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arxiv: 2605.22988 · v1 · pith:DWKL76AAnew · submitted 2026-05-21 · 🧬 q-bio.NC · cs.LG· cs.RO· cs.SY· eess.SY

Active Sensing Subserves Task-Level Control

Andrew Lamperski , Debojyoti Biswas , Eric S. Fortune , John Guckenheimer , Kathleen Hoffman , Noah J. Cowan This is my paper

Pith reviewed 2026-05-25 05:15 UTC · model grok-4.3

classification 🧬 q-bio.NC cs.LGcs.ROcs.SYeess.SY
keywords active sensingtask-level controlmode switchingadaptive sensorsfeedback controlbiological controlexplore exploit
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The pith

Active sensing emerges as a necessity for task-level control rather than from goals of reducing uncertainty.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes that active sensing movements arise inevitably when adaptive sensors are linked to movement and the system must achieve task goals. This view treats active sensing as a requirement of control rather than a separate strategy for gathering information. Animals appear to switch between an explore mode that produces dynamic movements to shape feedback and an exploit mode that uses slower movements to meet task objectives. The claim rests on empirical patterns in organisms together with mathematical theory showing that the combination of adaptive sensing, movement linkage, and task control produces these behaviors. If correct, the same strategy may explain why biological systems maintain robust performance where many engineered controllers do not.

Core claim

The combination of reliance on adaptive sensors, the linkage between movement and sensing, and task-level control inevitably gives rise to the emergence of active sensing movements. In this way, active sensing is not driven by sensory goals, such as minimizing uncertainty about the state, but rather is necessary for task-level control. This hypothesis is supported by both empirical data from organisms and mathematical theory. Active sensing behaviors often occur in discrete epochs, interspersed with goal-oriented behavior, suggesting that animals switch between an explore mode in which they produce dynamic movements to shape sensory feedback and an exploit mode in which they produce slower,,

What carries the argument

The mode-switching control policy that alternates between an explore mode (dynamic movements shaping sensory feedback) and an exploit mode (compensatory movements tied directly to task goals) in systems that combine adaptive sensors with movement-linked sensing.

If this is right

  • Biological systems achieve robust and graceful behavior through discrete epochs of active sensing rather than continuous uncertainty-minimizing policies.
  • Engineered control systems that lack this mode-switching strategy remain insufficient even when their sensors and actuators exceed biological hardware.
  • Active sensing is a direct consequence of task requirements rather than an optional information-gathering step.
  • Switching between explore and exploit modes constitutes a distinct feedback-control architecture used across organisms.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same necessity argument may apply to other sensorimotor loops in which movement alters the sensor's operating point.
  • Robotic designs could test whether adding explicit explore/exploit switching improves robustness in unstructured environments.
  • The framework predicts that disrupting the linkage between movement and sensing should eliminate discrete active-sensing epochs.

Load-bearing premise

Task-level control with adaptive sensors and movement-linked sensing cannot be achieved without generating active sensing movements.

What would settle it

A mathematical model or engineered controller that uses adaptive sensors linked to movement, performs task-level control, and produces no active sensing movements.

Figures

Figures reproduced from arXiv: 2605.22988 by Andrew Lamperski, Debojyoti Biswas, Eric S. Fortune, John Guckenheimer, Kathleen Hoffman, Noah J. Cowan.

Figure 1
Figure 1. Figure 1: The weakly electric glass knifefish, Eigenmannia virescens, as a model for active sensing. (A) This species has independent image-forming sensory systems, vision and electrosense, and a unique locomotor plant, a ventral ribbon fin that allows them to swim nearly equally well forwards and backwards. (B) The fish exhibit a natural behavior in which they maintain their position inside a longitudinally moving … view at source ↗
Figure 2
Figure 2. Figure 2: Computational models of exploration in biological active sensing and reinforcement learning. (A) Schematic illustrating the proposed interaction between task-level control and active sensing, adapted from [12, 14]. Sensory feedback is processed jointly by a task controller (ufb, standard state-feedback control law) and an active-sensing submodule (time varying, ua), whose outputs combine to generate motor … view at source ↗
Figure 3
Figure 3. Figure 3: Mode switching and sensory salience [13]. (A) Mode switching during station-keeping tasks is found in species from humans to nematodes. The phylogeny highlights species in which mode switching has been quantified (grey) and the sensory modalities that mediate switching (red). Asterisks indicate species in which the effects of sensory salience are known. Adapted from [13]. (B) Two distinct behavioral modes,… view at source ↗
Figure 4
Figure 4. Figure 4: Comparison between active-sensing-for-control and infotaxis-style information maxi￾mization. In the active sensing framework, task control is primary: the controller minimizes tracking error, while an active sensing generator adds exploratory movements as needed to improve state estimation. In the infotaxis framework, information acquisition is primary: actions are selected to maximize expected informa￾tio… view at source ↗
Figure 5
Figure 5. Figure 5: Different perspectives on the same animal–environment interaction. Colors denote corresponding elements across representations. (A) Biological perspective. An animal (illustrated with a weakly electric fish) interacts with its environment through closed-loop sensorimotor dynamics. The central nervous system (CNS) generates motor commands that drive the musculoskeletal system, which in turn acts on the envi… view at source ↗
read the original abstract

