The text below is the un-edited preprint of a commentary I wrote on Andy Clark's 'Whatever Next? Predictive Brains, Situated Agents, and the Future of Cognitive Science' in Behavioral and Brain Sciences. The published version of Clark's paper, with commentaries and a response, is here (probably behind a paywall.) Because behavioral and brain scientists were just falling over themselves to comment on Clark's target article, my commentary ended up appearing in Frontiers in Philosophical and Theoretical Psychology. That journal is open access, and so you can read the final version of my commentary at this link. You can read Clark's response to all of the commentaries at this link.
I've included the preprint text on this blog because it's one of the recent pieces in which I've directly argued for a thesis that has something to do with common currencies.
I've included the preprint text on this blog because it's one of the recent pieces in which I've directly argued for a thesis that has something to do with common currencies.
Commentary text
Clark’s
synthesis of much recent work on sensory and motor systems in the brain is at
once radical and curiously traditional. It is radical, among other things, concerning
what representations are, how they are constructed, and what sensory and motor
representations have in common. But it is traditionally cognitivist in viewing
the main task of brains as being that of representing the world.
What this
traditional orientation tends to neglect is the role of the brain as a system
for selecting among available actions. This phenomenon has an ultimate aspect
regarding the external standards relevant to assessing actions. Various behavioral
ecological schemes for ranking actions in terms of their contribution to quantities
such as fitness, and economic models of revealed preference, are the leading
theoretical players here. The phenomenon also has a proximal aspect, which
concerns the specific biological mechanisms, including neural ones, by means of
which the values of different available actions might be represented, and
selections between them made. On this topic the recent explosion of neuroeconomic
research on decision processes in the brain is urgently relevant.
Natural agents
have limited means of action, and those means have alternative – sometimes
mutually exclusive – uses. That is to say the predicament of natural agents is fundamentally an economic one,
even if it is not necessary that selection converge on a system for responding
to the predicament in which economic variables are explicitly represented.
Furthermore there is considerable evidence from behavioural ecology and other
fields that many vertebrate behaviours in natural settings are economically
efficient.
Neither the
ultimate nor the proximal aspects of the problem of selecting between
behaviours play a significant role in Clark’s account. Natural selection,
fitness and biological descendents are not mentioned at all, and cognate
concepts like adaptiveness feature in diluted form. There’s similarly little
mention of decision and choice as theoretically understood in economics
including neuroeconomics, none of incentives, and reward and utility appear
only in the course of musing over whether it’s possible that cognitive
neuroscience could do without reference to either (section 5.1). Clark does
make some important points about action-centric representations, but even here does not consider the problem of
action selection.
Of course, no
survey can cover anything that anyone thinks is relevant, and it’s very easy to
complain about things that are left out. Clark’s lack of engagement with
neuroeconomics means missing a specific opportunity to make his general case
even more compelling, because what is emerging in that field complements his
case about sensory and motor systems in deep ways.
In his section
(3.2) Clark apparently takes seriously the concern that an agent with the sort
of brain that he’s been describing would be expected to ‘seek a nice dark room
and stay in it’. Clark disposes of the worry by pointing out that creatures
with real biological needs should ‘expect’ to follow exploratory strategies,
and that these expectations themselves should recruit both perception and
action. This is part of a reasonable and interesting response, but action
selection under those conditions (as with most others) would still require some
way of dealing with specific questions, such as where and how to forage, and
how to trade off foraging with other expected behaviours such as predator
avoidance and reproduction.
A related move
appears later, in section (5.1) when he considers an austere vision of cognition
that does without reference to goals and rewards, in favour of comprehensive
analysis in terms of expectations. Clark correctly holds back from endorsing
this possibility, but for relatively generic reasons to the effect that even if
some description is in principle replaceable, it may be convenient to continue
using it. This misses the main chance. Recent work on the neural implementation
of decision in various vertebrates including humans has produced a body of
results highly congenial to the unifying vision Clark supports.
Consider
saccadic movements in rhesus monkeys. A key component in the neural
implementation of these movements is the lateral intraparietal area (LIP),
which comprises a topographic map integrating locations in the visual field and
aspects of the muscular plans that would effect the centering of gaze on those
locations. It, along with a network of other maps with varying topographies in
the frontal eye fields, superior colliculus and related areas, provides a
striking illustration of what Clark calls an ‘action-centric’ representation.
In addition, as studies including Platt & Glimcher (1999) and Dorris &
Glimcher (2004) have shown, some activity in LIP neurons of rhesus monkeys on visually identical trials varies in
precise ways with the relative expected rewards (or relative subjective value)
from saccades to the represented location. These representations are not merely
‘action-centric’ insofar as they combine answers to the questions ‘where is
it?’ with ‘how do I gaze at it?’ They also include identifiable activity
corresponding to the answer to ‘what’s it probably worth for me to look at it?’
There’s more.
The expected relative reward values attached to saccadic and other movements
are not sui generis. They’re
predictions, and ones that get updated in the light of ongoing experience.
Among the key findings on this topic is that dopamine neurons do not – as
previously supposed – directly encode hedonic value (because if they did they
would respond in the same to expected and unexpected rewards of equivalent
hedonic worth). Rather it turns out that they encode some aspects of the difference between experienced and
expected reward (Montague et al 1997, see also Bayer & Glimcher 2005). While
many details about the operation of this system, and its interaction with other
neural systems, have yet to be determined, it is nonetheless clear that crucial
features of the neural systems for attaching values to sensory events and
actions operate by means of prediction error. In this respect they suggest a
way of expanding the scope of Clark’s claim about the importance of minimizing
prediction error as a general goal of neural systems.
References
Bayer, H.M.
and Glimcher, P.W. (2005). Midbrain dopamine neurons encode a quantitative
reward prediction error signal. Neuron
47, 1 – 13.
Dorris, M.C. and Glimcher, P.W. (2004). Activity in posterior parietal cortex is correlated with the subjective desirability of an action. Neuron 44, 365 – 378.
Montague , P.R., Dayan , P., and Sejnowski, T.J. (1997). A framework for mesencephalic dopamine systems based on predictive Hebbian learning. J. Neurosci. 16, 1936 – 1947.
Platt, M.L. and Glimcher, P.W. (1999). Neural correlates of decision variables in parietal cortex. Nature 400, 233 – 238.
Version notes:
First posted May 20, 2013. Link to Clark's reply to commentaries added May 21, 2013.
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