Temporal Cognition Field

Mechanism

The temporal cognition field is a cognitive domain field whose tuple structure parallels the other affective dimensions but whose semantic content encodes the agent's relationship to time rather than to outcomes or to other agents. The field carries at minimum three independently addressable dimensions: urgency, which represents the perceived rate at which available action time is decreasing; patience, which represents the agent's tolerance for delay before reallocating attention; and deadline pressure, which represents the proximity of externally imposed temporal commitments.

Each dimension is maintained as a value with an associated decay function and an accumulation rule. Urgency rises when externally observed deadlines approach or when execution delays accumulate beyond the agent's prior expectation, and it decays when commitments are completed or when the agent transitions to a regime of slack time. Patience decreases under sustained blocking conditions and recovers during productive activity, with the recovery rate itself modulated by the agent's broader affective state. Deadline pressure tracks the worst-case proximity across all active commitments, weighted by the agent's integrity-derived assessment of its capability to meet each commitment.

The temporal structure of affect is realized through three operations the field continuously performs. Decay reduces the influence of past affective events at a configured rate, ensuring that distant emotional history does not dominate present evaluation. Accumulation integrates ongoing affective input over a sliding window, producing a present-state estimate that reflects recent rather than instantaneous experience. Modulation couples the field's outputs to other cognitive primitives so that temporal context shapes the behavior of forecast, integrity, and the coherence engine. The combination of decay, accumulation, and modulation is what gives affective state its temporal character: the present is informed by, but not enslaved to, the past.

Coupling functions link the temporal dimensions to specific behavioral parameters. Urgency couples inversely to forecast horizon length: as urgency rises, the forecasting engine narrows the projected horizon to focus speculative resources on near-term outcomes. Urgency couples positively to promotion threshold sensitivity: speculative branches must clear a higher bar to be promoted to execution under time pressure. Patience couples to empathy weighting in the coherence trifecta: an impatient agent weights its own immediate goals more heavily relative to the projected states of other agents. Deadline pressure couples to confidence: confidence margins tighten as deadlines approach, reflecting reduced opportunity for course correction.

The field is realized internally as a structured tuple whose components are addressed by name and whose values are typed scalars or short vectors. Each dimension exposes a current value, a derivative estimate, a recent-history buffer of bounded length, and a metadata block recording the most recent contributing event identifiers. Read accesses to the field are non-destructive and observation-stable: a consumer reading the urgency value does not perturb the value or the field's internal accumulators. Write accesses occur only through the decay and accumulation operators, which are themselves invoked only by the temporal-cognition update cycle. This separation between read and write paths ensures that downstream consumers - the forecasting engine, the promotion threshold evaluator, the empathy weighting block, the confidence calculator - obtain consistent values within a single mutation cycle even as the field is being updated by upstream affective input.

The update cycle itself is staged. In the first stage, the field receives affective input from upstream sources: the integrity register, the coherence trifecta, the trust-slope evaluator, and the external commitment ledger. Each input is timestamped and tagged with a contributing source. In the second stage, the accumulation operator integrates the inputs into the field's recent-history buffer, applying the recency weighting function. In the third stage, the decay operator advances the field's per-dimension state by the configured time step. In the fourth stage, the modulation outputs are recomputed from the updated field state and exposed for downstream consumption. The cycle is rate-limited to a configured frequency, typically one update per mutation lifecycle, ensuring deterministic behavior under varying load.

An additional structural property of the field is its support for derivative observation. Each dimension exposes not only its present value but also a smoothed first derivative, computed across the recent-history buffer using a configurable estimator. The derivative channel is consumed by downstream primitives that must reason about the direction of temporal change rather than its absolute level: the forecast horizon adjuster, for example, may narrow more aggressively when urgency is rising rapidly than when urgency is high but stable, recognizing that a rapidly worsening temporal context warrants a more conservative posture than a steady-state high-pressure context. The derivative is itself decay-conditioned to prevent transient input spikes from producing spurious large derivative values; the smoothing window length is a governance-scoped parameter, and the choice of estimator (finite-difference, Savitzky-Golay, exponential-derivative) is declared in the configuration credential.

