Operation Span Score Calculator

Actual operation-span set inventory: independently serial-position-coded recall yields partial credit across correct items and absolute credit only for completely correct sets. Processing accuracy reported separately, unknown sets retain possible bounds. Protocol, item-order scoring, instrument applicability and actual task administration external; not a universal validity cutoff, maximum span, IQor clinical diagnosis.

Description

Operation span combines serial-position recall with a separately measured processing task.

Operation span combines serial-position recall with a separately measured processing task.^[1]

Partial and absolute credit reward different recall patterns and need the actual task protocol.

When to use Operation Span Score Calculator

Use this tool to calculate or review the supplied observations under the stated rules. Establish the assessment or recording protocol, units, instrument version and independently applicable reference or calibration.

How to use Operation Span Score Calculator and understand its results

Establish the assessment or recording protocol, units, instrument version and independently applicable reference or calibration.
Enter callerTrialSetIds, independentlyCodedSerialPositionRecallFlags, independentlyAssessedProcessingCorrectCounts, actualProcessingProblemCounts, setEvidenceKnownFlags, using the field meanings and rules below, then run the tool.
Read result.knownPartialCreditRecallScore, result.knownAbsoluteCreditRecallScore, result.maximumPossiblePartialCreditScore, result.maximumPossibleAbsoluteCreditScore, result.knownProcessingAccuracyPercent, result.unknownSetIds. Use the units and sign conventions below. Review unresolved records and any possible bounds separately from known results.
Correct any reported input issue and run again. Before using a successful result, check the stated units, signs, reference scope and observation coverage.

Input field	Meaning	Value type	Requirement
callerTrialSetIds	Actual trial set IDs	list of string	Required
independentlyCodedSerialPositionRecallFlags	Independently position-scored recall flags by set	numeric matrix	Required
independentlyAssessedProcessingCorrectCounts	Actual correctly solved processing problems by set	list of number	Required
actualProcessingProblemCounts	Actual processing problem counts by set	list of number	Required
setEvidenceKnownFlags	Set evidence known flags; binary 1 means the matched observation is known, 0 means unknown	list of number	Required

Require unique nonblank set IDs and aligned recall rows, processing-correct counts, actual-problem counts and known flags. Recall rows contain 1–32 binary serial-position flags. Actual problems are integers 1–1000000; correct counts are integers 0–actual. Known partial credit sums recalled positions; absolute credit adds a set's size only when all positions are correct. Unknown sets add their full lengths to maximum bounds and retain their IDs. Processing accuracy pools correct/actual problems over known sets, which must have positive total actual problems. No automatic processing-accuracy cutoff excludes a set.

partial=Σ_known ΣrecallFlags; absolute=Σ_knownAllCorrect setLength; maximumBounds=knownScore+Σ_unknown setLength; processingPercent=100×Σ_known correct/Σ_known actual

Use the matching field units and the exact boundary, ordering and reference rules above.

Output field	Meaning	Value type
result	Structured result; its nested fields below contain the actual calculation or selected reference	object
result.knownPartialCreditRecallScore	Correct serial positions summed over known sets	number
result.knownAbsoluteCreditRecallScore	Full set lengths credited only for completely correct known sets	number
result.maximumPossiblePartialCreditScore	Known partial recall plus all serial positions in unknown sets	number
result.maximumPossibleAbsoluteCreditScore	Known absolute recall plus all serial positions in unknown sets	number
result.knownProcessingAccuracyPercent	Pooled correct processing problems divided by pooled actual problems for known sets, times 100	number
result.unknownSetIds	Unknown Set Ids; unresolved records kept separate from known results	list

json

{
  "input": {
    "callerTrialSetIds": [
      "set-a",
      "set-b"
    ],
    "independentlyCodedSerialPositionRecallFlags": [
      [
        1,
        1
      ],
      [
        1,
        0,
        1
      ]
    ],
    "independentlyAssessedProcessingCorrectCounts": [
      2,
      2
    ],
    "actualProcessingProblemCounts": [
      2,
      3
    ],
    "setEvidenceKnownFlags": [
      1,
      1
    ]
  },
  "output": {
    "result": {
      "knownPartialCreditRecallScore": 4,
      "knownAbsoluteCreditRecallScore": 2,
      "maximumPossiblePartialCreditScore": 4,
      "maximumPossibleAbsoluteCreditScore": 2,
      "knownProcessingAccuracyPercent": 80,
      "unknownSetIds": []
    }
  }
}

Limitations and assumptions

Actual acquisition, assessment protocol, instrument version and calibration determine applicability. The tool does not examine a person, classify images, administer a questionnaire or choose diagnostic cutoffs.

Missing required fields, unknown fields, wrong types and violated rules return input issues. Numeric values and outputs must be finite. Lists and matrices must be dense, without missing cells. Numeric lists allow at most 10000 entries; ordinary string lists allow at most 128 strings of 128 UTF-16 units each; numeric matrices allow at most 128 rows and 128 columns, subject to narrower requirements above. Extremely large composite outputs are rejected.

Alternative or Complementary methods

For a related step, use Saccadic Intrusion Rate Calculator: Saccadic-intrusion rate standardizes independently counted events by valid observation time. For a complementary comparison, use Memory Guided Saccade Error Calculator: Memory-guided saccade error compares a measured endpoint with its remembered target coordinate.

References

Operation Span Score Calculator — tool.io

Similar or alternative tools

Saccadic Intrusion Rate Calculator
Saccadic-intrusion rate standardizes independently counted events by valid observation time.
Memory Guided Saccade Error Calculator
Memory-guided saccade error compares a measured endpoint with its remembered target coordinate.