What is a mental process?

What is a “mental” process? The stuff we’re conscious of or a limbo between real, wet, neural processes and observable behavior?

A well known analogy is the computer. The hardware stuff you can kick is analogous to the brain; the stuff you see on the screen is, I suppose, the phenomenology; then the software, all of which correlates with processes you could detect in the hardware if you looked hard enough, some but not all of which affects the screen, is cognition.

Forget for a moment about minds and consider the engineering perspective; then the point of the levels is clear. When you want, say, to check your email, you probably don’t want to fiddle around directly with the chips in your PC. It’s much less painful to rely on years of abstraction and just click or tap on the appropriate icon. You intervene at the level of software, and care very little about what the hardware is doing being the scenes.

What is the point of the levels for understanding a system? Psychologists want to explain, tell an empirically grounded story about, people-level phenomena, like remembering things, reasoning about things, understanding language, feeling and expressing emotions. Layers of abstraction are necessary to isolate the important points of this story. The effect of phonological similarity on remembering or pragmatic language effects when reasoning would be lost if expressed in terms of (say) gene expression.

I don’t understand when the neural becomes the cognitive or the mental. There are many levels of neural, not all of which you can poke. At the top level I’m thinking here about the sorts of things you can do with EEG where the story is tremendously abstract (for instance event-related potentials or the frequency of oscillations) though dependent on stuff going on in the brain. “Real neuroscientists” sometimes get a bit sniffy about that level: it’s not brain science unless you are able to talk about actual bits of brain like synapses and vesicles. But what are actual bits of brain?

Maybe a clue comes from how you intervene on the system. You can intervene with TMS, you can intervene with drugs, or you can intervene with verbal instructions. How do you intervene cognitively or mentally?  Is this the correct way to think about it?

Death and furniture

Found this paper by Edwards, Ashmore, and Potter (1995) amusing as recently I tapped a table to make a point about different levels of analysis. From the intro:

“When relativists talk about the social construction of reality, truth, cognition, scientific knowledge, technical capacity, social structure, and so on, their realist opponents sooner or later start hitting the furniture, invoking the Holocaust, talking about rocks, guns, killings, human misery, tables and chairs. The force of these objections is to introduce a bottom line, a bedrock of reality that places limits on what may be treated as epistemologically constructed or deconstructible. There are two related kinds of moves: Furniture (tables, rocks, stones, etc. — the reality that cannot be denied), and Death (misery, genocide, poverty, power — the reality that should not be denied). Our aim is to show how these “but surely not this” gestures and arguments work, how they trade off each other, and how unconvincing they are, on examination, as refutations of relativism.”

And the point about levels is made:

“It is surprisingly easy and even reasonable to question the table’s given reality. It does not take long, in looking closer, at wood grain and molecule, before you are no longer looking at a “table”. Indeed, physicists might wish to point out that, at a certain level of analysis, there is nothing at all “solid” there, down at the (most basic?) levels of particles, strings and the contested organization of sub-atomic space. Its solidity is then, ineluctably, a perceptual category, a matter of what tables seem to be like to us, in the scale of human perception and bodily action. Reality takes on an intrinsically human dimension, and the most that can be claimed for it is an ‘experiential realism'”


Edwards, D., Ashmore, M., & Potter, J. (1995). Death and furniture: The rhetoric, politics and theology of bottom line arguments against relativism, History of the Human Sciences, 8, 25-49.

Personal and sub-personal

Reading Da Silva Neves et al.’s (2002) An empirical test of patterns for nonmonotonic inference [Annals of Mathematics and Art. Intel., 34: 107-130]. Interesting paragraph (p. 110):

… even if we expect human inference to corroborate these properties, we know of no sufficient reason to think that lay reasoners would recognize any rationality postulate as valid, neither that they would conscientiously use them to guide their reasoning.

Then later (p. 111):

… we assume that human inference is constrained by knowledge organisation in memory and that its formal properties emerge from a spreading activation process operating directly on knowledge structures. We make the hypothesis that this spreading activation process is by and large consistent with TP [a set of properties they provide].

This is wonderful stuff, and an example of where the personal/sub-personal distinction recently exposited by Keith Frankish [link updated 2020] would come in handy.

A Connectionist Computational Model for Epistemic and Temporal Reasoning

Many researchers argue that logics and connectionist systems complement each other nicely. Logics are an expressive formalism for describing knowledge, they expose the common form across a class of content, they often come with pleasant meta-properties (e.g. soundness and completeness), and logic-based learning makes excellent use of knowledge. Connectionist systems are good for data driven learning and they’re fault tolerant, also some would argue that they’re a good candidate for tip-toe-towards-the-brain cognitive models. I thought I’d give d’Avila Garcez and Lamb (2006) a go [A Connectionist Computational Model for Epistemic and Temporal Reasoning, Neural Computation 18:7, 1711-1738].

I’m assuming you know a bit of propositional logic and set theory.

The modal logic bit

There are many modal logics which have properties in common, for instance provability logics, logics of tense, deontic logics. I’ll follow the exposition in the paper. The gist is: take all the usual propositional logic connectives and add the operators □ and ◊. As a first approximation, □P (“box P”) means “it’s necessary that P” and ◊P (“diamond P”) means “it’s possible that P”. Kripke models are used to characterise when a model logic sentence is true. A model, M, is a triple (Ω, R, v), where:

  • Ω is a set of possible worlds.
  • R is a binary relation on Ω, which can be thought of as describing connectivity between possible worlds, so if R(ω,ω’) then world ω’ is reachable from ω. Viewed temporally, the interpretation could be that ω’ comes after ω.
  • v is a lookup table, so v(p), for an atom p, returns the set of worlds where p is true.

