SCHEMA.md

psyche — data dictionary

Every table and every column in psyche.duckdb, explained once.

• built: 2026-08-06T03:48:05+00:00
• from: psyche-org/psyche-private @ 844d2abd1720
• verification policy: recheck decides: CORRECT|CORRECT_WITH_NOTES; self-check: VALID with no hard errors

Generated at build time from the registries in buildDB/ — see the last section. Do not edit by hand.

What is published here

file	what it is
psyche.duckdb	this database
paper_index.json.gz	keyword index over every study's paper (term -> {study: count}, non-positional)
source_manifest.json.gz	content hash of every input the build read
metadata_cache.json.gz	answers already fetched from outside the corpus
SCHEMA.md	this document

How to read this

Keys. An experiment is (datasource, study_id, experiment_id) — that triple identifies one experiment everywhere in the database. A study is (datasource, study_id). study_id is the study's slug, and slugs are unique across datasources with one known exception (gershman-bhui-2020 appears under both openalex and opencogdata), which is why the datasource is always part of the key.

Naming. The column name tells you its shape:

pattern	meaning
n_*	a count — integer, never null unless the underlying column is absent from the corpus
*_rate	a proportion in 0–1, DOUBLE; null when the denominator was zero
max_* / min_* / mean_* / median_*	a statistic over the rows one grain below
*_spec	the full specification as released upstream, usually high-cardinality text
is_* / has_*	boolean
*_id	an identifier, compared for equality only — never ordered or arithmetic

Null vs unknown. They mean different things and are not interchangeable:

• NULL — not applicable, or genuinely absent from the released data.
• 'unknown' — we looked and there was no record. Used where a row must exist for every experiment or study even though the upstream file does not cover it. has_agent_profile and studies.record_status say which case you are in.

Missing values in trials. The upstream corpus encodes "nothing happened" both as NULL and as the sentinel strings 'none' and 'psyche_sentinel'. Anything derived here normalises all three away; raw trials columns still carry them, so filter accordingly.

Which column means what. Several columns describe "what the participant did" and they are not interchangeable — action is populated in 99% of experiments, response in only 55%, and keypress is frequently a placeholder. Derived columns are built on action, restricted to decision_freedom = 'free' so that filler trials (press space to continue) cannot be mistaken for choices. The same split applies to stimulus_id (identity), stimulus_type (category) and stimulus_spec (unique per trial). See the trials table below.

Sequences and switches. A transition is a consecutive pair of trials within one participant's one block — never across a block boundary and never across participants. It is a switch when the value changed and a repeat when it did not. Trials whose value is missing are skipped rather than breaking the chain, so filler trials interleaved with real ones cannot hide a switch. At experiment level, counts pool every participant's transitions, so response_switch_rate is over all transitions while mean_response_switch_rate averages the participants' individual rates.

Only verified experiments are here. trials never contains an experiment that failed verification, so there is nothing to filter out. experiment_verification explains every experiment, including the ones that were excluded and why.

New columns. Task-data columns are declared in buildDB/postprocessing.py and metadata columns in buildDB/metadata_sources.py, each with the description reproduced here. This document and the column_dictionary table are regenerated from those declarations on every build, so a column added upstream of this file appears here automatically — with the conventions above, which is what a new column is expected to follow. SELECT * FROM column_dictionary is the same content, queryable.

Tables

experiments

one row per VERIFIED experiment (view) — 947 rows

The convenience join: verification + study + paradigm + all experiment-level task data. Start here.

