Digital Design
Matlab App for Behavioural and Physiological Experiments
- Replaced a fragmented manual setup with a single system that runs a full hour of experiments unattended
- Eliminated 7–8 researcher interruptions per session that were directly contaminating physiological measurements
- Unified all data into one labelled dataset, making it easier to organise, analyse, and trace back
- Halved study duration and cut expected participant dropouts by 80%
Study duration
−50%
4–5 months → 2 months
Dropouts
−80%
5 expected → 1 actual
Sessions
0
Interruptions per session
Data quality
Cleaner
Less disturbance, better signal
Turn a short manual experiment into a one-hour automated study — without disturbing participants or affecting the physiological data.
Bright white buttons and misaligned layout were distracting — and affected pupil size as screens changed
Adding new SNR conditions meant stopping the session and making manual changes mid-experiment
Each manual switch introduced errors, strained the researcher, and disturbed participants' physiological state
One hour is at the limit of what participants can sustain — the design had to work with that, not against it
Revealing issues through observation, not just self-report
Participants rarely articulate confusion mid-task. Direct observation was combined with in-the-moment probing and post-session interviews to capture both behaviour and experience.
Observational Testing
Observed participants mid-session — watching for hesitation, pauses, and unexpected reactions that self-report would never surface
Progressive Piloting
Tested on myself first, then fellow researchers, then 10 participants in a pilot study before the full experiment
In-the-moment Probing
Asked in the moment when something looked off — "Is there anything you are looking for?" kept things conversational
Continuous Iteration
Refined continuously: changes after self-testing, then again after each participant round — no fixed iteration count
From observation to pattern
Field Notes
Kept written notes during each session — recording what participants stopped at, asked about, or visibly reacted to
Affinity Grouping
Grouped observations by type: unclear instructions, visual confusion, flow disruptions, emotional reactions
Interface clarity reduces cognitive load
When participants stopped to search the screen, the interface was competing with the task. Cognitive load belongs to the experiment, not the app.
Confusion rarely surfaced as words
Pauses and hesitations said it instead — each one needed a follow-up question. Behaviour reveals what self-report misses.
Researcher intervention contaminates data
Stepping in mid-session isn't just inconvenient — it shifts physiological state and contaminates the data.
Long sessions need designed pacing
At one hour, fatigue is expected. Breaks weren't a comfort feature — they were part of keeping the data clean.
Every change came from observed behaviour.
Each entry names the observation that prompted it.
Experiment Flow
Before
Manual switching between conditions
Each new SNR condition required researcher intervention — up to 7–8 times per session. That's 7–8 disturbances, and 7–8 chances for error.
After
Automated progression across conditions
Continuous, standardised execution. Sessions ran without intervention for the full hour.
Break Timing
Before
Unclear waiting time
Participants didn't know when the next part would begin — adding uncertainty to an already long session.
After
Countdown + user-controlled continuation
Clear pacing. Participants knew where they were, and could continue when ready.
Break Content
Before
Blank screen or uncontrolled content
A blank screen creates discomfort; engaging content shifts emotional state before the next condition.
After
Neutral, narration-free video
Kept participants at ease without introducing new stimuli that could affect later measures.
Screen Design
Before
Bright white, high contrast
Affected pupil diameter as screens changed — directly interfering with physiological measurement.
After
Calm background, controlled light
Consistent visual environment throughout. Less stimulus noise in the data.
Interface Clarity
Before
Small targets, misaligned layout
Participants stopped mid-task to search the screen — adding cognitive load that belonged to the task, not the interface.
After
Larger elements, simplified layout
Reduced visual search time. Interaction faded into the background.
Faster studies. Higher retention. More reliable data.
Study duration
60 experiments completed in 2 months. The same volume typically takes 4–5 months.
Dropouts
1 dropout from 30 participants. Based on prior experiments, we budgeted for 5.
Sessions
Uninterrupted hour-long sessions
No researcher intervention required from start to finish.
Workload
No manual adjustments
Eliminated 7–8 manual condition switches per session, and all associated error risk.
Data quality
Cleaner physiological data
Less emotional and physical disturbance during measurement improved signal integrity.
What I'd do differently
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Being both researcher and designer on the same study meant every decision had to serve data integrity and participant experience at once — more demanding than either role alone.
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I'd document each change more systematically next time — noting what changed, why, and what it replaced. UX documentation was secondary to building a system that served the experiment, and it shows.
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Participants varied widely. Some were cooperative to the point of bias; others disengaged. The design had to hold up regardless.