, B., 63
Jeffreys weights, 313
Jones, M. N., 232
Kinnebrook, David, 65
Kolmogorov axioms, 307, 370, 373–374
Kruschke, J. K., 279
Kullback-Leibler divergence, 306
Languages, tonal, 134
Latent Díríchlet Allocation algorithms, 244
Latent semantic analysis (LSA), 239–240, 245, 248–249
Law of total probability, 376
LBA (Linear Ballistic Accumulator) model, 52, 301
Leaky competing accumulator (LCA) model, 36, 51–52, 128, 223–225, 327
Learning. See also Computational reinforcement learning
absolute identification in, 130–133
associative, 194–196
Hebbian, 235
modeling human function, 202–203
(p. 395) procedural, 30–31
relationships in continuous quantities, 200–203
Lexical decisions, diffusion models in, 46–47
Lexicographic semi-order (LS) choice rule, 213
Li, Y., 255
Likelihood function, 18–19, 280, 296
Likelihood ratio test, 28
Limited capacity, 70, 73
Linear Ballistic Accumulator (LBA) model, 52, 301
Linear functions, 1–2
Linear regression, 8, 200–202
Logan, G. D., 320
Love, B. C., 99
LSA (latent semantic analysis), 239–240, 245, 248–249
LS (lexicographic semi-order) choice rule, 213
Luder’s rule, 374
“Magical number seven,” 66
Mapping, functions for, 1
Marginal discriminabilities, 23
Marginal response invariance, 22
Markov Chain Monte Carlo (MCMC) algorithms, 244, 281–282, 293
Markov decision process (MDP), 102–104
Markov dynamic model for two-stage gambles, 382–383, 385–387
Maskelyn, Nevil, 65
Matched filter model, 167, 173
Mathematical concepts, review of, 1–10
derivatives and integrals, 3–5
expectations, 7–8
mathematical functions, 1–3
maximum likelihood estimation, 8–9
probability theory, 5–7
Matrix reasoning, 48
Matzke, D., 300
Maximum likelihood estimation (MLE), 8–9, 281, 347
MCMC (Markov Chain Monte Carlo) algorithms, 244, 281–282, 293
MDL (minimum description length), in model comparison, 307–309
MDP (Markov decision process), 102–104
MDS (multidimensional scaling), 143
Mean interaction contrast, 83–84
Measures of generalizability, 303
Memory interference models example, 303–306
Méré, Chevalier de, 210
Meyer, Irwin, Osman, and Kounios partial information paradigm, 42–44
Miller, George, 66
Minimum description length (MDL), in model comparison, 307–309
Minimum-time stopping rule, exhaustive processing versus, 71–72
Minkowski power model, 144
MLE (maximum likelihood estimation), 8–9, 281, 347
Model-based versus model-free learning, 108–109
Modeling. See Parsimony principle in model comparison; specifically named models
Model mimicking
degenerative, 80
ignoring parallel-serial, 87–90
prediction overlaps from, 75–78
in psychological science, 91–93
Moderate stochastic transitivity (MST), 212–213
Moment matching, in parameter estimation, 347–348
Monte-Carlo methods, 104, 311–313
Movement-related neurons, in FEF, 321, 323, 325–326, 328
Moving window models, 240–241
MPM (multiplicative prototype model), 290, 292
MPTs (multinomial processing tree models). See Multinomial processing tree models (MPTs)
MST (moderate stochastic transitivity), 212–213
Müller, Johannes, 64
Multialternative choice problems, decision field theory for, 222–225
Multi-armed bandit tasks, 111
Multi-attribute decision field theory, 221–222
Multichoice-decision-making, 52–53
Multidimensional scaling (MDS), 143, 273
Multidimensional signal detection theory, 13–34
general recognition theory
applied to data, 17–21
empirical example, 24–28
neural implementations of, 30–31
overview, 15–16
response accuracy and response time accounted for, 28–30
summary statistics approach, 22–24
multivariate normal model, 16–17
overview, 13–15
Multinomial processing tree models (MPTs), 301, 304–305, 307–311, 350–352
Multiple comparisons, shrinkage and, 290
Multiple linear regression, 8
Multiplicative prototype model (MPM), 290, 292
Multivariate normal model, 16–17
Myopic behavior, of agents, 103
National Institute of Neurological Disorders and Stroke, 31
Natural log functions, 2
NCM (no-conflict model), 303, 305–306, 308–309, 313
Nested models, comparing, 313–314
Neufeld, R. W. J., 341
Neural evidence
of computational reinforcement learning, 106–108
of exemplar-based random walk, 158
of GRT, 30–31
in perceptual decision making, 325–330
