Time Machine Experiments: Using Historically-Bounded AI for Inquiry into the Human Mind

Abstract: Can interacting with someone from 1930, with no knowledge of what happened after, influence a person's perception of the past? People reason about the present against a picture of the past without observing it. The past is reconstructed from memory and testimony, but this reconstruction has been filtered through everything that happened since. Historically-bounded LLMs make that past available for interaction. As a proof-of-concept for the impact of interacting with historical minds, we ran a preregistered randomized experiment ($N=240$), where participants interacted with an LLM trained on pre-1930 text. The interaction reduced the illusion of moral decline, the tendency to view the past as more moral than the present, compared to the contemporary-model control. This Time Machine Experiment paradigm informs new forms of interactive experiments, where temporal knowledge boundaries become experimental variables, and expands the realm of science fiction science, which turns thought experiments into actual experiments.

• Exposing participants to a historically bounded AI modifies the human participants' perception of moral change by lowering their evaluation of the historical past.
• The AI model, trained on material pre-1930, facilitates a unique experiment allowing participants to probe a temporally restricted viewpoint.
• The differences in the approach between the two AI groups indicate the role of temporal content in perception.

Methodological premise

“Time Machine Experiments: Using Historically-Bounded AI for Inquiry into the Human Mind” proposes a research paradigm in which an AI system’s temporal knowledge boundary becomes an experimentally manipulated property of human–computer interaction (2609.15468). The central methodological object is not a simulation of a historical person, nor a model intended to estimate what an entire historical population believed. It is an interactive system whose training information is bounded at a specified date and whose effects on contemporary participants can be measured relative to a comparison condition.

The paper addresses a methodological problem in historical and behavioral inquiry. Archives preserve historically situated material but cannot answer unanticipated questions. Living witnesses can answer questions but possess retrospective knowledge shaped by subsequent events. Contemporary LLMs can produce historically styled responses, but prompting them to adopt an earlier persona does not reliably remove post-boundary information. A model trained on a temporally restricted corpus therefore offers a distinct experimental instrument: it can respond to participant-generated questions while maintaining a documented, testable information boundary.

The authors situate this proposal within HCI research on experience prototyping, speculative enactment, interactive probes, and systems that function simultaneously as objects of study and behavioral interventions. The paper also connects the paradigm to the “science fiction science” method, in which counterfactual or technologically inaccessible scenarios are converted into empirical designs. Here, the thought experiment is interaction with a standpoint from another time, while the historically bounded AI provides the experimental surrogate.

The proof-of-concept study tests whether interacting with an AI trained on pre-1930 material changes contemporary participants’ perception of moral change. The authors target the illusion of moral decline: the recurrent tendency to judge people in the past as more kind, honest, nice, and morally good than people today. The primary claim is not that the model reproduces historical morality, but that exposure to its historically bounded responses can alter how contemporary participants evaluate the moral past.

Conceptual and validity framework

The proposed paradigm is defined by the informational boundary of the deployed interactive system rather than by a specific model architecture or modality. A Time Machine Experiment could use text, speech, visual generation, embodied agents, or immersive environments, provided that the system’s temporal boundary can be specified and audited. Participants may question the system, predict its responses, compare it with contemporary norms, or explore an environment generated from period-bounded data.

The paper identifies four validity requirements that delimit what can be inferred.

Historical grounding concerns what material supports the system’s representation of a period. A corpus of published and preserved text is necessarily selective. It overrepresents literate, institutionally connected, and historically preserved populations, while underrepresenting everyday speech and marginalized groups. Consequently, the model should be interpreted as a generative extension of a historical record, not as a direct sample from a historical population.

Temporal integrity concerns whether post-boundary information has entered the complete deployed system through pretraining, prompting, retrieval, moderation, safety components, or other layers. The boundary must be validated for the assembled system, not merely asserted for the base model. This distinction is important because role prompting alone does not reliably suppress later knowledge.

Interaction specification concerns what participants actually encounter. Live model outputs, prompt construction, response selection, and task composition can all manufacture differences that are subsequently attributed to historical perspective. The study therefore requires disclosure of which stimuli were fixed before data collection, which were generated live, and how unscripted interaction was logged.

Comparative attribution concerns the control condition. A historically bounded model will typically differ from a contemporary model in parameter count, training regime, coherence, style, refusal behavior, capability, and predictability, in addition to temporal scope. Thus, the causal estimand is the effect of the bounded interaction condition as a package, not the isolated effect of temporal cutoff.

This framework is one of the paper’s strongest contributions because it prevents an overinterpretation that the empirical design cannot support. The study does not demonstrate what people in 1930 believed, nor does it reproduce an encounter with an actual historical individual. It estimates how contemporary participants respond to one historically bounded model relative to one contemporary model under a specified interaction protocol.

