The Associative-Cognitive Distinction Today: A Survey of Practitioners
This Quarto document was built with R version 4.4.1 (2024-06-14) (Race for Your Life).
We received a total of 392 responses, of which 220 were completed, 3 did not consent to the study, 12 stopped the survey before being asked for consent (exited the survey after opening the starting page), 116 consented to the survey but did not complete all the questions, and the rest, 41, were identified as spam by the Qualtrics software used to collect the responses, on account of a lack of or an anomalous IP address.
Discipline
Participants were asked: Please rank how well the following terms describe your research (1: best description, 7: worst description). Drag and drop the statements into your preferred order. They were provided with the following options, which were randomly ordered (with ‘other’ always at the bottom) for each participant:
- Animal behaviour
- Animal cognition
- Animal learning
- Behavioural ecology
- Behavioural neuroscience
- Comparative psychology
- Other
Below is the plot for the participants who fully completed the survey. 4 participants were dropped as they did not provide an answer to this question.
There were 36 other research areas mentioned.
Publications
Years in the Discipline
Primary Country of Research
Below is a table of countries and the frequency of their mentions:
Methodology of Choice
Participants were asked what methods they use in their research. They had the following choices, presented at random (with ‘Other’ always at the end), of which they could choose as many as they liked:
- Computational Modelling
- Behavioural testing (e.g., string-pulling)
- Ethological/Observational methods
- Other (please specify)
And here’s a table of the other methodologies mentioned:
Participants were asked to what extent they agree with the following statements. It was noted that by behaviour, we mean specific behavioural sequences such as ‘food caching’, ‘pecking’, or ‘string pulling’.
Important note: We forgot to add a ‘not sure’ option on this question…
We can also examine responses to this question by top-ranked domain. Due to the complexity of the plots, the questions have been simplified to: * Exclusive: A candidate explanation for a particular behaviour cannot be both a cognitive and an associative explanation at the same time. * Exhaustive: An explanation for a behaviour must be either cognitive or associative, it cannot be neither. * Depends: Whether an explanation for a behaviour is cognitive, associative, both, or neither depends on the behaviour in question.
We can normalise this by the number of respondents in each domain, producing a proportion:
Relative Simplicity
Participants were asked a series of questions regarding parsimony considerations in comparative psychology.
We can also examine responses to this question by top-ranked domain. Due to the complexity of the plots, we have again simplified the questions, but they should be straightforward to work out.
We can normalise this by the number of respondents in each domain, producing a proportion:
Morgan’s Canon
Before asking more in depth and targeted questions about Morgan’s Canon, we asked participants what Morgan’s Canon means to them, asking them to be as detailed as they would like.
Then they were given the following prompt:
Morgan’s original (1903) Canon stated that: “In no case is an animal activity to be interpreted in terms of higher psychological processes if it can be fairly interpreted in terms of processes which stand lower in the scale of psychological evolution and development.”
They were asked:
Does Morgan’s Canon have a modern interpretation?
We can also examine responses to this question by top-ranked domain.
We can normalise this by the number of respondents in each domain, producing a proportion:
If they answered Yes or No, they were then prompted to explain why they think Morgan’s Canon does or does not have a modern interpretation.
We then asked some more targeted questions. First, they were asked to what extent they agree with the following statements about Morgan’s Canon.
We can also examine responses to this question by top-ranked domain. Due to the complexity of the plots, the questions have again been simplified to: * Ubiquity: ‘Lower processes’ are more common across the animal kingdom. * Recency: ‘Higher processes’ evolved more recently. * Conservation: ‘Lower processes’ are more evolutionarily conserved. * No hierarchy: There is no hierarchy of processes. * AL is lower: Associative learning is a ‘lower process’. * Cognition is higher: Cognition is a ‘higher process’. * MC is useful for interpretation: Morgan’s Canon is useful for interpreting results in animal research. * MC is useful for controls: Morgan’s Canon is useful for coming up with control conditions for experiments.
We can normalise this by the number of respondents in each domain, producing a proportion:
Morgan’s Canon Day-to-Day
Participants were then prompted to think about their day-to-day research and how Morgan’s Canon and the associative-cognitive distinction fit into it.
We can also examine responses to this question by top-ranked domain. Due to the complexity of the plots, we have again simplified the questions, but they should be straightforward to work out.
We can normalise this by the number of respondents in each domain, producing a proportion:
Next we asked more in depth questions about the distinction and its potential role in their work.
Inferences and Controls
We can also examine responses to this question by top-ranked domain. Due to the complexity of the plots, the questions have again been simplified to: * Useful distinction: The associative-cognitive distinction is useful for coming up with controlfor experiments. * Falsifying AL: Falsifying associative explanations is evidence for cognitive explanations. * Falsifying cognition: Falsifying cognitive explanations isfor associative explanations. * Existential Threat: As more sophisticated associative modelsavailable to explain behaviour, is there less need for cognitive explanation(even if they are just as explanatory)?.
We can normalise this by the number of respondents in each domain, producing a proportion:
Finally, they were asked for final comments about the role an associative-cognitive distinction might play in their day-to-day research.
Expertise in the Fields
Participants were asked what their understanding of the literature on associative learning and comparative cognition is like. If they said that they had no understanding, then no further questions were asked on that topic, apart from the final free-text one asking if they had any further comments on the topic of associative learning and its definition.
We can also examine responses to this question by top-ranked domain. Due to the complexity of the plots, the questions have again been simplified.
We can normalise this by the number of respondents in each domain, producing a proportion:
Those that selected ‘Other’ for associative learning specified the following:
Those that selected ‘Other’ for comparative cognition specified the following:
Free Text
Those who professed to having at least basic understanding (or other) were then presented with the following prompts:
To contextualise the following questions, please read these sentences: An animal solves a task via associative learning. This animal is associative or learns by association. Associative explanations account for the animal’s behaviour in the task.
or
To contextualise the following questions, please read these sentences: An animal solves a task cognitively. This animal is cognitive. Cognitive explanations account for the animal’s behaviour in the task
They were then asked:
In your opinion, given the above context, what are the defining characteristics of an ‘associative’/a ‘cognitive’ explanation of animal behaviour? (Please be as detailed as you like)
Specific Properties
Next they were asked about a variety of potential properties that associative explanations might be described as having or producing.
Note that the questions on associative learning were on a Never-Always scale, while questions on cognition were on a Strongly disagree - Strongly agree scale
Well-Defined Concepts?
They were then asked to what extent they agreed with the following statements:
‘Associative learning’/‘Cognition’ is a well-defined theoretical concept
‘Associative’ explanations of animal behaviour are becoming increasingly powerful
We can also examine responses to this question by top-ranked domain. Due to the complexity of the plots, the questions have again been simplified to: * Associative learning: ‘Associative learning’ is a well-defined theoretical concept. * Cognition: ‘Cognition’ is a well-defined concept. * Power argument: ‘Associative’ explanations of animal behaviour are becoming increasingly powerful.
We can normalise this by the number of respondents in each domain, producing a proportion:
Further Terminology
Are there any other terms that you would use to describe ‘associative’ behaviour?
Removing common stop-words using the {tidytext} package gives the following frequent terms:
Are there any other terms that you would use to describe ‘cognitive’ behaviour?
Removing common stop-words using the {tidytext} package gives the following frequent terms:
Finally, they were asked for any further comments on the topic of cognition and its definition.
Below are some kendall’s correlations between question responses, to see if certain responses to questions were associated to other responses.
First, we look at correlations between all ordinal questions. We removed all missing data and responses of Not sure or Don’t know. This left us with a sample size of 19.