column	type	meaning
datasource	VARCHAR	Part of the experiment key.
study_id	VARCHAR	Part of the experiment key.
experiment_id	VARCHAR	Part of the experiment key.
verdict	VARCHAR	The verdict that actually decided.
decided_by	VARCHAR	Which signal decided: recheck, self, or none.
title	VARCHAR	Study title, 'unknown' where the repo has no record.
year	BIGINT	One row per ingested study: title, publication year, DOI and task type. Year and title are completed from OpenAlex by DOI where the repo does not carry them. Studies with no record at all read 'unknown' rather than going missing.
paper_doi	VARCHAR	One row per ingested study: title, publication year, DOI and task type. Year and title are completed from OpenAlex by DOI where the repo does not carry them. Studies with no record at all read 'unknown' rather than going missing.
first_author	VARCHAR	One row per ingested study: title, publication year, DOI and task type. Year and title are completed from OpenAlex by DOI where the repo does not carry them. Studies with no record at all read 'unknown' rather than going missing.
author_list	VARCHAR	One row per ingested study: title, publication year, DOI and task type. Year and title are completed from OpenAlex by DOI where the repo does not carry them. Studies with no record at all read 'unknown' rather than going missing.
task_type	VARCHAR	Study task type, 'unknown' where the repo has no record.
paradigm	VARCHAR	Paradigm from the agent's profile, 'unknown' where it has no entry for this experiment.
paradigm_status	VARCHAR	Status of that paradigm label, 'unknown' where absent.
has_agent_profile	BOOLEAN	False when the agent has no entry for this experiment, in which case paradigm and paradigm_status read 'unknown'.
n_congruency_labelled_trials	BIGINT	Trials carrying a congruency label. Zero for most experiments — see congruency. Non-zero is what makes an interference contrast possible at all.
n_trials	BIGINT	Rows of trial data.
n_subjects	BIGINT	Participants with at least one trial.
n_free_trials	BIGINT	Trials that were a real choice rather than filler.
n_unique_stimulus	BIGINT	Distinct stimuli presented, by identity.
n_unique_stimulus_type	BIGINT	Distinct stimulus categories.
n_unique_stimulus_spec	BIGINT	Distinct fully specified stimulus descriptions.
n_unique_response	BIGINT	Distinct responses the participant actually made — the task's action space on free trials. The one to reach for.
n_unique_action	BIGINT	Distinct raw action values, including on forced trials.
n_unique_response_label	BIGINT	Distinct raw response values — the rawer label, null for ~45% of experiments. Prefer n_unique_response.
n_unique_keypress	BIGINT	Distinct physical keys. Describes the apparatus, not the behaviour — often a single placeholder value.
n_blocks	BIGINT	Distinct blocks the trials are grouped into.
max_trials_per_block	BIGINT	Longest block, in trials.
n_steps	BIGINT	Distinct within-trial step indices. 1 means every trial is a single stimulus/response exchange; more means multi-step trials.
max_step	BIGINT	Deepest within-trial step index reached.
mean_steps_per_trial	DOUBLE	Average number of rows per trial — 1.0 for single-step tasks.
n_experiment_phases	BIGINT	Distinct experiment phases (practice, main, ...).
n_task_phases	BIGINT	Distinct task phases within a trial (study, test, ...).
correct_coverage	VARCHAR	Where in a trial this experiment records correctness: all_steps, first_only, later_only, none, or partial. Read it before pooling anything built on prev_correct, which is EXCHANGE-WISE and therefore heterogeneous in a way the switch columns are not: a step-wise post-error contrast is well defined where correctness is recorded at every step and undefined where it is recorded only at one, and nothing else in the schema distinguishes them. Cross with n_multi_step_trials — the classification only bites where trials have more than one row.
n_multi_step_trials	BIGINT	Trials made of more than one row. Zero in a single-step experiment, where a row IS a trial and every EXCHANGE-WISE column is therefore trial-wise. Non-zero is the signal that response_switch and its relatives are comparing steps within a trial as often as trials.
n_unique_feedback	BIGINT	Distinct feedback values shown.
n_trials_with_reaction_time	BIGINT	Trials carrying a usable reaction time — the no-response sentinel does not count.
median_reaction_time_ms	DOUBLE	Median reaction time in milliseconds, comparable across studies.
n_imputed_trials	BIGINT	Trials the standardisation had to reconstruct.
n_response_transitions	BIGINT	Consecutive pairs of trials with a response, within one participant's block — the denominator of the switch rate.
n_response_switches	BIGINT	Transitions where the response changed.
n_response_repeats	BIGINT	Transitions where the response stayed the same.
response_switch_rate	DOUBLE	Share of response transitions that were a switch, 0-1. NULL when there were no transitions at all.
n_stimulus_transitions	BIGINT	Consecutive pairs of trials with a stimulus, within one participant's block — the denominator of the switch rate.
n_stimulus_switches	BIGINT	Transitions where the stimulus changed.
n_stimulus_repeats	BIGINT	Transitions where the stimulus stayed the same.
stimulus_switch_rate	DOUBLE	Share of stimulus transitions that were a switch, 0-1. NULL when there were no transitions at all.
n_stimulus_category_transitions	BIGINT	Consecutive pairs of trials with a stimulus category, within one participant's block — the denominator of the switch rate.
n_stimulus_category_switches	BIGINT	Transitions where the stimulus category changed.
n_stimulus_category_repeats	BIGINT	Transitions where the stimulus category stayed the same.
stimulus_category_switch_rate	DOUBLE	Share of stimulus category transitions that were a switch, 0-1. NULL when there were no transitions at all.
min_trials_per_subject	BIGINT	How the per-subject task data is spread within an experiment — statistics over rows, which a flat aggregate over trials cannot express. The mean switch rates are the average of the PARTICIPANTS' rates, unlike the experiment-level rate, which pools all transitions.
median_trials_per_subject	DOUBLE	How the per-subject task data is spread within an experiment — statistics over rows, which a flat aggregate over trials cannot express. The mean switch rates are the average of the PARTICIPANTS' rates, unlike the experiment-level rate, which pools all transitions.
max_trials_per_subject	BIGINT	How the per-subject task data is spread within an experiment — statistics over rows, which a flat aggregate over trials cannot express. The mean switch rates are the average of the PARTICIPANTS' rates, unlike the experiment-level rate, which pools all transitions.
mean_unique_stimulus_per_subject	DOUBLE	How the per-subject task data is spread within an experiment — statistics over rows, which a flat aggregate over trials cannot express. The mean switch rates are the average of the PARTICIPANTS' rates, unlike the experiment-level rate, which pools all transitions.
mean_unique_response_per_subject	DOUBLE	How the per-subject task data is spread within an experiment — statistics over rows, which a flat aggregate over trials cannot express. The mean switch rates are the average of the PARTICIPANTS' rates, unlike the experiment-level rate, which pools all transitions.
mean_response_switch_rate	DOUBLE	How the per-subject task data is spread within an experiment — statistics over rows, which a flat aggregate over trials cannot express. The mean switch rates are the average of the PARTICIPANTS' rates, unlike the experiment-level rate, which pools all transitions.
mean_stimulus_switch_rate	DOUBLE	How the per-subject task data is spread within an experiment — statistics over rows, which a flat aggregate over trials cannot express. The mean switch rates are the average of the PARTICIPANTS' rates, unlike the experiment-level rate, which pools all transitions.

trials

one response window per row - see below — 55,228,793 rows

Column meanings and the definition of a row come from convention.yaml — Behavioral Data Standard — which is the authority, reproduced here rather than paraphrased. The fill and distinct figures are measured from the data.

What a row is. Each row represents exactly ONE response window: a period during which the participant can or should produce a behavioral response. A response window exists when the experiment waits for or accepts participant input (keypress, click, slider), OR when the participant may deliberately withhold a response (go/no-go). A row MUST NOT exist for pure display events (fixation cross, blank screen, feedback with auto-advance, reward display with no keypress) or inter-trial intervals — these are presentation details, not behavioral observations. Data from display-only events (reward_value, feedback) attaches to the nearest decision row. If a presentation parameter varies across trials (SOA, ISI, cue duration), it is a feature of the associated decision row, not a separate row. A row MUST NOT contain multiple responses or reaction times for different events. If a trial involves multiple response windows, each window MUST be a separate row with its own step value. The tuple (subject_id, trial_global, step) MUST uniquely identify rows. step MUST always be populated: use 0 for every row in single-step trials, or 0, 1, 2, … for multi-step trials.

