Unlike the average and active swim speeds, the floating or inactivity measure identifies the extent to which there were periods of inactivity or particularly low speed during the trial. It’s not a direct measure of performance, but provides context for other behavioral data – preventing misinterpretation of commonly used metrics such as latency, path length and platform crossings. Periods of slow speed can inflate latency and path length in learning trials, and reduce the number of platform crossings in probe trials, giving a misleading impression of the cognitive function being tested (e.g. implying poor spatial memory).

Periods of slow speed could be due to strategic pausing (e.g. to take bearings at the start of a trial or when getting close to the learned platform location), or could be a sign of disengagement, fatigue, sensory or motor impairment, confusion or poor spatial memory. It’s therefore important to use floating or inactivity alongside other measures, to show which of those is occuring. Time slice analysis may also be valuable in showing whether slowness occurs at the start, end or throughout a trial.

HVS Image systems measure the percentage of the trial time that the subject spends below a defined speed threshold (5 cm/sec by default, but you can set whatever speed threshold is appropriate for your study), as well as complementary measures such as the average and active speeds (was the subject fast when active, or fairly slow even when above the threshold?), time slices (at what point(s) did slownesss occur?), heading angle, path ratio and corridor test (showing spatial intent and follow-through), and other measures that show use or lack of spatial or procedural strategies.

The floating or inactivity measure is particularly relevant in:

• Depression and mood disorder models, where subjects may disengage from the task or lose motivation.
• Chronic stress studies, where stress may suppress engagement and increase immobility.
• Aging models, as older animals may disengage more readily or experience fatigue.
• Neurodegenerative models (e.g., Alzheimer’s), where inactivity may result from apathy or confusion, masking preserved spatial memory.
• Pharmacological studies, where sedatives, anxiolytics, antipsychotics, or dopaminergic drugs can alter activity levels.
• Sensorimotor lesion models, where impaired balance, proprioception, or motor control may increase passive behavior.

It’s also useful in:

• Early training trials where subjects are unfamiliar with task demands.
• Probe trials where a subject no longer expects to find the platform.
• Screening for trials or subjects that may distort group-level statistics, and supporting more accurate subgroup analyses, e.g. separating the cognitively impaired from the disengaged.
• Protocol evaluation, as more inactivity in a cohort than expected may suggest poor task design, stress induction, or effects of environmental factors such as lighting, water temperature or cue placement.

You can use it to help disentangle cognitive deficits from performance artifacts caused by reduced movement. Poor scores on spatial learning measures with a low float time suggests genuine cognitive impairment, whereas poor spatial learning scores with a high float time may point to issues with motivation, mood, or sensorimotor ability.

Float or inactivity time is therefore especially valuable when considered alongside measures such as Gallagher proximity, path efficiency ratio, heading angle, or strategy and behavior classification, because together they add deeper insight than can be gained just from commonly used metrics like latency, path length, or platform crossings.

For example, long latencies or path lengths considered alone may suggest poor learning, but if high float time is present, the length may reflect disengagement, sedation, or motor slowing, rather than impaired spatial memory. Likewise, few platform crossings in a probe trial might suggest poor memory of the platform location, but could be due to general inactivity. Using float time, along with spatial measures such as the time close to the platform and the Gallagher proximity measures, allows you to see whether long latency learning trials or low platform crossing probe trials were accurate but inactive, active but inaccurate, or both inaccurate and inactive.

By pairing float/inactivity with navigation-focused analyses, you can distinguish genuine cognitive deficits from motivational, sensorimotor, or environmental influences, giving a more accurate and scientifically robust interpretation of behavior.