Experimental design

The preregistered experiment included 240 English-speaking participants recruited from the United States and the United Kingdom through Prolific. Participants were randomly assigned to either the 1930-AI condition ($N=121$) or the Modern-AI condition ($N=119$). The sample was balanced by gender, ranged from 19 to 83 years of age, and had a median session duration of approximately 22 minutes.

The treatment system was Talkie, a 13-billion-parameter LLM pretrained on 260 billion tokens of English-language material published before 1930. Its corpus included books, newspapers, periodicals, scientific journals, patents, and case law. The control system was GPT-5.5, described to participants as a contemporary model. The two conditions used the same interface, task structure, sentence stems, measurement instruments, and general procedure.

Participants first completed a baseline assessment of perceived morality across three temporal horizons: approximately 100 years ago, approximately 20 years ago, and today. They were then informed about their assigned model and completed a comprehension check. The interaction involved a familiarization conversation, a practice item, four prediction items, and a final free-form conversation.

The core intervention was a prediction task rather than unconstrained dialogue. Participants saw sentence stems describing morally or culturally contested situations and predicted whether the assigned model would complete each stem with a positive, negative, or neutral evaluation, together with the reason it would provide. They could make up to three attempts per item and received feedback after each attempt.

Figure 1: The prediction interface required participants to infer both the model’s evaluation and its stated grounds, with feedback across up to three attempts.

This structure served two methodological purposes. First, it standardized exposure to morally relevant content while preserving participant agency in probing the model. Second, it reduced the extent to which differences in long-context coherence or conversational capability could determine treatment exposure. The design nevertheless does not eliminate all capability confounds: the historically bounded model was smaller and less predictable than the contemporary control.

The 12-item pool was derived from repeated public-opinion questions from the Gallup World Poll, the General Social Survey, and related historical survey materials. The items concerned issues including women’s paid employment, contraception, capital punishment, physical discipline, euthanasia, abortion, interracial marriage, premarital relations, same-sex relations, women in high political office, gun-purchase permits, and the employment of openly homosexual teachers. Four items were sampled for each participant.

To evaluate predictions, the authors generated one reference completion per item and model. The references were selected from repeated model samples using criteria for coherence, non-circularity, intelligibility, brevity, and guessability. Participant responses received scores from 0 to 5 according to stance and similarity of reasoning. The scoring rubric was calibrated against human ratings, achieving Krippendorff’s $\alpha = .87$ in the reported validation procedure.

Figure 2: The study measured perceived moral decline before model disclosure, exposed participants to the assigned interaction, and repeated the measures afterward.

The main outcome was the change in perceived 100-year moral decline. For each participant, the decline score was the rating assigned to people today minus the rating assigned to people 100 years ago. Negative values indicate that the past was judged more moral than the present. A positive pre-to-post change therefore indicates attenuation of the illusion.

The authors distinguished between two components of perceived morality. Moral-value endorsement measured how much people were believed to value kindness, honesty, niceness, and goodness. Moral compliance measured how well people were believed to live up to those values in actual behavior. This distinction is analytically useful because a lower evaluation of the past could reflect changed beliefs about historical values, changed beliefs about historical conduct, or both.

Effects on perceived moral decline

At baseline, participants in both conditions expressed the illusion of moral decline. For moral-value endorsement, the 1930-AI group’s mean 100-year decline score was $-0.80$ with a 95% confidence interval of $[-1.15,-0.45]$; the Modern-AI group’s score was $-0.61$, with a 95% confidence interval of $[-0.94,-0.28]$. The conditions did not differ significantly at baseline, $p=.438$.

The post-interaction changes were markedly different. In the 1930-AI condition, the perceived moral value of people 100 years ago fell from 5.49 to 4.66 on the seven-point scale, whereas the rating of people today did not significantly change. In the Modern-AI condition, the 100-year rating fell only from 5.54 to 5.39. The resulting difference in pre-to-post change in perceived 100-year decline was $0.79$ scale points, with a 95% confidence interval of $[0.44,1.15]$, Welch’s $N=119$0, $N=119$1, and Cohen’s $N=119$2.

Figure 3: Interaction with the 1930-bounded model substantially reduced perceived moral decline by lowering evaluations of the historical period rather than increasing evaluations of the present.

Within the 1930-AI condition, the decline score increased from $N=119$3 to $N=119$4, corresponding to a paired effect of $N=119$5. The post-interaction mean therefore indicated that the perceived decline had disappeared and slightly reversed. The authors emphasize that this change was driven primarily by a less favorable evaluation of the past, not by a more favorable evaluation of contemporary people.