Internal consistency between the field's dimensions is maintained by an invariant checker that runs at the close of each update cycle. The checker verifies that derived quantities (such as the overall temporal-pressure scalar that aggregates urgency, deadline pressure, and the inverse of patience) are consistent with their constituent dimensions, that no dimension has departed from its configured saturation envelope, and that the recent-history buffer remains well-formed after the accumulation pass. Checker violations are themselves recorded in lineage and propagated to the integrity register, where they may degrade the agent's self-reported integrity score. This integrity coupling closes a feedback path: a temporal-cognition field that has fallen into an inconsistent state produces an integrity signal that downstream consumers can act upon, even if the field's nominal outputs continue to be exposed.

Operating Parameters

Decay constants are configured per dimension and are typically expressed as half-lives in operational time units. Urgency half-lives are short, on the order of the agent's mutation lifecycle period, to ensure responsiveness to changing temporal context. Patience half-lives are longer, reflecting the slower dynamics of attentional reallocation. Deadline pressure does not decay autonomously; it is recomputed each cycle from the current set of active commitments.

Accumulation windows are bounded to prevent unbounded memory growth. The window length is configured as a number of mutation cycles or as a wall-clock duration, whichever is shorter. Within the window, affective input is integrated with a recency weighting that emphasizes recent events without discarding the older portion of the window outright.

Coupling weights are policy-governed and bounded. Each coupling specifies a weight, a saturation point beyond which additional temporal input produces no further behavioral change, and a floor below which the coupling is treated as inactive. These bounds prevent pathological behavior in extreme temporal regimes, such as an agent with infinitely high urgency collapsing its forecast horizon to zero or an agent with infinite patience refusing to ever reallocate attention.

The bidirectional coupling between the temporal field and the broader affective state is rate-limited to prevent oscillation. Temporal input modifies affect, and affect modifies temporal input, but the loop is dampened by a configured time constant that ensures convergence to a stable state under steady external conditions.

Saturation behavior is specified per dimension. Urgency saturates upward at a configured ceiling, beyond which additional deadline approach produces no further forecast-horizon narrowing; this floor on horizon length prevents the agent from collapsing into purely reactive behavior under extreme pressure. Patience saturates downward at a configured floor, below which additional blocking produces no further attentional reallocation; this floor preserves a minimum commitment to the agent's current task even under sustained frustration. Deadline pressure saturates at the highest pressure attributable to any single active commitment, ensuring that the addition of further low-priority commitments does not artificially inflate aggregate pressure.

Configuration of all parameters is governance-scoped. The decay constants, accumulation windows, coupling weights, saturation bounds, and rate limits are declared in the agent's configuration credential and may be revised only through the configuration-update procedure. Operational drift in these parameters is itself recorded as lineage, so an external auditor may reconstruct the temporal-cognition policy under which any historical behavior occurred. This auditability is essential when the agent's behavior is consumed by downstream systems that must reason about the temporal context in which the agent's outputs were produced.

Initial conditions for the field at agent instantiation are themselves parameters subject to governance. The initial values for urgency and patience may be drawn from a configured prior distribution, from a population-mean snapshot of comparable agents, or from a deterministic neutral state. The choice of initialization regime is declared in the configuration credential and is recorded in the agent's birth lineage. This declared initialization is essential to reproducibility: an auditor reconstructing the agent's behavior from scratch must instantiate the temporal field with the same initial conditions used during the original execution. Embodiments that draw initial values from population means additionally specify the population identifier, the snapshot timestamp, and the inclusion criteria, so that the population itself is reconstructible.

