Knowledge Representations: Individual Differences in Novel Problem Solving
<p>Figural analogy example. <span class="html-italic">Note.</span> An example figural analogy item used in the current study. The answer to this item is B.</p> "> Figure 2
<p>Diagram of figural analogy procedure.</p> "> Figure 3
<p>RSJT comparison stimuli example. <span class="html-italic">Note.</span> The same version comparison example includes two swap rules, the different version comparison includes two slightly different swap rules, and the different rule comparison includes a swap rule and a size change rule.</p> "> Figure 4
<p>Numeracy A:B items. <span class="html-italic">Note.</span> Examples of the RSJT items used for the numeracy rule.</p> "> Figure 5
<p>Example of paper folding item. <span class="html-italic">Note.</span> A paperfolding example from <a href="#B17-jintelligence-11-00077" class="html-bibr">Ekstrom et al.</a> (<a href="#B17-jintelligence-11-00077" class="html-bibr">1976</a>).</p> "> Figure 6
<p>Interaction between RSJT different-version scores and training. <span class="html-italic">Note.</span> The plot was generated using the predict function in R to generate log odds ratio accuracy data based upon Model 1 (<a href="#jintelligence-11-00077-t003" class="html-table">Table 3</a>). The data points represent item level data for all participants and the linear slopes were generated with the geom_smooth function in ggplot. The circles represent trained-rule items and the squares represent novel-rule items.</p> "> Figure 7
<p>Interaction between WMC, training, and trial. <span class="html-italic">Note.</span> The plot was generated using the predict function in R to generate log odds ratio accuracy data based upon the model in <a href="#jintelligence-11-00077-t004" class="html-table">Table 4</a>. The data points represent item-level data for all participants and the linear slopes were generated with the geom_smooth function in ggplot. The model was analyzed with WMC as a continuous predictor but a tertiary split was used to plot the data. The circles represent trained-rule items and the squares represent novel-rule items.</p> "> Figure 8
<p>Interaction between RSJT different version scores, training, and item type. <span class="html-italic">Note.</span> The plot was generated using the predict function in R to generate log odds ratio accuracy data based upon the different-version model in <a href="#jintelligence-11-00077-t007" class="html-table">Table 7</a>. The data points represent item-level data for all participants and the linear slopes were generated with the geom_smooth function in ggplot. The circles represent trained-rule items and the squares represent novel-rule items.</p> "> Figure 9
<p>Interaction between different rule–different version and the training manipulation. <span class="html-italic">Note.</span> The plot was generated using the predict function in R to generate log odds ratio accuracy data based upon the model in <a href="#jintelligence-11-00077-t009" class="html-table">Table 9</a>. The data points represent item-level data for all participants and the linear slopes were generated with the geom_smooth function in ggplot. The circles represent trained-rule items and the squares represent novel-rule items.</p> "> Figure 10
