<p>Previous studies have shown that visual feature estimation is influenced by internal (sensory) and external (physical) noise, as well as the perception–action mapping process – the transformation of internal perceptual representations into motor responses. However, their interactions remain poorly understood. To investigate this, we conducted two experiments. In Experiment 1, participants estimated self-motion direction (heading) from 3D dot-cloud optic flow, with or without a preceding color-discrimination task to manipulate internal noise magnitudes. In Experiment 2, we introduced randomly moving noise dots (0% or 40% replacement) to vary internal and external noise concurrently. We employed a mixed experimental design that included both between- and within-subject comparisons across participant groups. Results showed systematic variations in estimation errors and response variability across conditions. We developed computational models with one {c}, two {<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(c\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>c</mi> </math></EquationSource> </InlineEquation>, <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\({\sigma }_{E}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>σ</mi> <mi>E</mi> </msub> </math></EquationSource> </InlineEquation>}, or three free parameters {<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(c\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>c</mi> </math></EquationSource> </InlineEquation>, <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\({\sigma }_{E}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>σ</mi> <mi>E</mi> </msub> </math></EquationSource> </InlineEquation>, <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(a\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>a</mi> </math></EquationSource> </InlineEquation>}, where these parameters represented internal noise magnitude, external noise, and perception–action mapping scaling, respectively. Model comparisons revealed that the color-discrimination task increased internal noise {<InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(c\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>c</mi> </math></EquationSource> </InlineEquation>}, whereas noise dots elevated both {<InlineEquation ID="IEq7"> <EquationSource Format="TEX">\(c\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>c</mi> </math></EquationSource> </InlineEquation>} and {<InlineEquation ID="IEq8"> <EquationSource Format="TEX">\({\sigma }_{E}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>σ</mi> <mi>E</mi> </msub> </math></EquationSource> </InlineEquation>}. A negative correlation between noise magnitudes {<InlineEquation ID="IEq9"> <EquationSource Format="TEX">\(c\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>c</mi> </math></EquationSource> </InlineEquation>, <InlineEquation ID="IEq10"> <EquationSource Format="TEX">\({\sigma }_{E}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>σ</mi> <mi>E</mi> </msub> </math></EquationSource> </InlineEquation>} and scaling magnitude {<InlineEquation ID="IEq11"> <EquationSource Format="TEX">\(a\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>a</mi> </math></EquationSource> </InlineEquation>} suggested that noise effects counteract perception–action mapping. In summary, these results provide evidence for a dynamic interaction mechanism in visual feature estimation, whereby increased sensory noise (either internal or external) is counterbalanced by adaptive scaling of perception–action transformation. Our findings highlight the interaction of sensory noise and sensorimotor mapping in perception, and our model serves as a tool for dissecting sensory uncertainty and action scaling across different perceptual domains.</p>