11. The magical number seven, plus-or-minus two or some limits on our capacity for processing information

Miller, George A.

doi:10.1525/9780520318267-011

Cited by 12 publications

(8 citation statements)

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“…On average, normal healthy adults have a digit span between five and nine. This range is also known as the magical number seven, plus or minus two (Miller, 1956). As the aim of this study is not to measure digit span by itself, the length of the sequences is fixed at five target digits.…”

Section: Experimental Design Backgroundmentioning

confidence: 99%

“…Although this is a possible explanation, the authors argue that precautions were taken in the experimental design to limit the cognitive load. Miller (1956) stated that normal healthy adults have on average a digit span between five and nine. The participants of this experiment were selected to be "normal healthy adults" and the amount of target digits was fixed at five.…”

Section: Difference Between Rhythmic and Non-rhythmic Auditory Supportmentioning

confidence: 99%

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With No Attention Specifically Directed to It, Rhythmic Sound Does Not Automatically Facilitate Visual Task Performance

et al. 2022

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In a century where humans and machines—powered by artificial intelligence or not—increasingly work together, it is of interest to understand human processing of multi-sensory stimuli in relation to attention and working memory. This paper explores whether and when supporting visual information with rhythmic auditory stimuli can optimize multi-sensory information processing. In turn, this can make the interaction between humans or between machines and humans more engaging, rewarding and activating. For this purpose a novel working memory paradigm was developed where participants are presented with a series of five target digits randomly interchanged with five distractor digits. Their goal is to remember the target digits and recall them orally. Depending on the condition support is provided by audio and/or rhythm. It is expected that the sound will lead to a better performance. It is also expected that this effect of sound is different in case of rhythmic and non-rhythmic sound. Last but not least, some variability is expected across participants. To make correct conclusions, the data of the experiment was statistically analyzed in a classic way, but also predictive models were developed in order to predict outcomes based on a range of input variables related to the experiment and the participant. The effect of auditory support could be confirmed, but no difference was observed between rhythmic and non-rhythmic sounds. Overall performance was indeed affected by individual differences, such as visual dominance or perceived task difficulty. Surprisingly a music education did not significantly affect the performance and even tended toward a negative effect. To better understand the underlying processes of attention, also brain activation data, e.g., by means of electroencephalography (EEG), should be recorded. This approach can be subject to a future work.

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Section: Experimental Design Backgroundmentioning

confidence: 99%

Section: Difference Between Rhythmic and Non-rhythmic Auditory Supportmentioning

confidence: 99%

With No Attention Specifically Directed to It, Rhythmic Sound Does Not Automatically Facilitate Visual Task Performance

et al. 2022

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“…Concurrently considering multiple factors and their conflicts and interactions would likely be difficult for designers, such that an automated model might be important for weighting their complexities. We were inspired by the recent modeling work [20,31,40,43,75] and appealed to psychophysical laws [31,52,69,82], entropy [15,68,70,71], perceptual proxies [39,58], serial-position and ordering effects [36], visual memory (e.g., [3,10,54]), neighborhood effects [87], and distractors [77]. We are capable of providing (very) preliminary recommendations given the inputs (see Fig.…”

Section: Implications and Future Directionsmentioning

confidence: 99%

Rethinking the ranks of visual channels

McColeman¹,

Yang²,

Brady³

et al. 2021

Preprint

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Data can be visually represented using visual channels like position, length or luminance. An existing ranking of these visual channels is based on how accurately participants could report the ratio between two depicted values. There is an assumption that this ranking should hold for different tasks and for different numbers of marks. However, there is surprisingly little existing work that tests this assumption, especially given that visually computing ratios is relatively unimportant in real-world visualizations, compared to seeing, remembering, and comparing trends and motifs, across displays that almost universally depict more than two values.To simulate the information extracted from a glance at a visualization, we instead asked participants to immediately reproduce a set of values from memory after they were shown the visualization. These values could be shown in a bar graph (position (bar)), line graph (position (line)), heat map (luminance), bubble chart (area), misaligned bar graph (length), or `wind map' (angle). With a Bayesian multilevel modeling approach, we show how the rank positions of visual channels shift across different numbers of marks (2, 4 or 8) and for bias, precision, and error measures. The ranking did not hold, even for reproductions of only 2 marks, and the new probabilistic ranking was highly inconsistent for reproductions of different numbers of marks. Other factors besides channel choice had an order of magnitude more influence on performance, such as the number of values in the series (e.g., more marks led to larger errors), or the value of each mark (e.g., small values were systematically overestimated). Every visual channel was worse for displays with 8 marks than 4, consistent with established limits on visual memory. These results point to the need for a body of empirical studies that move beyond two-value ratio judgments as a baseline for reliably ranking the quality of a visual channel, including testing new tasks (detection of trends or motifs), timescales (immediate computation, or later comparison), and the number of values (from a handful, to thousands)

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“…Different from logistic regression (which is only interpretable in low dimensional problem settings because humans can handle at most 7 ± 2 cognitive entities at once (Miller, 2020;Cowan, 2010)), the rule set algorithm is not limited by the number of input variables. The number of arguments in each rule can be controlled, as simple as determining the largest allowed depth of a tree.…”

Section: Interpretable Classificationmentioning

confidence: 99%

An Interpretable Algorithm for Uveal Melanoma Subtyping from Whole Slide Cytology Images

Chen¹,

Liu²,

Gómez³

et al. 2021

Preprint

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Algorithmic decision support is rapidly becoming a staple of personalized medicine, especially for high-stakes recommendations in which access to certain information can drastically alter the course of treatment, and thus, patient outcome; a prominent example is radiomics for cancer subtyping. Because in these scenarios the stakes are high, it is desirable for decision systems to not only provide recommendations but supply transparent reasoning in support thereof. For learning-based systems, this can be achieved through an interpretable design of the inference pipeline. Herein we describe an automated yet interpretable system for uveal melanoma subtyping with digital cytology images from fine needle aspiration biopsies. Our method embeds every automatically segmented cell of a candidate cytology image as a point in a 2D manifold defined by many representative slides, which enables reasoning about the cell-level composition of the tissue sample, paving the way for interpretable subtyping of the biopsy. Finally, a rule-based slide-level classification algorithm is trained on the partitions of the circularly distorted 2D manifold. This process results in a simple rule set that is evaluated automatically but highly transparent for human verification. On our in house cytology dataset of 88 uveal melanoma patients, the proposed method achieves an accuracy of 87.5 % that compares favorably to all competing approaches, including deep "black box" models. The method comes with a user interface to facilitate interaction with cell-level content, which may offer additional insights for pathological assessment.

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11. The magical number seven, plus-or-minus two or some limits on our capacity for processing information

Cited by 12 publications

References 0 publications

With No Attention Specifically Directed to It, Rhythmic Sound Does Not Automatically Facilitate Visual Task Performance

With No Attention Specifically Directed to It, Rhythmic Sound Does Not Automatically Facilitate Visual Task Performance

Rethinking the ranks of visual channels

An Interpretable Algorithm for Uveal Melanoma Subtyping from Whole Slide Cytology Images

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