This plot might not render well in the HTML file. You can visually inspect the correlation coefficients below the plot:
Exclusivity Exhaustivity
Exclusivity 1.000000000 0.471939904
Exhaustivity 0.471939904 1.000000000
Depends 0.478859189 0.162032544
AL is Simple -0.085524442 -0.251143803
Cog is Complex 0.334297924 0.545793834
AL is simpler than Cog -0.133038021 -0.403623970
AL Default -0.284679353 -0.034671367
Ubiquity -0.122456374 -0.222277112
Recency 0.000000000 0.164121988
Conservation 0.167689131 -0.033321765
No Hierarchy -0.115620664 0.134316320
MC Utility 0.121218305 -0.200227144
AL = Lower 0.142857143 -0.092702481
Cog = Higher 0.355755889 0.260237870
MC for Controls 0.121927402 -0.053116067
MC Day-to-Day -0.231501330 -0.131105658
AC Day-to-Day -0.208643066 0.008562347
AL as Null -0.262017292 -0.153504516
AC for Controls 0.174527778 -0.296519841
Eliminative AL 0.598499600 0.166695252
Eliminative Cog 0.416702394 0.069923018
Existential Threat -0.027105237 0.085280287
Flexible AL -0.020102787 -0.113847688
Automatic AL -0.079242885 0.037397880
Explicit AL -0.106315487 0.025087260
Representational AL -0.063245553 -0.068224229
Symbolic AL -0.043795667 -0.066140553
Deterministic AL 0.116527507 0.101527348
Intentional AL -0.018758062 0.088526779
Intelligent AL 0.414039336 -0.018609684
Propositional AL 0.133408459 -0.041973850
Information-Theoretic AL -0.268909631 0.025381837
Computational AL -0.308205651 -0.254545455
Neural Network AL 0.019622938 0.388955588
Conditioning AL 0.009260053 0.008740377
AL Well Defined -0.084049772 0.105777177
Power Argument -0.063245553 -0.221728745
Flexible Cog 0.172582986 0.144797862
Automatic Cog -0.144561264 -0.281424946
Explicit Cog 0.393919299 0.026558034
Representational Cog -0.057270266 -0.171178118
Symbolic Cog 0.133928571 -0.109557478
Deterministic Cog 0.231501330 0.218509431
Intentional Cog 0.086682839 0.090909091
Intelligent Cog 0.096314266 0.081818182
Propositional Cog 0.082648845 0.242699568
Information-Theoretic Cog -0.099533288 0.084552714
Computational Cog -0.211891385 -0.272727273
Neural Network Cog -0.165985001 0.226300953
Conditioning Cog -0.186771842 -0.176290170
Cog Well Defined 0.423635700 0.324887212
Publications -0.057270266 -0.261271864
Years 0.287494454 0.100503782
Depends AL is Simple
Exclusivity 0.47885919 -0.085524442
Exhaustivity 0.16203254 -0.251143803
Depends 1.00000000 0.151453138
AL is Simple 0.15145314 1.000000000
Cog is Complex 0.28800000 -0.412289099
AL is simpler than Cog -0.27766409 0.389380531
AL Default -0.37403319 -0.059864055
Ubiquity -0.16681153 0.298257089
Recency -0.21554410 -0.089060797
Conservation -0.16410758 -0.213697026
No Hierarchy -0.11024989 -0.215347221
MC Utility -0.08049845 0.216365800
AL = Lower 0.01581139 0.266076042
Cog = Higher 0.20474980 0.447259612
MC for Controls 0.14117732 0.087343740
MC Day-to-Day -0.44275683 0.241460074
AC Day-to-Day -0.41767405 -0.135166765
AL as Null -0.46400000 0.058898443
AC for Controls 0.25498025 0.373823166
Eliminative AL 0.41699735 -0.146193421
Eliminative Cog 0.06559360 -0.189718629
Existential Threat 0.07200000 -0.109382822
Flexible AL -0.10679860 -0.383781422
Automatic AL -0.33328205 0.221388649
Explicit AL -0.09413574 0.123760017
Representational AL -0.25600000 -0.092554696
Symbolic AL -0.27144836 -0.228398364
Deterministic AL -0.07936758 0.116865670
Intentional AL -0.17439551 -0.253296846
Intelligent AL -0.13093073 -0.192790408
Propositional AL 0.09449991 0.000000000
Information-Theoretic AL -0.50001575 -0.125213218
Computational AL -0.51168172 -0.188357853
Neural Network AL -0.50387180 -0.045685444
Conditioning AL -0.24597602 0.025870722
AL Well Defined -0.10749677 0.269605993
Power Argument -0.34400000 0.109382822
Flexible Cog 0.00000000 0.071431419
Automatic Cog -0.41600000 -0.084140632
Explicit Cog -0.09135003 -0.139749984
Representational Cog -0.41412558 0.097778743
Symbolic Cog -0.27669930 -0.216186784
Deterministic Cog 0.03279680 -0.344942962
Intentional Cog -0.06822423 0.026908265
Intelligent Cog -0.07675226 -0.080724794
Propositional Cog -0.03252463 -0.119728111
Information-Theoretic Cog -0.35252212 -0.278075944
Computational Cog -0.40934538 0.008969422
Neural Network Cog -0.38375339 0.163163779
Conditioning Cog -0.29924971 -0.356151173
Cog Well Defined -0.14847829 -0.106848513
Publications -0.21974011 -0.080000790
Years 0.32055507 0.337146333
Cog is Complex
Exclusivity 0.334297924
Exhaustivity 0.545793834
Depends 0.288000000
AL is Simple -0.412289099
Cog is Complex 1.000000000
AL is simpler than Cog -0.488015668
AL Default -0.219541220
Ubiquity -0.450391134
Recency 0.338712158
Conservation 0.093775760
No Hierarchy 0.007874992
MC Utility -0.026832816
AL = Lower -0.308322072
Cog = Higher 0.133874869
MC for Controls 0.107959124
MC Day-to-Day -0.483752836
AC Day-to-Day -0.152611672
AL as Null -0.472000000
AC for Controls -0.193166852
Eliminative AL 0.364872684
Eliminative Cog 0.139386410
Existential Threat -0.024000000
Flexible AL -0.026699650
Automatic AL -0.324511467
Explicit AL -0.062757163
Representational AL -0.080000000
Symbolic AL -0.015511335
Deterministic AL -0.198418948
Intentional AL 0.099654576
Intelligent AL -0.061101009
Propositional AL -0.062999938
Information-Theoretic AL 0.015873516
Computational AL -0.102336344
Neural Network AL -0.130311673
Conditioning AL -0.139386410
AL Well Defined -0.041344912
Power Argument -0.328000000
Flexible Cog -0.203748653
Automatic Cog -0.296000000
Explicit Cog -0.199309152
Representational Cog -0.304255532
Symbolic Cog -0.015811388
Deterministic Cog 0.057394404
Intentional Cog -0.034112115
Intelligent Cog -0.051168172
Propositional Cog 0.170754282
Information-Theoretic Cog -0.158634954
Computational Cog -0.281424946
Neural Network Cog -0.155134350
Conditioning Cog -0.125999877
Cog Well Defined 0.156292933
Publications -0.329610159
Years 0.028284271
AL is simpler than Cog
Exclusivity -0.133038021
Exhaustivity -0.403623970
Depends -0.277664087
AL is Simple 0.389380531
Cog is Complex -0.488015668
AL is simpler than Cog 1.000000000
AL Default 0.307872284
Ubiquity 0.578969644
Recency 0.008096436
Conservation 0.410955819
No Hierarchy -0.182216879
MC Utility 0.272809052
AL = Lower 0.390799187
Cog = Higher 0.265042733
MC for Controls 0.366843708
MC Day-to-Day 0.465672999
AC Day-to-Day 0.278781452
AL as Null 0.294492213
AC for Controls 0.357569984
Eliminative AL -0.201015954
Eliminative Cog 0.241460074
Existential Threat 0.185109391
Flexible AL 0.187210450
Automatic AL 0.405879190
Explicit AL 0.206266695
Representational AL 0.286078150
Symbolic AL 0.318126293
Deterministic AL 0.191993601
Intentional AL 0.165953106
Intelligent AL 0.321317347
Propositional AL -0.024847756
Information-Theoretic AL 0.100170574
Computational AL 0.197327274
Neural Network AL 0.292386842
Conditioning AL 0.319072240
AL Well Defined 0.313090830
Power Argument 0.479601605
Flexible Cog -0.160720692
Automatic Cog 0.471187541
Explicit Cog 0.148484358
Representational Cog 0.257780324
Symbolic Cog 0.299335547
Deterministic Cog 0.060365018
Intentional Cog 0.242174382
Intelligent Cog 0.089694215
Propositional Cog -0.008552008
Information-Theoretic Cog 0.157576368
Computational Cog 0.134541323
Neural Network Cog 0.463728636
Conditioning Cog 0.016565171
Cog Well Defined -0.016438233
Publications 0.115556697
Years 0.317314196
AL Default Ubiquity
Exclusivity -0.284679353 -0.12245637
Exhaustivity -0.034671367 -0.22227711
Depends -0.374033189 -0.16681153
AL is Simple -0.059864055 0.29825709
Cog is Complex -0.219541220 -0.45039113
AL is simpler than Cog 0.307872284 0.57896964
AL Default 1.000000000 0.50016167
Ubiquity 0.500161670 1.00000000
Recency 0.359913604 0.15248875
Conservation 0.317710566 0.45625061
No Hierarchy 0.088045091 -0.33662013
MC Utility 0.190909091 0.37300192
AL = Lower 0.152670782 0.33793249
Cog = Higher 0.008004099 0.19704593
MC for Controls 0.185695338 0.33766550
MC Day-to-Day 0.450015626 0.64111932
AC Day-to-Day 0.457177132 0.40195874
AL as Null 0.642361346 0.54213748
AC for Controls 0.078533493 0.24166844
Eliminative AL -0.423833831 -0.16303117
Eliminative Cog 0.183339699 0.29064076
Existential Threat 0.398426658 0.25855787
Flexible AL 0.515610180 0.01855754
Automatic AL 0.543776557 0.58521356
Explicit AL 0.207304623 0.14721495
Representational AL 0.333377408 0.30026076
Symbolic AL 0.394140895 0.33960537
Deterministic AL 0.080668774 0.38890818
Intentional AL 0.430475557 0.03463236
Intelligent AL 0.097590007 0.05460189
Propositional AL -0.024012297 0.23809716
Information-Theoretic AL 0.242006321 0.03309857
