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Marr’s Threefold Distinction

If I apply the Teleological Stance successfully, do I thereby come to know a fact about the goal of an action?

To answer this question, we need to get beyond the Teleological Stance and consider the representations and algorithms that underpin it. Let me explain.
Consider Csibra & Gergely’s own answer, which is ‘Yes!’

‘when taking the teleological stance one-year-olds apply the same inferential principle of rational action that drives everyday mentalistic reasoning about intentional actions in adults’

(György Gergely and Csibra 2003; cf. Csibra, Bíró, et al. 2003; Csibra and Gergely 1998: 259)

\citet[p.~22ff]{Marr:1982kx} distinguishes:
\begin{itemize}
\item computational description---What is the thing for and how does it achieve this?
\item representations and algorithms---How are the inputs and outputs represented, and how is the transformation accomplished?
\item hardware implementation---How are the representations and algorithms physically realised?
\end{itemize}
One possibility is to appeal to David Marr’s famous three-fold distinction bweteen levels of description of a system: the computational theory, the representations and algorithm, and the hardware implementation.
This is easy to understand in simple cases. To illustrate, consider a GPS locator. It receives information from four satellites and tells you where on Earth the device is.
There are three ways in which we can characterise this device.

1. computational description

First, we can explain how in theory it is possible to infer the device’s location from it receives from satellites. This involves a bit of maths: given time signals from four different satellites, you can work out what time it is and how far you are away from each of the satellites. Then, if you know where the satellites are and what shape the Earth is, you can work out where on Earth you are.

-- What is the thing for and how does it achieve this?

The computational description tells us what the GPS locator does and what it is for. It also establishes the theoretical possibility of a GPS locator.
But merely having the computational description does not enable you to build a GPS locator, nor to understand how a particular GPS locator works. For that you also need to identify representations and alogrithms ...

2. representations and algorithms

At the level of representations and algorthms we specify how the GPS receiver represents the information it receives from the satellites (for example, it might in principle be a number, a vector or a time). We also specify the algorithm the device uses to compute the time and its location. The algorithm will be different from the computational theory: it is a procedure for discovering time and location. The algorithm may involve all kinds of shortcuts and approximations. And, unlike the computational theory, constraints on time, memory and other limited resources will be evident.
So an account of the representations and algorithms tells us ...

-- How are the inputs and outputs represented, and how is the transformation accomplished?

3. hardware implementation

The final thing we need to understand the GPS locator is a description of the hardware in which the algorithm is implemented. It’s only here that we discover whether the device is narrowly mechanical device, using cogs, say, or an electronic device, or some new kind of biological entity.

-- How are the representations and algorithms physically realised?

The hardware implementation tells us how the representations and algorithms are represented physically.

Marr (1992, 22ff)

How is this relevant to the teleological stance? It provides a computational description of goal ascription. (Whereas the Motor Theory provides an account of the representations and algorithms )
The teleological stance provides a computational description of goal ascription.
For deeper insight into goal ascription, we need an account of representations and algorithms.
Compare our research on infants’ abilities concerning physical objects. Spelke’s principles of object perception provide a computational description of infants’ abilities to segment, etc. But to understand the nature of these abilities and their relation to knowledge, and to explain the otherwise puzzling patterns of development, we needed to identify representations and alogrithms. (We did this by appeal to the operations of a system of object indexes.)
The Teleological Stance:

‘an action can be explained by a goal state if, and only if, it is seen as the most justifiable action towards that goal state that is available within the constraints of reality’

\citep[p.~255]{Csibra:1998cx}

Csibra & Gergely, 1998 p. 255

1. Consider goals to which the action might be directed.

2. For each goal, determine how justifiable the observed actions are as a means to achieving that goal.

3. Ascribe the goal with the highest rationality score.

The teleological stance is a computational description. What’s the algorithm?

‘when taking the teleological stance one-year-olds apply the same inferential principle of rational action that drives everyday mentalistic reasoning about intentional actions in adults’

(\citealp{Gergely:2003gb}; compare \citealp{Csibra:2003jv}, \citealp[p.~259]{Csibra:1998cx} )

(György Gergely and Csibra 2003; cf. Csibra, Bíró, et al. 2003; Csibra and Gergely 1998: 259)

Csibra and Gergely seem aware that this would make the Teleological Stance quite complex to apply ...

`Such calculations require detailed knowledge of biomechanical factors that determine the motion capabilities and energy expenditure of agents. However, in the absence of such knowledge, one can appeal to heuristics that approximate the results of these calculations on the basis of knowledge in other domains that is certainly available to young infants.

For example, the length of pathways can be assessed by geometrical calculations, taking also into account some physical factors (like the impenetrability of solid objects).

Similarly, the fewer steps an action sequence takes, the less effort it might require, and so infants’ numerical competence can also contribute to efficiency evaluation.’

Csibra & Gergely, forthcoming ms p. 8

What heuristics.
Csibra and Gergely’s newer proposal seems to assume the inferential integration of core systems. But principles governing object indexes are not typically available for general reasoning.

Is there an alternative?