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Cognitive science for machine learning 2: Empirical methods
Published on Jun 15, 20104554 Views
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Cognitive science for machine learning 2: Empirical Methods00:00
Cognitive Science for Machine Learning 200:00
Overview01:43
The Experimental Method - 102:15
Observations and Experiments03:22
The Contrast03:24
The Experimental Method - 204:11
The Experimental Method - 306:14
The Experimental Method - 406:24
The Experimental Method - 506:27
The Experimental Method - 606:38
The Experimental Method - 706:39
The Experimental Method - 806:40
The Experimental Method - 906:44
The Experimental Method - 1006:46
Psychophysical Method07:59
Traditional Psychophysical Methods08:01
Modern Psychophysical Methods11:00
Example: Human category learning13:05
Forced-choice staircases with fixed step sizes: asymptotic and small-sample properties16:03
Categories are defined by different types of dimension16:06
But for substances... - 117:05
But for substances... - 217:52
A Bayesian approach18:46
and furthermore ...20:28
Now compare with experiments ...20:41
Standard Three-Step Routine in Modelling Data20:44
Definitions and Notation - 120:46
Definitions and Notation - 221:50
Definitions and Notation - 322:18
Parameter Estimation - 122:40
Parameter Estimation - 223:04
Parameter Estimation - 323:21
Parameter Estimation - 423:23
... and conduct new experiments23:28
Parameter Estimation - Third Source of Error: Lapses 23:33
Parameter Estimation - 525:26
Parameter Estimation - 627:28
The qualitative observations that people routinely ...27:31
Statistical Considerations28:01
Goodness-of-fit - 129:34
Goodness-of-fit - 229:37
Inferring causality from observation30:09
Goodness-of-fit - 330:54
Goodness-of-fit - 430:55
Goodness-of-fit - 531:39
Goodness-of-fit - 632:09
Estimates of Variability - the way of Bayes ...33:43
Inferring agency from observation33:46
... or the Traditional Way34:00
Literature on Psychometric Methods - 134:32
The House of Blackwell34:47
Literature on Psychometric Methods - 236:14
Essentials of Psychometric Methods 36:37
Inferring communicative intentions from observation - 137:02
Inferring communicative intentions from observation - 237:58
Inferring communicative intentions from observation - 338:22
Inferring communicative intentions from observation - 438:34
Inferring communicative intentions from observation - 538:37
Common knowledge is crucial - 138:44
Common knowledge is crucial - 239:03
Common knowledge is crucial - 339:10
The study of vision is heavily based on observations, not merely experiments40:18
Common knowledge is crucial - 440:20
A 3D variation...42:43
Note the difference...43:02
Signal Detection Theory43:42
Psychological science can improve diagnostic decision44:27
Observations may suggest general principles44:46
Binary World46:27
If so, can only share information wihin a single object - 147:50
If so, can only share information wihin a single object - 248:39
The patterns in language50:35
Displaying the structure53:21
Semantic categories53:26
Analysis of the structure of the environment53:27
Observations53:31
The spectrum of experimental methods54:07
Simple psychopysics-detection - 154:18
Decision Theory Applied to Perception & Cognition54:42
Simple psychopysics-detection - 256:37
Detection, Estimation and Modulation Theory - 156:58
Detection, Estimation and Modulation Theory - 257:01
Detection, Estimation and Modulation Theory - 357:01
Appeals of SDT57:09
From responses to reaction times - 157:38
From responses to reaction times - 258:13
From responses to reaction times - 358:27
From responses to reaction times - 458:34
From responses to reaction times - 558:36
From responses to reaction times - 659:09
Incidental variables59:10