Computational AL 0.494066978 0.31118796
Neural Network AL 0.520962417 0.31700506
Conditioning AL 0.175006077 0.35902682
AL Well Defined 0.294159195 0.29311434
Power Argument 0.552918627 0.58384036
Flexible Cog -0.224346435 -0.20357171
Automatic Cog 0.357770876 0.51711575
Explicit Cog -0.059084880 0.16450371
Representational Cog -0.042950508 0.20266087
Symbolic Cog 0.088388348 0.23078317
Deterministic Cog 0.108337095 0.30773727
Intentional Cog -0.078010575 -0.08891084
Intelligent Cog -0.294706618 -0.08891084
Propositional Cog -0.066115702 0.15259170
Information-Theoretic Cog 0.089575389 -0.08269419
Computational Cog 0.078010575 0.01778217
Neural Network Cog 0.365148372 0.38306544
Conditioning Cog 0.392200858 0.13136395
Cog Well Defined -0.142969755 -0.04888399
Publications -0.042950508 0.16741550
Years 0.143739894 0.32437230
Recency Conservation
Exclusivity 0.000000000 0.16768913
Exhaustivity 0.164121988 -0.03332177
Depends -0.215544100 -0.16410758
AL is Simple -0.089060797 -0.21369703
Cog is Complex 0.338712158 0.09377576
AL is simpler than Cog 0.008096436 0.41095582
AL Default 0.359913604 0.31771057
Ubiquity 0.152488747 0.45625061
Recency 1.000000000 0.32334483
Conservation 0.323344832 1.00000000
No Hierarchy -0.310686310 -0.29231634
MC Utility 0.361478446 0.23590010
AL = Lower -0.152145155 0.17761809
Cog = Higher 0.075777149 0.16154324
MC for Controls 0.143838990 0.28392485
MC Day-to-Day 0.189352427 0.23226773
AC Day-to-Day -0.061831853 0.38445861
AL as Null 0.100074047 0.15629293
AC for Controls -0.066914961 0.20378664
Eliminative AL -0.016718995 0.16123712
Eliminative Cog 0.118345267 0.60870163
Existential Threat -0.046188022 0.33602981
Flexible AL -0.042819583 0.24342305
Automatic AL 0.303821810 0.40266654
Explicit AL 0.045291081 0.09961759
Representational AL 0.230940108 0.15629293
Symbolic AL 0.305978670 0.31819095
Deterministic AL -0.099282935 0.38764349
Intentional AL 0.159821100 0.21091560
Intelligent AL 0.050395263 0.43485040
Propositional AL 0.037888574 0.24616113
Information-Theoretic AL 0.282574508 0.27910331
Computational AL 0.451335467 0.44984383
Neural Network AL 0.217346940 0.24609876
Conditioning AL -0.015779369 0.32837851
AL Well Defined 0.167093471 0.01615479
Power Argument 0.277128129 0.38291769
Flexible Cog -0.318593003 -0.29854195
Automatic Cog -0.015396007 0.59391315
Explicit Cog -0.279686926 0.33259768
Representational Cog -0.138252510 0.21464891
Symbolic Cog 0.174966928 0.61780206
Deterministic Cog -0.110455583 0.45652622
Intentional Cog -0.303625678 0.05831309
Intelligent Cog -0.328243976 0.08330441
Propositional Cog 0.031296835 0.28593951
Information-Theoretic Cog -0.067842914 0.17217724
Computational Cog -0.032824398 0.02499132
Neural Network Cog 0.133564614 0.35093477
Conditioning Cog 0.303108595 0.21539099
Cog Well Defined 0.135353650 0.15267176
Publications -0.130120010 0.15685882
Years 0.018144368 0.37759563
No Hierarchy MC Utility
Exclusivity -0.115620664 0.121218305
Exhaustivity 0.134316320 -0.200227144
Depends -0.110249892 -0.080498447
AL is Simple -0.215347221 0.216365800
Cog is Complex 0.007874992 -0.026832816
AL is simpler than Cog -0.182216879 0.272809052
AL Default 0.088045091 0.190909091
Ubiquity -0.336620128 0.373001923
Recency -0.310686310 0.361478446
Conservation -0.292316341 0.235900095
No Hierarchy 1.000000000 -0.167285672
MC Utility -0.167285672 1.000000000
AL = Lower -0.163425362 -0.061871843
Cog = Higher -0.031007752 0.149676654
MC for Controls -0.098097377 0.287827774
MC Day-to-Day 0.096852963 0.302510504
AC Day-to-Day 0.189760381 0.017960530
AL as Null 0.094499908 0.286216701
AC for Controls -0.197754372 0.086386843
Eliminative AL -0.102620342 0.058277152
Eliminative Cog 0.161421605 0.385013369
Existential Threat -0.062999938 -0.241495342
Flexible AL 0.402997442 -0.009950372
Automatic AL 0.069068252 0.245145169
Explicit AL 0.200773697 0.236805665
Representational AL 0.110249892 0.429325052
Symbolic AL 0.053441343 0.346843988
Deterministic AL -0.148442030 0.008873565
Intentional AL 0.049048689 0.232119173
Intelligent AL 0.034369284 0.117108009
Propositional AL -0.062015504 0.220112727
Information-Theoretic AL 0.093752861 0.248459823
Computational AL 0.050368620 0.333711906
Neural Network AL 0.222344075 0.155405738
Conditioning AL -0.274416728 0.137504775
AL Well Defined 0.138376116 0.120185043
Power Argument 0.000000000 0.536656315
Flexible Cog 0.217278625 -0.170848439
Automatic Cog -0.173249831 0.152052622
Explicit Cog -0.073573033 -0.009284767
Representational Cog -0.166389581 -0.037796447
Symbolic Cog -0.365761524 0.114904852
Deterministic Cog -0.064568642 0.009166985
Intentional Cog 0.058763390 -0.085811633
Intelligent Cog 0.134316320 -0.276504151
Propositional Cog -0.128065586 0.072727273
Information-Theoretic Cog 0.034701362 -0.305452076
Computational Cog 0.033579080 0.162088640
Neural Network Cog 0.168784988 0.027386128
Conditioning Cog 0.007751938 0.123263127
Cog Well Defined -0.023077606 -0.017474081
Publications -0.124792186 -0.047245559
Years -0.287703791 0.147572957
AL = Lower Cog = Higher
Exclusivity 0.142857143 0.355755889
Exhaustivity -0.092702481 0.260237870
Depends 0.015811388 0.204749800
AL is Simple 0.266076042 0.447259612
Cog is Complex -0.308322072 0.133874869
AL is simpler than Cog 0.390799187 0.265042733
AL Default 0.152670782 0.008004099
Ubiquity 0.337932493 0.197045929
Recency -0.152145155 0.075777149
Conservation 0.177618093 0.161543241
No Hierarchy -0.163425362 -0.031007752
MC Utility -0.061871843 0.149676654
AL = Lower 1.000000000 0.435800964
Cog = Higher 0.435800964 1.000000000
MC for Controls 0.402125951 0.343340820
MC Day-to-Day 0.259281489 0.153350524
AC Day-to-Day 0.388937889 0.110693556
AL as Null 0.379473319 -0.023624977
AC for Controls 0.503948958 0.273813746
Eliminative AL 0.025755106 0.153930513
Eliminative Cog 0.113435652 0.250203487
Existential Threat 0.110679718 0.000000000
Flexible AL 0.017589939 -0.148933837
Automatic AL 0.182011002 0.379875384
Explicit AL -0.007752171 0.200773697
Representational AL -0.134396801 -0.015749985
Symbolic AL -0.145620593 -0.106882686
Deterministic AL 0.243139124 0.273445845
Intentional AL -0.139513085 -0.147146066
Intelligent AL -0.034503278 0.060146247
Propositional AL -0.389108004 0.000000000
Information-Theoretic AL -0.305884705 -0.257820368
Computational AL 0.050564990 0.000000000
Neural Network AL 0.145945599 0.179585599
Conditioning AL 0.275486583 0.258274567
AL Well Defined 0.335031731 0.545364691
Power Argument 0.118585412 0.015749985
Flexible Cog -0.083894507 0.075211832
Automatic Cog 0.339944848 -0.141749862
Explicit Cog 0.016413304 -0.114446940
Representational Cog 0.267261242 0.166389581
Symbolic Cog 0.093750000 0.077821601
Deterministic Cog 0.016205093 -0.096852963
Intentional Cog -0.126412474 0.092342470
Intelligent Cog 0.033709993 0.167895400
Propositional Cog -0.136600174 0.248127074
Information-Theoretic Cog -0.156764928 -0.347013622
Computational Cog -0.294962440 -0.209869250
Neural Network Cog 0.072618438 0.337569976
Conditioning Cog -0.295722083 -0.480620155
Cog Well Defined 0.409293867 0.361549158
Publications -0.133630621 -0.349418121
Years 0.083852549 0.426915302
MC for Controls MC Day-to-Day
Exclusivity 0.121927402 -0.231501330
Exhaustivity -0.053116067 -0.131105658
Depends 0.141177316 -0.442756833
AL is Simple 0.087343740 0.241460074
Cog is Complex 0.107959124 -0.483752836
AL is simpler than Cog 0.366843708 0.465672999
AL Default 0.185695338 0.450015626
Ubiquity 0.337665502 0.641119317
Recency 0.143838990 0.189352427
Conservation 0.283924848 0.232267727
No Hierarchy -0.098097377 0.096852963
MC Utility 0.287827774 0.302510504
AL = Lower 0.402125951 0.259281489
Cog = Higher 0.343340820 0.153350524
MC for Controls 1.000000000 0.204271965
MC Day-to-Day 0.204271965 1.000000000
AC Day-to-Day 0.283490872 0.329287821
AL as Null 0.024913644 0.590342444
AC for Controls 0.497290652 0.007919069
Eliminative AL 0.378762839 -0.178075258
Eliminative Cog 0.391521266 0.252100840
Existential Threat -0.016609096 -0.073792806
Flexible AL 0.277160651 0.009121491
Automatic AL 0.091044630 0.701139529
Explicit AL -0.122149189 0.225119375
Representational AL 0.348791015 0.418159231
Symbolic AL 0.362291128 0.310002578
Deterministic AL 0.041194492 0.366047270
Intentional AL -0.043103448 -0.144692642
Intelligent AL -0.018122009 0.008946061
Propositional AL -0.228893880 0.145279444
Information-Theoretic AL -0.304839239 0.187090827
Computational AL 0.070821423 0.463239993
Neural Network AL 0.009018163 0.489706960
Conditioning AL 0.178737969 0.235294118