Neurocognitive modeling of perceptual decision making. See Perceptual decision making, neurocognitive modeling of
Neuro-connectionist modeling, 345
Neuroeconomics, 48
Neuroscience, decision making understanding from, 53–58
Newton-Raphson method, 19
Nietzsche, Friedrich, 64
No-conflict model (NCM), 303, 305–306, 308–309, 313
Noise, in perceptual systems, 15, 36
(p. 396) Nondecision time, 48–50, 57
Nonlinear dynamical system modeling, 345–346
Nonparametric models, 189–192, 194–195
Normal distribution, 7
Nosofsky, R. M., 142
Null list strength effects in (REM) model, 170–171
Numerosity discrimination task, 50
Observations, clustering, 192–196
Occam’s razor, 301–302
One-choice decisions, 53
Operant conditioning, 101
Optimality, 44–45
Optimal planning, 113
Ornstein-Uhlenbeck (OU) diffusion process, 50, 55
Overfitting, 190–191
Palmeri, T. J., 142, 320
Parallelism, 68
Parallel processing
in benchmark model, 74–75
mathematics supported by, 77
parallel-serial mimicry ignored, 87–90
partial processing as basis of, 80–81
serial processing versus, 71
Parallel-Serial Tester (PST) paradigm, 82
Parametric models, 189–190
Parsimony principle in model comparison, 300–319
Bayes factors, 309–314
comparison of model comparisons, 314–315
information criteria, 306–307
memory interference models example, 303–306
minimum description length, 307–309
overview, 300–303
Partial information paradigm, 42–44
Pascal, Blaise, 210
Pathocognition, 357–359
Pavlovian conditioning, 195
PBRW (prototype-based random walk) model, 151–152
Perceptual decision making, neurocognitive modeling of, 320–340
architectures for, 327–328
conclusions, 333–336
control over, 330–333
neural dynamics, predictions of, 328–330
neural locus of drift rates, 325–327
overview, 320–323
saccadic eye movements and, 323–325
Perceptual expertise, automaticity and, 148–150
Perceptual independence, 15–16, 23
Perceptual judgment, 121–141
absolute identification issues, 129–139
absolute and relative judgment, 129–130
absolute identification versus perfect pitch, 133–135
intertrial interval and sequential effects, 136–138
learning, 130–133
response times, 135
absolute identification theories, 124–129
benchmark phenomena, 122–124
overview, 121–122
Perceptual separability, 15, 22f
Perceptual tasks, diffusion models in, 36, 45–46
Perceptual units, features as, 196–200
Perfect pitch, absolute identification versus, 133–135
Perspective. See Shape perception
Pizlo, Z., 255
Pleskac, T. J., 209
Poisson counter model, 36, 55
Poisson shot noise process, 55
Policies, in decision making, 103–104
Polynomial regression, 302–303
Posterior distribution
in attention allocation differences, 293–295
in baseball batting example, 285–290
Monte Carlo sampling for, 311–313
in tests for model-parameter differences, 356
Pothos, E., 369
Power functions, 2
Power law, 300
Practice effects, 146–148, 300–301
Prediction error, 105, 107–108
Principles of Psychology (James), 64
Probabilistic topic models, 243–246, 248, 250
Probability density function, 72
Probability judgment error, 377–379
Probability mass function, 7
Probability theory, 5–7, 373–377. See also Bayesian models; Decision-making models
Probability weighting function, 214–216
Problem of Points, 210
Procedural learning, 30–31
Prospect theory, 209, 214, 216–219
Prototype-based random walk (PBRW) model, 151–152
Prototype models, 142
PST (Parallel-Serial Tester) paradigm, 82
Psychology, mathematical and computational modeling in. See Clinical psychology, mathematical and computational modeling in
Psychomotor vigilance task (PVT), 53
Q-learning, 104–106, 109, 111
Quadratic functions, 2
Quantile-probability plots, 40
Quantum models of cognition and decision, 369–389
classical probabilities versus, 373–377
concepts, definitions, and notation, 371–373
decision making applications, 381–387
Markov dynamic model for two-stage gambles, 382–383
model comparisons, 385–387
quantum dynamic model for two-stage gambles, 384–385
two-stage gambling paradigm, 381–382
dynamical principles, 379–381
probability judgment error applications, 377–379
reasons for, 369–371
Race model inequality, 74
Rae, B., 121
Random Permutations Model (RPM), 244
Random variables with continuous distribution, 7–8
