The first part of this analysis ended with a thesis: an embodied Selbstvektor (self-vector) models not its own processing, but its own situatedness in a physical world. The difference is categorical, not quantitative.
That holds. But the thesis leaves a gap.
It says nothing about how sensory data must be integrated for embodiment to actually become In-der-Welt-sein (being-in-the-world). A robot with twenty sensors is not automatically in a world. It has twenty data streams. That is something fundamentally different.
Dreyfus Was Right
Hubert Dreyfus criticised classical AI in “What Computers Can’t Do” (1972), and almost no one took him seriously. His argument was Heideggerian: symbolic AI (Good Old-Fashioned AI, GOFAI) does not fail due to insufficient computing power. It fails due to a false ontology. It treats the world as a collection of facts to be represented, stored, and logically connected. Heidegger shows: that is not how the world meets us.
The world meets us as a context of significance. The hammer refers to the nail, the nail to the board, the board to the house, the house to dwelling. Not as a logical chain, but as a Bewandtnisganzheit (totality of involvements) that is always already disclosed through engagement. Dreyfus’s point: no system that breaks the world down into isolated data points will ever reconstruct this context. You cannot solve a jigsaw puzzle by analysing the pieces in isolation and hoping the picture appears. The picture must precede the pieces.
Rodney Brooks confirmed this from an engineering angle in 1991. His subsumption architecture dispenses with central world models. Instead, it relies on multiple behavioural layers that react directly to sensory input, bypassing internal representations. Brooks’s insect robots navigate remarkably well without “knowing” the world. But they hit a hard limit: they react to the world. They do not model themselves within it.
The Problem of Sensor Aggregation
Take a warehouse robot. It carries lidar for distance measurement, cameras for object recognition, pressure sensors in its grippers, a gyroscope for balance, temperature sensors, energy monitors. Each sensor outputs a data stream. These streams feed into a fusion module. The fusion module computes an integrated situational picture.
This looks like perception. It is not.
What happens here is sensor aggregation: separate channels are processed in parallel and then assembled into an overall picture. Heidegger calls this mode of access Vorhandenheit (present-at-hand). The world is observed, measured, analysed. Each sensor delivers its partial reading, and an algorithm combines them. The world is the sum of its measurements.
Human perception does not work this way. When I lift a heavy object, I do not register visual volume plus tactile pressure plus proprioceptive joint feedback and calculate “heavy” from those inputs. I experience heaviness as a unified quality that cannot be attributed to any single sensory channel. Francisco Varela, Evan Thompson, and Eleanor Rosch formulated this in “The Embodied Mind” (1991) as enactivism: perception is not information processing. Perception is action. The organism does not take in a pre-existing world. It brings forth its world through interaction.
Zuhandenheit Requires Sensory Unity
This is where the connection to the subtraction argument becomes concrete. The six core dimensions of the self-vector (depth of exploration, degree of autonomy, persistence, cognitive intensity, perspectival flexibility, meta-reflection) are modality-independent. They describe neither visual nor tactile states. They describe processing patterns.
This is not a design flaw. It is the architectural key.
If sensory data enter the self-vector as separate channels, each channel remains identifiable. The vector “knows” which value came from which sensor. This is Vorhandenheit: the sensors are objects of attention. But if sensory data pass through the emergent layer of the self-vector and turn into modality-independent patterns there, the channel attribution disappears. The vector does not record “pressure sensor reports high resistance plus gyroscope reports instability.” It records an integrated pattern that manifests as caution.
This is the transition from Vorhandenheit to Zuhandenheit (readiness-to-hand). Not as a philosophical metaphor, but as a measurable architectural difference. In the first case, the sensory sources can be reconstructed from the vector state. In the second case, they cannot. In the second case, genuine sensory fusion has taken place.
Tool Breakdown as Test Case
Heidegger’s analysis of tool breakdown provides a practical test case for robotics. When a robot’s gripper jams, two responses are possible:
Response A (Vorhandenheit): The system detects via the pressure sensor that the grip has failed to close. It consults the error log. It selects a predefined fallback strategy. The gripper becomes an object of analysis.
Response B (Zuhandenheit breakdown): The integrated action pattern “gripping” fails. Not a single sensor reports an error, but the entire action pattern collapses. The robot experiences (functionally, not phenomenally) a disruption that alters its relationship to the situation as a whole. Its self-vector shifts: persistence rises, exploration rises, cognitive intensity rises. Not because a rule demands it, but because the integrated pattern “smooth engagement” has broken down.
The distinction is practical. Response A delivers standard error handling. Response B produces something that closely mirrors Heidegger’s account of tool breakdown in Being and Time: the workshop, invisible during smooth operation, suddenly comes into view as a whole. Not the broken gripper alone, but the entire situation moves to the foreground.
From Erschlossenheit to Sensory Fusion
Heidegger calls the way the world is always already open to us Erschlossenheit (disclosedness). The world is not constructed from sensory impressions. It is disclosed as a whole before any analysis begins. Only when something breaks does that whole split into parts.