AL Well Defined 0.515026203 0.372894747
Power Argument 0.406922851 0.598541645
Flexible Cog -0.431822827 -0.043504585
Automatic Cog 0.307268275 0.352565627
Explicit Cog -0.008620690 0.042556659
Representational Cog -0.131599201 0.311831518
Symbolic Cog 0.049239912 0.121538198
Deterministic Cog 0.238317292 0.201680672
Intentional Cog 0.017705356 -0.201028676
Intelligent Cog -0.141642846 0.043701886
Propositional Cog -0.059084880 -0.050001736
Information-Theoretic Cog -0.402536318 0.090324987
Computational Cog -0.168200880 0.104884527
Neural Network Cog 0.127136907 0.460255075
Conditioning Cog -0.196194755 0.096852963
Cog Well Defined 0.219027740 0.112129247
Publications -0.254425122 0.095281853
Years 0.009787004 -0.048314253
AC Day-to-Day AL as Null
Exclusivity -0.208643066 -0.262017292
Exhaustivity 0.008562347 -0.153504516
Depends -0.417674051 -0.464000000
AL is Simple -0.135166765 0.058898443
Cog is Complex -0.152611672 -0.472000000
AL is simpler than Cog 0.278781452 0.294492213
AL Default 0.457177132 0.642361346
Ubiquity 0.401958739 0.542137477
Recency -0.061831853 0.100074047
Conservation 0.384458608 0.156292933
No Hierarchy 0.189760381 0.094499908
MC Utility 0.017960530 0.286216701
AL = Lower 0.388937889 0.379473319
Cog = Higher 0.110693556 -0.023624977
MC for Controls 0.283490872 0.024913644
MC Day-to-Day 0.329287821 0.590342444
AC Day-to-Day 1.000000000 0.618478883
AL as Null 0.618478883 1.000000000
AC for Controls 0.155155349 0.015453348
Eliminative AL -0.226782518 -0.460434577
Eliminative Cog 0.345752212 0.139386410
Existential Threat 0.273094572 0.128000000
Flexible AL 0.277006630 0.133498248
Automatic AL 0.369846726 0.508693651
Explicit AL 0.126019416 0.329475107
Representational AL 0.040160966 0.168000000
Symbolic AL 0.210245690 0.178380349
Deterministic AL 0.318747868 0.230165979
Intentional AL 0.141745436 0.215918248
Intelligent AL 0.043819207 -0.043643578
Propositional AL -0.086973508 -0.007874992
Information-Theoretic AL 0.175311327 0.246039495
Computational AL 0.308244485 0.486097633
Neural Network AL 0.488454654 0.529934136
Conditioning AL 0.271662452 0.245976019
AL Well Defined 0.356997095 0.223262522
Power Argument 0.401609664 0.488000000
Flexible Cog -0.076713214 0.016979054
Automatic Cog 0.674704236 0.424000000
Explicit Cog 0.208449171 0.008304548
Representational Cog 0.229109929 0.236643191
Symbolic Cog 0.111125111 0.079056942
Deterministic Cog 0.246965866 0.049195204
Intentional Cog 0.068498775 -0.255840860
Intelligent Cog 0.008562347 -0.162032544
Propositional Cog 0.163277547 -0.008131156
Information-Theoretic Cog -0.079636663 -0.008813053
Computational Cog 0.059936428 0.042640143
Neural Network Cog 0.508265023 0.253113940
Conditioning Cog 0.055346778 0.275624731
Cog Well Defined 0.219690633 0.078146467
Publications 0.059398870 0.042257713
Years -0.028398092 -0.037712362
AC for Controls Eliminative AL
Exclusivity 0.174527778 0.598499600
Exhaustivity -0.296519841 0.166695252
Depends 0.254980245 0.416997353
AL is Simple 0.373823166 -0.146193421
Cog is Complex -0.193166852 0.364872684
AL is simpler than Cog 0.357569984 -0.201015954
AL Default 0.078533493 -0.423833831
Ubiquity 0.241668438 -0.163031173
Recency -0.066914961 -0.016718995
Conservation 0.203786644 0.161237116
No Hierarchy -0.197754372 -0.102620342
MC Utility 0.086386843 0.058277152
AL = Lower 0.503948958 0.025755106
Cog = Higher 0.273813746 0.153930513
MC for Controls 0.497290652 0.378762839
MC Day-to-Day 0.007919069 -0.178075258
AC Day-to-Day 0.155155349 -0.226782518
AL as Null 0.015453348 -0.460434577
AC for Controls 1.000000000 0.243328325
Eliminative AL 0.243328325 1.000000000
Eliminative Cog 0.277167411 0.418476856
Existential Threat 0.216346875 -0.364872684
Flexible AL 0.163320430 0.019329311
Automatic AL 0.135534830 -0.152387864
Explicit AL -0.174262428 -0.357787103
Representational AL -0.169986830 0.026062335
Symbolic AL -0.097378960 -0.050532699
Deterministic AL 0.076655927 0.206850440
Intentional AL -0.224582875 -0.351708351
Intelligent AL 0.168609852 0.132702912
Propositional AL -0.167330622 0.059861866
Information-Theoretic AL -0.252964559 -0.284419355
Computational AL -0.049419974 -0.203738641
Neural Network AL -0.067125055 -0.292452830
Conditioning AL -0.071271620 0.008903763
AL Well Defined 0.111810530 -0.080815869
Power Argument 0.309066964 0.000000000
Flexible Cog -0.180388477 -0.082971337
Automatic Cog 0.339973660 -0.026062335
Explicit Cog 0.096249804 0.279563048
Representational Cog -0.097953472 -0.055066732
Symbolic Cog -0.045813542 0.171700705
Deterministic Cog -0.102947895 0.382861805
Intentional Cog 0.008236662 -0.009260847
Intelligent Cog -0.197679894 0.037043389
Propositional Cog -0.251307178 -0.017659743
Information-Theoretic Cog -0.383037683 -0.162696188
Computational Cog -0.074129960 -0.009260847
Neural Network Cog 0.047316022 -0.248264416
Conditioning Cog -0.220572184 -0.265102551
Cog Well Defined 0.098119495 0.356418887
Publications 0.057139525 0.045888944
Years 0.291391128 -0.122859023
Eliminative Cog
Exclusivity 0.416702394
Exhaustivity 0.069923018
Depends 0.065593605
AL is Simple -0.189718629
Cog is Complex 0.139386410
AL is simpler than Cog 0.241460074
AL Default 0.183339699
Ubiquity 0.290640757
Recency 0.118345267
Conservation 0.608701629
No Hierarchy 0.161421605
MC Utility 0.385013369
AL = Lower 0.113435652
Cog = Higher 0.250203487
MC for Controls 0.391521266
MC Day-to-Day 0.252100840
AC Day-to-Day 0.345752212
AL as Null 0.139386410
AC for Controls 0.277167411
Eliminative AL 0.418476856
Eliminative Cog 1.000000000
Existential Threat 0.155784812
Flexible AL 0.401345603
Automatic AL 0.404503574
Explicit AL 0.273359241
Representational AL 0.377163228
Symbolic AL 0.413336771
Deterministic AL 0.366047270
Intentional AL 0.127669978
Intelligent AL 0.483087310
Propositional AL 0.314772129
Information-Theoretic AL 0.146418908
Computational AL 0.314653580
Neural Network AL 0.258209124
Conditioning AL 0.159663866
AL Well Defined -0.008474881
Power Argument 0.442756833
Flexible Cog -0.052205502
Automatic Cog 0.377163228
Explicit Cog 0.348964607
Representational Cog 0.017323973
Symbolic Cog 0.299794222
Deterministic Cog 0.478991597
Intentional Cog 0.104884527
Intelligent Cog 0.183547922
Propositional Cog 0.208340568
Information-Theoretic Cog -0.054194992
Computational Cog -0.069923018
Neural Network Cog 0.284521319
Conditioning Cog 0.112995123
Cog Well Defined 0.216249263
Publications -0.060633906
Years 0.289885518
Existential Threat Flexible AL
Exclusivity -0.027105237 -0.020102787
Exhaustivity 0.085280287 -0.113847688
Depends 0.072000000 -0.106798598
AL is Simple -0.109382822 -0.383781422
Cog is Complex -0.024000000 -0.026699650
AL is simpler than Cog 0.185109391 0.187210450
AL Default 0.398426658 0.515610180
Ubiquity 0.258557873 0.018557539
Recency -0.046188022 -0.042819583
Conservation 0.336029806 0.243423048
No Hierarchy -0.062999938 0.402997442
MC Utility -0.241495342 -0.009950372
AL = Lower 0.110679718 0.017589939
Cog = Higher 0.000000000 -0.148933837
MC for Controls -0.016609096 0.277160651
MC Day-to-Day -0.073792806 0.009121491
AC Day-to-Day 0.273094572 0.277006630
AL as Null 0.128000000 0.133498248
AC for Controls 0.216346875 0.163320430
Eliminative AL -0.364872684 0.019329311
Eliminative Cog 0.155784812 0.401345603
Existential Threat 1.000000000 0.302596028
Flexible AL 0.302596028 1.000000000
Automatic AL 0.105246962 0.146357136
Explicit AL 0.141203617 -0.017454107
Representational AL -0.120000000 0.373795094
Symbolic AL 0.131846345 0.431403334
Deterministic AL -0.047620547 -0.052977164
Intentional AL 0.290659179 0.471173107
Intelligent AL 0.244404037 0.446686159
Propositional AL 0.000000000 -0.078847326
Information-Theoretic AL 0.000000000 -0.026488582
Computational AL -0.008528029 0.218208069
Neural Network AL 0.251935901 0.193293111
Conditioning AL 0.000000000 0.100336401
AL Well Defined -0.132303717 0.036796488
Power Argument 0.008000000 0.391594860
Flexible Cog -0.127342908 -0.094444751
Automatic Cog 0.288000000 0.275896379
Explicit Cog 0.083045480 0.230967209
Representational Cog -0.143676223 -0.141033265
Symbolic Cog 0.007905694 0.140719509
Deterministic Cog 0.163984012 0.246280256
Intentional Cog 0.179088602 0.294106528
Intelligent Cog 0.042640143 -0.056923844
Propositional Cog 0.073180407 -0.144732682
Information-Theoretic Cog 0.158634954 0.127457106
Computational Cog -0.307009032 0.218208069
Neural Network Cog 0.253113940 0.090834052
Conditioning Cog 0.125999877 0.332910930
Cog Well Defined -0.257883340 -0.104324164