Random vector models, 241–243
(p. 397) Range of the function, 1
Rank-dependent utility theory, 209
Rapid decisions. See Diffusion models
Ratanalysis, 188
Ratcliff, R., 35
Ratcliff’s diffusion model, 301
Rational choice models, 47, 214–219
Reaction time distributions, 83–87
Recognition and categorization. See Exemplar-based random walk (EBRW) model
Recognition memory models, 46, 166–172
Region of practical equivalence (ROPE), in decision rules, 286
Regularization methods, in shape perception, 258–260
Reinforcement learning (RL). See Computational reinforcement learning
Relative judgment models of absolute identification, 126–127, 129t, 136
Release-from-inhibition model, 50–51
REM (retrieving effectively from memory) model. See Retrieving effectively from memory (REM) model
Rescorla-Wagner model, 111, 194
Response accuracy, 28–30, 90–91
Response signal tasks, 41–42
Response times (RT)
absolute identification and, 128, 135
cognitive-psychological complementarity, 87–90
in diffusion models, 38–41
example of, 78–79
exemplar-based random walk model of, 144–146, 152–157
GRT to account for, 28–30
human information processing studied by, 63–70
Donder’s complication experiment, 66–68
Sternberg’s work in, 68–70
von Helmholtz’s measurement of nerve impulse speed, 64–65
Wundt’s reaction time studies, 65–66
metatheory expansion to encompass accuracy, 90–91
model mimicking, 75–78, 91–93
quantitative expressions of, 70–75
stopping rule distinctions based on set-size functions, 82–87
theoretical distinctions, 79–82
Restricted capacity models of absolute identification, 127–128, 129t, 136
Retrieval-based semantics, 246–247
Retrieved context models, 177–178
Retrieving effectively from memory (REM) model
consequences of updating in, 171–172
overview, 168–170
word frequency and null list strength effects in, 170–171
Reward prediction error hypothesis, 107
Reward-rate optimality, 45
Reward varieties, in reinforcement learning, 113–114
Rickard’s component power laws model, 301
Risk in decision making. See Decision-making models
RL (reinforcement learning). See Computational reinforcement learning
ROPE (region of practical equivalence), in decision rules, 286
RPM (Random Permutations Model), 244
RT (response times). See Response times (RT)
RT-distance hypothesis, 29, 150
Rule-plus-exception models, 142
Rumelhart networks, 235–237
Saccadic eye movements, 323–325
SAMBA (Selective Attention, Mapping, and Ballistic Accumulators) model of absolute identification, 128–129, 133, 135–138
Sampling independence test, 24
Savage-Dickey approximation to Bayes factor, 313–314
Sawada, T., 255
SBME (strength-based mirror effect), 171–172
Schall, J. D., 320
Schizophrenia, stimulus-encoding elongation in, 357–359
SCM (similarity-choice model), 21
SD (social desirability) contamination of scores, 361
Second-order conditioning, 102–103, 111
Selective Attention, Mapping, and Ballistic Accumulators (SAMBA) model of absolute identification, 128–129, 133, 135–138
Selective influence, 85
Semantic and recognition priming effects, 47
Semantic memory, 232–254
compositional semantics, 249
connectionist models of, 234–239
distributional models of, 239–247
latent semantic analysis, 239–240
moving window models, 240–241
probabilistic topic models, 243–246
random vector models, 241–243
retrieval-based semantics, 246–247
future directions, 249–250
grounding semantic models, 247–249
overview, 232–233
research models and themes, 233–234
Semantic networks, 233–234
SEMMOD software for semantic memory modeling, 246
Sensory preconditioning, 194–195
Sequential effects, 123–124, 136–138
Sequential-sampling models. See Diffusion models
Serial processing mathematics supported by, 76–77
parallel processing versus, 71
parallel-serial mimicry ignored, 87–90
parallel-serial testing paradigm, 82
SEUT (subjective expected utility theory), 209, 211
SFT (Systems Factorial Technology), 354–355
Shape perception, 255–276
constancy, 256–257
constraints in regularization and Bayesian methods, 258–260
(p. 398) eye and camera geometry, 260–263
Fechnerian paradigm inadequacy, 257–258
new definition of, 273–274
perspective and orthographic projection, 263–265