<p>Interaction between different rule–different version, training, and item type. <span class="html-italic">Note.</span> The plot was generated using the predict function in R to generate log odds ratio accuracy data based upon the different rule–different version model in <a href="#jintelligence-11-00077-t010" class="html-table">Table 10</a>. The data points represent item-level data for all participants and the linear slopes were generated with the geom_smooth function in ggplot. The circles represent trained-rule items and the squares represent novel-rule items.</p> ">
Abstract
:1. Knowledge Representations: Individual Differences in Novel Problem Solving
2. Working Memory Capacity and Problem Solving
3. Individual Differences in Knowledge
3.1. Expertise and Representation
3.2. Analogical Transfer
3.3. Learning on Novel Tasks
4. Summary
5. The Present Study
6. Method
6.1. Participants
6.2. Materials
6.2.1. Modified Figural Analogies Task
Figural Analogies Training
Rule-Similarity Judgement Task (RSJT)
Figural Analogies Test
6.2.2. Working Memory Capacity
Automated Operation Span
Automated Symmetry Span
6.2.3. Gf Measures
Paper Folding Task
Letter Series
6.3. Procedure
7. Results
7.1. Summary Statistics and Correlations
7.2. Predicting Figural Analogies Accuracy with Training, RSJT Scores, WMC, and Gf
7.2.1. Interim Discussion: Task Learning
7.2.2. Task Learning Post Hoc Analysis
7.3. Predicting RSJT Different-Version Scores
7.3.1. Interim Discussion: Further Investigating the RSJT Scores
7.3.2. Further Investigating the RSJT Scores Post Hoc Analyses
7.3.3. Interim Discussion: Measuring Different Version Relative to Same Version and Different Rule
7.3.4. Relative Rule Post Hoc Analysis
8. Discussion
8.1. Knowledge Representations and Transfer
8.2. The Rule-Similarity Judgement Task
8.3. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Rule | Version | Description | Set |
---|---|---|---|
Rotation | 1 | Object rotates 45 degrees clockwise | A |
Rotation | 2 | Object rotates 90 degrees clockwise | A |
Color Change | 1 | Object fill changes from either dark fill or white fill | A |
Color Change | 2 | Object fill changes from one pattern to another pattern | A |
Shift | 1 | Object moves diagonally to another location | A |
Shift | 2 | Object moves vertically to another location | A |
Merge | 1 | Two objects combine, such as a puzzle piece or slice of pie, creating a whole shape | A |
Merge | 2 | Two objects combine, such as a puzzle piece or slice of pie, creating a shape where the added piece juts out | A |
Pattern | 1 | The number of notches in the object determines the number of sides | A |
Pattern | 2 | The color of the object (e.g., black or white) determines the type of object | A |
Duplicate | 1 | The object is duplicated, resulting in 2 of the same object | A |
Duplicate | 2 | The object is duplicated, resulting in 3 of the same object | A |
Count | 1 | The number of sides increases by 2 | A |
Count | 2 | The number of sides decreases by 1 | A |
Sharpness | 1 | The corners of the object go from sharp to round | B |
Sharpness | 2 | The edges of the object go from straight to curved | B |
Location | 1 | Three objects in a straight line move to create a diagonal line | B |
Location | 2 | Three objects in a straight line move to make a triangle shape | B |