Publications 0.033806170 0.009402218
Years 0.603397787 -0.020977226
Automatic AL Explicit AL
Exclusivity -0.07924289 -0.106315487
Exhaustivity 0.03739788 0.025087260
Depends -0.33328205 -0.094135745
AL is Simple 0.22138865 0.123760017
Cog is Complex -0.32451147 -0.062757163
AL is simpler than Cog 0.40587919 0.206266695
AL Default 0.54377656 0.207304623
Ubiquity 0.58521356 0.147214948
Recency 0.30382181 0.045291081
Conservation 0.40266654 0.099617589
No Hierarchy 0.06906825 0.200773697
MC Utility 0.24514517 0.236805665
AL = Lower 0.18201100 -0.007752171
Cog = Higher 0.37987538 0.200773697
MC for Controls 0.09104463 -0.122149189
MC Day-to-Day 0.70113953 0.225119375
AC Day-to-Day 0.36984673 0.126019416
AL as Null 0.50869365 0.329475107
AC for Controls 0.13553483 -0.174262428
Eliminative AL -0.15238786 -0.357787103
Eliminative Cog 0.40450357 0.273359241
Existential Threat 0.10524696 0.141203617
Flexible AL 0.14635714 -0.017454107
Automatic AL 1.00000000 0.275208366
Explicit AL 0.27520837 1.000000000
Representational AL 0.28065857 0.266717944
Symbolic AL 0.23807596 0.182521381
Deterministic AL 0.59168476 0.124522103
Intentional AL -0.01820893 0.399020684
Intelligent AL 0.27751514 0.017118420
Propositional AL 0.34534126 0.262550220
Information-Theoretic AL 0.22623241 0.326870520
Computational AL 0.56096819 0.225785343
Neural Network AL 0.60002721 0.161856070
Conditioning AL 0.35056976 0.064319821
AL Well Defined 0.34448792 -0.040542021
Power Argument 0.57885829 -0.047067872
Flexible Cog 0.18614520 0.183142659
Automatic Cog 0.25434683 -0.070601809
Explicit Cog 0.12746248 -0.171008865
Representational Cog 0.34282781 0.082874193
Symbolic Cog 0.32935324 -0.007752171
Deterministic Cog 0.14382349 -0.096479732
Intentional Cog -0.00934947 0.000000000
Intelligent Cog 0.09349470 0.275959863
Propositional Cog 0.24960235 0.263117406
Information-Theoretic Cog 0.02898585 0.120986732
Computational Cog 0.24308622 -0.317771964
Neural Network Cog 0.66240626 0.216173008
Conditioning Cog 0.16403710 0.247106089
Cog Well Defined 0.20561696 -0.168583613
Publications 0.09265616 -0.505532577
Years 0.23773324 0.351310124
Representational AL Symbolic AL
Exclusivity -0.06324555 -0.043795667
Exhaustivity -0.06822423 -0.066140553
Depends -0.25600000 -0.271448357
AL is Simple -0.09255470 -0.228398364
Cog is Complex -0.08000000 -0.015511335
AL is simpler than Cog 0.28607815 0.318126293
AL Default 0.33337741 0.394140895
Ubiquity 0.30026076 0.339605371
Recency 0.23094011 0.305978670
Conservation 0.15629293 0.318190949
No Hierarchy 0.11024989 0.053441343
MC Utility 0.42932505 0.346843988
AL = Lower -0.13439680 -0.145620593
Cog = Higher -0.01574998 -0.106882686
MC for Controls 0.34879102 0.362291128
MC Day-to-Day 0.41815923 0.310002578
AC Day-to-Day 0.04016097 0.210245690
AL as Null 0.16800000 0.178380349
AC for Controls -0.16998683 -0.097378960
Eliminative AL 0.02606233 -0.050532699
Eliminative Cog 0.37716323 0.413336771
Existential Threat -0.12000000 0.131846345
Flexible AL 0.37379509 0.431403334
Automatic AL 0.28065857 0.238075958
Explicit AL 0.26671794 0.182521381
Representational AL 1.00000000 0.775566734
Symbolic AL 0.77556673 1.000000000
Deterministic AL 0.04762055 -0.107720995
Intentional AL 0.34048647 0.394494784
Intelligent AL 0.25313275 0.372333580
Propositional AL 0.18899982 0.206130895
Information-Theoretic AL 0.06349406 0.084637924
Computational AL 0.28995297 0.297632490
Neural Network AL 0.21718612 0.227397147
Conditioning AL 0.35256563 0.190770817
AL Well Defined 0.15711066 0.200410863
Power Argument 0.46400000 0.504118377
Flexible Cog 0.04244764 -0.057611705
Automatic Cog 0.09600000 0.248181355
Explicit Cog 0.15778641 0.241527419
Representational Cog 0.10141851 -0.049160514
Symbolic Cog 0.26088791 0.229927252
Deterministic Cog 0.26237442 0.214617170
Intentional Cog 0.23878480 0.223224368
Intelligent Cog 0.22172875 0.124013538
Propositional Cog 0.17888544 0.220718901
Information-Theoretic Cog 0.07931748 0.008543888
Computational Cog 0.13644846 0.074408123
Neural Network Cog 0.18779421 0.292877009
Conditioning Cog 0.35437465 0.496241043
Cog Well Defined 0.01562929 0.015151950
Publications -0.27890090 -0.139288124
Years -0.07542472 -0.027420425
Deterministic AL Intentional AL
Exclusivity 0.116527507 -0.018758062
Exhaustivity 0.101527348 0.088526779
Depends -0.079367579 -0.174395508
AL is Simple 0.116865670 -0.253296846
Cog is Complex -0.198418948 0.099654576
AL is simpler than Cog 0.191993601 0.165953106
AL Default 0.080668774 0.430475557
Ubiquity 0.388908179 0.034632359
Recency -0.099282935 0.159821100
Conservation 0.387643491 0.210915601
No Hierarchy -0.148442030 0.049048689
MC Utility 0.008873565 0.232119173
AL = Lower 0.243139124 -0.139513085
Cog = Higher 0.273445845 -0.147146066
MC for Controls 0.041194492 -0.043103448
MC Day-to-Day 0.366047270 -0.144692642
AC Day-to-Day 0.318747868 0.141745436
AL as Null 0.230165979 0.215918248
AC for Controls 0.076655927 -0.224582875
Eliminative AL 0.206850440 -0.351708351
Eliminative Cog 0.366047270 0.127669978
Existential Threat -0.047620547 0.290659179
Flexible AL -0.052977164 0.471173107
Automatic AL 0.591684759 -0.018208926
Explicit AL 0.124522103 0.399020684
Representational AL 0.047620547 0.340486467
Symbolic AL -0.107720995 0.394494784
Deterministic AL 1.000000000 -0.098866780
Intentional AL -0.098866780 1.000000000
Intelligent AL 0.103916554 0.244647127
Propositional AL 0.335947752 0.008174781
Information-Theoretic AL 0.094488189 0.280122544
Computational AL 0.152291022 0.070821423
Neural Network AL 0.232706745 0.234472234
Conditioning AL 0.593810016 0.255339956
AL Well Defined 0.016407228 -0.111589011
Power Argument 0.238102737 0.149481864
Flexible Cog 0.303206949 0.017625422
Automatic Cog 0.261913011 0.083045480
Explicit Cog 0.403706019 0.094827586
Representational Cog 0.519853315 -0.122825921
Symbolic Cog 0.509807842 0.205166301
Deterministic Cog 0.504331795 0.093624651
Intentional Cog 0.160751635 0.327549082
Intelligent Cog 0.304582045 -0.053116067
Propositional Cog 0.419477623 0.202576733
Information-Theoretic Cog 0.166124286 0.256159475
Computational Cog 0.203054696 0.026558034
Neural Network Cog 0.372619302 0.042378969
Conditioning Cog -0.039063692 0.482312105
Cog Well Defined 0.248091834 -0.227139878
Publications 0.109001502 -0.263198402
Years 0.187071178 0.215314080
Intelligent AL Propositional AL
Exclusivity 0.414039336 0.133408459
Exhaustivity -0.018609684 -0.041973850
Depends -0.130930734 0.094499908
AL is Simple -0.192790408 0.000000000
Cog is Complex -0.061101009 -0.062999938
AL is simpler than Cog 0.321317347 -0.024847756
AL Default 0.097590007 -0.024012297
Ubiquity 0.054601891 0.238097164
Recency 0.050395263 0.037888574
Conservation 0.434850405 0.246161129
No Hierarchy 0.034369284 -0.062015504
MC Utility 0.117108009 0.220112727
AL = Lower -0.034503278 -0.389108004
Cog = Higher 0.060146247 0.000000000
MC for Controls -0.018122009 -0.228893880
MC Day-to-Day 0.008946061 0.145279444
AC Day-to-Day 0.043819207 -0.086973508
AL as Null -0.043643578 -0.007874992
AC for Controls 0.168609852 -0.167330622
Eliminative AL 0.132702912 0.059861866
Eliminative Cog 0.483087310 0.314772129
Existential Threat 0.244404037 0.000000000
Flexible AL 0.446686159 -0.078847326
Automatic AL 0.277515138 0.345341258
Explicit AL 0.017118420 0.262550220
Representational AL 0.253132753 0.188999815
Symbolic AL 0.372333580 0.206130895
Deterministic AL 0.103916554 0.335947752
Intentional AL 0.244647127 0.008174781
Intelligent AL 1.000000000 0.309323557
Propositional AL 0.309323557 1.000000000
Information-Theoretic AL 0.034638851 0.125003815
Computational AL 0.195401684 0.184684940
Neural Network AL 0.274884604 -0.128275428
Conditioning AL 0.196813349 0.040355401
AL Well Defined -0.135332990 -0.162795430
Power Argument 0.314233762 0.157499846
Flexible Cog 0.296410674 0.317561067
Automatic Cog 0.218217890 -0.047249954
Explicit Cog 0.598026310 0.376039946
Representational Cog 0.258198890 0.207986977
Symbolic Cog 0.483045892 0.280157763
Deterministic Cog 0.143136981 0.209848086
Intentional Cog 0.465242105 0.050368620
Intelligent Cog 0.372193684 0.218264020
Propositional Cog 0.221795471 0.424217255
Information-Theoretic Cog 0.211548241 0.026026022
Computational Cog 0.334974316 0.235053560
Neural Network Cog 0.231626410 0.104485945
Conditioning Cog 0.274954273 0.387596899
Cog Well Defined 0.247267877 -0.192313382
Publications 0.258198890 0.174709060
Years 0.308606700 0.296984558
Information-Theoretic AL
Exclusivity -0.268909631
Exhaustivity 0.025381837
Depends -0.500015748