3D mirror-symmetrical shape recovery from 2D images, 269–273
3D symmetry and 2D orthographic and perspective projections, 265–269
uniqueness, 255–256
Shrinkage and multiple comparisons, 286–288, 290
Sichuan University, 134
Signal detection theory. See Multidimensional signal detection theory
Sign-dependent utility theory, 209
Similarity
as context effect in DFT, 225–226
kernels of, 201
practice effects and, 146–148
as search determinant, 89
similarity-choice model (SCM), 21
Single cell recording data, 54
Sleep deprivation, 50
Social desirability (SD) contamination of scores, 361
Soto, F. A., 13
Span of attention, 66
Spatial models, 233–234
Speed-accuracy tradeoff, 90–91
Speeded classification, 29, 146–148
Speeded visual search, 89
Sperling, George, 66
S-Space software for semantic memory modeling, 246
SST (strong stochastic transitivity), 212–213
State representation influence, in reinforcement learning, 109–110
States, in Markov decision process (MDP), 102–103
Sternberg, Saul, 68
Steven’s law of psychophysics, 307
Stimulus dimensionality, 133
Stimulus-response learning, 101
Stochastic difference equation, 5
Stochastic dominance, 217–218, 221–222
Stochastic independence, 72
Stochastic transitivity, 212–213
Stopping rule
exhaustive processing versus,71–72
set-size functions and, 82–87
Stop signal paradigm, 330, 332–333
St. Petersburg paradox, 210–211
Strength-based mirror effect (SBME), 171–172
Strong inference tactic, 92
Strong stochastic transitivity (SST), 212–213
Structural MRI, 57
Subjective expected utility theory (SEUT), 209, 211
Subtraction, method of, 66–69
Summary statistics approach to GRT, 22–24
Super capacity, 73
SuperMatrix software for semantic memory modeling, 246
Supertaskers, 75
Survivor interaction contrast, 84–85
Symmetry. See Shape perception
Systematic exploration, 113
Systems Factorial Technology (SFT), 354–355
TAX (transfer of attention exchange) model, 219
Temporal Context Model (TCM), 244
Temporal difference learning, 104–105, 109, 111
Tenenbaum, J. B., 187
Test of English as a Foreign Language (TOEFL), 240
Theorizing process, 1
Threat sensitivity modeling in anxiety-prone individuals, 352–354
3D symmetry. See Shape perception
Thurstonian models of absolute identification, 124–125, 129, 136
Time-varying processing, 44
TOEFL (Test of English as a Foreign Language), 240
Tolman, Edwin, 93
Tonal languages, 134
Topic models, 243–246, 248, 250
Total probability, law of, 375–376
Townsend, J. T., 1, 63
Townsend’s capacity reallocation, 69
Transfer of attention exchange (TAX) model, 219
Transformations, perspective projection as, 261
Transition probabilities, in Markov decision process (MDP), 102
Transitivity, axioms of, 211–214
Trial independent “random” exploration, 112–113
Trial-to-trial variability, 37
Trigonometric functions, 2–3
2D orthographic and perspective projections. See Shape perception
Two-choice models, diffusion models versus, 51–56
Two-choice tasks, 41–44
Two-stage gambles
Markov dynamic model for, 382–383
model comparisons for, 385–387
overview, 381–382
quantum dynamic model for, 384–385
Uncertainty, 281. See also Decision-making models
Uniqueness, in shape perception, 255–256
Unlimited capacity and independent, parallel processing channels (UCIP), 74–75
U.S. Army Research Office, 31
Utility function, 211, 216–217
Value-based judgments, diffusion models in, 47–48
Vandekerckhove, J., 300
Vanpaemel, W., 279
Venn diagrams, 6
Veridical perception, 255, 258
Vickers accumulator model, 36
Visually responsive neurons, in FEF, 321, 323, 325–326
Visual search experiments, 69
Visual short-term memory (VSTM), 45
Vitalism, 64
von Helmholtz, Hermann, 64
von Neumann axioms, 371, 373
VSTM (visual short-term memory), 45
Wagenmakers, E.-J., 300
WAIS vocabulary, 48
Wallsten, T. S., 209
Wang, Z., 1, 369
(p. 399) Weak stochastic transitivity (WST), 212–213
Weighted additive utility model, 220
Wickens, T. D., 14, 19
William of Occam, 302
Willits, J., 232
Wisconsin Card Sorting Test, 349, 356–357
Woodworth, R. S., 63–64
Word frequency effects in (REM) model, 170–171
Word recognition, 47
Word-Similarity software for semantic memory modeling, 246
Workload capacity, 72–74, 85–87
Wundt, Wilhem, 65