Skip to content
OpenTrain AIFor AI Companies

HFEPX · Eval paper review

How Psychological Learning Paradigms Shaped and Constrained Artificial Intelligence

Alex Anvi Eponon, Ildar Batyrshin, Christian E. Maldonado-Sifuentes, Grigori Sidorov

Published

Mar 18, 2026

Citations

0

Trust level

Low

Usefulness score

40/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Apr 8, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Tags

Critique Edit General

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

Background context only.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Detected
From extracted signals
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
40/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Current artificial intelligence systems struggle with systematic compositional reasoning: the capacity to recombine known components in novel configurations. This paper argues that the failure is architectural, not merely a matter of scale or training data, and that its origins lie in the psychological learning theories from which AI paradigms were derived. The argument proceeds in three stages. First, drawing on the systematicity debate in cognitive science and on the demonstration of Aizawa that neither connectionism nor classicism can make systematicity a structural consequence of the architecture, the paper establishes that the corrective techniques proliferating in modern AI, from chain-of-thought prompting to alignment through human feedback, function as auxiliary hypotheses that address symptoms without resolving the underlying architectural indifference to systematicity. Second, it traces the genealogy from psychological learning theory to AI methodology, showing that behaviourism, cognitivism, and constructivism each bequeathed a specific structural limitation to the AI paradigm it inspired: the exclusion of internal structure, the opacity of representation, and the absence of formal construction operators. A cross-cultural reappraisal of rote learning reveals a further underexploited pathway. Third, the paper introduces ReSynth, a trimodular conceptual framework that proposes the principled separation of reasoning, identity, and memory as a path toward architectures in which systematic behaviour is a structural consequence of design rather than a correction applied after the fact.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

partial

Critique Edit

Directly usable for protocol triage.

"Current artificial intelligence systems struggle with systematic compositional reasoning: the capacity to recombine known components in novel configurations."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Current artificial intelligence systems struggle with systematic compositional reasoning: the capacity to recombine known components in novel configurations."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Current artificial intelligence systems struggle with systematic compositional reasoning: the capacity to recombine known components in novel configurations."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Current artificial intelligence systems struggle with systematic compositional reasoning: the capacity to recombine known components in novel configurations."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Current artificial intelligence systems struggle with systematic compositional reasoning: the capacity to recombine known components in novel configurations."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
Yes
Feedback types
Critique Edit
Rater population
Not reported
Expertise required
General
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Current artificial intelligence systems struggle with systematic compositional reasoning: the capacity to recombine known components in novel configurations.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Current artificial intelligence systems struggle with systematic compositional reasoning: the capacity to recombine known components in novel configurations.
  • This paper argues that the failure is architectural, not merely a matter of scale or training data, and that its origins lie in the psychological learning theories from which AI paradigms were derived.
  • First, drawing on the systematicity debate in cognitive science and on the demonstration of Aizawa that neither connectionism nor classicism can make systematicity a structural consequence of the architecture, the paper establishes that the corrective techniques proliferating in modern AI, from chain-of-thought prompting to alignment through human feedback, function as auxiliary hypotheses that address symptoms without resolving the underlying architectural indifference to systematicity.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Human demonstrations evaluation

Contribution summary

  • The dominant paradigms of artificial intelligence were shaped by learning theories from psychology: behaviorism inspired reinforcement learning, cognitivism gave rise to deep learning and memory-augmented architectures, and constructivism…
  • This paper argues that each AI paradigm inherited not only the strengths but the structural limitations of the psychological theory that inspired it.
  • Reinforcement learning cannot account for the internal structure of knowledge, deep learning compresses representations into opaque parameter spaces resistant to principled update, and current integrative approaches lack a formal account of…

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Critique Edit

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.