AL is Simple -0.125213218
Cog is Complex 0.015873516
AL is simpler than Cog 0.100170574
AL Default 0.242006321
Ubiquity 0.033098568
Recency 0.282574508
Conservation 0.279103314
No Hierarchy 0.093752861
MC Utility 0.248459823
AL = Lower -0.305884705
Cog = Higher -0.257820368
MC for Controls -0.304839239
MC Day-to-Day 0.187090827
AC Day-to-Day 0.175311327
AL as Null 0.246039495
AC for Controls -0.252964559
Eliminative AL -0.284419355
Eliminative Cog 0.146418908
Existential Threat 0.000000000
Flexible AL -0.026488582
Automatic AL 0.226232408
Explicit AL 0.326870520
Representational AL 0.063494063
Symbolic AL 0.084637924
Deterministic AL 0.094488189
Intentional AL 0.280122544
Intelligent AL 0.034638851
Propositional AL 0.125003815
Information-Theoretic AL 1.000000000
Computational AL 0.321503269
Neural Network AL 0.396463343
Conditioning AL -0.113881373
AL Well Defined -0.246108415
Power Argument 0.198418948
Flexible Cog -0.134758644
Automatic Cog 0.238102737
Explicit Cog -0.049433390
Representational Cog -0.033538924
Symbolic Cog 0.070588778
Deterministic Cog 0.040671919
Intentional Cog -0.084606124
Intelligent Cog -0.152291022
Propositional Cog 0.008066877
Information-Theoretic Cog 0.437169174
Computational Cog 0.160751635
Neural Network Cog 0.348318043
Conditioning Cog 0.328135014
Cog Well Defined -0.178316006
Publications 0.184464079
Years -0.009353559
Computational AL
Exclusivity -0.308205651
Exhaustivity -0.254545455
Depends -0.511681719
AL is Simple -0.188357853
Cog is Complex -0.102336344
AL is simpler than Cog 0.197327274
AL Default 0.494066978
Ubiquity 0.311187957
Recency 0.451335467
Conservation 0.449843831
No Hierarchy 0.050368620
MC Utility 0.333711906
AL = Lower 0.050564990
Cog = Higher 0.000000000
MC for Controls 0.070821423
MC Day-to-Day 0.463239993
AC Day-to-Day 0.308244485
AL as Null 0.486097633
AC for Controls -0.049419974
Eliminative AL -0.203738641
Eliminative Cog 0.314653580
Existential Threat -0.008528029
Flexible AL 0.218208069
Automatic AL 0.560968194
Explicit AL 0.225785343
Representational AL 0.289952974
Symbolic AL 0.297632490
Deterministic AL 0.152291022
Intentional AL 0.070821423
Intelligent AL 0.195401684
Propositional AL 0.184684940
Information-Theoretic AL 0.321503269
Computational AL 1.000000000
Neural Network AL 0.277825420
Conditioning AL 0.218509431
AL Well Defined 0.123406707
Power Argument 0.366705232
Flexible Cog -0.162897595
Automatic Cog 0.289952974
Explicit Cog -0.141642846
Representational Cog 0.297309363
Symbolic Cog 0.421374914
Deterministic Cog 0.008740377
Intentional Cog -0.318181818
Intelligent Cog 0.009090909
Propositional Cog 0.138685468
Information-Theoretic Cog 0.000000000
Computational Cog 0.218181818
Neural Network Cog 0.182781539
Conditioning Cog 0.360975110
Cog Well Defined 0.116626178
Publications 0.072074997
Years -0.070352647
Neural Network AL Conditioning AL
Exclusivity 0.019622938 0.009260053
Exhaustivity 0.388955588 0.008740377
Depends -0.503871802 -0.245976019
AL is Simple -0.045685444 0.025870722
Cog is Complex -0.130311673 -0.139386410
AL is simpler than Cog 0.292386842 0.319072240
AL Default 0.520962417 0.175006077
Ubiquity 0.317005058 0.359026818
Recency 0.217346940 -0.015779369
Conservation 0.246098755 0.328378510
No Hierarchy 0.222344075 -0.274416728
MC Utility 0.155405738 0.137504775
AL = Lower 0.145945599 0.275486583
Cog = Higher 0.179585599 0.258274567
MC for Controls 0.009018163 0.178737969
MC Day-to-Day 0.489706960 0.235294118
AC Day-to-Day 0.488454654 0.271662452
AL as Null 0.529934136 0.245976019
AC for Controls -0.067125055 -0.071271620
Eliminative AL -0.292452830 0.008903763
Eliminative Cog 0.258209124 0.159663866
Existential Threat 0.251935901 0.000000000
Flexible AL 0.193293111 0.100336401
Automatic AL 0.600027213 0.350569764
Explicit AL 0.161856070 0.064319821
Representational AL 0.217186121 0.352565627
Symbolic AL 0.227397147 0.190770817
Deterministic AL 0.232706745 0.593810016
Intentional AL 0.234472234 0.255339956
Intelligent AL 0.274884604 0.196813349
Propositional AL -0.128275428 0.040355401
Information-Theoretic AL 0.396463343 -0.113881373
Computational AL 0.277825420 0.218509431
Neural Network AL 1.000000000 0.258209124
Conditioning AL 0.258209124 1.000000000
AL Well Defined 0.233468065 0.135598090
Power Argument 0.443059688 0.196780815
Flexible Cog 0.073752299 0.226223844
Automatic Cog 0.330122904 0.229577617
Explicit Cog 0.126254280 0.297896615
Representational Cog 0.137666831 0.640987009
Symbolic Cog 0.094435388 0.704921549
Deterministic Cog 0.204786547 0.411764706
Intentional Cog 0.194477794 0.410797730
Intelligent Cog 0.037043389 0.393316975
Propositional Cog 0.105958458 0.550019098
Information-Theoretic Cog 0.258399827 0.126454982
Computational Cog 0.120391015 0.270951694
Neural Network Cog 0.700460316 0.192470304
Conditioning Cog -0.017103390 -0.032284321
Cog Well Defined 0.288529575 0.296341582
Publications 0.091777887 -0.077957879
Years 0.143335527 0.164268460
AL Well Defined Power Argument
Exclusivity -0.084049772 -0.06324555
Exhaustivity 0.105777177 -0.22172875
Depends -0.107496770 -0.34400000
AL is Simple 0.269605993 0.10938282
Cog is Complex -0.041344912 -0.32800000
AL is simpler than Cog 0.313090830 0.47960160
AL Default 0.294159195 0.55291863
Ubiquity 0.293114340 0.58384036
Recency 0.167093471 0.27712813
Conservation 0.016154794 0.38291769
No Hierarchy 0.138376116 0.00000000
MC Utility 0.120185043 0.53665631
AL = Lower 0.335031731 0.11858541
Cog = Higher 0.545364691 0.01574998
MC for Controls 0.515026203 0.40692285
MC Day-to-Day 0.372894747 0.59854165
AC Day-to-Day 0.356997095 0.40160966
AL as Null 0.223262522 0.48800000
AC for Controls 0.111810530 0.30906696
Eliminative AL -0.080815869 0.00000000
Eliminative Cog -0.008474881 0.44275683
Existential Threat -0.132303717 0.00800000
Flexible AL 0.036796488 0.39159486
Automatic AL 0.344487919 0.57885829
Explicit AL -0.040542021 -0.04706787
Representational AL 0.157110664 0.46400000
Symbolic AL 0.200410863 0.50411838
Deterministic AL 0.016407228 0.23810274
Intentional AL -0.111589011 0.14948186
Intelligent AL -0.135332990 0.31423376
Propositional AL -0.162795430 0.15749985
Information-Theoretic AL -0.246108415 0.19841895
Computational AL 0.123406707 0.36670523
Neural Network AL 0.233468065 0.44305969
Conditioning AL 0.135598090 0.19678081
AL Well Defined 1.000000000 0.28941438
Power Argument 0.289414381 1.00000000
Flexible Cog -0.131624532 -0.09338480
Automatic Cog 0.033075929 0.55200000
Explicit Cog -0.163091631 0.31557282
Representational Cog 0.061149949 0.07606388
Symbolic Cog -0.089886562 0.18183097
Deterministic Cog -0.135598090 0.20498002
Intentional Cog -0.061703353 0.07675226
Intelligent Cog -0.114591942 -0.26436889
Propositional Cog 0.025213645 -0.04878694
Information-Theoretic Cog -0.382593641 -0.12338274
Computational Cog -0.017629530 0.40081735
Neural Network Cog 0.405095747 0.41641326
Conditioning Cog -0.236053374 0.29924971
Cog Well Defined 0.258476700 0.07814647
Publications -0.218392674 0.26199782
Years -0.107195980 -0.00942809
Flexible Cog Automatic Cog
Exclusivity 0.17258299 -0.14456126
Exhaustivity 0.14479786 -0.28142495
Depends 0.00000000 -0.41600000
AL is Simple 0.07143142 -0.08414063
Cog is Complex -0.20374865 -0.29600000
AL is simpler than Cog -0.16072069 0.47118754
AL Default -0.22434644 0.35777088
Ubiquity -0.20357171 0.51711575
Recency -0.31859300 -0.01539601
Conservation -0.29854195 0.59391315
No Hierarchy 0.21727862 -0.17324983
MC Utility -0.17084844 0.15205262
AL = Lower -0.08389451 0.33994485
Cog = Higher 0.07521183 -0.14174986
MC for Controls -0.43182283 0.30726828
MC Day-to-Day -0.04350459 0.35256563
AC Day-to-Day -0.07671321 0.67470424
AL as Null 0.01697905 0.42400000
AC for Controls -0.18038848 0.33997366
Eliminative AL -0.08297134 -0.02606233
Eliminative Cog -0.05220550 0.37716323
Existential Threat -0.12734291 0.28800000
Flexible AL -0.09444475 0.27589638
Automatic AL 0.18614520 0.25434683
Explicit AL 0.18314266 -0.07060181
Representational AL 0.04244764 0.09600000
Symbolic AL -0.05761171 0.24818135
Deterministic AL 0.30320695 0.26191301
Intentional AL 0.01762542 0.08304548
Intelligent AL 0.29641067 0.21821789
Propositional AL 0.31756107 -0.04724995
Information-Theoretic AL -0.13475864 0.23810274
Computational AL -0.16289760 0.28995297
Neural Network AL 0.07375230 0.33012290
Conditioning AL 0.22622384 0.22957762
AL Well Defined -0.13162453 0.03307593
Power Argument -0.09338480 0.55200000
Flexible Cog 1.00000000 -0.30562298
Automatic Cog -0.30562298 1.00000000
