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. 2020 May 5;5(1):20.
doi: 10.1186/s41235-020-00211-y.

Strengthening Spatial Reasoning: Elucidating the Attentional and Neural Mechanisms Associated With Mental Rotation Skill Development

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Free PMC article

Strengthening Spatial Reasoning: Elucidating the Attentional and Neural Mechanisms Associated With Mental Rotation Skill Development

Katherine C Moen et al. Cogn Res Princ Implic. .
Free PMC article

Abstract

Spatial reasoning is a critical skill in many everyday tasks and in science, technology, engineering, and mathematics disciplines. The current study examined how training on mental rotation (a spatial reasoning task) impacts the completeness of an encoded representation and the ability to rotate the representation. We used a multisession, multimethod design with an active control group to determine how mental rotation ability impacts performance for a trained stimulus category and an untrained stimulus category. Participants in the experimental group (n = 18) showed greater improvement than the active control group (n = 18) on the mental rotation tasks. The number of saccades between objects decreased and saccade amplitude increased after training, suggesting that participants in the experimental group encoded more of the object and possibly had more complete mental representations after training. Functional magnetic resonance imaging data revealed distinct neural activation associated with mental rotation, notably in the right motor cortex and right lateral occipital cortex. These brain areas are often associated with rotation and encoding complete representations, respectively. Furthermore, logistic regression revealed that activation in these brain regions during the post-training scan significantly predicted training group assignment. Overall, the current study suggests that effective mental rotation training protocols should aim to improve the encoding and manipulation of mental representations.

Keywords: Encoding; Eye-tracking; Mental rotation; Object representations; STEM education; Spatial reasoning; Working memory; fMRI.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The current study used a multimethod, multisession training design (top panel). Participants were matched and assigned to the experimental group or active control training group. Participants were asked to make same/different judgments for three-dimensional cube arrangements (bottom left panel), three-dimensional chemical structures (bottom center panel), and random dot arrays (bottom right panel, control task). If presented with A and B, participants responded quickly and accurately because the angular disparity (40 degrees) is small (bottom left and center panels) or there is a large log difference (0.5) between the arrays (bottom right panel). When presented with A and C, participants responded more slowly and less accurately because of the larger angular disparity (120 degrees, bottom left and center panels) or because of the smaller log differences (0.2, bottom right panel). Object D in the bottom left and center panels represents a mirror image of object A (i.e., different object). Object D in the bottom right panel contains the same number of dots as object A (i.e., log difference of zero)
Fig. 2
Fig. 2
Before training, the training groups did not differ in accuracy on the cubes task (a) or the molecules task (b). After training, both training conditions improved on the cubes task (a), but the experimental group improved significantly more than the control group. Only the experimental group improved on the molecules task (b)
Fig. 3
Fig. 3
Response time increased with angular disparity for both the cubes (a) and molecules (b) tasks. Response time decreased after training for the cubes task (c) and the molecules task (d). Training did not impact the response time slope for the cubes task (e), but the experimental group had a significant increase in slope for the molecules task after training (f)
Fig. 4
Fig. 4
The top panel represents a sample trial during the pre-training session of one participant. Blue circles represent fixations, and the numbers inside circles represent fixation order. The diameter of the circle represents the duration of the fixation. The red arrows from one circle to the next represent saccades within an object. Long black arrows represent switches between objects. This participant initially used a global strategy (fixations 1–3; long fixations, long saccades) and switched to a local strategy (fixations 4–9; short fixations, short saccades) after the first examination on the first object (left). In the current study, saccade amplitude increases after training for the experimental group for both cubes task (a) and the molecules task (b). The number of saccades between objects decreased after training for the experimental group for the cubes task (c) and the molecules task (d)
Fig. 5
Fig. 5
For the experimental training (a), accuracy (gray line) increased across training sessions and RT (black line) decreased. For the control training (b), accuracy (gray line) increased across training sessions and RT (black line) did not change
Fig. 6
Fig. 6
Rotation blocks (red/yellow) resulted in significantly greater activation in the visuospatial network than nonrotation blocks (blue), which activated the default mode network
Fig. 7
Fig. 7
Several covariate analyses were conducted using pre-training brain activation data (session 2). Activation in the posterior lateral occipital cortex (a; red) and supramarginal gyrus (b; blue) significantly predicted pre-training accuracy (during session 1). Activation in the right lateral occipital cortex (c; green) and the left motor cortex (d; yellow) significantly predicted pre-training saccade amplitude during the first observation of an object. Activation in the right motor cortex (e; pink) significantly predicted accuracy improvement (session 9 accuracy − session 1 accuracy)
Fig. 8
Fig. 8
Masks were created from rotation blocks during pre-training (session 2) for the visuospatial network (yellow and green), lateral occipital cortex (pink and red), and motor areas (blue and cyan). We used these regions to conduct logistic regression to determine which brain areas could predict training group assignment. The dashed box indicates the two brain regions that significantly predicted group assignment
Fig. 9
Fig. 9
Brain activation in the visuospatial network (a), lateral occipital cortex (b), and motor cortex (c) as a function of angular disparity. Error bars represented standard error

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