Protzko & Colom 2021: why WM training doesn't increase G?
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Gwern Branwen
Sep 18, 2021, 6:00:04 PM9/18/21
to N-back
"Testing the structure of human cognitive ability using evidence
obtained from the impact of brain lesions over abilities", Protzko &
Colom 2021: https://www.gwern.net/docs/iq/2021-protzko.pdf
> - Focal cortical lesions lead to local, not global, deficits.
> - Measurement models to explain the positive manifold are causal models with unique predictions going beyond model fit statistics.
> - Correlated factor, network, process sampling, mutualism, investment models, make causal predictions inconsistent with lesion evidence.
> - Hierarchical and bifactor models are consistent with the pattern of lesion effects, as well as possibly one form of bonds sampling models.
> - Future models and explanations of the positive manifold have to accommodate focal lesions leading to local not global deficits.
>
> Here we examine 3 classes of models regarding the structure of human cognition: common cause models, sampling/network models, and interconnected models. That disparate models can accommodate one of the most globally replicated psychological phenomena---namely, the positive manifold---is an extension of underdetermination of theory by data. Statistical fit indices are an insufficient and sometimes intractable method of demarcating between the theories; strict tests and further evidence should be brought to bear on understanding the potential causes of the positive manifold.
> The cognitive impact of focal cortical lesions allows testing the necessary causal connections predicted by competing models. This evidence shows focal cortical lesions lead to local, not global (across all abilities), deficits. Only models that can accommodate a deficit in a given ability without effects on other covarying abilities can accommodate focal lesion evidence.
> After studying how different models pass this test, we suggest bifactor models (class: common cause models) and bond models (class: sampling models) are best supported. In short, competing psychometric models can be informed when their implied causal connections and predictions are tested.
Training the specific ability of WM could only cause true latent _g_
increases in models with 'upwards causation' like hierarchical models
or dynamic mutual causation like mutualism/investment models; these
are ruled out by the lesion literature which finds that (physically
tiny & localized) brain lesions damage specific abilities such as
executive functions or WM but not _g_. If decreasing a specific
ability like WM cannot decrease _g_, then it's hard to see how
increasing that ability could ever increase _g_.
This provides some insight into why empirically WM training can
increase measured IQ test scores but the increases are artifactual, on
specific sub-tests & due to motivation etc, and not across the board
like true latent intelligence boost would be: it is not an issue of
insufficient training time or use of strategies or differing
personalities or the USA vs foreigners or any of the defenses proposed
- the causal structure of intelligence and other brain abilities is
not connected in a way that lets WM improvements cause _g_
improvements (_g_ → WM, but not WM → _g_), so dual n-back could never
have increased _g_.
--
gwern
obtained from the impact of brain lesions over abilities", Protzko &
Colom 2021: https://www.gwern.net/docs/iq/2021-protzko.pdf
> - Focal cortical lesions lead to local, not global, deficits.
> - Measurement models to explain the positive manifold are causal models with unique predictions going beyond model fit statistics.
> - Correlated factor, network, process sampling, mutualism, investment models, make causal predictions inconsistent with lesion evidence.
> - Hierarchical and bifactor models are consistent with the pattern of lesion effects, as well as possibly one form of bonds sampling models.
> - Future models and explanations of the positive manifold have to accommodate focal lesions leading to local not global deficits.
>
> Here we examine 3 classes of models regarding the structure of human cognition: common cause models, sampling/network models, and interconnected models. That disparate models can accommodate one of the most globally replicated psychological phenomena---namely, the positive manifold---is an extension of underdetermination of theory by data. Statistical fit indices are an insufficient and sometimes intractable method of demarcating between the theories; strict tests and further evidence should be brought to bear on understanding the potential causes of the positive manifold.
> The cognitive impact of focal cortical lesions allows testing the necessary causal connections predicted by competing models. This evidence shows focal cortical lesions lead to local, not global (across all abilities), deficits. Only models that can accommodate a deficit in a given ability without effects on other covarying abilities can accommodate focal lesion evidence.
> After studying how different models pass this test, we suggest bifactor models (class: common cause models) and bond models (class: sampling models) are best supported. In short, competing psychometric models can be informed when their implied causal connections and predictions are tested.
Training the specific ability of WM could only cause true latent _g_
increases in models with 'upwards causation' like hierarchical models
or dynamic mutual causation like mutualism/investment models; these
are ruled out by the lesion literature which finds that (physically
tiny & localized) brain lesions damage specific abilities such as
executive functions or WM but not _g_. If decreasing a specific
ability like WM cannot decrease _g_, then it's hard to see how
increasing that ability could ever increase _g_.
This provides some insight into why empirically WM training can
increase measured IQ test scores but the increases are artifactual, on
specific sub-tests & due to motivation etc, and not across the board
like true latent intelligence boost would be: it is not an issue of
insufficient training time or use of strategies or differing
personalities or the USA vs foreigners or any of the defenses proposed
- the causal structure of intelligence and other brain abilities is
not connected in a way that lets WM improvements cause _g_
improvements (_g_ → WM, but not WM → _g_), so dual n-back could never
have increased _g_.
--
gwern
Pontus Granström
Sep 19, 2021, 3:25:48 AM9/19/21
to Dual N-Back, Brain Training & Intelligence
G doesnt exists, this article is bullshit. G is a statistical variable. Why bother increasing something that is a mirage of many subprocesses?
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Thomas
Sep 19, 2021, 6:18:47 AM9/19/21
to Dual N-Back, Brain Training & Intelligence
G is simply IQ to me and IQ is measuring the simultaneous interaction between a bunch of "neural processes" that add to the number.
G is our abstraction of IQ, IQ is our measurement of it and the "neural processes" add to the function of G and reflect the structure of IQ.
We have to remember we're dealing with biology here, I think Gwern knows this already as well. Studies will get better and better at correlating structure with function. "Where we see slight breakdown of axonal strength here, we see reductions in intelligence here but not here" this kind of speak.
Pontus Granström
Sep 19, 2021, 2:54:54 PM9/19/21
to Dual N-Back, Brain Training & Intelligence
Nback improves functions that are important for intellectual/cognitive work, who cares if they dont call it G. What difference does it make? Everyone who nbacks does so because they experience benefits, I do nback every day, as I said after a run its amazing, you truly think clearly. There is a synergetic effect between running and nback.
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