Cognitive Performance Data Visualization

An Excel-based team project using four visualizations to explore patterns in cognitive-performance data.

Project Overview

For this INEG 21003 team project, we used Microsoft Excel to explore a cognitive-performance dataset and communicate its patterns through four visualizations. The variables included caffeine concentration, hours of sleep, environmental noise, reaction time, focus scores, time since the last meal, and task-completion time.

The goal was exploratory: ask a clear question, choose a chart suited to that question, and describe what the available data show without treating association as proof of causation.

At a glance

• Tool: Microsoft Excel
• Methods: data organization, scatter plots, histogram, and time series
• Focus: chart selection, visual clarity, interpretation, and team communication
• Important boundary: the charts describe patterns in the dataset; they do not establish cause and effect

Caffeine Concentration and Sleep

Scatter plot comparing caffeine concentration with hours of sleep.

Question: Is caffeine concentration associated with hours of sleep in this dataset?

The points are broadly dispersed rather than concentrated around a clear upward or downward pattern. Within this sample, caffeine concentration by itself does not appear to be a strong predictor of reported sleep duration. Other variables and individual differences may account for more of the variation.

Environmental Noise and Reaction Time

Distribution of reaction times under the noise conditions in the dataset.

Question: How does reaction-time performance appear across the observed noise conditions?

The reaction-time distribution remains centered in a similar range rather than showing an obvious shift toward much slower responses. This visualization does not provide strong visual evidence of a large noise-related difference in the observed sample. A formal comparison would be needed to determine whether smaller differences are statistically meaningful.

Focus Across Trial Days

Time series of focus score across the trial.

Question: How did focus scores change over the course of the trial?

The series is more variable at the beginning and then settles into a narrower, generally higher band. That pattern is consistent with participants becoming more familiar with the task, although the chart alone cannot establish the cause. The visualization is useful because it shows both the level of performance and how its variability changes over time.

Time Since Last Meal and Task Completion

Scatter plot comparing time since the last meal with task-completion time.

Question: Is time since the last meal associated with task-completion time?

Completion times cluster within a similar range across the observed values for time since eating. There is no obvious strong upward or downward pattern in the scatter plot, so this variable alone does not appear to explain much of the variation in completion time within the sample.

What the Project Demonstrates

This project strengthened my ability to organize data in Excel, match a chart to a specific question, and write conclusions that are proportional to the evidence. It also reinforced an important analytical habit: a clear visualization can reveal a pattern, but it does not automatically explain why the pattern exists.

From an Industrial Engineering perspective, that discipline matters when using operational data. Good analysis should help a team see what is happening, identify what deserves further investigation, and avoid claiming more certainty than the data support.