column	type	filled	distinct	meaning
datasource	VARCHAR	100%	5	Which upstream collection the study came from. Added by this build.
study_id	VARCHAR	100%	464	Study slug. Added by this build.
experiment_id	VARCHAR	100%	18	Experiment within the study, from exp_<id>.parquet. Added by this build.
subject_id	VARCHAR	100%	369,533	Unique subject identifier.
age	DOUBLE	44%	3,958	Age of the subject (years, unless otherwise specified).
sex	VARCHAR	44%	3	Reported/recorded sex of the subject.
gender	VARCHAR	35%	4	Gender of the subject.
trial_global	BIGINT	100%	41,269	0-based sequential trial_global index within subject, reflecting global chronological order.
step	BIGINT	100%	47,799	0-based index of sub-steps within a trial. MUST always be populated: 0 for every row in single-step trials, 0, 1, 2, … for multi-step trials where each trial involves multiple participant responses or behavioral observations.
trial_block	DOUBLE	100%	2,560	Within-subject index (0-based) of the temporal block: the finest-grain contiguous chunk of trials in chronological order that the paper describes (e.g. separated by instructions, pauses, procedure changes, or learning- criterion resets). When the paper describes multiple levels of temporal structure (e.g. conditions containing learning blocks), trial_block MUST encode the innermost / most granular level — not the outermost. This encodes temporal run structure only — it is NOT an experimental factor, condition, or manipulation.
trial_within_block	DOUBLE	100%	8,475	0-based sequential trial index within the current temporal block (see column trial_block).
imputed	BIGINT	100%	2	Whether this row is a synthetic observation added during standardization rather than recorded in the raw data. 0 == observed trial; 1 == imputed trial (e.g. padding uncompleted learning blocks after a stopping criterion so paper-aligned analyses can use standard trial-level columns). Default 0 when no imputation was applied.
experiment_phase	VARCHAR	100%	232	The session segment this row belongs to — the coarsest level of the experiment's temporal hierarchy (experiment_phase → block → trial → step). A FREEFORM name (the set of segments is not fixed and varies by study), e.g. consent, instructions, demographics, practice, main_task, debrief. Non-task segments (consent, surveys, instructions) carry no behavioral rows; the task segment(s) contain the blocks/trials/steps. Distinct from task_phase (the functional stage WITHIN the task).
task_phase	VARCHAR	71%	3	Functional stage of the task itself, within a task experiment_phase (NOT the session segment — that is experiment_phase). 'study' = exposure or encoding of task-relevant information; 'distractor' = intervening task intended to disrupt rehearsal or introduce delay; 'test' = performance measurement phase (retrieval, decision, or response).
subject_spec	VARCHAR	100%	40,391	JSON-encoded object of factor(s) attached to the SUBJECT: constant within a subject (across all their rows), varying across subjects (a factor that may repeat, not the subject_id). Between- subject manipulations and counterbalancing groups, e.g. {"response_mapping": "B", "reward_layout": "variant_2"}. {} (empty object) when the study has no between-subject factor. Resolves raw ids to the SEMANTIC labels in action / outcome when the study counterbalances them. Constant within subject_id.
block_spec	VARCHAR	98%	137,793	JSON-encoded object of factor(s) attached to the temporal BLOCK: constant within a block, varying across blocks (e.g. a per-block reward regime or active rule), {"reward_regime": "rich"}. {} when none. Constant within a trial_block (per subject).
trial_spec	VARCHAR	100%	1,556,671	JSON-encoded object of factor(s) attached to the TRIAL: constant within a trial (across its steps), varying across trials (e.g. the trial's latent state, transition type, true category, or cue value), {"transition_type": "common"}. {} when none. Constant within a trial_global (per subject). Whether a key is visible to the participant or latent is decided downstream, not here.
step_spec	VARCHAR	90%	411	JSON-encoded object of factor(s) attached to the STEP position: the same at each step index across trials, e.g. {"stage": "first"} at step 0. {} when none. Constant within step.
decision	BIGINT	100%	2	Whether this step involves a variable behavioral decision. 1 = decision step: the participant's response varies across trials (choice, classification, judgment, or go/no-go). 0 = non-decision step: a response window exists but the response is constant or forced (e.g., spacebar to dismiss feedback, forced acknowledgment). The participant performs an action but has no meaningful alternative. When decision is 0, encoding columns (encoding_applicable, response_type, feature_vector) MUST be null.
decision_freedom	VARCHAR	99%	3	Whether the response on this decision step was freely chosen. 'free' (default) = participant chose from multiple available options; 'forced' = only one option was available — no meaningful alternative existed (e.g., single-option stimulus, spacebar acknowledgment press). 'instructed' = multiple options were available but the participant was explicitly told which response to make (e.g., instructed sampling trials in bandit tasks, directed training presses). The participant still physically executes the press; they observe the outcome. Both 'forced' and 'instructed' trials still have a response_type and feature_vector (the structure is observable) but the choice itself was constrained: on 'forced' trials a single option was available, while on 'instructed' trials the participant was told which of several options to choose. Null when decision is 0.
correct_response	VARCHAR	27%	137,840	Task-defined correct response for this observation, in the same coding as response.
feedback	DOUBLE	100%	2	Whether performance feedback was provided for this observation.
stimulus_id	VARCHAR	94%	8,248,024	Unique stimulus identifier.
stimulus_type	VARCHAR	95%	6	Type of stimulus at the experimental/representational level. This describes the kind of object presented to participants (e.g., text string, geometric symbol, image, abstract feature vector), independent of the underlying storage format (e.g., PNG/BMP/JPG). The value determines the expected schema of stimulus_spec.
stimulus_spec	VARCHAR	96%	7,029,051	JSON-encoded stimulus specification for the row. If step is null, this is the trial-level stimulus; if step is present, this is the step-level stimulus/event for that row. The JSON object must be parseable and consistent with stimulus_type. Minimal schemas by stimulus_type: - text: {'text': } (optional: 'color', 'font', 'language') - symbol: {'shape': } (optional: 'color', 'size', 'orientation', 'fill', '_asset_ref') - feature_vector: {'features': <object|list>} (optional: 'feature_names') - mixed: {'components': } (each component should include its own type) When a stable identifier exists in the source data (e.g., filename, item id), include it as 'asset_ref' or 'id' to preserve traceability.
response	VARCHAR	59%	371,260	Participant response for this observation (e.g., keypress, option id, category label).
correct	DOUBLE	38%	2	Behavioral accuracy outcome for this observation. 1 == correct response, 0 == incorrect response.
