The Delayed Match-to-Sample (DMTS) task evaluates whether an evolved Dynamic Neural Field (DNF) architecture can perform memory-guided decision-making.

The desired behaviour is:

1. A sample stimulus is presented at a particular feature/location.
2. The system must encode and maintain this sample internally during a delay period.
3. Two or more test stimuli are then presented.
4. The system should select the stimulus matching the memorised sample, while suppressing non-matching alternatives.

This task is a canonical paradigm in cognitive neuroscience for studying working memory and comparison-based decision processes.

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DMTS consistently requires a three-field architecture, with explicit separation between perception, memory, and decision:

• Fields: 3 (input, memory, output)

• Hidden fields: typically 1–3

• Connections: multiple feedforward couplings

• Dynamics:

  • Input field: stimulus-driven encoding of samples and test cues
  • Memory field: sustained representation of the sample across the delay
  • Output field: competitive selection biased toward the matching stimulus

dmts-phenotype-wbg

The figure above illustrates:

• Encoding of the sample in a dedicated memory field
• Maintenance of the sample representation during the delay
• Matching-based selection in the output field

The evolved solution closely mirrors classical Dynamic Field Theory accounts of DMTS.

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Analysed 100 runs; 100 reached the fitness threshold (100.0% success, threshold = 0.950)

• Mean: 48.44
• Median: 41.00
• Std: 32.01

• Mean convergence rate (fitness gain/gen): 0.0177
• Mean fitness improvement/gen: 0.0201

• Hidden fields (mean ± std): 1.30 ± 0.50
• Enabled connections (mean ± std): 3.46 ± 1.23

label	run	value
Highest max fitness	2026年01月22日 12h47m03s	0.987305
Lowest max fitness	2026年01月28日 05h08m57s	0.950243
Fastest to threshold	2026年01月23日 00h27m03s	10.0
Slowest to threshold	2026年01月23日 21h18m55s	159.0
Most hidden fields	2026年01月23日 21h18m55s	3.0
Most enabled connections	2026年01月23日 21h18m55s	8.0

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This section reports detailed statistics from the evolutionary run that produced the highest-fitness solution for the Delayed Match-to-Sample task.

Final generation: g = 30

• Best fitness: 0.9873
• Target fitness: 0.950
• Average fitness: 0.4851

Overall run statistics

• Max best fitness: 0.9873 (reached at generation 30)
• Mean best fitness over run (AUC): 0.6965
• Mean average fitness over run (AUC): 0.4010
• Longest stagnation period: 3 generations
• Target fitness first reached: generation 30 (best fitness ≈ 0.9873)

Final generation (g = 30)

• Species: 92
• Active species: 56

Across run

• Total distinct species created: 91
• Species extinct by final generation: 29
• Average species per generation: 30.32
• Average active species per generation: 17.94
• Max active species in a generation: 62 (at g = 28)

Species lifetime & size

• Average species lifespan: 9.32 generations
• Longest-lived species: 0 (lifespan 30)
• Average max members per species: 73.02
• Average offspring per species: 318.68

Final generation (g = 30)

• Avg genome size: 6.00
• Avg field genes: 3.00
• Avg connection genes: 3.00

Growth over run

• Genome size change: +4.00 (≈ +0.133 / gen)
• Field genes change: +1.00 (≈ +0.033 / gen)
• Connection genes change: +3.00 (≈ +0.100 / gen)

Ratios

• Avg connections per field (final gen): 1.00

• Field kernels:

  • Gaussian: 56 544 (85.2%)
  • Mexican-hat: 9 835 (14.8%)

• Interaction kernels:

  • Gaussian: 10 627 (36.2%)
  • Mexican-hat: 18 746 (63.8%)

Generation: 30 Fitness: 0.9873

Field	Role	Kernel type	Field parameters	Kernel parameters
nf 1	Input	Gaussian	h = -11.85, τ = 19.20	A = 8.33, σ = 4.20, A_glob = -0.02
nf 2	Output	Gaussian	h = -6.88, τ = 18.27	A = 4.18, σ = 4.93, A_glob = -0.13
nf 3	Hidden	Gaussian	h = -11.85, τ = 19.20	A = 23.95, σ = 1.00, A_glob = -0.20

Interaction gene	From → To	Kernel parameters
gk cg 1-2-0	nf 1 → nf 2	A = 11.53, σ = 5.42, A_glob = -0.05
gk cg 1-3-18	nf 1 → nf 3	A = 15.73, σ = 5.57, A_glob = 0.00
gk cg 3-2-19	nf 3 → nf 2	A = 3.28, σ = 2.20, A_glob = 0.00

dmts-cut-compressed.mp4

• Nearly every generation introduces new species
• Many species go extinct, indicating strong selection pressure
• A dedicated hidden memory field reliably emerges
• Interaction kernels shift toward Mexican-hat dominance

The best-performing solution is interpretable, implementing perception → memory → decision as distinct dynamical components.

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DMTS represents a qualitative leap in cognitive complexity:

• Evolution must coordinate memory maintenance and stimulus comparison.
• Convergence is slower and architectures are more elaborate.
• Nevertheless, neat-dnfs reliably evolves interpretable, modular solutions aligned with neurocognitive theory.

This task demonstrates that the framework supports compositional cognition, not just isolated instabilities.