Research Platform

Use Dual N-Back in
Your Research

A web-based adaptive n-back platform with d‑prime signal detection scoring, CSV data export, and trial-by-trial replay data — ready for cognitive training studies.

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Platform Capabilities

Research-grade data collection and adaptive algorithms, accessible through a standard web browser.

Signal Detection Scoring

  • › d‑prime (d′) with loglinear correction
  • › Per-modality breakdowns (visual, audio, color, shape)
  • › Hit rate and false alarm rate per modality
  • › Dynamically normalized for match density

Comprehensive CSV Export

  • › 34 fields: accuracy, d′, RT, and more
  • › Hit, miss, and false alarm counts per modality
  • › Filterable by date range
  • › Progression outcomes and session flags

Adaptive Difficulty

  • › d′-driven progression (not raw accuracy)
  • › Continuous difficulty scale (0.00–0.99)
  • › Controls ISI, stimulus duration, lure density
  • › Automatic level advancement (N=2+)

Trial-by-Trial Replay

  • › Full stimulus sequences with timestamps
  • › User responses and correct/incorrect marking
  • › Positions, letters, colors, and shapes recorded
  • › Session replay reconstruction

Multiple N-Back Modes

  • › Dual: visual position + audio letter
  • › Triple: adds color modality
  • › Quad: adds shape modality
  • › N-back levels 2 and up

WMC Assessment

  • › Counting Span Task (complex span paradigm)
  • › Partial Credit Unit (PCU) scoring
  • › Processing accuracy metrics
  • › Independent of n-back training skill
Data Specification

What You Get Per Session

Every session produces a structured record with 34 fields. All scoring is computed server-side to ensure consistency.

Sample Export Fields
Field Example
nBack 3
difficulty 0.45
overallDprime 2.34
visualDprime 2.51
audioDprime 2.18
visualHits 7
visualFalseAlarms 1
meanRT 523
durationMs 127450

+ 25 more fields including per-modality accuracy, target counts, progression outcomes, and session flags.

Applications

Research Use Cases

Longitudinal Training Studies

Track d′ learning curves and n-back progression over weeks or months. Daily stat aggregations provide ready-made temporal analysis.

Working Memory Interventions

Measure training effects with the built-in Counting Span Task as a pre/post assessment, independent of n-back skill gains.

Cross-Modal Interference

Use Triple and Quad modes to study attention distribution across visual, auditory, color, and shape modalities.

Individual Differences

Identify high vs. low training responders through progression trajectories and difficulty adaptation patterns.

Response Time Analysis

Mean RT, standard deviation, and lapse metrics (RT > 800ms) for fatigue and sustained attention modeling.

Foundational Research

Dual n-back is an established paradigm in working memory research. Our platform implements it with research-grade metrics.

Jaeggi, S. M., Buschkuegl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences, 105(19), 6829–6833.

Au, J., Sheehan, E., Tsai, N., Duncan, G. J., Buschkuegl, M., & Jaeggi, S. M. (2015). Improving fluid intelligence with training on working memory: A meta-analysis. Psychonomic Bulletin & Review, 22(2), 366–377.

Soveri, A., Antfolk, J., Karlsson, L., Salo, B., & Laine, M. (2017). Working memory training revisited: A multi-level meta-analysis of n-back training studies. Psychonomic Bulletin & Review, 24(4), 1077–1096.

Interested in Using the Platform?

Tell us about your research and we'll discuss how the platform can support your study.

Or email us directly at support@dualnback.com