reaction_time	DOUBLE	74%	7,067,383	Reaction time of the behavioral response for this observation. EXACTLY THREE mutually-exclusive states, each tied to the response space (keypress/response/subject_input/action) so RT-missingness and response-presence can never be conflated: > 0 — the participant RESPONDED and the latency was measured (units in reaction_time_unit). The response space is populated. null — the participant RESPONDED but the latency was NOT recorded: an untimed / self-paced task, or a source with no per-item RT (e.g. a Qualtrics survey, a self-paced rating). The response space is populated; reaction_time_unit is null. Use null — NOT -1 — for a real response with no RT. -1 — the participant DID NOT respond within the deadline (timeout / missing response). The response space MUST be 'none' (no fabricated choice; keypress never holds a fabricated key). A no-response is STILL scored and MAY carry a real outcome — the consequence of not responding: a correctly-withheld Go/No-Go no-go trial is correct, a missed go trial or a deadline miss is incorrect. What it must NOT carry is a choice that was never made, or an outcome/reward contingent on a choice or event that never happened (a reward credited to a phantom choice, a second-stage screen never reached). -1 is RESERVED for no-response; never use it for "RT unavailable" (that is null) or for a synthetic/padding row (imputed=1 carries no real behavior — null + 'none'). validate_no_response.py enforces this pairing.
reaction_time_unit	VARCHAR	72%	3	Unit of measurement for reaction_time (e.g., ms, s). null when reaction_time is null (untimed / RT not recorded) or -1 (no response).
reward	DOUBLE	38%	3	Scalar reinforcement outcome associated with this observation from the participant perspective. +1 = positive outcome / gain / correct / rewarded. -1 = negative outcome / loss / incorrect / punished. 0 = neutral outcome (no gain, no loss, no correctness signal). This column represents the task-defined feedback signal used for learning or performance evaluation, not necessarily monetary reward.
reward_value	DOUBLE	27%	563,584	Magnitude of the outcome the participant OBSERVED on this trial, in native task units (e.g., points, coins, dollars). This is the trial-level feedback signal the participant saw and could learn from — NOT the amount of money actually paid out. The two diverge whenever an observed outcome does not translate directly to money: practice/training trials whose points do not count toward the bonus, 'one random trial is paid' designs, or points that are converted to cash only at the end. In all such cases reward_value records the magnitude shown to the participant (the points/coins displayed), never 0 — a phase being unincentivized is not a reason to zero this column, because the participant still saw the outcome and learned from it. Use null (not 0) when NO outcome was shown on the trial (e.g., a decision step with no feedback): 0 means 'an outcome of zero magnitude was observed', whereas null means 'no outcome was observed'. Do not conflate the two. Whether a trial counted toward payment is a separate property — record it in a dedicated column or state it in the preamble, not by altering reward_value. This is the pre-normalization value used to derive reward via sign coding, when applicable.
reward_unit	VARCHAR	29%	5	Unit/scale of reward_value. 'points' = arbitrary task points; 'usd' = US dollars; 'eur' = euros; 'binary' = outcome coded as -1/0/1 only (no magnitude beyond sign); 'other' = known unit not covered above (prefer adding a new level if it becomes common).
response_type	VARCHAR	66%	4	Computational response type for this trial step. Determines how to interpret feature_vector. 'choice' = select one of N presented options (response is index into options). 'classify' = assign a category label to a stimulus (response is category index). 'judgment' = produce a scalar rating or estimate about a single stimulus. 'press_or_withhold' = binary motor response (press key or withhold). Null when encoding_applicable is 0.
encoding_applicable	DOUBLE	95%	2	Whether the trial encoding columns (feature_vector, response_type) are applicable for this decision step. 1 = applicable (feature_vector and response_type should be filled). 0 = not applicable because the task fundamentally requires semantic processing (math verification, logical reasoning, lexical decision, etc.); feature_vector and response_type are null by design. Null when decision is 0 (non-decision steps have no encoding).
feature_vector	VARCHAR	60%	5,167,913	JSON-encoded object describing the abstract decision structure of this trial step. Structure: options[option][dimension][encoding]. Contains an "options" key (array of per-option feature arrays). len(options) matches number of chooseable alternatives for choice, or 1 for classify/judgment/press_or_withhold. Each option is an array of feature dimensions. Each dimension is a list: categorical features are one-hot encoded (length = number of levels), continuous features are single-element lists ([value]). Empty option (identity only): []. Optional keys: "transformed" (bool) and "transform" (string) when features are not raw observable values (see Rule 16). Null when encoding_applicable is 0 or decision is 0. Example: {"options": [[[1, 0], [0, 1], [1, 0]]]}
keypress	VARCHAR	99%	15,722	The literal key the participant pressed on this decision row, when the raw data records one (e.g. an E-Prime RESP field): the ground-truth physical key (e.g. "f"). "none" when no real key was recorded — this column NEVER holds a fabricated key. Null when decision is 0.
subject_input	VARCHAR	99%	722,857	What the participant selected on this decision row, in a single response space used for the whole study, abstracted from the input DEVICE and from trial CONTENT — the link between keypress and action, equal to neither. The value is one of three kinds: a DISCRETE option/position token, a NUMERIC value (a typed number, slider, or estimate), or a FREEFORM value (free text, or any response that is neither an enumerable choice nor a single number). The column always holds the REAL value the participant produced — an actual token, number, or text. For a SMALL positional choice (a fixed handful of slots — left/right, option_1..N) the value is the SLOT/side the response landed on, never the stimulus that happened to sit there (that identity is the action); so subject_input stays distinct from action and the two are never collapsed. For an OPEN or LARGE positional response — one drawn from a big or CONTENT-DEPENDENT space (a board square, a clicked grid cell, a map location, a drawn point, a selected target) — there is NO small fixed slot set, so the "slot/side" rule above does NOT apply: subject_input is the FREEFORM/positional kind and MUST hold the REAL location token the participant produced (e.g. the coordinate "j6" or "[3,4]" read from the raw click), NEVER a single collapsed placeholder like "board_square" that throws the location away (that discards the very behaviour being studied). Model such a response as a value step (response_kind: value): it renders verbatim as <<j6>> and is never mapped to a keypress — the keypress response modes are for SMALL enumerable choices only. Render the trial CONTENT (e.g. the board) as the stimulus so the token is meaningful. "none" when the participant produced no valid input (timeout / non-response; reaction_time=-1). Null when decision is 0.
action	VARCHAR	99%	722,857	The conceptual choice the subject_input corresponds to on this row's option layout: the chosen option identity, arm, or category — NOT the raw token and NOT the screen position. A SEMANTIC label (e.g. "pine_trail", "birch_trail"), resolved per subject from subject_spec when the study counterbalances raw ids. The single source of truth for "what the participant chose", independent of where the option sat on screen. Always non-null on a decision row: a non-response is "none" (or a task-defined default action). Null when decision is 0.