Alternative Embodiments

The dimension set may be extended beyond the minimal urgency-patience-deadline triple. Additional dimensions may include rhythm, encoding the agent's perception of regular temporal patterns; anticipation, encoding forward-looking emotional engagement with future events; and retrospection, encoding the weight given to past events in present evaluation. The field's structural behavior is invariant under dimension extension.

Decay functions may be embodied as exponential, polynomial, or piecewise schedules. Accumulation may be implemented as a sliding-window integral, as an exponential moving average, or as a learned aggregator. Coupling functions may be linear, sigmoidal, or learned mappings from temporal state to behavioral parameter. The disclosure encompasses all embodiments in which decay, accumulation, and modulation jointly produce the temporal structure of affect.

The temporal field may be embodied as a single shared field across an agent population, as a per-agent field with no cross-agent coupling, or as a hybrid in which agents share a global temporal context while maintaining individual urgency and patience states. Multi-agent embodiments admit coupling functions that propagate temporal pressure between agents through the empathy mechanism.

The field may be persisted across agent lifecycle boundaries or reset at each lifecycle. In a persistence embodiment, the field's recent-history buffer and decay state are serialized at lifecycle end and restored at lifecycle begin, producing an agent whose temporal context survives restarts. In a reset embodiment, each lifecycle begins with a freshly initialized field, with initial values drawn either from a configured prior or from a population-level mean. Hybrid embodiments persist the slow-decaying patience and retrospection dimensions while resetting the fast-decaying urgency and deadline-pressure dimensions, producing an agent that remembers its dispositional temporal posture but not its momentary deadline pressure.

Embodiments differ in their treatment of clock sources. The simplest embodiment derives all temporal quantities from the agent's local mutation clock; this is appropriate when the agent operates in isolation. A second embodiment couples the field to a mesh-wide synchronized clock, ensuring that urgency derived from external deadlines is consistent across cooperating agents. A third embodiment supports multiple clock sources simultaneously, with the field's deadline-pressure dimension distinguishing commitments tied to different clocks and applying clock-specific synchronization tolerances when computing pressure.

Embodiments also differ in their granularity of temporal-event tagging. A coarse-grained embodiment treats all upstream affective inputs as undifferentiated contributions to the dimension they modulate. A fine-grained embodiment tags each input with a category drawn from a declared taxonomy (deadline-derived, blocking-derived, latency-derived, anticipation-derived, retrospection-derived) and accumulates per-category sub-buffers in addition to the dimension-level buffer. The fine-grained embodiment supports downstream introspection: a consumer may query not only the present urgency value but also the categorical decomposition of the inputs that produced it, distinguishing urgency arising from external deadline approach from urgency arising from accumulated execution delay. The taxonomy itself is governance-scoped and versioned, so that historical events tagged under an earlier taxonomy remain interpretable after taxonomy revision.

Composition with Trust-Slope Modulation

The temporal cognition field composes directly with the trust-slope modulation mechanism disclosed elsewhere in the affective-state corpus. Trust slope, as a measure of the rate at which the agent's trustworthiness is evolving, is itself a temporal quantity, and its modulation is naturally sensitive to temporal context. The composition operates in two directions.

In the first direction, the temporal field modulates the trust slope's evaluation horizon. Under high urgency, the trust slope is computed over a shorter window, emphasizing recent behavior and permitting faster trust adaptation. Under high patience, the slope is computed over a longer window, smoothing transient fluctuations and producing more stable trust assessments. The forecasting horizon coupling, which is already present in the temporal field's outputs, ensures that the trust-slope evaluation horizon remains consistent with the broader cognitive horizon the agent is operating under.

In the second direction, the trust slope feeds back into the temporal field's deadline pressure dimension. A trust slope that is degrading rapidly under deadline pressure increases the perceived deadline pressure further, because the agent recognizes that its capacity to meet commitments is itself eroding. This feedback is bounded by the same rate limits that govern the temporal-affective loop, preventing runaway escalation.

