Since first described, multiple properties of classical conditioning have been discovered, establishing the need for mathematical models to help explain the defining features. The mathematical complexity of the models puts our understanding of their workings beyond the ability of our intuitive thinking and makes computer simulations irreplaceable. The complexity of the models frequently results in function redundancy, a natural property of biologically evolved systems that is much desired in technologically designed products. Experts provide the latest advancements in the field and present detailed descriptions of how the models simulate conditioned behaviour and its physiological bases. It offers advanced students and researchers examples of how the models are used to analyse existing experimental results and design future experiments. This volume is of great interest to psychologists and neuroscientists, as well as computer scientists and engineers searching for ideas applicable to the design of robots that mimic animal behaviour.
Author(s): Nestor Schmajuk
Edition: 1
Publisher: Cambridge University Press
Year: 2010
Language: English
Pages: 285
Cover......Page 1
Half-title......Page 3
Title......Page 5
Copyright......Page 6
Contents......Page 7
Contributors......Page 8
The models......Page 11
Beyond parsimony: redundancy and reliability......Page 15
Evaluation of the data......Page 16
References......Page 17
Abstract......Page 20
Fast learning favors selective attention......Page 21
Attention to what? The representation......Page 22
Learning of what? The environment......Page 23
An environment with context-dependent relevance......Page 24
Designing fast learners......Page 25
Assessing selective attention: exhibiting highlighting......Page 26
Other signatures of attentional learning?......Page 28
Design space and functional desiderata......Page 30
Results: optimal learners exhibit highlighting......Page 32
Evolution: genetic algorithms discover fast learners......Page 36
Agents......Page 37
Environment and fitness......Page 38
Assessing selective attention by highlighting......Page 39
Simulation parameters......Page 40
Results: evolved learners exhibit highlighting......Page 43
The dynamics of context duration......Page 48
Environments that encourage attentional learning......Page 52
Linearly separable, four outcomes, with contextual dependency......Page 53
Linearly separable with no contextual dependency......Page 56
Summary: environments that encourage attentional learning......Page 58
Author note......Page 59
References......Page 60
Abstract......Page 63
Configural solutions to non-linear discriminations......Page 65
Elemental solutions to non-linear discriminations......Page 67
Acknowledgments......Page 77
References......Page 78
Abstract......Page 81
CS processing and US processing......Page 82
Summed error......Page 85
Individual error......Page 86
CS processing......Page 89
Evidence for the Mackintosh model: positive transfer......Page 90
Evidence for the Pearce–Hall model: negative transfer......Page 92
A reconciliation: two CS-processing mechanisms......Page 93
A hybrid model......Page 98
Component 4: “salience associability,” s (CS-processing)......Page 99
Simulation 1: Baxter, et al. (1999, Experiment 2)......Page 102
Simulation 3: Haselgrove, Esber, Pearce, and Jones (in press)......Page 105
CS-processing mechanisms in human contingency learning......Page 107
Falsifiability and parsimony......Page 111
References......Page 113
Within-compound associations: models and data......Page 118
The sometimes competing retrieval model (SOCR)......Page 128
The US-processing (USP) model......Page 130
Scaling......Page 132
Hill climbing......Page 133
Simulation 1: counteraction between two blocking stimuli......Page 134
Results and discussion......Page 136
Simulation 2: within-compound associations in second-order conditioning......Page 137
Results and discussion......Page 139
Simulation 3: the role of CS–context associations in extinction......Page 140
Results and discussion......Page 141
Simulation 4: the effect of CS duration on the CS’s and context’s response potential......Page 142
Results and discussion......Page 143
Simulation 5: counteraction between latent inhibition and overshadowing......Page 145
Results and discussion......Page 146
Simulation 6: CS preexposure attenuates conditioned inhibition......Page 147
Results and discussion......Page 148
Results and discussion......Page 150
Conclusions......Page 151
Author note......Page 155
References......Page 156
Abstract......Page 160
Diminished processing of expected USs......Page 163
CER potentiation of defensive responses......Page 165
Separating specific diminution and generalized potentiation effects......Page 168
Simulations via the quantitative assumptions of SOP and AESOP......Page 174
Observations and conclusions......Page 187
References......Page 192
Schmajuk, Lam, and Gray’s (1996) attentional–associative model......Page 196
Context-dependent and context-independent latent inhibition......Page 197
Excitatory conditioning tends to be context independent......Page 198
Limiting term for VCS,US associations......Page 199
Attentional and associative representations in the brain......Page 200
Attention and conditioned inhibition......Page 202
Latent inhibition......Page 205
Interactions between latent inhibition and overshadowing......Page 206
Extinction......Page 209
Spontaneous recovery......Page 211
Schmajuk and Di Carlo’s (1992) associational–configural model......Page 214
Occasion setting......Page 215
Schmajuk and Kutlu (2010) attentional–configural version of the SLG model......Page 217
Additivity training preceding blocking......Page 218
Conclusion......Page 221
Evaluation of the models......Page 222
References......Page 224
Abstract......Page 229
Eyelid conditioning and the cerebellum......Page 231
What the cerebellum computes......Page 234
A brief summary of previous findings......Page 239
Computer simulation of cerebellar learning......Page 240
Sites and rules for synaptic plasticity......Page 243
Successes and failures......Page 244
Rescorla–Wagner in the cerebellum......Page 246
Back to conditioned response timing......Page 249
References......Page 250
8 The operant/respondent distinction: a computational neural-network analysis......Page 254
The behavioral framework......Page 255
The computational neural framework......Page 259
Neurocomputational submodel......Page 260
Network submodel......Page 266
Taking stock......Page 268
The analysis......Page 269
Concluding remarks......Page 275
References......Page 277
Index......Page 282