outcome	VARCHAR	40%	495,865	JSON-encoded consequence of THIS row's action, with a kind: {"kind": "state"|"end_state", "value": <...>} - "state": a NEW state the action transitions into and that the trial continues from (e.g. the second-stage state of a two-step task, a map location). Non-terminal. - "end_state": a TERMINAL outcome (reward, feedback, win/loss, points). Ends the trial's response sequence. The "value" is a SEMANTIC label (e.g. "ridge", "berry"), resolved per subject from subject_spec when the study counterbalances raw ids. null when the action produced no observed consequence on this row. A non-response (subject_input="none") usually has a null outcome, but a task may define a real consequence (e.g. an end_state of 0 / a penalty) when non-responses are scored; never fabricate a state/reward the participant did not earn. The value is the outcome the participant ACTUALLY OBSERVED, read from the raw data in whatever format it is stored there (a column, a code, a nested field) — for both kinds (state and end_state). It is never derived from action or from the action→outcome contingency. Row grammar (structural): outcome.kind == "state" -> the next response window is step+1 of the SAME trial; outcome.kind == "end_state" -> the next response window RESETS to step 0 of the NEXT trial. ==================================================================== EXCEPTION — repeated-measures / repeated-sampling trials (READ THIS) ==================================================================== The end_state rule above assumes a trial has exactly ONE terminal outcome, at its last step. That assumption does NOT hold for repeated-measures paradigms — decisions-from-experience / repeated sampling — where the SAME problem is faced for several consecutive responses, each a complete choose->observe event carrying its OWN observed outcome (e.g. CPC-style feedback gambles faced 5 times per problem; peterson2021_using). In that case — and ONLY that case — a single trial MAY carry repeated end_state steps: step 0..k all with outcome.kind == "end_state", each holding the outcome observed on that repetition, and the trial still RESETS to step 0 of the NEXT trial only after its final step. Recognize it by a CONSTANT problem/stimulus across the trial's steps (the grouping key, e.g. problem_id, lives in trial_spec) paired with a FRESH per-step outcome. This is the SOLE situation in which end_state is not the last step of its trial; in every other trial there is exactly one terminal end_state. Do NOT use this exception to mask a misencoded sequential trial: a genuine multi-step task (e.g. a two-step task) still uses "state" for its non-terminal steps and "end_state" only at the end. ==================================================================== Null when decision is 0.
correct_outcome	VARCHAR	4%	56,847	The correct answer revealed in feedback, expressed in the SAME space as action (e.g. "beta", "vowel"). Taken directly from the data whenever the feedback the participant saw revealed the correct answer. null when no correct answer was revealed to the participant (e.g. reward-learning feedback that showed only an outcome, never the right action). Null when decision is 0.
reaction_time_ms	DOUBLE	72%	6,794,219	Reaction time in milliseconds, on one scale for the whole corpus. reaction_time is NOT comparable across studies as released: 401 experiments record it in ms and 127 in seconds, so averaging them raw mixes 691 with 0.62. Negative values are the corpus's no-response sentinel (-1) and become NULL rather than a reaction time of minus one. Rows whose unit is absent are exactly those sentinels. A unit this does not recognise yields NULL rather than a guess.
congruency	VARCHAR	2%	3	Whether the trial's features pointed to the same response or competing ones: congruent, incongruent, neutral, or NULL where the study did not label it. Lifted out of trial_spec and onto the row because interference is one of the most studied effects in the corpus and should not require every consumer to parse JSON — nor to know that the same contrast is spelled congruent/consistent/compatible depending on the literature it came from. IMPORTANT: this is NOT a universal property of a trial. Only the studies that set out to manipulate congruency record it — 33 of 831 experiments — so an analysis over this column is an analysis of the interference literature, not of the corpus.
response_switch	BOOLEAN	90%	2	EXCHANGE-WISE. Compares this row to the PREVIOUS ROW in (trial_global, step) order — not necessarily the previous trial. TRUE when the response differs, FALSE when it repeats, NULL at a block boundary or where either row lacks a response. In a single-step experiment a row IS a trial, so this is trial-wise. Where n_multi_step_trials is non-zero it crosses within-trial step boundaries, and most comparisons are then against another step of the SAME trial. Use prev_row_same_trial to separate the two, or response_switch_trial for the strictly trial-wise construct. Both are real transitions — a step is a genuine stimulus/response exchange — but they are not interchangeable: median reaction time by step runs 1933 / 715 / 623 / 428 ms, so mixing them compares an encoding step against a response step.
stimulus_switch	BOOLEAN	90%	2	EXCHANGE-WISE. Compares this row to the PREVIOUS ROW in (trial_global, step) order — not necessarily the previous trial. TRUE when the stimulus differs, FALSE when it repeats, NULL at a block boundary or where either row lacks a stimulus. In a single-step experiment a row IS a trial, so this is trial-wise. Where n_multi_step_trials is non-zero it crosses within-trial step boundaries, and most comparisons are then against another step of the SAME trial. Use prev_row_same_trial to separate the two, or stimulus_switch_trial for the strictly trial-wise construct. Both are real transitions — a step is a genuine stimulus/response exchange — but they are not interchangeable: median reaction time by step runs 1933 / 715 / 623 / 428 ms, so mixing them compares an encoding step against a response step.
stimulus_category_switch	BOOLEAN	91%	2	EXCHANGE-WISE. Compares this row to the PREVIOUS ROW in (trial_global, step) order — not necessarily the previous trial. TRUE when the stimulus category differs, FALSE when it repeats, NULL at a block boundary or where either row lacks a stimulus category. In a single-step experiment a row IS a trial, so this is trial-wise. Where n_multi_step_trials is non-zero it crosses within-trial step boundaries, and most comparisons are then against another step of the SAME trial. Use prev_row_same_trial to separate the two, or stimulus_category_switch_trial for the strictly trial-wise construct. Both are real transitions — a step is a genuine stimulus/response exchange — but they are not interchangeable: median reaction time by step runs 1933 / 715 / 623 / 428 ms, so mixing them compares an encoding step against a response step.
prev_row_same_trial	BOOLEAN	96%	2	TRUE when the previous row — the one every EXCHANGE-WISE column compares against — belongs to the same trial as this row. Lets an analysis separate within-trial step transitions from between-trial ones without recomputing any lag. NULL at the start of a sequence. Always FALSE in a single-step experiment; TRUE for 65.5% of comparisons in the multi-step ones.
prev_correct	DOUBLE	42%	2	EXCHANGE-WISE. Whether the previous ROW was correct, 1 or 0 — the last earlier row that recorded correctness at all, within the same participant and block. In a single-step experiment that is the previous trial. In a multi-step one it is usually a previous STEP of the same trial. Use prev_trial_correct for the trial-wise construct. Unlike the switch columns this one is NOT uniformly available: across the 144 multi-step experiments, 41 record correctness at every step, 12 only at step 0, 10 only at later steps, and 81 not at all. Read correct_coverage on the experiment before pooling — a step-wise post-error contrast is well defined in 41 of them and undefined in 22.
is_primary_step	BOOLEAN	100%	2	(undocumented)
response_switch_trial	BOOLEAN	66%	2	(undocumented)
stimulus_switch_trial	BOOLEAN	67%	2	(undocumented)
stimulus_category_switch_trial	BOOLEAN	68%	2	(undocumented)
prev_trial_correct	DOUBLE	29%	2	(undocumented)