Size | 1 | Object increases in size | B |
Size | 2 | Object decreases in size | B |
Invert | 1 | Notches in an object change to instead stick out of the object | B |
Invert | 2 | Parts of an object that jut out change to become notches | B |
Reflect | 1 | Object reflects over an imaginary x axis | B |
Reflect | 2 | Object reflects over an imaginary y axis | B |
Swap | 1 | Two objects swap locations in within a grid-like design | B |
Swap | 2 | Two objects swap locations but one object was inside the other object | B |
Divide | 1 | A fourth of an object is removed | B |
Divide | 2 | A third of an object is removed | B |
Predictors | β | SE | Odds Ratio | z | p | |
---|---|---|---|---|---|---|
Trained Items Only | ||||||
(Intercept) | −0.49 | 0.14 | 0.61 | −3.48 | 0.001 | |
RSJT DV | 0.04 | 0.06 | 1.04 | 0.59 | 0.553 | |
Item Type | 0.02 | 0.14 | 1.02 | 0.18 | 0.855 | |
RSJT DV × Item Type | 0.05 | 0.05 | 1.05 | 1.01 | 0.314 | |
Novel Items Only | ||||||
(Intercept) | −0.63 | 0.14 | 0.53 | −4.44 | 0.000 | |
RSJT DV | 0.18 | 0.06 | 1.19 | 2.79 | 0.005 | |
Item Type | −0.05 | 0.14 | 0.95 | −0.40 | 0.690 | |
RSJT DV × Item Type | −0.11 | 0.05 | 0.90 | −2.33 | 0.020 | |
Distinct-Rule Items Only | ||||||
(Intercept) | −0.57 | 0.18 | 0.56 | −3.27 | 0.001 | |
RSJT DV | 0.06 | 0.06 | 1.06 | 0.96 | 0.339 | |
Training | 0.11 | 0.06 | 1.11 | 1.72 | 0.086 | |
RSJT DV × Training | 0.01 | 0.05 | 1.01 | 0.14 | 0.892 | |
Paired-Rule Items Only | ||||||
(Intercept) | −0.54 | 0.19 | 0.58 | −2.80 | 0.005 | |
RSJT DV | 0.14 | 0.06 | 1.14 | 2.30 | 0.021 | |
Training | 0.03 | 0.05 | 1.03 | 0.63 | 0.530 | |
RSJT DV × Training | −0.14 | 0.05 | 0.87 | −3.20 | 0.001 |
Predictors | β | SE | Odds Ratio | z | p | |
---|---|---|---|---|---|---|
Trained Items Only | ||||||
(Intercept) | −0.52 | 0.14 | 0.60 | −3.72 | <.001 | |
WMC | 0.39 | 0.08 | 1.47 | 5.13 | <.001 | |
Trial | 0.10 | 0.04 | 1.11 | 2.57 | 0.010 | |
WMC × Trial | −0.11 | 0.06 | 0.90 | −1.81 | 0.070 | |
Trained Items Only | ||||||
(Intercept) | −0.64 | 0.14 | 0.52 | −4.60 | <.001 | |
WMC | 0.38 | 0.07 | 1.46 | 5.10 | <.001 | |
Trial | 0.09 | 0.04 | 1.09 | 2.33 | 0.020 | |
Novel Items Only | ||||||
(Intercept) | −0.64 | 0.14 | 0.52 | −4.60 | <.001 | |
WMC | 0.49 | 0.07 | 1.64 | 6.58 | <.001 | |
Trial | 0.08 | 0.04 | 1.08 | 1.91 | 0.056 | |
WMC × Trial | 0.14 | 0.06 | 1.15 | 2.30 | 0.021 |
Predictors | β | SE | Odds Ratio | z | p | |
---|---|---|---|---|---|---|
Trained Items Only | ||||||
(Intercept) | −0.51 | 0.14 | 0.60 | −3.75 | <.001 | |
RSJT DR | −0.11 | 0.05 | 0.90 | −2.15 | 0.031 | |
Gf | 0.48 | 0.06 | 1.61 | 8.17 | <.001 | |
RSJT DR × Gf | −0.06 | 0.06 | 0.94 | −0.97 | 0.330 | |
Novel Items Only | ||||||
(Intercept) | −0.62 | 0.14 | 0.54 | −4.45 | <.001 | |
RSJT DR | −0.07 | 0.05 | 0.93 | −1.36 | 0.175 | |
Gf | 0.43 | 0.06 | 1.53 | 6.86 | <.001 | |
RSJT DR × Gf | 0.10 | 0.06 | 1.10 | 1.55 | 0.122 | |
High Gf Only | ||||||
(Intercept) | −0.29 | 0.16 | 0.75 | −1.83 | 0.067 | |
RSJT DR | −0.12 | 0.07 | 0.89 | −1.81 | 0.070 | |
Training | 0.11 | 0.05 | 1.11 | 2.24 | 0.025 | |
RSJT DR × Training | −0.08 | 0.04 | 0.92 | −1.96 | 0.050 | |
Low Gf Only | ||||||
(Intercept) | −0.83 | 0.12 | 0.44 | −7.14 | <.001 | |
RSJT DR | −0.12 | 0.06 | 0.89 | −1.98 | 0.048 | |
