> m2h=read.table("M2H_observations.csv",sep=",",header=TRUE)
> m2h_model=lm(OBSERVATIONS~GENDER*MEDIA*WOCHENTAG*OBSERVER,m2h)
> anova(m2h_model)
Analysis of Variance Table
Response: OBSERVATIONS
Df Sum Sq Mean Sq F value Pr(>F)
GENDER 1 0.9 0.88 0.0347 0.8523733
MEDIA 7 15653.3 2236.18 88.1365 < 2.2e-16 ***
WOCHENTAG 3 495.5 165.17 6.5099 0.0003290 ***
OBSERVER 4 1328.1 332.02 13.0862 2.123e-09 ***
GENDER:MEDIA 7 57.7 8.25 0.3250 0.9419604
GENDER:WOCHENTAG 3 15.4 5.14 0.2026 0.8945048
MEDIA:WOCHENTAG 21 1217.0 57.95 2.2841 0.0018619 **
GENDER:OBSERVER 4 20.0 5.01 0.1973 0.9395594
MEDIA:OBSERVER 28 1636.4 58.44 2.3034 0.0005379 ***
WOCHENTAG:OBSERVER 1 84.5 84.49 3.3302 0.0696387 .
GENDER:MEDIA:WOCHENTAG 21 172.8 8.23 0.3244 0.9980821
GENDER:MEDIA:OBSERVER 28 127.2 4.54 0.1790 0.9999992
GENDER:WOCHENTAG:OBSERVER 1 0.0 0.05 0.0018 0.9657824
MEDIA:WOCHENTAG:OBSERVER 7 150.1 21.44 0.8449 0.5514657
GENDER:MEDIA:WOCHENTAG:OBSERVER 7 60.1 8.58 0.3383 0.9355206
Residuals 184 4668.4 25.37
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
We suggest that smartphones may also impair cognitive performance by affecting the allocation of attentional resources, even when consumers successfully resist the urge to multitask, mind-wander, or otherwise (consciously) attend to their phones—that is, when their phones are merely present.
"...the attractiveness of these high-priority stimuli should predict not just their ability to capture the orientation of attention, but also the cognitive costs associated with inhibiting this automatic attention response. "
We propose that the mere presence of one’s smartphone may impose a “brain drain” as limited-capacity attentional resources are recruited to inhibit automatic attention to one’s phone, and are thus unavailable for engaging with the task at hand.
We propose that the mere presence of one’s smartphone may impose a “brain drain” as limited-capacity attentional resources are recruited to inhibit automatic attention to one’s phone, and are thus unavailable for engaging with the task at hand.
Prior research suggests that smartphones are chronically salient for many individuals, even when they are located out of sight in one’s pocket or bag (e.g., Deb 2015).
We posit that individual differences in dependence on one’s smartphone will moderate the effects of smartphone salience on available cognitive capacity, such that individuals who most depend on their phones will suffer the most from their presence— and benefit the most from their absence.
To determine whether there is a general capacity for all working memory tasks, Turner and Engle (1989) developed a task called operation-word-spanor OSPAN. In this task, participants are asked to read and verify a simple math problem (such as "Is (4/2)-1=1 ?) and then read a word after the operation (such as SNOW). After a series of problems and words has been presented, the participants recall the words that followed each operation. The number of operation-word strings in a sequence is increased and decreased to measure the participant's operation span. Operation span measures predict verbal abilities and reading comprehension even though the subjects are solving mathematical problems. Engle and his colleagues have argued that this implies a general pool of resources that is used in every type of working memory situation.
Date | Topic |
---|---|
12.4 | Introduction |
19.4 | NO COURSE (Karfreitag) |
26.4 | How to read scientific articles |
3.5 | Google, Brain & co. |
10.5 | Cognitive sciences |
17.5 | Cognitive psychology |
24.5 | Abstracts |
31.5 | OPTIONAL COURSE (Christihimmelfahrt Brückentag) |
7.6 | Memetic theory |
14.6 | Theory of multiple intelligences |
21.6 | Developmental aspects |
28.6 | Socrates & Gestalt |
5.7 | Symposion |
12.7 | Summa Summarum |
> m2h=read.table("M2H_observations.csv",sep=",",header=TRUE)
> m2h_model=lm(OBSERVATIONS~GENDER*MEDIA*WOCHENTAG*OBSERVER,m2h)
> anova(m2h_model)
Analysis of Variance Table
Response: OBSERVATIONS
Df Sum Sq Mean Sq F value Pr(>F)
GENDER 1 0.9 0.88 0.0347 0.8523733
MEDIA 7 15653.3 2236.18 88.1365 < 2.2e-16 ***
WOCHENTAG 3 495.5 165.17 6.5099 0.0003290 ***
OBSERVER 4 1328.1 332.02 13.0862 2.123e-09 ***
GENDER:MEDIA 7 57.7 8.25 0.3250 0.9419604
GENDER:WOCHENTAG 3 15.4 5.14 0.2026 0.8945048
MEDIA:WOCHENTAG 21 1217.0 57.95 2.2841 0.0018619 **
GENDER:OBSERVER 4 20.0 5.01 0.1973 0.9395594
MEDIA:OBSERVER 28 1636.4 58.44 2.3034 0.0005379 ***
WOCHENTAG:OBSERVER 1 84.5 84.49 3.3302 0.0696387 .
GENDER:MEDIA:WOCHENTAG 21 172.8 8.23 0.3244 0.9980821
GENDER:MEDIA:OBSERVER 28 127.2 4.54 0.1790 0.9999992
GENDER:WOCHENTAG:OBSERVER 1 0.0 0.05 0.0018 0.9657824
MEDIA:WOCHENTAG:OBSERVER 7 150.1 21.44 0.8449 0.5514657
GENDER:MEDIA:WOCHENTAG:OBSERVER 7 60.1 8.58 0.3383 0.9355206
Residuals 184 4668.4 25.37
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
To determine whether there is a general capacity for all working memory tasks, Turner and Engle (1989) developed a task called operation-word-spanor OSPAN. In this task, participants are asked to read and verify a simple math problem (such as "Is (4/2)-1=1 ?) and then read a word after the operation (such as SNOW). After a series of problems and words has been presented, the participants recall the words that followed each operation. The number of operation-word strings in a sequence is increased and decreased to measure the participant's operation span. Operation span measures predict verbal abilities and reading comprehension even though the subjects are solving mathematical problems. Engle and his colleagues have argued that this implies a general pool of resources that is used in every type of working memory situation.