experiment_verification

one row per experiment that has trial data — 1,131 rows

Why each experiment is in the database — or is not. Both verification signals are recorded for every experiment regardless of which one decided, so a different policy can be evaluated without a rebuild.

column	type	meaning
datasource	VARCHAR	Part of the experiment key.
study_id	VARCHAR	Part of the experiment key.
experiment_id	VARCHAR	Part of the experiment key.
verified	BOOLEAN	Whether this experiment's rows are in trials.
decided_by	VARCHAR	Which signal decided: recheck, self, or none.
verdict	VARCHAR	The verdict that actually decided.
self_verdict	VARCHAR	The standardisation pipeline's own verdict for this experiment: VALID, INVESTIGATE or ERROR.
self_error_count	INTEGER	Hard errors the pipeline's own reanalysis reported.
recheck_verdict	VARCHAR	The independent audit's standing verdict for the study: CORRECT, CORRECT_WITH_NOTES or HAS_ERRORS. Null where no audit exists.
recheck_severity	VARCHAR	Severity the auditor assigned.
recheck_round	INTEGER	Which audit round the standing verdict comes from.
recheck_pipeline_verdict	VARCHAR	What the pipeline had said, as recorded by the auditor.
reason	VARCHAR	One sentence explaining the decision, for a human.

studies

one row per ingested study — 488 rows

The bibliographic record. Title and task type come from the repo; authors and publication year are resolved from OpenAlex by DOI, because the repo's own authors field is a placeholder in every study that has one.

column	type	meaning
datasource	VARCHAR	Part of the study key.
study_id	VARCHAR	Part of the study key.
title	VARCHAR	One row per ingested study: title, publication year, DOI and task type. Year and title are completed from OpenAlex by DOI where the repo does not carry them. Studies with no record at all read 'unknown' rather than going missing.
year	BIGINT	One row per ingested study: title, publication year, DOI and task type. Year and title are completed from OpenAlex by DOI where the repo does not carry them. Studies with no record at all read 'unknown' rather than going missing.
paper_doi	VARCHAR	One row per ingested study: title, publication year, DOI and task type. Year and title are completed from OpenAlex by DOI where the repo does not carry them. Studies with no record at all read 'unknown' rather than going missing.
openalex_id	VARCHAR	One row per ingested study: title, publication year, DOI and task type. Year and title are completed from OpenAlex by DOI where the repo does not carry them. Studies with no record at all read 'unknown' rather than going missing.
task_type	VARCHAR	One row per ingested study: title, publication year, DOI and task type. Year and title are completed from OpenAlex by DOI where the repo does not carry them. Studies with no record at all read 'unknown' rather than going missing.
n_authors	BIGINT	One row per ingested study: title, publication year, DOI and task type. Year and title are completed from OpenAlex by DOI where the repo does not carry them. Studies with no record at all read 'unknown' rather than going missing.
author_list	VARCHAR	One row per ingested study: title, publication year, DOI and task type. Year and title are completed from OpenAlex by DOI where the repo does not carry them. Studies with no record at all read 'unknown' rather than going missing.
first_author	VARCHAR	One row per ingested study: title, publication year, DOI and task type. Year and title are completed from OpenAlex by DOI where the repo does not carry them. Studies with no record at all read 'unknown' rather than going missing.
abstract	VARCHAR	One row per ingested study: title, publication year, DOI and task type. Year and title are completed from OpenAlex by DOI where the repo does not carry them. Studies with no record at all read 'unknown' rather than going missing.
record_status	VARCHAR	enriched (repo record plus OpenAlex), no_openalex_record, repo_only (no DOI to look up) or missing (no repo record at all — title and task_type read 'unknown').

study_authors

one row per author per study — 3,983 rows

So an author can be queried directly rather than by matching against a joined string. Position 1 is the first author.

column	type	meaning
datasource	VARCHAR	Part of the study key.
study_id	VARCHAR	Part of the study key.
author_position	BIGINT	1 for the first author, ascending.
author_name	VARCHAR	Author's display name as OpenAlex has it.
author_surname	VARCHAR	Last whitespace-separated part of the name; convenient, not authoritative.
is_first_author	BOOLEAN	True at position 1.
is_last_author	BOOLEAN	True at the final position — the senior author, by convention.
openalex_author_id	VARCHAR	One row per author per study, so authors can be queried individually: WHERE author_name = '...' rather than LIKE over a joined string. Resolved from OpenAlex by DOI — the repo itself has no real author data.
affiliation	VARCHAR	One row per author per study, so authors can be queried individually: WHERE author_name = '...' rather than LIKE over a joined string. Resolved from OpenAlex by DOI — the repo itself has no real author data.

experiment_capabilities

one row per ingested experiment — 947 rows

The agent's profile of the experiment. Not every experiment has one; those read 'unknown' and have has_agent_profile = false.

column	type	meaning
datasource	VARCHAR	Part of the study key.
study_id	VARCHAR	Part of the study key.
experiment_id	VARCHAR	Part of the experiment key.
paradigm	VARCHAR	Per-experiment profile written by the agent's dataset profiler: paradigm, subject count, which canonical columns are populated and what values they take. Every ingested experiment gets a row; the agent has no entry for some, and those read 'unknown'.
paradigm_status	VARCHAR	Per-experiment profile written by the agent's dataset profiler: paradigm, subject count, which canonical columns are populated and what values they take. Every ingested experiment gets a row; the agent has no entry for some, and those read 'unknown'.
has_agent_profile	BOOLEAN	False when the agent has no entry for this experiment, in which case paradigm and paradigm_status read 'unknown'.
n_subjects	BIGINT	Per-experiment profile written by the agent's dataset profiler: paradigm, subject count, which canonical columns are populated and what values they take. Every ingested experiment gets a row; the agent has no entry for some, and those read 'unknown'.
trials_per_subject	JSON	Per-experiment profile written by the agent's dataset profiler: paradigm, subject count, which canonical columns are populated and what values they take. Every ingested experiment gets a row; the agent has no entry for some, and those read 'unknown'.
columns_present	JSON	Per-experiment profile written by the agent's dataset profiler: paradigm, subject count, which canonical columns are populated and what values they take. Every ingested experiment gets a row; the agent has no entry for some, and those read 'unknown'.
column_values	JSON	Per-experiment profile written by the agent's dataset profiler: paradigm, subject count, which canonical columns are populated and what values they take. Every ingested experiment gets a row; the agent has no entry for some, and those read 'unknown'.
demographic_coverage	JSON	Per-experiment profile written by the agent's dataset profiler: paradigm, subject count, which canonical columns are populated and what values they take. Every ingested experiment gets a row; the agent has no entry for some, and those read 'unknown'.
applicable_methods	JSON	Per-experiment profile written by the agent's dataset profiler: paradigm, subject count, which canonical columns are populated and what values they take. Every ingested experiment gets a row; the agent has no entry for some, and those read 'unknown'.
jingle_jangle_note	VARCHAR	Per-experiment profile written by the agent's dataset profiler: paradigm, subject count, which canonical columns are populated and what values they take. Every ingested experiment gets a row; the agent has no entry for some, and those read 'unknown'.
fingerprint	VARCHAR	Per-experiment profile written by the agent's dataset profiler: paradigm, subject count, which canonical columns are populated and what values they take. Every ingested experiment gets a row; the agent has no entry for some, and those read 'unknown'.
profiled_at	VARCHAR	Per-experiment profile written by the agent's dataset profiler: paradigm, subject count, which canonical columns are populated and what values they take. Every ingested experiment gets a row; the agent has no entry for some, and those read 'unknown'.

