Evidence map›Paper›PMID 41426388›Full record

ArticleFrontiers in psychology2025

The neuroscience of algorithmic suffering: short comparative analysis between human and AI.

Esen K Tütüncü, Mar Gonzalez-Franco

Abstract read
In one paragraph

Article in Frontiers in psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Esen K TütüncüInstitute of Neurosciences, Universitat de Barcelona, Barcelona, Spain.
Mar Gonzalez-FrancoGoogle, Seattle, WA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Across cultures and centuries, humans have sought to explain suffering, be it as moral failure, biological necessity, or existential condition. Today, as artificial intelligence begins to mimic aspects of thought and emotion, the question resurfaces: can a machine suffer? In this paper, we revisit suffering not as a sentimental analogy but as a comparative lens between human and algorithmic cognition. Building on earlier reflections on "painful intelligence", we examine how frustration, reward, and prediction take shape in neural and computational systems, grounding our analysis in Bayesian inference, behavioral psychology, and theories of consciousness. While both humans and machines respond to errors and unmet goals, only the former experience these as violations of meaning and integrity. By tracing this divide, we suggest that suffering remains the last frontier between optimization and awareness-a reminder that consciousness cannot be reduced to performance, however convincing the imitation.

Indexed as

artificial intelligencebehavioral psychologyconsciousnessneurosciencepredictive processingreinforcement learningsuffering

Identifiers

PMID41426388
PMCPMC12712916

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.