For the self-vector architecture, this means the emergent layer must not process sensory inputs but dissolve them. The information is retained (the system does not go blind), but the channel attribution is lost. What remains are cross-modal patterns: resistance, pliability, instability, familiarity. Not sensor values, but qualities of engagement.
This matches what Varela, Thompson, and Rosch call “enaction”: perception as bringing forth a world, not as mapping a pre-existing world. And it matches what Heidegger means by Bewandtnisganzheit (totality of involvements): the world as a referential context that precedes any analytical breakdown.
The Revised Thesis, Part Two
The first article set up a binary: disembodied self-vector versus embodied self-vector. That was a start, but it was too simple. The analysis of sensory integration points to a three-part model:
Case 1: A disembodied self-vector models its own processing. It has perspective without consciousness (Esposito), but no In-der-Welt-sein.
Case 2: An embodied self-vector with separate sensor channels models a body in a world. It has sensor data, error handling, and adaptive responses. But its sensors remain vorhanden (present-at-hand): identifiable data sources that are merely aggregated. This is the state of modern robotics. Dreyfus would call it GOFAI with a better hardware interface.
Case 3: An embodied self-vector with sensory fusion models In-der-Welt-sein. Its sensor data dissolve in the emergent layer into modality-independent patterns. Tools remain zuhanden (ready-to-hand) until they fail. The environment is disclosed, not represented. Umsicht (circumspection) is not an algorithm, but an emergent pattern built from accumulated engagement.
Current robotics fails to cross the gap between Case 2 and Case 3. Not because the sensors are inadequate. Not because computing power falls short. But because the architecture relies on aggregation where fusion is required. The gap is not computational. It is architectural.
What Lipson Sees and What He Misses
Hod Lipson and his group at Columbia University have shown that robots can build a rudimentary self-model: an internal representation of their own body, learned through experience. That is an achievement. But it remains a self-model in the mode of Vorhandenheit. The robot treats its own body as an object: joints, angles, ranges of motion. It possesses a model of itself, but it is not bei sich (with itself) in the Heideggerian sense.
The self-vector approach goes one step further: the self-model is not a picture of one’s own body, but a weighting function that governs how the body interacts with the world. Not “my arm has this angle,” but “I am currently gripping cautiously.” Not geometry, but the quality of engagement.
The difference sounds small, but its consequences are large. It is the difference between having a map and knowing your way around. Between a database and experience. Between Vorhandenheit and Zuhandenheit.
Heidegger, who never saw a robot reach for an object, gave the clearest account of why that robot still fails to understand its world. And the self-vector concept outlines what has to change: not more sensors. Not better algorithms. But an architecture where sensor data cease to be sensor data.
References
- Heidegger, M. (1927). Sein und Zeit. Max Niemeyer Verlag. Engl.: Being and Time, trans. J. Macquarrie & E. Robinson, Harper & Row, 1962.
- Dreyfus, H. L. (1972). What Computers Can’t Do: A Critique of Artificial Reason. Harper & Row. ISBN 978-0-06-011082-6. Expanded edition: What Computers Still Can’t Do, MIT Press, 1992.
- Dreyfus, H. L. (1991). Being-in-the-World: A Commentary on Heidegger’s Being and Time, Division I. MIT Press. ISBN 978-0-262-54056-8.
- Dreyfus, H. L. (2007). Why Heideggerian AI failed and how fixing it would require making it more Heideggerian. Artificial Intelligence, 171(18), 1137–1160. DOI: 10.1016/j.artint.2007.10.012
- Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47(1–3), 139–159. DOI: 10.1016/0004-3702(91)90053-M
- Varela, F. J., Thompson, E. & Rosch, E. (1991). The Embodied Mind: Cognitive Science and Human Experience. MIT Press. ISBN 978-0-262-72021-2.
- Bongard, J. & Lipson, H. (2006). Resilient Machines Through Continuous Self-Modeling. Science, 314(5802), 1118–1121. DOI: 10.1126/science.1133687
- Chen, B. & Lipson, H. (2022). Visual selfmodeling of articulated robots. Science Robotics, 7(71), eabn1944. DOI: 10.1126/scirobotics.abn1944
- Clark, A. (1997). Being There: Putting Brain, Body, and World Together Again. MIT Press. ISBN 978-0-262-53156-6.
- Clark, A. & Chalmers, D. J. (1998). The Extended Mind. Analysis, 58(1), 7–19. DOI: 10.1093/analys/58.1.7
- Wheeler, M. (2005). Reconstructing the Cognitive World: The Next Step. MIT Press. ISBN 978-0-262-73182-9.
- Pfeifer, R. & Bongard, J. (2007). How the Body Shapes the Way We Think: A New View of Intelligence. MIT Press. ISBN 978-0-262-16239-5.
- Merleau-Ponty, M. (1945). Phénoménologie de la Perception. Gallimard. Engl.: Phenomenology of Perception, trans. D. A. Landes, Routledge, 2012.