Active sensing is traditionally defined as the expenditure of energy, typically in the form of movement, for obtaining information. Here, we propose that the combination of reliance on adaptive sensors, the linkage between movement and sensing, and task-level control inevitably gives rise to the emergence of active sensing movements. In this way, active sensing is not driven by sensory goals, such as minimizing uncertainty about the state, but rather is necessary for task-level control. This hypothesis, that active sensing subserves control, is supported by both empirical data from organisms and mathematical theory. Interestingly, active sensing behaviors often occur in discrete epochs, interspersed with goal-oriented behavior. This suggests that animals switch between two behavioral modes with distinct control policies, an `explore' mode in which animals produce dynamic movements to shape sensory feedback, and an `exploit' mode in which animals produce slower compensatory movements that are directly related to achieving task goals. This strategy for feedback control that relies on adaptive sensors, active sensing, and mode switching is not commonly used in engineered systems despite being ubiquitous in biology. Engineered systems comprising state-of-the-art sensors, actuators, and mechanical designs can outperform animals with respect to ``cost functions'' such as maximum force generation, precision, and speed. Nevertheless, animals routinely achieve robust, graceful behaviors that are currently unmatched by engineered systems, suggesting that current control systems are insufficient. These insights, expressed in the language of control theory, may be critical for improving robotic sensing and control.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes that active sensing movements emerge inevitably from the combination of adaptive sensors, movement-sensing linkage, and task-level control, serving task-level control rather than sensory goals such as uncertainty minimization. This is framed as a hypothesis supported by empirical data from organisms and mathematical theory. The paper further describes discrete behavioral epochs with mode switching between an 'explore' mode (dynamic movements shaping sensory feedback) and an 'exploit' mode (slower compensatory movements for task goals), and contrasts this biological strategy with engineered control systems.

Significance. If the mathematical theory derives active sensing strictly as a deductive necessity from the three stated premises without auxiliary information-gain objectives, the result would offer a substantive reframing of active sensing in neuroscience and control theory, with direct implications for designing more robust robotic systems. The empirical observations of mode switching and the explicit contrast with engineered systems would strengthen the contribution if rigorously linked to the theory.

major comments (2)
  1. [Abstract] Abstract: the central claim that the three premises 'inevitably gives rise to the emergence of active sensing movements' that are 'necessary for task-level control' rather than 'driven by sensory goals, such as minimizing uncertainty about the state' requires the mathematical theory to produce active sensing as a closed-loop requirement without introducing entropy-reduction, mutual-information, or exploration-bonus terms. No equations or model structure are provided to verify this separation.
  2. [Abstract] Abstract: the support from 'mathematical theory' is asserted but not instantiated; without explicit derivation or simulation showing active sensing appearing from the premises alone (rather than from a joint cost function that includes sensory objectives), the 'inevitably' and the claimed distinction from sensory goals cannot be evaluated.
minor comments (1)
  1. [Abstract] Abstract: the phrasing 'this strategy for feedback control... is not commonly used in engineered systems' would benefit from a brief citation or example of current sensorimotor control architectures that do incorporate adaptive sensing.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful reading and for identifying the need to make the mathematical support for our central claim more explicit and verifiable. We address each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the three premises 'inevitably gives rise to the emergence of active sensing movements' that are 'necessary for task-level control' rather than 'driven by sensory goals, such as minimizing uncertainty about the state' requires the mathematical theory to produce active sensing as a closed-loop requirement without introducing entropy-reduction, mutual-information, or exploration-bonus terms. No equations or model structure are provided to verify this separation.

    Authors: We agree that the abstract, as a summary, does not display the equations. The manuscript's mathematical theory section presents active sensing as a deductive necessity arising solely from the requirement for closed-loop stability under the three premises (adaptive sensors, movement-sensing linkage, task-level control). No information-gain or entropy terms appear in the argument; the movements are required to generate the feedback signals that enable any task-level controller to function. To allow direct verification of this separation, we will revise the abstract to include a concise outline of the model structure and a pointer to the relevant theoretical section. revision: yes

  2. Referee: [Abstract] Abstract: the support from 'mathematical theory' is asserted but not instantiated; without explicit derivation or simulation showing active sensing appearing from the premises alone (rather than from a joint cost function that includes sensory objectives), the 'inevitably' and the claimed distinction from sensory goals cannot be evaluated.

    Authors: The manuscript frames the mathematical theory as a logical derivation rather than a simulation or optimization problem. We accept that the abstract does not instantiate the steps, which limits evaluability. In revision we will add a brief, explicit statement of the derivation (showing emergence from control requirements alone) directly in the abstract and will ensure the main-text theory section contains numbered steps that readers can follow without reference to sensory objectives. revision: yes

Circularity Check

0 steps flagged

Hypothesis framed from premises; no equations or self-citations reduce claim to inputs

full rationale

The paper states a hypothesis that three premises (adaptive sensors, movement-sensing linkage, task-level control) inevitably produce active sensing as necessary for control rather than uncertainty minimization. No derivation chain, equations, fitted parameters, or load-bearing self-citations appear in the provided text. The claim is presented as interpretive support from external empirical data and unspecified mathematical theory, not as a quantity obtained by construction from its own inputs. This is the most common honest non-finding for a hypothesis paper.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review yields minimal explicit ledger entries; the hypothesis implicitly rests on standard control-theoretic assumptions about sensor-actuator coupling and task goals.

axioms (1)
  • domain assumption Task-level control in biological systems requires linkage between movement and adaptive sensing
    Invoked as the basis for why active sensing emerges; stated in the abstract's central proposal.

pith-pipeline@v0.9.0 · 5824 in / 1187 out tokens · 21556 ms · 2026-05-25T05:15:26.898220+00:00 · methodology

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Reference graph

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