Together with the coherence engine, the composition of temporal cognition and trust-slope modulation produces an agent whose behavior under time pressure is structurally reasonable: it narrows its focus, tightens its confidence margins, prioritizes commitments it is most capable of meeting, and signals degradation through the trust-slope channel before failures occur.

The composition also produces a principled treatment of recovery. When deadline pressure subsides, the temporal field's decay operators relax urgency at their configured rate, and the trust-slope window widens accordingly. Recovery is therefore not instantaneous; an agent that has operated under sustained pressure carries the residue of that pressure into the immediate post-deadline period, just as a human reasoner does. This residue is the temporal-structural signature of affective state: the past does not disappear when the deadline passes; it decays.

Prior-Art Distinction

Real-time scheduling systems track deadlines and adjust task priority but do not maintain affective dimensions and do not modulate forecast horizons or empathy weighting through structural coupling. Affective computing literature describes momentary affective state but rarely addresses the temporal structure of affect through explicit decay, accumulation, and modulation operators. Reinforcement learning systems with discount factors implement a primitive form of temporal weighting but do not expose the discount as a cognitive field that other primitives can consult. The temporal cognition field is distinguished by its tuple-structured representation, its bidirectional coupling with affective state, and its compositional role with trust-slope modulation and the coherence trifecta.

Cognitive architectures in the prior art that include explicit affect components - SOAR with emotional extensions, ACT-R with subsymbolic activation - typically treat time as an external parameter rather than as a domain over which affect itself is structured. The disclosed field treats the agent's relationship to time as a first-class cognitive object whose evolution is governed by the same operator vocabulary used elsewhere in the architecture, making it composable with the trust, integrity, coherence, and forecasting primitives without ad-hoc bridging logic. Discount-factor mechanisms in temporal-difference learning produce numerically similar effects on near-versus-far valuation but do not expose a structural field that downstream primitives can read; they operate within the value-function calculation rather than as an architecturally visible state.

Process-control and real-time-operating-system literature provides further background but does not anticipate the disclosed field. Earliest-deadline-first and rate-monotonic scheduling reason about deadline proximity but do so only to determine task ordering, not to modulate cognitive parameters such as forecast horizon or empathy weighting. Dynamic-priority schedulers that increase priority as deadlines approach do so monotonically and irreversibly within the deadline interval, whereas the disclosed field admits decay, recovery, and bidirectional modulation. Hybrid scheduler-cognition systems that bolt scheduling primitives onto cognitive architectures typically do so via ad-hoc adapters; the disclosed field is internal to the cognitive architecture itself, sharing the operator vocabulary used by other affective dimensions, which permits the field to compose without bridging logic.

Disclosure Scope

This disclosure covers the temporal cognition field as a cognitive domain field with at least the dimensions of urgency, patience, and deadline pressure; the decay, accumulation, and modulation operators that give the field its temporal structure; the coupling functions that link the field to forecast horizon, promotion threshold, empathy weighting, and confidence; the bidirectional coupling with the broader affective state; and the composition with trust-slope modulation and the coherence engine. The scope encompasses any embodiment in which past affect informs current state through structurally specified decay, accumulation, and modulation, regardless of dimension extension, function form, or deployment topology.

The disclosure further encompasses configuration regimes in which the decay constants, accumulation windows, coupling weights, and saturation bounds are declared in a governance-scoped configuration credential whose modifications are themselves recorded in lineage; persistence regimes in which the field state is serialized across agent lifecycle boundaries and restored at lifecycle begin; multi-clock regimes in which the field tracks deadline pressure against simultaneously active clock sources with clock-specific synchronization tolerances; and population-level regimes in which the field is shared, partitioned, or hybridized across cooperating agents with empathy-mediated propagation of temporal pressure. The disclosure additionally encompasses the use of the temporal cognition field as input to downstream auditing, explanation, and post-hoc behavioral reconstruction systems, in which the field's recorded state and update lineage are consumed to reproduce the temporal context of any historical agent decision.