Let’s start with an easy rule:

(M, ω) ⊨ p iff ω ∈ v(p), for a propositional atom p

This says that to check whether p is true in ω, you just look it up. Now a recursive rule:

(M, ω) ⊨ A & B iff (M, ω) ⊨ A and (M, ω) ⊨ B

This lifts “&” up to our natural language (classical logic interpretation thereof) notion of “and”, and recurses on A and B. There are similar rules for disjunction and implication. The more interesting rules:

(M, ω) ⊨ □A iff for all ω’ ∈ Ω such that R(ω,ω’), (M, ω’) ⊨ A

(M, ω) ⊨ ◊A iff there is an ω’ ∈ Ω such that R(ω,ω’) and (M, ω’) ⊨ A

The first says that A is necessarily true in world ω if it’s true for all connected worlds. The second says that A is possibly true if there is at least one connected world for which it is true. “Is R reflexive?”, I hear you ask. I’m not sure. It depends on the exact flavour of modal logic, I guess.

A sketch of logic programs and a connectionist implementation

Logic programs are sets of Horn clauses, A1 & A2 & … & An → B, where Ai is a propositional atom or the negation of an atom. (This doesn’t preclude inferences about predicate logic: the first step is to look at the grounding of the predicate logic program which, very crudely, you get by working out what the various variables can be instantiated by. Details in a textbook – a keyword you’ll find helpful is “Herbrand”.) Below is a picture of the network that represents the program {B & C & ~D → A, E & F → A, B}.



A network representing a program

The thresholds are configured so that the units in the hidden layer, Ni, are only active when the antecedents are all true, e.g. N1 is only active when B, C, and ~D have the truth value true. The thresholds of the output layer’s units are only active when at least one of the hidden layer connections to them is active. Additionally, the output feeds back to the inputs. The networks do valuation calculations through the magic of backpropagation, but can’t infer new sentences as such, as far as I can tell. To do so would involve growing new nets and some mechanism outside the net interpreting what the new bits mean.

Aside on biological plausibiliy

Biological plausibility raises its head here. Do the units in this network model – in any way at all – individual neurons in the brain? My gut instinct says, “Absolutely no way”, but perhaps it would be better not even to think this as (a) the units in the model aren’t intended to characterise biological neurons and (b) we can’t test this particular hypothesis. Mike Page has written in favour of localists nets, of which this is an instance [Behavioral and Brain Sciences (2000), 23: 443-467]. Maybe more on that in another post.

Moving to modal logic programs and nets

Modal logic programs are like the vanilla kind, but the literals may (optionally) have one of the modal operators. There is also a set of connections between the possible worlds, i.e. a specification of the relation, R. The central idea of the translation is to use one network to represent each possible world and then apply an algorithm to wire up the different networks correctly, giving one unified network. Take the following program: {ω1 : r → □q, ω1 : ◊s → r, ω2 : s, ω3 : q → ◊p, R(ω1,ω2), R(ω1,ω3)}. This wires up to:

A network representing a modal logic program

Each input and output neuron can now represent □A, ◊A, A, □~A, ◊~A, or ~A. The individual networks are connected to maintain the properties of the modality operators, for instance □q in ω1 connects to q in ω2 and ω3 since R(ω1, ω2), R(ω1, ω3), so q must be true in these worlds.

The Connectionist Temporal Logic of Knowledge

Much the same as before, except we now have a set of agents, A = {1, …, n}, and a timeline, T, which is the set of naturals, each of which is a possible world but with a temporal intepretation. Take a model M = (T, R1, …, Rn, π). Ri specifies what bits of the timeline agent i has access to, and π(t) gives a set of propositions that are true at time t.

Recall the following definition from before

(M, ω) ⊨ p iff ω ∈ v(p), for a propositional letter p

Its analogue in the temporal logic is

(M, t) ⊨ p iff t ∈ π(p), for a propositional letter p

There are two extra model operators: O, which intuitively means “at the next time step” and K which is the same as □, except for agents. More formally:

(M, t) ⊨ OA iff (M, t+1) ⊨ A

(M, t) ⊨ KA iff for all u ∈ T such that Ri(t,u), (M, u) ⊨ A

Now in the translation we have network for each agent, and a collection of agent networks for each time step, all wired up appropriately.

Pages 1724-1727 give the algorithms for net construction. Have a look – I shan’t wade through them now. The proof of soundness of translation relies on d’Aliva Garcez, Broda, and Gabbay (2002), Neural-symbolic lerning systems: Foundations and applications.

Some questions I haven’t got around to working out the answers to

  • How can these nets be embedded in a static population coded network. Is there any advantage to doing so?
  • Where is the learning? In a sense it’s the bit that does the computation, but it doesn’t correspond to the usual notion of “learning”.
  • How can the construction of a network be related to what’s going on in the brain? Really I want a more concrete answer to how this could model the psychology. The authors don’t appear to care, in this paper anyway.
  • How can networks shrink again?
  • How can we infer new sentences from the networks?


I received the following helpful comments from one of the authors, Artur d’Avila Garcez (9 Aug 2006):

I am interested in the localist v distributed discussion and in the issue of biological plausibility; it’s not that we don’t care, but I guess you’re right to say that we don’t “in this paper anyway”. In this paper – and in our previous work – what we do is to say: take standard ANNs (typically the ones you can apply Backpropagation to). What logics can you represent in such ANNs? In this way, learning is a bonus as representation should preceed learning.

The above answers you question re. learning. Learning is not the computation, that’s the reasoning part! Learning is the process of changing the connections (initially set by the logic) progressively, according to some set of examples (cases). For this you can apply Backprop to each network in the ensemble. The result is a different set of weights and therefore a different set of rules – after learning if you go back to the computation you should get different results.