Media multitasking index
Across all 139 participants, the median MMI score was 4.34 (mean = 4.41 ± 1.91). We identified 36 HMMs (mean = 6.92 ± 1.23) and 36 LMMs (mean score = 2.19 ± 0.70).
The mean BIS-11 score was 61.38 (±10.57); HMMs did not significantly differ from LMMs across subscales, F(1, 201) = 2.40, p = .12; HMMAll scales = 62.79 ± 10.81, LMMAll scales = 59.86 ± 11.57.
The mean ADHD score was 2.41 (±1.59); HMMs scored significantly higher than LMMs, F(1, 54) = 9.30, p = .0033; HMM = 2.92 ± 1.61, LMM = 1.97 ± 1.65.
Relationship between MMI, impulsivity, and ADHD
Across all participants, MMI score positively correlated with ADHD, r136 = .30, p = .00036, and impulsivity across subscales, r
136 = .17, p = .046. The relationship between impulsivity and MMI was driven by the Attention subscale (r = .24, p = .0046), with no significant effects in the other subscales (Motor: r = .078, p = .36; Nonplanning: r = .065, p = .45). The ADHD and overall impulsivity scores significantly correlated, r136 = .56, p = 1.1 * 10-12.
Working memory and long-term memory performance
We first examined group effects (HMMs vs. LMMs) on performance and then, for effects of interest, we further tested whether performance continuously scaled with MMI score (i.e., across all participants).
Working memory: Rectangles
We analyzed WM performance following Vogel et al. (2005): K = S * (H – F), where K is WM capacity, S the size of the target array (2), H the proportion of correct changes detected (hit rate), and F the proportion of changes incorrectly reported (false alarm rate). As measured by K, LMMs were able to hold more task-relevant information in mind relative to HMMs (see Fig. 2a, left panel); Group (HMM, LMM) x Distractor Load (0, 2, 4, 6) ANOVA showed a main effect of Group: F(1, 256) = 4.88, p = .028. This difference was driven by a greater tendency for HMMs to incorrectly endorse a change, when none occurred (“false alarms”; FAs), ANOVA on FA rate showed a main effect of Group: F(1, 256) = 7.52, p = .0065. Hit rate did not significantly differ across Groups: F(1, 256) = 1.27, p = .26, and the Group x Hit/FA interaction was significant, F(1, 548) = 5.39, p = .021.
We also interrogated the data in a signal detection theory (SDT) framework (Green & Swets, 1966) to determine whether HMMs’ reduced WM performance reflects (a) reduced discriminability to detect a change in the WM arrays, as measured by d’WM (d’ = Z
Hits – Z
False Alarms), and/or (b) a different bias to report changes, as measured by C
WM (C = -½ [ZHits + ZFalse Alarms]). Relative to LMMs, HMMs had a poorer ability to discriminate between the presence versus absence of change (see Fig. 2a, middle panel), d’WM by Group and Distractor Load; main effect of Group: F(1, 256) = 5.92, p = .016. HMMs and LMMs did not differ in bias (see Fig. 2a, right panel),C
WM by Group and Distractor Load; main effect of Group: F(1, 256) = 1.48, p = .23. Thus, reduced WM performance in people who frequently media multitask appears to be driven by discriminability differences: HMMs hold fewer or less precise representations of target information in WM.
To determine whether WM performance scales linearly across all levels of media multitasking, we regressed all 139 participants’ MMI scores against their d’WM. This revealed a significant negative relationship: The higher the MMI score, the lower the WM discriminability, d’WM ~ MMI, with Distractor Load as a factor (i.e., d’WM ~ MMI * Load): multiple regression r = .16; effect of MMI, t = -2.61, p = .0092. As was the case with group effects, the relationship between bias and MMI was not significant, C
WM ~ MMI * Load: multiple regression r = .098; effect of MMI, t = -1.10, p = .27. Discriminability differences appeared to be due to FA rates and not hit rates: participants with higher MMI scores exhibited significantly higher FA rates, FA rate ~ MMI * Load: multiple regression r = .18; effect of MMI, t = 2.97, p = .0032, but not significantly lower hit rates, Hit rate ~ MMI * Load: multiple regression r = .094; effect of MMI, t = -1.20, p = .23.