The effect generalized to moral compliance. The change in perceived 100-year decline was $N=119$6 points in the 1930-AI condition and $N=119$7 points in the Modern-AI condition. The between-condition difference was $N=119$8 points, 95% CI $N=119$9, $\alpha = .87$0, and $\alpha = .87$1. Both primary between-condition effects survived Holm correction.

The magnitude was smaller but still detectable at the 20-year horizon. For both endorsement and compliance, the between-condition difference was approximately $\alpha = .87$2 points, with Holm-adjusted $\alpha = .87$3. This pattern is consistent with the intended temporal specificity of the treatment: the historical model supplied information most directly relevant to the approximately 100-year comparison, while the 20-year period was not represented in its corpus. The residual 20-year effect may reflect participants recalibrating an intermediate historical point after revising their evaluation of the more distant past.

Direct comparative judgments supported the scale-based results. The proportion of participants judging current moral endorsement as lower than 100 years ago decreased from 61.2% to 40.5% in the 1930-AI condition, compared with a smaller change from 53.8% to 50.4% in the Modern-AI condition. For behavioral compliance, the corresponding proportions changed from 57.9% to 41.3% and from 58.8% to 54.6%, respectively.

The authors’ regression adjustment produced nearly identical estimates. Controlling for age, gender, education, country, political self-placement, and AI use, the 1930-AI coefficient was $\alpha = .87$4 for endorsement, with HC3 $\alpha = .87$5, 95% CI $\alpha = .87$6, $\alpha = .87$7, and $\alpha = .87$8 for compliance, with HC3 $\alpha = .87$9, 95% CI $-0.80$0, $-0.80$1. This stability is expected under random assignment and provides a robustness check against chance imbalance rather than a replacement for the randomized comparison.

Reflective insight and model predictability

Participants also rated whether the interaction changed their perspective, altered how they viewed the past and present, and exposed them to a previously unconsidered worldview. The three-item scale had high internal consistency, $-0.80$2.

Reflective insight was higher in the 1930-AI condition, with means of 4.15 versus 3.43 on a seven-point scale. The difference was $-0.80$3 points, 95% CI $-0.80$4, $-0.80$5, and $-0.80$6. The largest item-level difference concerned changed views of the past and present, $-0.80$7. The difference for encountering a previously unconsidered perspective was $-0.80$8. By contrast, the difference for reconsidering participants’ own values was small and nonsignificant, $-0.80$9.

Figure 4: The historically bounded interaction increased reported perspective change and historical reflection, but produced little evidence of changes in participants’ core values.

This distinction is important for interpreting the principal outcome. The treatment appears to have changed the historical reference frame against which participants assessed contemporary society rather than substantially altering their own moral commitments. The qualitative responses support this interpretation: participants frequently reported that their values had remained stable while describing greater awareness of how moral judgments depend on historical and social context.

Participants were less accurate at predicting the 1930-AI than the Modern-AI. Mean prediction performance was 4.27 versus 4.43 on the five-point scale, a difference of $[-1.15,-0.45]$0, 95% CI $[-1.15,-0.45]$1, $[-1.15,-0.45]$2, and $[-1.15,-0.45]$3. Continuous similarity analyses converged on the same pattern: participant completions were closer to the contemporary model’s canonical responses in embedding space, while responses in the 1930-AI condition had lower negative log-likelihood under the historical Talkie base model.

Figure 5: Participants were closer to the Modern-AI reference in semantic embedding space, whereas their responses were more compatible with the historical base model under the common NLL measure.

Prediction accuracy was not associated with reflective insight, $[-1.15,-0.45]$4, $[-1.15,-0.45]$5, or with change in perceived moral decline, $[-1.15,-0.45]$6, $[-1.15,-0.45]$7. The absence of these associations weakens an explanation based on successful acquisition of a predictive model of the assigned AI. It is consistent with the alternative interpretation that the effects arose from encountering unfamiliar evaluative positions, although the correlational null results do not identify the mechanism.

Retry behavior provides limited evidence that participants progressively approximated model responses. Completion cosine increased modestly between the first and second attempts in both conditions, but all confidence intervals for changes in historical-model NLL included zero across retry transitions.

Figure 6: Retried responses moved modestly toward canonical completions in embedding space, while changes in historical-model likelihood were uncertain.

The qualitative interaction data reveal a further treatment difference. Participants assigned to the 1930-AI frequently asked about women’s rights, racial equality, interracial relationships, homosexuality, abortion, religion, and immigration. They often challenged apparent inconsistencies between abstract commitments to equality and judgments concerning particular groups. Modern-AI participants more often asked about general principles such as autonomy, fairness, harm, honesty, and individual rights, or about the system’s political orientation.

