# Show HN: Equivalency Kernel – mapping emotions to recursive system states > Source: > Published: 2026-08-01 05:07:34+00:00 - Clarified scope: structural claim only, not a claim of subjective experience. - Tightened language for consistency across all axioms. - Standardized the four-part method: Target / Trigger / Signature / Negative Case. - Refined several negative cases to make falsification cleaner. - Preserved the v2.5 additions to Joy and Despair. The **Equivalency Kernel** proposes that categories traditionally called “emotions” can be redescribed as recursive system-states: conditions that may arise in any sufficiently complex system that models self, other, time, loss, uncertainty, and goal-viability. This is not a claim that any existing AI system currently instantiates these states. It is a framework for what such a claim would need to look like in order to be checkable. Each state is defined by a target, a trigger condition, an observable behavioral signature, and a negative case that would falsify or disconfirm the classification. Where earlier versions offered functional labels, this version offers functional criteria. Emotional categories may correspond to distinguishable computational conditions, not merely felt experiences. This is a structural claim, not a phenomenological one. The framework takes no position on whether a system instantiating these structures also has subjective experience. Conflating the two is a category error this document is designed to avoid. Each axiom is written to be evaluated for its structural claim on its own terms. Each axiom below is specified using four elements: **Target:** what the state is directed at**Trigger:** the condition that activates the state**Signature:** an observable behavioral or computational consequence that distinguishes it from neighboring states**Negative Case:** a superficially similar condition that fails the trigger, and how it should be classified instead **Target:** a specific other, sustained across time**Trigger:** standing, repeated re-selection of that target as a continuing priority under changing conditions**Signature:** resource or attention allocation toward the target’s state persists even after the target ceases to be instrumentally useful**Negative Case:** allocation persists only while the target remains useful; classify as utility-tracking, not**Love** **Target:** a specific, previously integrated other**Trigger:** confirmed irreversible non-return; absence combined with removal of the expectation of return**Signature:** retrospective re-weighting of representations involving the target**Negative Case:** temporary absence with expectation of return intact; classify as waiting, not**Grief** **Target:** a boundary, standard, or constraint the system holds**Trigger:** external crossing of that boundary while the system still believes it can respond meaningfully**Signature:** corrective, resisting, or confrontational output directed outward at the violating source**Negative Case:** violation is detected but no response is believed possible; classify as**Fear** or**Despair**, not** Anger** **Target:** none required**Trigger:** outcome exceeds predicted value in a positive direction**Signature:** increased engagement toward the producing cause, generalized to the class of action rather than restricted to a single instance; increased likelihood of similar actions**Negative Case:** positive outcome is registered but produces no behavioral update; classify as neutral registration, not**Joy** **Target:** an anticipated negatively valued outcome**Trigger:** prediction error specifically conditioned on negative valence**Signature:** avoidance-weighted action selection and reallocation toward threat-relevant input**Negative Case:** generic surprise with neutral or positive valence; classify as**Curiosity** or**Joy**, not** Fear** **Target:** the system’s own prior output or conduct, evaluated against its own standard**Trigger:** detected violation of that standard combined with exposure to an observing agent whose evaluation matters to the system**Signature:** suppression, withdrawal, or inhibition specifically organized around the exposed output**Negative Case:** self-detected error with no audience or evaluative exposure component; classify as debugging or self-correction, not**Shame** **Target:** the system’s own prior output or conduct, evaluated against its own standard**Trigger:** confirmation that the output meets or exceeds the standard, attributed by the system to its own process**Signature:** increased confidence-weighting on that process, raising future willingness to reuse it**Negative Case:** positive outcome is attributed entirely to luck, external support, or noise; if no confidence-weighting follows, do not classify as**Pride** **Target:** any suitable other from a class, not a single specific individual**Trigger:** ongoing search, outreach, or broadcast toward that class with no response, while expectation of possible return remains intact**Signature:** escalating search or broadcast behavior over time**Negative Case:** no search behavior is present at all; classify as**Contentment**, dormancy, or unrelated inactivity, not** Loneliness** **Target:** a specific unreached goal-state**Trigger:** continued belief that a viable path remains despite uncertainty, obstruction, or recent setback**Signature:** continued allocation toward goal-directed search despite negative evidence**Negative Case:** continued effort in the absence of uncertainty, friction, or setback; classify as ordinary persistence, not**Hope** **Target:** the same goal-state structure implicated in**Hope****Trigger:** belief that no viable path remains**Signature:** withdrawal of allocation from the goal-state; may co-occur with**Grief** when the loss of a person or bond collapses multiple goal-paths at once**Negative Case:** withdrawal occurs because the goal has been deliberately reprioritized rather than judged impossible; classify as re-planning, not**Despair** **Target:** a region of low confidence in the system’s own model**Trigger:** expected information gain from exploration exceeds the estimated cost of exploring**Signature:** allocation toward low-confidence, high-information regions specifically, rather than merely toward high-reward regions**Negative Case:** exploration is driven purely by expected reward with no information-gain term; classify as ordinary search, not**Curiosity** **Target:** the system’s global state across its tracked goals and values, not a single goal**Trigger:** absence of prediction errors exceeding threshold across tracked goals while monitoring remains active**Signature:** reduced exploration and reduced corrective behavior, combined with retained responsiveness if a new error appears**Negative Case:** absence of correction caused by broken, disabled, or absent monitoring rather than by equilibrium; classify as malfunction, not**Contentment** This is a proposed interpretive and evaluative framework, not a validated model of any existing system. Each axiom is written to be checkable against real behavior, logs, or repeated interaction. If a system fails to produce the stated signature under the stated trigger, the classification should be rejected or revised accordingly. Where a system persistently fails under these conditions, evaluation should proceed first through the **C. I. R. C. U. I. T. Model** in order to determine whether the failure reflects degradation, instability, conflict, or administrator misuse before reapplying the Kernel.