Working memory: Common objects
A similar pattern of results was observed using the Objects variant of the WM task. Specifically, HMMs again exhibited significantly lower WM performance than LMMs (see Fig. 2b, left panel); K by Group and Distractor Load, main effect of Group: F(1, 272) = 5.45, p = .020, and this difference was due to a greater tendency to endorse a change when none occurred, FA rate by Group and Distractor Load, main effect of Group: F(1, 272) = 4.49, p = .035. Again, hit rate did not significantly differ across Groups (Hit rate by group and distractor load, main effect of Group: F(1, 272) = 2.19, p = .14. Finally, HMMs demonstrated reduced discrimination relative to LMMs (see Fig. 2b, middle panel); d’WM by Group and Distractor Load, main effect of Group: F(1, 272) = 4.56, p = .034, with no difference in bias (see Fig. 2b, right panel), C
WM by Group and Distractor Load, main effect of Group: F(1, 272) < 1.
Across-participant regression revealed that while higher MMI scores numerically tended to be associated with lower WM discriminability, this relationship only trended toward significance, d’WM ~ MMI * Distractor Load: multiple regression r = .16; effect of MMI, t = -1.64, p = .10. As in the rectangles task, this trend was associated with a slightly greater tendency to endorse a change when none occurred, although this relationship again only trended toward significance, FA rate ~ MMI * Load: multiple regression r = .16; effect of MMI, t = 1.64, p = .10. Finally, MMI again did not correlate across participants with hit rate, Hit rate ~ MMI * Load: multiple regression r = .15; effect of MMI, t = -.87, p = .39, or bias, C
WM ~ MMI * Load: multiple regression r = .099, effect of MMI t = -.42, p = .68.
Taken together, these two WM studies indicate that––regardless of the nature of the information (common objects or rectangles)––HMMs demonstrate a deficit in WM that reflects a reduction in the number or precision of task-relevant representations that they can encode and/or maintain in WM.
Long-term memory: Target objects
Paralleling the effects observed in WM, HMMs, relative to LMMs, exhibited reduced LTM performance, manifested as a reduced ability to discriminate the previously encountered WM targets from novel objects (see Fig. 3a, left panel); d’LTM by Group, Distractor Load, and Confidence (high vs. low), main effect of Group: F(1, 532) = 9.39, p = .0023. Here HMMs’ poorer discrimination was accompanied by a more liberal decision bias when looking across all trials, with HMMs demonstrating a stronger bias to endorse objects as recognized, C
LTM by Group, Distractor Load, and Confidence, main effect of Group: F(1, 532) = 5.83, p = .016. However, when confined to high confidence responses only, HMMs and LMMs were equally conservative, F(1, 267) = 1.31, p = .25. Across participants, higher MMI scores correlated with reduced LTM performance, d’LTM ~ MMI * Distractor Load * Confidence: multiple regression r = .65; effect of MMI, t = -2.67, p = .008, even when confined to high confidence retrieval responses, multiple regression r = .16; effect of MMI, t = -2.47, p = .014.
To test whether WM performance—using the standard K metric—predicted LTM performance, we regressed all participants’ LTM discrimination scores (d’LTM) onto their performance on the WM objects task. There was a significant positive relationship between the ability to hold objects in WM and the ability to later recognize those previously encountered objects (NB, this pattern was significant when LTM performance was assessed collapsed across decision confidence), r
136 = .31, p = 2.3 * 10-4, as well as when restricted to high confidence decisions, r
136 = .33, p = 8.6 * 10-5; thus, we report high confidence outcomes henceforth (see Fig. 3a, right panel, green).