Explicit Cog 0.40538469 0.39031376
Representational Cog 0.40359150 0.24509473
Symbolic Cog 0.13423121 0.30041638
Deterministic Cog -0.13921467 0.39356163
Intentional Cog 0.41629385 0.06822423
Intelligent Cog 0.54299198 -0.05116817
Propositional Cog 0.40554933 -0.06504925
Information-Theoretic Cog 0.03740933 0.13219579
Computational Cog 0.24434639 0.17056057
Neural Network Cog 0.10397505 0.32659863
Conditioning Cog 0.10863931 0.15749985
Cog Well Defined 0.23219929 0.08596111
Publications -0.08071830 0.35496479
Years 0.00000000 0.06599663
Explicit Cog Representational Cog
Exclusivity 0.393919299 -0.05727027
Exhaustivity 0.026558034 -0.17117812
Depends -0.091350028 -0.41412558
AL is Simple -0.139749984 0.09777874
Cog is Complex -0.199309152 -0.30425553
AL is simpler than Cog 0.148484358 0.25778032
AL Default -0.059084880 -0.04295051
Ubiquity 0.164503706 0.20266087
Recency -0.279686926 -0.13825251
Conservation 0.332597679 0.21464891
No Hierarchy -0.073573033 -0.16638958
MC Utility -0.009284767 -0.03779645
AL = Lower 0.016413304 0.26726124
Cog = Higher -0.114446940 0.16638958
MC for Controls -0.008620690 -0.13159920
MC Day-to-Day 0.042556659 0.31183152
AC Day-to-Day 0.208449171 0.22910993
AL as Null 0.008304548 0.23664319
AC for Controls 0.096249804 -0.09795347
Eliminative AL 0.279563048 -0.05506673
Eliminative Cog 0.348964607 0.01732397
Existential Threat 0.083045480 -0.14367622
Flexible AL 0.230967209 -0.14103327
Automatic AL 0.127462482 0.34282781
Explicit AL -0.171008865 0.08287419
Representational AL 0.157786412 0.10141851
Symbolic AL 0.241527419 -0.04916051
Deterministic AL 0.403706019 0.51985331
Intentional AL 0.094827586 -0.12282592
Intelligent AL 0.598026310 0.25819889
Propositional AL 0.376039946 0.20798698
Information-Theoretic AL -0.049433390 -0.03353892
Computational AL -0.141642846 0.29730936
Neural Network AL 0.126254280 0.13766683
Conditioning AL 0.297896615 0.64098701
AL Well Defined -0.163091631 0.06114995
Power Argument 0.315572823 0.07606388
Flexible Cog 0.405384695 0.40359150
Automatic Cog 0.390313755 0.24509473
Explicit Cog 1.000000000 0.38602432
Representational Cog 0.386024323 1.00000000
Symbolic Cog 0.418539255 0.63474545
Deterministic Cog 0.468123253 0.16457775
Intentional Cog 0.495749962 0.19820624
Intelligent Cog 0.354107116 0.54056248
Propositional Cog 0.236339521 0.34360407
Information-Theoretic Cog 0.210416712 0.20483070
Computational Cog 0.398370505 0.37839373
Neural Network Cog 0.152564288 0.13801311
Conditioning Cog 0.204369536 -0.01663896
Cog Well Defined 0.210915601 0.39627491
Publications 0.359704483 0.12500000
Years 0.107657040 0.02988072
Symbolic Cog Deterministic Cog
Exclusivity 0.133928571 0.231501330
Exhaustivity -0.109557478 0.218509431
Depends -0.276699295 0.032796802
AL is Simple -0.216186784 -0.344942962
Cog is Complex -0.015811388 0.057394404
AL is simpler than Cog 0.299335547 0.060365018
AL Default 0.088388348 0.108337095
Ubiquity 0.230783166 0.307737272
Recency 0.174966928 -0.110455583
Conservation 0.617802063 0.456526221
No Hierarchy -0.365761524 -0.064568642
MC Utility 0.114904852 0.009166985
AL = Lower 0.093750000 0.016205093
Cog = Higher 0.077821601 -0.096852963
MC for Controls 0.049239912 0.238317292
MC Day-to-Day 0.121538198 0.201680672
AC Day-to-Day 0.111125111 0.246965866
AL as Null 0.079056942 0.049195204
AC for Controls -0.045813542 -0.102947895
Eliminative AL 0.171700705 0.382861805
Eliminative Cog 0.299794222 0.478991597
Existential Threat 0.007905694 0.163984012
Flexible AL 0.140719509 0.246280256
Automatic AL 0.329353242 0.143823493
Explicit AL -0.007752171 -0.096479732
Representational AL 0.260887907 0.262374420
Symbolic AL 0.229927252 0.214617170
Deterministic AL 0.509807842 0.504331795
Intentional AL 0.205166301 0.093624651
Intelligent AL 0.483045892 0.143136981
Propositional AL 0.280157763 0.209848086
Information-Theoretic AL 0.070588778 0.040671919
Computational AL 0.421374914 0.008740377
Neural Network AL 0.094435388 0.204786547
Conditioning AL 0.704921549 0.411764706
AL Well Defined -0.089886562 -0.135598090
Power Argument 0.181830965 0.204980015
Flexible Cog 0.134231211 -0.139214673
Automatic Cog 0.300416378 0.393561630
Explicit Cog 0.418539255 0.468123253
Representational Cog 0.634745450 0.164577746
Symbolic Cog 1.000000000 0.388922234
Deterministic Cog 0.388922234 1.000000000
Intentional Cog 0.244397450 0.166067167
Intelligent Cog 0.387664921 0.192288299
Propositional Cog 0.474082956 0.216674190
Information-Theoretic Cog 0.296111531 0.352267449
Computational Cog 0.328672433 0.026221132
Neural Network Cog 0.112962014 0.133892385
Conditioning Cog 0.249029123 0.024213241
Cog Well Defined 0.293455980 0.048055392
Publications 0.150334449 0.190563705
Years 0.139754249 0.115954207
Intentional Cog Intelligent Cog
Exclusivity 0.086682839 0.096314266
Exhaustivity 0.090909091 0.081818182
Depends -0.068224229 -0.076752258
AL is Simple 0.026908265 -0.080724794
Cog is Complex -0.034112115 -0.051168172
AL is simpler than Cog 0.242174382 0.089694215
AL Default -0.078010575 -0.294706618
Ubiquity -0.088910845 -0.088910845
Recency -0.303625678 -0.328243976
Conservation 0.058313089 0.083304413
No Hierarchy 0.058763390 0.134316320
MC Utility -0.085811633 -0.276504151
AL = Lower -0.126412474 0.033709993
Cog = Higher 0.092342470 0.167895400
MC for Controls 0.017705356 -0.141642846
MC Day-to-Day -0.201028676 0.043701886
AC Day-to-Day 0.068498775 0.008562347
AL as Null -0.255840860 -0.162032544
AC for Controls 0.008236662 -0.197679894
Eliminative AL -0.009260847 0.037043389
Eliminative Cog 0.104884527 0.183547922
Existential Threat 0.179088602 0.042640143
Flexible AL 0.294106528 -0.056923844
Automatic AL -0.009349470 0.093494699
Explicit AL 0.000000000 0.275959863
Representational AL 0.238784802 0.221728745
Symbolic AL 0.223224368 0.124013538
Deterministic AL 0.160751635 0.304582045
Intentional AL 0.327549082 -0.053116067
Intelligent AL 0.465242105 0.372193684
Propositional AL 0.050368620 0.218264020
Information-Theoretic AL -0.084606124 -0.152291022
Computational AL -0.318181818 0.009090909
Neural Network AL 0.194477794 0.037043389
Conditioning AL 0.410797730 0.393316975
AL Well Defined -0.061703353 -0.114591942
Power Argument 0.076752258 -0.264368888
Flexible Cog 0.416293855 0.542991984
Automatic Cog 0.068224229 -0.051168172
Explicit Cog 0.495749962 0.354107116
Representational Cog 0.198206242 0.540562478
Symbolic Cog 0.244397450 0.387664921
Deterministic Cog 0.166067167 0.192288299
Intentional Cog 1.000000000 0.490909091
Intelligent Cog 0.490909091 1.000000000
Propositional Cog 0.416056403 0.520070503
Information-Theoretic Cog 0.187894921 0.263052889
Computational Cog 0.363636364 -0.009090909
Neural Network Cog 0.339451429 0.182781539
Conditioning Cog -0.075552930 -0.067158160
Cog Well Defined 0.049982648 0.283235005
Publications -0.027028124 -0.207215616
Years 0.251259454 0.140705294
Propositional Cog
Exclusivity 0.082648845
Exhaustivity 0.242699568
Depends -0.032524625
AL is Simple -0.119728111
Cog is Complex 0.170754282
AL is simpler than Cog -0.008552008
AL Default -0.066115702
Ubiquity 0.152591696
Recency 0.031296835
Conservation 0.285939509
No Hierarchy -0.128065586
MC Utility 0.072727273
AL = Lower -0.136600174
Cog = Higher 0.248127074
MC for Controls -0.059084880
MC Day-to-Day -0.050001736
AC Day-to-Day 0.163277547
AL as Null -0.008131156
AC for Controls -0.251307178
Eliminative AL -0.017659743
Eliminative Cog 0.208340568
Existential Threat 0.073180407
Flexible AL -0.144732682
Automatic AL 0.249602354
Explicit AL 0.263117406
Representational AL 0.178885438
Symbolic AL 0.220718901
Deterministic AL 0.419477623
Intentional AL 0.202576733
Intelligent AL 0.221795471
Propositional AL 0.424217255
Information-Theoretic AL 0.008066877
Computational AL 0.138685468
Neural Network AL 0.105958458
Conditioning AL 0.550019098
AL Well Defined 0.025213645
Power Argument -0.048786938
Flexible Cog 0.405549325
Automatic Cog -0.065049250
Explicit Cog 0.236339521
Representational Cog 0.343604066
Symbolic Cog 0.474082956
Deterministic Cog 0.216674190
Intentional Cog 0.416056403
Intelligent Cog 0.520070503
Propositional Cog 1.000000000
Information-Theoretic Cog -0.089575389
Computational Cog 0.112681942
Neural Network Cog 0.282160105
Conditioning Cog 0.096049190
Cog Well Defined 0.230340160
Publications -0.146031728
Years 0.249149149
Information-Theoretic Cog
Exclusivity -0.099533288
Exhaustivity 0.084552714
Depends -0.352522119
AL is Simple -0.278075944
Cog is Complex -0.158634954
AL is simpler than Cog 0.157576368
AL Default 0.089575389
