Attentional strategy choice is not predicted by cognitive ability or academic performance

Figures & data

Figure 1. Depiction of the Adaptive Choice Visual Search (ACVS) task (Irons & Leber, 2018, Experiment 2).

Top: stimulus from a sample trial, in which the subset of blue squares contains fewer items than the subset of red squares. Searching the smaller subset – blue, in this example – is considered the “optimal” choice, as it yields substantially faster performance. Bottom: Sequence of successive trials, showing that the colour of the smaller subset varies unpredictably, in randomized run lengths of 1–6. Figure reproduced from Irons and Leber (2020).

Visual search display containing an array of 54 squares in 3 concentric circles around a fixation cross. This particular display contains 3 subsets of squares: 14 green distractor items (never a target present), and 13 blue and 27 red squares (target subsets). Two targets appear on the display: the blue target in this trial condition is considered the "optimal" target, the red target is considered the "suboptimal" target. Below the example display is an array of example trials showing that throughout the experiment, sometimes there will be fewer blue than red squares, other times there will be fewer red than blue squares on any given trial.

Table 1. ACVS descriptive statistics.

Variable	N	M	SD	Accuracy	Response Time	Proportion of optimal choices	Proportion of switches
Intro Psychology Grade	88	3.52	0.66	r(87) = 0.03, p = 0.76, pHB = 1.0	r(87) = 0.04, p = 0.71, pHB = 1.0	r(87) = 0.05, p = 0.62, pHB = 1.0	r(87) = 0.14, p = 0.19, pHB = 0.97
GPA (AU19)	92	3.45	0.43	r(91) = 0.15, p = 0.14, pHB = 0.56	r(91) = 0.02, p = 0.86, pHB=0.86	r(91)=−0.09, p = 0.39, pHB = 1.0	r(91) = 0.07, p = 0.48, pHB = 1.0
GPA (AU20)	77	3.51	0.35	r(76) = 0.2, p = 0.08, pHB = 0.4	r(76) = 0.06, p = 0.58, pHB = 1.0	r(76)=−0.04, p = 0.73, pHB = 1.0	r(76) = 0.1, p = 0.38, pHB = 1.0
ACT	69	29.0	3.25	r(68) = 0.43, p = 0.0002, pHB = 0.001	r(68)=−0.35, p = 0.003, pHB = 0.02	r(68) = 0.15, p = 0.23, pHB = 1.0	r(68) = 0.04, p = 0.72, pHB = 1.0
ICAR	93	0.56	0.18	r(92) = 0.06, p = 0.59, pHB = 1.0	r(92)=−0.36, p = 0.0004, pHB = 0.002	r(92) = 0.14, p = 0.2, pHB = 1.0	r(92)=−0.06, p = 0.56, pHB = 1.0
MAAS	93	3.67	0.79	r(92) = 0.01, p = 0.91, pHB = 0.91	r(92)=−0.15, p = 0.14, pHB = 0.56	r(92) = 0.02, p = 0.84, pHB = 0.84	r(92) = 0.01, p = 0.89, pHB = 0.89

Note: N = sample size; M = mean; SD = standard deviation. p = uncorrected p-value; p_HB = Holm-Bonferroni corrected p-value.

Figure 2. Correlation matrix: ACVS and survey metrics.

Note: Complete correlation matrix of comparisons of the ACVS and survey metrics. Pearson’s r and uncorrected p-values (denoted in italics) are shown below the diagonal. Graphical depiction of Pearson’s r coefficients, in absolute values, above the diagonal. Note that Intro Psych Grade, GPA(AU19), and GPA(AU20) are not independent, as they are calculated based on some degree of shared data.

A correlation matrix and heatmap. The top right shows the correlation heatmap, with stronger correlations depicted in darker blue colors, medium correlations shown in lighter blue and green colors, and weaker correlations shown in light green colors. The bottom left of the figure contains both R and p-values for correlations with each of the measures of interest: 1. ACVS optimal choices, 2. ACVS switch rates, 3. ACVS response times, 4. ACVS accuracy, 5. ICAR scores, 6. ACT scores, 7. MAAS scores, 8. Introduction to Psychology course grade, 9. GPA for Autumn 2019 semester, and 10. GPA for Autumn 2020 semester.

Irons, J. L., & Leber, A. B. (2018). Characterizing individual variation in the strategic use of attentional control. Journal of Experimental Psychology: Human Perception and Performance, 44(10), 1637–1654. https://doi.org/10.1037/xhp0000560

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Irons, J. L., & Leber, A. B. (2020). Developing an individual profile of attentional control strategy. Current Directions in Psychological Science, 29(4), 364–371. https://doi.org/10.1177/0963721420924018

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