study_files

one row per tracked file per study — 14,212 rows

The agent's integrity manifest — content hashes that let a consumer verify a local copy. Studies the agent does not track have no rows.

column	type	meaning
study_slug	VARCHAR	The study's slug.
datasource	VARCHAR	Part of the study key.
relative_path	VARCHAR	Content hash and size of every file the agent tracks per study — the integrity manifest that lets a consumer verify a local copy. Studies the agent does not track simply have no rows here.
rfilename	VARCHAR	Content hash and size of every file the agent tracks per study — the integrity manifest that lets a consumer verify a local copy. Studies the agent does not track simply have no rows here.
sha256	VARCHAR	Content hash and size of every file the agent tracks per study — the integrity manifest that lets a consumer verify a local copy. Studies the agent does not track simply have no rows here.
remote_id	VARCHAR	Content hash and size of every file the agent tracks per study — the integrity manifest that lets a consumer verify a local copy. Studies the agent does not track simply have no rows here.
remote_scheme	VARCHAR	Content hash and size of every file the agent tracks per study — the integrity manifest that lets a consumer verify a local copy. Studies the agent does not track simply have no rows here.
size_bytes	BIGINT	Content hash and size of every file the agent tracks per study — the integrity manifest that lets a consumer verify a local copy. Studies the agent does not track simply have no rows here.
synced_at	VARCHAR	Content hash and size of every file the agent tracks per study — the integrity manifest that lets a consumer verify a local copy. Studies the agent does not track simply have no rows here.

task_data_subject

one row per participant per experiment — 495,709 rows

Derived description of what the task looked like for one participant. Every column is declared in postprocessing.py.

column	type	meaning
datasource	VARCHAR	Part of the experiment key.
study_id	VARCHAR	Part of the experiment key.
experiment_id	VARCHAR	Part of the experiment key.
subject_id	VARCHAR	Participant, as text — pinned so this table's schema is stable whichever studies are loaded. Join with CAST(trials.subject_id AS VARCHAR).
n_congruency_labelled_trials	BIGINT	Trials carrying a congruency label. Zero for most experiments — see congruency. Non-zero is what makes an interference contrast possible at all.
n_trials	BIGINT	Rows of trial data.
n_free_trials	BIGINT	Trials that were a real choice rather than filler.
n_unique_stimulus	BIGINT	Distinct stimuli presented, by identity.
n_unique_stimulus_type	BIGINT	Distinct stimulus categories.
n_unique_stimulus_spec	BIGINT	Distinct fully specified stimulus descriptions.
n_unique_response	BIGINT	Distinct responses the participant actually made — the task's action space on free trials. The one to reach for.
n_unique_action	BIGINT	Distinct raw action values, including on forced trials.
n_unique_response_label	BIGINT	Distinct raw response values — the rawer label, null for ~45% of experiments. Prefer n_unique_response.
n_unique_keypress	BIGINT	Distinct physical keys. Describes the apparatus, not the behaviour — often a single placeholder value.
n_blocks	BIGINT	Distinct blocks the trials are grouped into.
max_trials_per_block	BIGINT	Longest block, in trials.
n_steps	BIGINT	Distinct within-trial step indices. 1 means every trial is a single stimulus/response exchange; more means multi-step trials.
max_step	BIGINT	Deepest within-trial step index reached.
mean_steps_per_trial	DOUBLE	Average number of rows per trial — 1.0 for single-step tasks.
n_experiment_phases	BIGINT	Distinct experiment phases (practice, main, ...).
n_task_phases	BIGINT	Distinct task phases within a trial (study, test, ...).
n_unique_feedback	BIGINT	Distinct feedback values shown.
n_trials_with_reaction_time	BIGINT	Trials carrying a usable reaction time — the no-response sentinel does not count.
median_reaction_time_ms	DOUBLE	Median reaction time in milliseconds, comparable across studies.
n_imputed_trials	BIGINT	Trials the standardisation had to reconstruct.
n_response_transitions	BIGINT	Consecutive pairs of trials with a response, within one participant's block — the denominator of the switch rate.
n_response_switches	BIGINT	Transitions where the response changed.
n_response_repeats	BIGINT	Transitions where the response stayed the same.
response_switch_rate	DOUBLE	Share of response transitions that were a switch, 0-1. NULL when there were no transitions at all.
n_stimulus_transitions	BIGINT	Consecutive pairs of trials with a stimulus, within one participant's block — the denominator of the switch rate.
n_stimulus_switches	BIGINT	Transitions where the stimulus changed.
n_stimulus_repeats	BIGINT	Transitions where the stimulus stayed the same.
stimulus_switch_rate	DOUBLE	Share of stimulus transitions that were a switch, 0-1. NULL when there were no transitions at all.
n_stimulus_category_transitions	BIGINT	Consecutive pairs of trials with a stimulus category, within one participant's block — the denominator of the switch rate.
n_stimulus_category_switches	BIGINT	Transitions where the stimulus category changed.
n_stimulus_category_repeats	BIGINT	Transitions where the stimulus category stayed the same.
stimulus_category_switch_rate	DOUBLE	Share of stimulus category transitions that were a switch, 0-1. NULL when there were no transitions at all.

task_data_experiment

one row per experiment — 947 rows

The same, aggregated over the whole experiment, plus statistics over the per-participant rows.