Ubiquity -0.082694186
Recency -0.067842914
Conservation 0.172177237
No Hierarchy 0.034701362
MC Utility -0.305452076
AL = Lower -0.156764928
Cog = Higher -0.347013622
MC for Controls -0.402536318
MC Day-to-Day 0.090324987
AC Day-to-Day -0.079636663
AL as Null -0.008813053
AC for Controls -0.383037683
Eliminative AL -0.162696188
Eliminative Cog -0.054194992
Existential Threat 0.158634954
Flexible AL 0.127457106
Automatic AL 0.028985845
Explicit AL 0.120986732
Representational AL 0.079317477
Symbolic AL 0.008543888
Deterministic AL 0.166124286
Intentional AL 0.256159475
Intelligent AL 0.211548241
Propositional AL 0.026026022
Information-Theoretic AL 0.437169174
Computational AL 0.000000000
Neural Network AL 0.258399827
Conditioning AL 0.126454982
AL Well Defined -0.382593641
Power Argument -0.123382742
Flexible Cog 0.037409327
Automatic Cog 0.132195795
Explicit Cog 0.210416712
Representational Cog 0.204830704
Symbolic Cog 0.296111531
Deterministic Cog 0.352267449
Intentional Cog 0.187894921
Intelligent Cog 0.263052889
Propositional Cog -0.089575389
Information-Theoretic Cog 1.000000000
Computational Cog 0.093947460
Neural Network Cog 0.170900906
Conditioning Cog 0.225558855
Cog Well Defined -0.154959514
Publications 0.242072650
Years 0.020772565
Computational Cog
Exclusivity -0.211891385
Exhaustivity -0.272727273
Depends -0.409345375
AL is Simple 0.008969422
Cog is Complex -0.281424946
AL is simpler than Cog 0.134541323
AL Default 0.078010575
Ubiquity 0.017782169
Recency -0.032824398
Conservation 0.024991324
No Hierarchy 0.033579080
MC Utility 0.162088640
AL = Lower -0.294962440
Cog = Higher -0.209869250
MC for Controls -0.168200880
MC Day-to-Day 0.104884527
AC Day-to-Day 0.059936428
AL as Null 0.042640143
AC for Controls -0.074129960
Eliminative AL -0.009260847
Eliminative Cog -0.069923018
Existential Threat -0.307009032
Flexible AL 0.218208069
Automatic AL 0.243086217
Explicit AL -0.317771964
Representational AL 0.136448458
Symbolic AL 0.074408123
Deterministic AL 0.203054696
Intentional AL 0.026558034
Intelligent AL 0.334974316
Propositional AL 0.235053560
Information-Theoretic AL 0.160751635
Computational AL 0.218181818
Neural Network AL 0.120391015
Conditioning AL 0.270951694
AL Well Defined -0.017629530
Power Argument 0.400817347
Flexible Cog 0.244346393
Automatic Cog 0.170560573
Explicit Cog 0.398370505
Representational Cog 0.378393734
Symbolic Cog 0.328672433
Deterministic Cog 0.026221132
Intentional Cog 0.363636364
Intelligent Cog -0.009090909
Propositional Cog 0.112681942
Information-Theoretic Cog 0.093947460
Computational Cog 1.000000000
Neural Network Cog 0.147966008
Conditioning Cog 0.184684940
Cog Well Defined 0.008330441
Publications 0.558581227
Years -0.221108319
Neural Network Cog
Exclusivity -0.16598500
Exhaustivity 0.22630095
Depends -0.38375339
AL is Simple 0.16316378
Cog is Complex -0.15513435
AL is simpler than Cog 0.46372864
AL Default 0.36514837
Ubiquity 0.38306544
Recency 0.13356461
Conservation 0.35093477
No Hierarchy 0.16878499
MC Utility 0.02738613
AL = Lower 0.07261844
Cog = Higher 0.33756998
MC for Controls 0.12713691
MC Day-to-Day 0.46025507
AC Day-to-Day 0.50826502
AL as Null 0.25311394
AC for Controls 0.04731602
Eliminative AL -0.24826442
Eliminative Cog 0.28452132
Existential Threat 0.25311394
Flexible AL 0.09083405
Automatic AL 0.66240626
Explicit AL 0.21617301
Representational AL 0.18779421
Symbolic AL 0.29287701
Deterministic AL 0.37261930
Intentional AL 0.04237897
Intelligent AL 0.23162641
Propositional AL 0.10448594
Information-Theoretic AL 0.34831804
Computational AL 0.18278154
Neural Network AL 0.70046032
Conditioning AL 0.19247030
AL Well Defined 0.40509575
Power Argument 0.41641326
Flexible Cog 0.10397505
Automatic Cog 0.32659863
Explicit Cog 0.15256429
Representational Cog 0.13801311
Symbolic Cog 0.11296201
Deterministic Cog 0.13389239
Intentional Cog 0.33945143
Intelligent Cog 0.18278154
Propositional Cog 0.28216011
Information-Theoretic Cog 0.17090091
Computational Cog 0.14796601
Neural Network Cog 1.00000000
Conditioning Cog -0.03214952
Cog Well Defined 0.14356423
Publications 0.07763238
Years 0.22131760
Conditioning Cog Cog Well Defined
Exclusivity -0.186771842 0.423635700
Exhaustivity -0.176290170 0.324887212
Depends -0.299249708 -0.148478286
AL is Simple -0.356151173 -0.106848513
Cog is Complex -0.125999877 0.156292933
AL is simpler than Cog 0.016565171 -0.016438233
AL Default 0.392200858 -0.142969755
Ubiquity 0.131363953 -0.048883994
Recency 0.303108595 0.135353650
Conservation 0.215390988 0.152671756
No Hierarchy 0.007751938 -0.023077606
MC Utility 0.123263127 -0.017474081
AL = Lower -0.295722083 0.409293867
Cog = Higher -0.480620155 0.361549158
MC for Controls -0.196194755 0.219027740
MC Day-to-Day 0.096852963 0.112129247
AC Day-to-Day 0.055346778 0.219690633
AL as Null 0.275624731 0.078146467
AC for Controls -0.220572184 0.098119495
Eliminative AL -0.265102551 0.356418887
Eliminative Cog 0.112995123 0.216249263
Existential Threat 0.125999877 -0.257883340
Flexible AL 0.332910930 -0.104324164
Automatic AL 0.164037097 0.205616955
Explicit AL 0.247106089 -0.168583613
Representational AL 0.354374654 0.015629293
Symbolic AL 0.496241043 0.015151950
Deterministic AL -0.039063692 0.248091834
Intentional AL 0.482312105 -0.227139878
Intelligent AL 0.274954273 0.247267877
Propositional AL 0.387596899 -0.192313382
Information-Theoretic AL 0.328135014 -0.178316006
Computational AL 0.360975110 0.116626178
Neural Network AL -0.017103390 0.288529575
Conditioning AL -0.032284321 0.296341582
AL Well Defined -0.236053374 0.258476700
Power Argument 0.299249708 0.078146467
Flexible Cog 0.108639312 0.232199294
Automatic Cog 0.157499846 0.085961113
Explicit Cog 0.204369536 0.210915601
Representational Cog -0.016638958 0.396274912
Symbolic Cog 0.249029123 0.293455980
Deterministic Cog 0.024213241 0.048055392
Intentional Cog -0.075552930 0.049982648
Intelligent Cog -0.067158160 0.283235005
Propositional Cog 0.096049190 0.230340160
Information-Theoretic Cog 0.225558855 -0.154959514
Computational Cog 0.184684940 0.008330441
Neural Network Cog -0.032149521 0.143564226
Conditioning Cog 1.000000000 -0.223083523
Cog Well Defined -0.223083523 1.000000000
Publications 0.066555833 -0.082557273
Years -0.120649977 -0.221031586
Publications Years
Exclusivity -0.057270266 0.287494454
Exhaustivity -0.261271864 0.100503782
Depends -0.219740106 0.320555074
AL is Simple -0.080000790 0.337146333
Cog is Complex -0.329610159 0.028284271
AL is simpler than Cog 0.115556697 0.317314196
AL Default -0.042950508 0.143739894
Ubiquity 0.167415502 0.324372304
Recency -0.130120010 0.018144368
Conservation 0.156858819 0.377595625
No Hierarchy -0.124792186 -0.287703791
MC Utility -0.047245559 0.147572957
AL = Lower -0.133630621 0.083852549
Cog = Higher -0.349418121 0.426915302
MC for Controls -0.254425122 0.009787004
MC Day-to-Day 0.095281853 -0.048314253
AC Day-to-Day 0.059398870 -0.028398092
AL as Null 0.042257713 -0.037712362
AC for Controls 0.057139525 0.291391128
Eliminative AL 0.045888944 -0.122859023
Eliminative Cog -0.060633906 0.289885518
Existential Threat 0.033806170 0.603397787
Flexible AL 0.009402218 -0.020977226
Automatic AL 0.092656165 0.237733241
Explicit AL -0.505532577 0.351310124
Representational AL -0.278900904 -0.075424723
Symbolic AL -0.139288124 -0.027420425
Deterministic AL 0.109001502 0.187071178
Intentional AL -0.263198402 0.215314080
Intelligent AL 0.258198890 0.308606700
Propositional AL 0.174709060 0.296984558
Information-Theoretic AL 0.184464079 -0.009353559
Computational AL 0.072074997 -0.070352647
Neural Network AL 0.091777887 0.143335527
Conditioning AL -0.077957879 0.164268460
AL Well Defined -0.218392674 -0.107195980
Power Argument 0.261997819 -0.009428090
Flexible Cog -0.080718300 0.000000000
Automatic Cog 0.354964787 0.065996633
Explicit Cog 0.359704483 0.107657040
Representational Cog 0.125000000 0.029880715
Symbolic Cog 0.150334449 0.139754249
Deterministic Cog 0.190563705 0.115954207
Intentional Cog -0.027028124 0.251259454
Intelligent Cog -0.207215616 0.140705294
Propositional Cog -0.146031728 0.249149149
Information-Theoretic Cog 0.242072650 0.020772565
Computational Cog 0.558581227 -0.221108319
Neural Network Cog 0.077632375 0.221317603
Conditioning Cog 0.066555833 -0.120649977
Cog Well Defined -0.082557273 -0.221031586
Publications 1.000000000 -0.109562623
Years -0.109562623 1.000000000Now we look closer at the properties.
Removing all missing and non-ordinal data for the associative property questions gave us a sample of 51.