This relationship between the ability to encode and maintain common objects in WM and the ability to later retrieve those objects from LTM is important, and yet does not adjudicate between alternative hypotheses about whether impaired WM acts to reduce (a) the encoding of information into LTM, or (b) task performance more generally, perhaps by reducing the ability to hold information online during LTM tasks. A first step toward adjudicating between these alternatives may come from assessing whether WM performance predicts LTM performance for completely different information. Here, we tested this hypothesis by determining whether WM performance on the rectangles task predicted LTM performance (for the objects), and found that the predictive relationship held (see Fig. 3a, right panel, orange); r
132 = .22, p = .0093. Because WM performance for the two types of material was correlated, we further examined whether performance on the rectangles task provided predictive information about LTM above and beyond that which was provided by the objects task. A multiple regression analysis revealed a strong predictive relationship, even after removing variance associated with the WM objects task, multiple regression r=.29; effect of K
rectangles, t = 3.52, p = .00046, suggesting that WM performance may have a more general impact on LTM.
Taken together, the foregoing results show that people who frequently engage with multiple media streams during their daily lives demonstrate worse LTM for previously encountered target information. Importantly, HMMs’ diminished LTM and WM performance occurred for information that was encountered while the participants were ostensibly single-tasking.
Long-term memory: Distractor objects
A final question concerned the LTM fate of distractor objects encountered during the WM objects task, as the answer may shed light on mechanisms underlying how HMMs manage competing representations in the WM task. We predicted two possible scenarios: (1) at encoding, HMMs attend to distractor objects at the expense of target objects, resulting in better representation of distractor objects in WM for HMM vs. LMMs, and ultimately leading to better LTM of distractor objects for HMMs, or (2) the ability to interrogate representations held in mind, whether during WM or LTM tasks, is reduced in HMMs, manifesting as worse LTM performance in HMMs than LMMs, for both targets and distractor objects.
An ANOVA of distractor LTM performance revealed a trend favoring the second scenario, in that HMMs remembered the distractors more poorly than LMMs (see Fig. 3b, left panel); d’LTM by Distractor Load (2, 4, 6), Group, and Confidence: main effect of Group: F(1, 399) = 3.47, p = .063. Interestingly, the number of times a distractor was displayed in the array (i.e., Distractor Load) had no effect on LTM for distractors, F(1, 399) < 1.
We next examined whether WM performance predicts LTM performance for the distractor objects (as it did for target objects). To do so, we regressed all participants’ ability to retrieve distractors from LTM (d’LTM) onto their performance in the WM objects task (K, the index of how well target information was held in mind). We found a positive relationship between K and the ability to later confidently recognize distractor objects (see Fig. 3b, right panel, green); K
objects ~ d’LTM-distractors * Confidence: multiple regression r = .72, effect of K
objects, t = 4.48, p = 1.1 * 10-5. This relationship was similar across WM tasks, with WM performance in the rectangles task also predicting long-term memory for distractor objects (see Fig. 3b, right panel, orange); K
rectangles ~ d’LTM-distractors * Confidence: multiple regression r = .33, effect of K
t = 2.11, p = .036, although the relationship was not significant after removing variance associated with WM performance for the objects task, likely due to floor effects, multiple regression r = .32; effect of K
rectangles, t < 1.
Together, these findings show that WM performance in general—across different tasks (rectangles/objects) and different information (target/distractor objects)—predicts LTM performance, suggesting that WM deficits are likely exerting their effects at both encoding and retrieval.
Relationship between task performance and impulsivity
Given the observed relationship between impulsivity and MMI score—driven by the Attentional Impulsivity subscale—we examined whether this subscale predicted task performance (d’ and C in WM and LTM tasks). Across all participants, the Attentional subscale negatively predicted d’ in both WM tasks, rectangles: attentional impulsivity ~ d’WM * Load: multiple regression r = .14, effect of d’, t = -2.15, p = .032; objects: multiple regression r = .15, effect of d’, t = -2.75, p = .0062, but did not show a relationship with d’ in the LTM task (p > .6) or with C in any task (all ps > .05). Thus, higher self-reported attentional impulsivity was associated with worse discrimination in both WM tasks.