Animate monitoring is not uniform: implications for the animate monitoring hypothesis
Bibliographic record
Abstract
The animate monitoring hypothesis (AMH) purports that humans evolved specialized mechanisms that prioritize attention to animates over inanimates. Importantly, the hypothesis emphasizes that any animate—an entity that can move on its own—should take priority in attention. While many experiments have found general support for this hypothesis, there have yet been no systematic investigations into whether the type of animate matters for animate monitoring. In the present research we addressed this issue across three experiments. In Experiment 1, participants ( N = 53) searched for an animate or inanimate entity in a search task, and the animate was either a mammal or a non-mammal (e.g., bird, reptile, insect). Mammals were found significantly faster than inanimates, replicating the basic AMH finding. However, they were also found significantly faster than non-mammals, who were not found faster than inanimates. Two additional experiments were conducted to probe for differences among types of non-mammals using an inattentional blindness task. Experiment 2 ( N = 171) compared detection of mammals, insects, and inanimates, and Experiment 3 ( N = 174) compared birds and herpetofauna (reptiles and amphibians). In Experiment 2, mammals were spontaneously detected at significantly higher rates than insects, who were detected at only slightly higher rates than the inanimates. Furthermore, when participants did not consciously identify the target, they nonetheless could correctly guess the higher level category of the target (living vs. nonliving thing) for the mammals and the inanimates, but could not do so for the insects. We also found in Experiment 3 that reptiles and birds were spontaneously detected at rates similar to the mammals, but like insects they were not identified as living things at rates greater than chance when they were not consciously detected. These results do not support a strong claim that all animates are prioritized in attention, but they do call for a more nuanced view. As such, they open a new window into the nature of animate monitoring, which have implications for theories of its origin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".