Presentation at JALT 2014 Conference, Tsukuba, Japan. Some tentative ideas on the implications of neuroscience research for autonomy in language learning

1. Autonomy, language learning,
and the brain
Phil Benson
Macquarie University
Presentation at JALT 2014 Conference,
Tsukuba, Japan

2. Autonomy – definition
‘…the capacity to take control of one’s
own learning…’
Benson, P. (2011) Teaching and Researching Autonomy. London: Pearson. (p. 58)

3. Controlling what?
Learning
management
Learning
content
Cognitive
processes
AUTONOMY

4. Control over what?
Learning
management
Learning
content
Cognitive
processes
AUTONOMY

5. Control over cognition
• …is metacognitive in the sense that it is
– not concerned with direct control of behaviour
(learning management/content)
– is concerned with control of the cognitive
processes that control behaviour
• … it involves
– control over attention
– reflection
– metacognitive (metalinguistic) knowledge

6. Control of attention
‘The human animal is an attentive animal…. Our
attention enables us to organize the field in which
we are going to act. Here we have the organism as
acting and determining its environment. It is not
simply a set of passive senses played upon by the
stimuli that come from without [the behaviourist
view]. The organism goes out and determines what
it is going to respond to and organizes that world’
Mead, G. H. (1934). Mind, self and society: The viewpoint of a
social behaviorist. Chicago: University of Chicago Press.

7. How important is control of cognition
to a theory of autonomy?
“The nature of the autonomous learner’s
psychological relation to the learning process is
often described in general attitudinal terms or in
terms of capacities for ‘detachment’, ‘critical
thinking’, ‘creativity’, and so on. Here, I have
hypothesized that it may be described more
precisely as a capacity to control certain cognitive
processes that are central to the management of
language learning’
(Benson 2011: 112)

8. Neuroscience
• Thought and ideas are physical (Lakoff 2013)
• Mapping behaviour and thinking onto brain activity
– electrical activity (EEG) / blood flow (PET) / blood
oxygenation (fMRI)
– Locate activity in regions of the brain / neural networks
• Applications
– Educational neuroscience (Goswami 2006)
– Neurolinguistics (deGroot 2011; Ellis 2008)
– Neurosociology (Franks and Turner 2013)
– Neuroeconomics (Camerer, Loewenstein and Prelec 2005)

9. Neuroscience and decision-making
“The capacity to take long-term
consequences of our behaviour into
account seems to be the product of
our prefrontal cortex, which, tellingly
is the part of the brain that is uniquely
human.”
Camerer et al. 2005: 39

11. Controlled vs. automatic processes
Controlled Automatic
Serial (step-by-step)
Deliberative / Explicit
Effortful
Accessible to introspection
e.g., solving a mathematical
problem
Multiple and parallel
Reflexive / Implicit
Effortless
Not accessible to introspection
e.g., visual recognition,
language processing

12. Cognitive vs affective processes
Cognitive processes Affective processes
Concerned with ‘yes/no’
questions
Work with affective processes
to produce action
Can control affective processes
Concerned with ‘go/no go’
questions
Work with cognitive processes
to produce action
Can override cognitive
processes

13. Four quadrants
Cognitive +
controlled
Affective + controlled
Cognitive + automatic Affective + automatic

14. Foor challenges from neuroscience
1. Implicit learning cannot be controlled
2. We exagerrate the role control
3. Decision making depends on affect
4. Control may not lead to good decisions

15. 1. Implicit learning cannot be controlled
• Much brain activity (both affective and cognitive)
is automatized and inaccessible to introspection
and control
• Controlled processes only come into play when
automatic processes are interrupted
• Much of language processing involves automatic
processes and is implicit
• Much L2 learning is implicit, though conscious
control is also needed to overcome non-optimal
L1 implicit processes (Ellis 2008)

16. 2. We exagerrate the role of control
• Introspective access to cognitively controlled
processes is much stronger than access to
automated processes
• We exagerrate the degree to which we control
our behaviour – it is the controlling part of the
brain that thinks it is in control
• Brain activity that initiates behaviour is
recorded 300 msec before the activity
associated with intention

17. 3. Decision making depends on affect
• Cognition is insufficient in itself to cause
action without the support of emotion
processes
• “The affective system provides inputs in the
form of affective evaluations of behavioral
options…. It is not enough to ‘know’ what
should be done; it is also necessary to ‘feel’
it.” (Camerer et al 2005: 29)

18. 4. Control may not lead to good decisions
• Deliberative thinking blocks access to
emotional reactions influences the quality of
decisions
• Decision-making draws resources from the
cognitive regions of the brain and affects the
quality of later cognitive activity
• People who reflect upon and give reasons for
their decisions do not necessarily make better
decisions.

19. Recent critiques of autonomy
• Role of English as a lingua franca and in CMC is leading
to a shift in emphasis from language learning to
language use.
• “Learners can be considered autonomous only if they
can meet the increased problem-solving demands the
use of English in international and CMC settings
presents” (p. 512).
• A shift of emphasis in the definition of learner
autonomy away from control of teaching and learning
processes (which are best left to the pedagogical
expertise of teachers) to control over language use.
Illés (2012)

20. Recent critiques of autonomy
“The research hypothesis of this study is that
the learner autonomy model is no longer
pertinent to the learning of English in France
today, since language use and implicit learning
are already taking place through everyday
communicative activities in virtual
communities.”
Sockett and Toffoli (2012: 140)

21. Implications from neuroscience
• Treat findings from neuroscience cautiously – they are
often over-interpreted and turn out to be more complex
than than they seem
• They support a shift away from rational-deliberative
models of autonomy based on planning, decision-making
and reflection
• They support a shift towards more intuitive-responsive
models that incorporate implicit learning and emotion
• Our models of autonomy should not be bound to the
prefrontal cortex; they should account for language
learning as a complex process involving both controlled and
automatic processes, and cognition and affect
•

22. References
Benson, P. (2011). Teaching and Researching Autonomy (Second Edition, first published, 2001).
London: Pearson Education.
Camerer, C., Loewenstein, G., Prelec, D. (2005). Neuroeconomics: How neuroscience can inform
economics. Journal of Economic Literature, 43, 9-64.
deGroot, A. M. B. (2011). language and cognition in bilinguals and multilinguals. New York, NY:
Psychology Press.
Ellis, N. (2008). Implicit and explicit knowledge about language. In J. Cenoz and N. H. Hornberger
(Eds.), Encyclopedia of language and education (Second Edition), Volume 6 (pp. 1-13). New York,
NY: Springer.
Franks, D. D. and Turner, J. H. (Eds.) (2013). Handbook of neurosociology. Dordrecht: Springer.
Goswami, U. (2006). Neuroscience and education: From research to practice. Nature reviews
Neuroscience, 7, 406-413.
Illés, E. (2012). Learner autonomy revisited. ELT Journal, 66 (4), 505-513
Lakoff, G. (2013). Neural social science. In D. D. Franks and J. H. Turner (Eds.), Handbook of
neurosociology (pp. 9-26). Dordrecht: Springer.
Mead, G. H. (1934). Mind, self, and society from the standpoint of a social behaviorist. Chicago:
University of Chicago Press.
Sockett, G., and Toffoli, D. (2012). Beyond learner autonomy: A dynamic systems view of the
informal learning of English in virtual online communities. ReCALL, 24 (2), 138-151.