column	type	meaning
datasource	VARCHAR	Part of the experiment key.
study_id	VARCHAR	Part of the experiment key.
experiment_id	VARCHAR	Part of the experiment key.
n_congruency_labelled_trials	BIGINT	Trials carrying a congruency label. Zero for most experiments — see congruency. Non-zero is what makes an interference contrast possible at all.
n_trials	BIGINT	Rows of trial data.
n_subjects	BIGINT	Participants with at least one trial.
n_free_trials	BIGINT	Trials that were a real choice rather than filler.
n_unique_stimulus	BIGINT	Distinct stimuli presented, by identity.
n_unique_stimulus_type	BIGINT	Distinct stimulus categories.
n_unique_stimulus_spec	BIGINT	Distinct fully specified stimulus descriptions.
n_unique_response	BIGINT	Distinct responses the participant actually made — the task's action space on free trials. The one to reach for.
n_unique_action	BIGINT	Distinct raw action values, including on forced trials.
n_unique_response_label	BIGINT	Distinct raw response values — the rawer label, null for ~45% of experiments. Prefer n_unique_response.
n_unique_keypress	BIGINT	Distinct physical keys. Describes the apparatus, not the behaviour — often a single placeholder value.
n_blocks	BIGINT	Distinct blocks the trials are grouped into.
max_trials_per_block	BIGINT	Longest block, in trials.
n_steps	BIGINT	Distinct within-trial step indices. 1 means every trial is a single stimulus/response exchange; more means multi-step trials.
max_step	BIGINT	Deepest within-trial step index reached.
mean_steps_per_trial	DOUBLE	Average number of rows per trial — 1.0 for single-step tasks.
n_experiment_phases	BIGINT	Distinct experiment phases (practice, main, ...).
n_task_phases	BIGINT	Distinct task phases within a trial (study, test, ...).
correct_coverage	VARCHAR	Where in a trial this experiment records correctness: all_steps, first_only, later_only, none, or partial. Read it before pooling anything built on prev_correct, which is EXCHANGE-WISE and therefore heterogeneous in a way the switch columns are not: a step-wise post-error contrast is well defined where correctness is recorded at every step and undefined where it is recorded only at one, and nothing else in the schema distinguishes them. Cross with n_multi_step_trials — the classification only bites where trials have more than one row.
n_multi_step_trials	BIGINT	Trials made of more than one row. Zero in a single-step experiment, where a row IS a trial and every EXCHANGE-WISE column is therefore trial-wise. Non-zero is the signal that response_switch and its relatives are comparing steps within a trial as often as trials.
n_unique_feedback	BIGINT	Distinct feedback values shown.
n_trials_with_reaction_time	BIGINT	Trials carrying a usable reaction time — the no-response sentinel does not count.
median_reaction_time_ms	DOUBLE	Median reaction time in milliseconds, comparable across studies.
n_imputed_trials	BIGINT	Trials the standardisation had to reconstruct.
n_response_transitions	BIGINT	Consecutive pairs of trials with a response, within one participant's block — the denominator of the switch rate.
n_response_switches	BIGINT	Transitions where the response changed.
n_response_repeats	BIGINT	Transitions where the response stayed the same.
response_switch_rate	DOUBLE	Share of response transitions that were a switch, 0-1. NULL when there were no transitions at all.
n_stimulus_transitions	BIGINT	Consecutive pairs of trials with a stimulus, within one participant's block — the denominator of the switch rate.
n_stimulus_switches	BIGINT	Transitions where the stimulus changed.
n_stimulus_repeats	BIGINT	Transitions where the stimulus stayed the same.
stimulus_switch_rate	DOUBLE	Share of stimulus transitions that were a switch, 0-1. NULL when there were no transitions at all.
n_stimulus_category_transitions	BIGINT	Consecutive pairs of trials with a stimulus category, within one participant's block — the denominator of the switch rate.
n_stimulus_category_switches	BIGINT	Transitions where the stimulus category changed.
n_stimulus_category_repeats	BIGINT	Transitions where the stimulus category stayed the same.
stimulus_category_switch_rate	DOUBLE	Share of stimulus category transitions that were a switch, 0-1. NULL when there were no transitions at all.
min_trials_per_subject	BIGINT	How the per-subject task data is spread within an experiment — statistics over rows, which a flat aggregate over trials cannot express. The mean switch rates are the average of the PARTICIPANTS' rates, unlike the experiment-level rate, which pools all transitions.
median_trials_per_subject	DOUBLE	How the per-subject task data is spread within an experiment — statistics over rows, which a flat aggregate over trials cannot express. The mean switch rates are the average of the PARTICIPANTS' rates, unlike the experiment-level rate, which pools all transitions.
max_trials_per_subject	BIGINT	How the per-subject task data is spread within an experiment — statistics over rows, which a flat aggregate over trials cannot express. The mean switch rates are the average of the PARTICIPANTS' rates, unlike the experiment-level rate, which pools all transitions.
mean_unique_stimulus_per_subject	DOUBLE	How the per-subject task data is spread within an experiment — statistics over rows, which a flat aggregate over trials cannot express. The mean switch rates are the average of the PARTICIPANTS' rates, unlike the experiment-level rate, which pools all transitions.
mean_unique_response_per_subject	DOUBLE	How the per-subject task data is spread within an experiment — statistics over rows, which a flat aggregate over trials cannot express. The mean switch rates are the average of the PARTICIPANTS' rates, unlike the experiment-level rate, which pools all transitions.
mean_response_switch_rate	DOUBLE	How the per-subject task data is spread within an experiment — statistics over rows, which a flat aggregate over trials cannot express. The mean switch rates are the average of the PARTICIPANTS' rates, unlike the experiment-level rate, which pools all transitions.
mean_stimulus_switch_rate	DOUBLE	How the per-subject task data is spread within an experiment — statistics over rows, which a flat aggregate over trials cannot express. The mean switch rates are the average of the PARTICIPANTS' rates, unlike the experiment-level rate, which pools all transitions.

source_manifest

one row per input file the build reads — 4,120 rows

Content hash of every input, which is what lets an update tell exactly what moved. ingested marks the Parquet files whose rows are actually in trials.

column	type	meaning
source_path	VARCHAR	Path of the file in psyche-private.
source_hash	VARCHAR	Content hash — the LFS SHA-256 where there is one, else the git blob id.
source_size	UBIGINT	Size in bytes.
role	VARCHAR	What the build reads it as: trials, control or metadata.
ingested	BOOLEAN	True when this Parquet file's rows are in trials.
datasource	VARCHAR	Parsed from the path, where the path has one.
study_id	VARCHAR	Parsed from the path, where the path has one.
experiment_id	VARCHAR	Parsed from the path, where the path has one.

build_info

key/value — 10 rows

Provenance of this database: when it was built, from which revision of psyche-private, under which verification policy, and the fingerprints of the recipes that produced it.

column	type	meaning
key	VARCHAR	Name of the property.
value	VARCHAR	Its value, as text.

Where this comes from

part of the database	declared in
what a trial row is, and every trials column	buildDB/convention.yaml (the upstream standard)
which experiments are included	buildDB/verification.py
the metadata tables	buildDB/metadata_sources.py
the task-data columns	buildDB/postprocessing.py
this document	buildDB/schema_doc.py

Adding a task-data column is a name and a function in postprocessing.py; adding a metadata table is a declaration in metadata_sources.py. Either way the column appears here on the next build, with the description given at the declaration. Nothing about this file is maintained by hand except the conventions and the trials column notes, which describe data this build does not own.