Training | 0.00 | 0.05 | 1.00 | −0.02 | 0.987 | |
RSJT DR × Training | 0.05 | 0.04 | 1.05 | 1.12 | 0.263 | |
Low Gf Only | ||||||
(Intercept) | −0.83 | 0.12 | 0.44 | −7.14 | <.001 | |
RSJT DR | −0.12 | 0.06 | 0.89 | −2.06 | 0.040 | |
Training | 0.00 | 0.05 | 1.00 | −0.06 | 0.952 |
Predictors | β | SE | Odds Ratio | z | p | |
---|---|---|---|---|---|---|
Trained Items Only | ||||||
(Intercept) | −0.49 | 0.14 | 0.61 | −3.56 | <.001 | |
DR-DV | −0.18 | 0.05 | 0.84 | −3.30 | 0.001 | |
Item Type | 0.02 | 0.13 | 1.02 | 0.14 | 0.886 | |
DR-DV × Item Type | −0.07 | 0.04 | 0.93 | −1.71 | 0.087 | |
Novel Items Only | ||||||
(Intercept) | −0.62 | 0.14 | 0.54 | −4.45 | <.001 | |
DR-DV | −0.22 | 0.05 | 0.80 | −4.07 | <.001 | |
Item Type | −0.06 | 0.13 | 0.94 | −0.45 | 0.653 | |
DR-DV × Item Type | 0.06 | 0.04 | 1.06 | 1.46 | 0.143 | |
Distinct-Rule Items Only | ||||||
(Intercept) | −0.58 | 0.17 | 0.56 | −3.35 | 0.001 | |
DR-DV | −0.20 | 0.05 | 0.82 | −3.67 | <.001 | |
Training | 0.10 | 0.06 | 1.11 | 1.66 | 0.098 | |
DR-DV × Training | −0.04 | 0.04 | 0.96 | −1.05 | 0.293 | |
Paired-Rule Items Only | ||||||
(Intercept) | −0.53 | 0.19 | 0.59 | −2.80 | 0.005 | |
DR-DV | −0.19 | 0.05 | 0.83 | −3.76 | <.001 | |
Training | 0.03 | 0.05 | 1.03 | 0.48 | 0.630 | |
DR-DV × Training | 0.08 | 0.04 | 1.09 | 2.20 | 0.028 |
1 | To address concerns that composite variables for WMC and Gf may not be appropriate, several models were checked with each of the individual indicators for these variables, rather than the composite variables. In the WMC x Novel/Learned x Trial interaction, using Operation Span instead of the composite moved the p-value to p = .07, rather than being significant, while the interaction remained significant using only Symmetry Span. In the Representation Scores x Novel/Learning + WMC + Gf analysis, using the individual Gf tasks moved the main effect of DV representation scores from non-significant to significant. When predicting SV-DV training, changes from composites to individual measures changed nothing. As these are minor differences, composites were used throughout the paper for analyses. |
2 | Only 3 participants failed to use the highest marking on the scale (100) for any of their scores; all 3 instead used 95 as their top score. A total of 29 participants put their lowest raw score as something other than 0. For 2, the lowest score was 25, for 2 others it was 20, and for 4 it was 15. All of the rest used 5 or 10 as their lowest number. Because 86% of the sample used the full scale, it is not anticipated that this caused substantial changes to the analyses. |
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n | M | SD | Min | Max | Skew | Kurtosis | Reliability | |
---|---|---|---|---|---|---|---|---|
FA Accuracy | 220 | 11.59 | 4.21 | 4.00 | 24.00 | 0.48 | 0.68 | 0.65 |
RSJT SV A | 115 | 0.69 | 0.15 | 0.26 | 0.96 | −0.50 | 1.31 | 0.70 |
RSJT SV B | 105 | 0.77 | 0.15 | 0.26 | 1.00 | −1.18 | 2.51 | 0.76 |
RSJT DV A | 115 | 0.35 | 0.21 | −0.19 | 0.75 | −0.55 | 0.83 | 0.68 |
RSJT DV B | 105 | 0.20 | 0.23 | −0.40 | 0.63 | −0.25 | 0.47 | 0.71 |
RSJT DR A | 115 | −0.68 | 0.25 | −1.00 | −0.09 | 0.46 | 0.13 | 0.89 |
RSJT DR B | 105 | −0.68 | 0.26 | −1.00 | 0.01 | 0.50 | 0.24 | 0.88 |
Operation Span | 220 | 41.07 | 6.66 | 22.00 | 50.00 | −0.58 | 0.56 | 0.67 |