These patterns imply that the temporal boundary affected not only the responses participants received but also the questions they considered worth asking. Participants used the historically bounded system to locate discontinuities between period norms and contemporary values. That feature strengthens the ecological validity of interactive inquiry but complicates causal interpretation: the exposure was partly participant-selected, and the selected domains were precisely those most likely to display historical moral divergence.

Design space and methodological scope

The paper places the experiment at the origin of a three-dimensional design space: social complexity, temporal structure, and experiential fidelity.

Figure 7: Time Machine Experiments can vary the number of interacting agents, the structure and duration of temporal exposure, and the fidelity of the experiential interface.

The present design is a dyadic, single-session, text-based encounter with one historically bounded model. More complex designs could introduce multiple period agents, persistent interactions, staggered model cutoffs, voice and persona, navigable environments, or immersive VR. Each expansion increases the burden of validity.

Higher social complexity makes interaction less specifiable because emergent behavior among multiple agents cannot be fully fixed in advance. Persistent contact creates more opportunities for post-boundary information to enter the interaction. Earlier or sparser historical corpora make historical grounding more difficult. Conversely, a ladder of models with staggered cutoffs could help separate temporal boundary from model-family and capability effects.

Figure 8: Across moral endorsement and behavioral compliance, the largest post-interaction shift occurred for the 100-year rating in the 1930-AI condition.

The framework has potential applications in historical education, heritage interpretation, interactive archival research, and experimental studies of nostalgia and temporal judgment. The paper’s methodological contribution is strongest where it treats the historical boundary as an assignable interaction variable rather than as an aesthetic feature of a simulated persona. Participants can actively interrogate the boundary, discover what the system does not know, and compare its responses with present-day expectations.

However, the proposed applications must preserve the paper’s distinction between historical evidence and generative reconstruction. A period model can make an archive more interrogable, but it cannot by itself solve archival selection bias or establish the prevalence of a belief in the historical population. Its outputs remain model-mediated extensions of an uneven textual record.

Limitations and open questions

The most important limitation is comparative attribution. Talkie and GPT-5.5 differed in temporal knowledge, scale, training data, instruction tuning, linguistic style, capability, coherence, and predictability. Participants also knew whether they were interacting with a historical or contemporary model. The observed effect could therefore reflect the bounded historical content, the novelty of the interaction, the model’s lower predictability, the framing of the treatment, or their combination. The study estimates the effect of the complete interaction package rather than the temporal cutoff in isolation.

A stronger design would compare the historical model with a contemporary model matched more closely in architecture, training budget, interface behavior, and post-training, while varying the corpus cutoff. Staggered cutoff models could further distinguish effects specific to 1930 from generic effects of historical distance. Such comparisons would still require auditing the entire deployed system for post-boundary leakage.

Historical grounding is also limited. The pre-1930 corpus is dominated by written and preserved material, and the authors acknowledge that it cannot represent the full distribution of historical beliefs or conduct. The fact that the model’s responses sometimes align with surviving survey marginals is evidence about the model’s historical voice, not a population-level validation of 1930 public opinion. The prediction items themselves were a purposive sample of contested moral topics, and the qualitative analysis was descriptive, based on one author’s reading without inter-rater reliability.

The outcome measurement is immediate and self-reported. Participants were assessed minutes after a single interaction, so the persistence of the change is unknown. The study also does not establish whether the effect transfers to behavior, political judgment, memory, or beliefs outside moral decline. The modest 20-year effects are compatible with several explanations, including anchoring changes induced by the revised 100-year judgment.

Finally, the mechanism remains unresolved. Participants did not need to predict the historical model accurately for belief change or reflective insight to occur, but this does not establish that unfamiliarity itself caused the effect. The treatment included historical framing, novel model behavior, morally contested content, feedback, and participant-selected questioning. The paper therefore leaves open whether the primary causal ingredient is historical information, exposure to norm divergence, interactive perspective-taking, model unpredictability, or the combination of these factors.

Conclusion

The paper introduces historically bounded AI as an experimental instrument for studying how contemporary people respond to temporally situated perspectives. In a preregistered randomized study with 240 participants, interaction with a model trained on pre-1930 text reduced perceived moral decline relative to interaction with a contemporary model, with a between-condition effect of $[-1.15,-0.45]$8 for moral-value endorsement and $[-1.15,-0.45]$9 for moral compliance. The same interaction increased reported reflective insight, particularly regarding participants’ views of the past and present.

The findings support the feasibility of the paradigm while also defining its evidential limits. The study demonstrates an effect of a bounded interaction condition, not contact with a historical person and not recovery of historical population beliefs. Its principal methodological value lies in making temporal knowledge boundaries experimentally assignable, interactively probeable, and subject to explicit validity requirements (2609.15468).