Symmetry Span | 220 | 19.70 | 4.90 | 8.00 | 28.00 | −0.30 | 0.29 | 0.61 |
Paper Folding | 220 | 11.10 | 3.36 | 4.00 | 19.00 | −0.34 | 0.42 | 0.66 |
Letter Series | 220 | 8.85 | 3.20 | 2.00 | 15.00 | −0.07 | 0.23 | 0.76 |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
---|---|---|---|---|---|---|---|---|---|---|
1. FA Accuracy | - | |||||||||
2. RSJT SV | 0.29 * | - | ||||||||
3. RSJT DV | 0.11 | 0.45 * | - | |||||||
4. RSJT DR | −0.17 * | −0.14 * | 0.33 * | - | ||||||
5. Operation Span | 0.27 * | 0.05 | −0.11 | −0.10 | - | |||||
6. Symmetry Span | 0.37 * | 0.09 | 0.04 | −0.09 | 0.14 * | - | ||||
7. Paper Folding | 0.45 * | 0.15 * | 0.07 | −0.09 | 0.14 * | 0.30 * | - | |||
8. Letter Series | 0.42 * | 0.13 | 0.03 | −0.08 | 0.17 * | 0.30 * | 0.32 * | - | ||
9. WMC Composite | 0.43 * | 0.10 | −0.05 | −0.13 | 0.74 * | 0.77 * | 0.29 * | 0.31 * | - | |
10. Gf Composite | 0.54 * | 0.17 * | 0.06 | −0.10 | 0.19 * | 0.37 * | 0.81 * | 0.82 * | 0.37 * | - |
Predictors | β | SE | Odds Ratio | z | p | |
---|---|---|---|---|---|---|
Model 1 | ||||||
Intercept | −0.56 | 0.13 | 0.57 | −4.14 | <.001 | |
Training | 0.07 | 0.04 | 1.07 | 1.65 | 0.098 | |
RSJT DV | 0.10 | 0.05 | 1.10 | 1.81 | 0.071 | |
Training × RSJT DV | −0.07 | 0.03 | 0.93 | −2.28 | 0.022 | |
Model 2 | ||||||
Intercept | −0.59 | 0.13 | 0.55 | −4.43 | <.001 | |
Training | 0.07 | 0.04 | 1.08 | 1.75 | 0.080 | |
RSJT DV | 0.11 | 0.05 | 1.12 | 2.33 | 0.020 | |
WMC | 0.44 | 0.06 | 1.56 | 7.21 | <.001 | |
Training × RSJT DV | −0.08 | 0.03 | 0.93 | −2.34 | 0.019 | |
Training × WMC | −0.06 | 0.04 | 0.94 | −1.38 | 0.168 | |
Model 3 | ||||||
(Intercept) | −0.59 | 0.13 | 0.55 | −4.47 | <.001 | |
Training | 0.07 | 0.04 | 1.08 | 1.75 | 0.080 | |
RSJT DV | 0.08 | 0.04 | 1.09 | 1.93 | 0.053 | |
WMC | 0.28 | 0.06 | 1.32 | 4.74 | <.001 | |
Gf | 0.37 | 0.05 | 1.44 | 7.33 | <.001 | |
Training × RSJT DV | −0.08 | 0.03 | 0.92 | −2.50 | 0.012 | |
Training × WMC | −0.09 | 0.04 | 0.92 | −2.00 | 0.045 | |
Training × Gf | 0.07 | 0.04 | 1.07 | 1.90 | 0.058 | |
Model 4 (Final Model) | ||||||
Intercept | −0.59 | 0.13 | 0.55 | −4.47 | <.001 | |
Training | 0.07 | 0.04 | 1.07 | 1.68 | 0.094 | |
RSJT DV | 0.08 | 0.04 | 1.09 | 1.93 | 0.054 | |
WMC | 0.28 | 0.06 | 1.32 | 4.66 | <.001 | |
Gf | 0.37 | 0.05 | 1.44 | 7.29 | <.001 | |
Training × RSJT DV | −0.07 | 0.03 | 0.93 | −2.28 | 0.023 |
Predictors | β | SE | Odds Ratio | z | p |
---|---|---|---|---|---|
Intercept | −0.58 | 0.13 | 0.56 | −4.35 | <.001 |
Training | 0.07 | 0.04 | 1.07 | 1.62 | 0.105 |
WMC | 0.44 | 0.06 | 1.55 | 7.02 | <.001 |
Trial | 0.09 | 0.03 | 1.10 | 3.27 | 0.001 |
Training × WMC | −0.05 | 0.04 | 0.95 | −1.29 | 0.199 |
Training × Trial | 0.01 | 0.03 | 1.01 | 0.28 | 0.776 |
Trial × WMC | 0.02 | 0.04 | 1.02 | 0.43 | 0.666 |
Training × Trial × WMC | −0.13 | 0.04 | 0.88 | −2.99 | 0.003 |
Predictors | β | SE | df | t | p |
---|---|---|---|---|---|
Intercept | 0.00 | 0.18 | 13.36 | −0.02 | 0.983 |
WMC | −0.04 | 0.04 | 216.03 | −1.01 | 0.315 |
Gf | 0.04 | 0.03 | 216.02 | 1.40 | 0.162 |
Predictors | β | SE | Odds Ratio | z | p |
---|---|---|---|---|---|
Intercept | −0.58 | 0.14 | 0.56 | −4.19 | <.001 |
RSJT DV | 0.04 | 0.07 | 1.05 | 0.69 | 0.492 |
RSJT SV | 0.20 | 0.06 | 1.22 | 3.11 | 0.002 |
RSJT DR | −0.12 | 0.05 | 0.89 | −2.16 | 0.031 |
Predictors | β | SE | Odds Ratio | z | p | |
---|---|---|---|---|---|---|
SV Final Model | ||||||
Intercept | −0.58 | 0.13 | 0.56 | −4.30 | <.001 | |
Training | 0.06 | 0.04 | 1.06 | 1.48 | 0.140 | |
Item Type | −0.01 | 0.13 | 0.99 | −0.10 | 0.919 | |
RSJT SV | 0.24 | 0.05 | 1.27 | 4.53 | <.001 | |
DV Final Model | ||||||
Intercept | −0.56 | 0.13 | 0.57 | −4.13 | <.001 | |
Training | 0.07 | 0.04 | 1.07 | 1.72 | 0.085 | |
Item Type | −0.01 | 0.13 | 0.99 | −0.11 | 0.912 | |
RSJT DV | 0.09 | 0.05 | 1.10 | 1.76 | 0.079 | |
Training × Item Type | 0.04 | 0.04 | 1.04 | 0.88 | 0.379 | |
RSJT DV × Training | −0.07 | 0.03 | 0.93 | −2.12 | 0.034 | |
RSJT DV × Item Type | −0.04 | 0.03 | 0.96 | −1.13 | 0.260 | |
RSJT DV × Training × Item Type | 0.08 | 0.03 | 1.08 | 2.39 | 0.017 | |
DR Final Model | ||||||
Intercept | −0.55 | 0.13 | 0.57 | −4.11 | <.001 | |
Training | 0.06 | 0.04 | 1.06 | 1.52 | 0.130 | |
Item Type | −0.01 | 0.13 | 0.99 | −0.09 | 0.929 | |
RSJT DR | −0.13 | 0.05 | 0.88 | −2.63 | 0.009 |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
---|---|---|---|---|---|---|---|---|
1. FA Accuracy | - | |||||||
2. RSJT SV | 0.30 * | - | ||||||
3. RSJT DV | 0.14 * | 0.51 * | - | |||||
4. RSJT DR | −0.18 * | −0.11 | 0.30 * | - | ||||
5. SV-DV | 0.04 | −0.08 | −0.77 * | −0.65 * | - | |||
6. DR-DV | −0.28 * | −0.60 * | −0.42 * | 0.70 * | −0.07 | - | ||
7. WMC | 0.42 * | 0.11 | 0.00 | −0.12 | 0.08 | −0.12 | - | |
8. Gf | 0.53 * | 0.17 * | 0.08 | −0.10 | 0.03 | −0.16 * | 0.38 * | - |
Predictors | β | SE | Odds Ratio | z | p |
---|---|---|---|---|---|
(Intercept) | −0.58 | 0.13 | 0.56 | −4.47 | <.001 |
Training | 0.06 | 0.04 | 1.07 | 1.54 | 0.123 |
SV-DV | 0.00 | 0.04 | 1.00 | −0.12 | 0.907 |
DR-DV | −0.13 | 0.04 | 0.88 | −3.39 | 0.001 |
Gf | 0.35 | 0.05 | 1.43 | 6.99 | <.001 |
WMC | 0.26 | 0.06 | 1.29 | 4.25 | <.001 |
Training × SV-DV | 0.07 | 0.03 | 1.07 | 2.18 | 0.029 |
Predictors | β | SE | Odds Ratio | z | p | |
---|---|---|---|---|---|---|
SV-DV Model | ||||||
(Intercept) | −0.55 | 0.13 | 0.58 | −4.12 | <.001 | |
Training | 0.06 | 0.04 | 1.06 | 1.49 | 0.136 | |
Item Type | −0.02 | 0.13 | 0.98 | −0.13 | 0.895 | |
SV-DV | 0.03 | 0.05 | 1.03 | 0.57 | 0.571 | |
DR-DV Model | ||||||
(Intercept) | −0.56 | 0.13 | 0.57 | −4.18 | <.001 | |
Training | 0.06 | 0.04 | 1.07 | 1.58 | 0.115 | |
Item Type | −0.02 | 0.13 | 0.98 | −0.16 | 0.872 | |
DR-DV | −0.20 | 0.05 | 0.82 | −4.34 | <.001 | |
Training × Item Type | 0.04 | 0.04 | 1.04 | 0.94 | 0.345 | |
DR-DV × Training | 0.02 | 0.03 | 1.02 | 0.69 | 0.488 | |
DR-DV × Item Type | 0.00 | 0.03 | 1.00 | −0.16 | 0.873 | |
DR-DV × Training x Item Type | −0.06 | 0.03 | 0.94 | −2.25 | 0.025 |
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Raden, M.J.; Jarosz, A.F. Knowledge Representations: Individual Differences in Novel Problem Solving. J. Intell. 2023, 11, 77. https://doi.org/10.3390/jintelligence11040077
Raden MJ, Jarosz AF. Knowledge Representations: Individual Differences in Novel Problem Solving. Journal of Intelligence. 2023; 11(4):77. https://doi.org/10.3390/jintelligence11040077
Chicago/Turabian StyleRaden, Megan J., and Andrew F. Jarosz. 2023. "Knowledge Representations: Individual Differences in Novel Problem Solving" Journal of Intelligence 11, no. 4: 77. https://doi.org/10.3390/jintelligence11040077