Download Integrating Mental Processes: Thinking and Problem Solving

Integrating Mental Processes: Thinking and Problem Solving
“Recruitment
of executive attention is normally associated with a subjective
feeling of mental effort.”
Lionel Naccache, Stanislas Dehaene, Laurent Cohen, Marie-Odile Habert, Elodie
Guichart-Gomez, Damien Galanaud, and Jean-Claude Willer, 2004
Implicit and Explicit Problem Solving
Human problem-solving
comes in two varieties:
explicit and implicit.
These two modes differ
sharply: explicit problemsolving has clear, conscious
goals and clearly defned
steps for getting from a
starting point to a solution.
Mental arithmetic is an
example of explicit problemsolving
Subtract 209-45=?.
Implicit
Human problem-solving comes in two varieties: explicit and implicit.
Implicit problem-solving may be more common than explicit since we learn and
practice many kinds of skills from early on in life. These problem-solving skills
become more profcient, implicit unconsciouss, and automatic with practice.
Infants acquiring language is an example of implicit problem-solving.
Thinking and Problem Solving
Explicit problem-solving involves greater executive control, higher mental
workload, more frequent conscious access, and wider recruitment of cortical
regions in pursuit of explicit goals.
Implicit problem-solving takes less executive control than the explicit kind, less
conscious access, lower cognitive load, and less cortical involvement.
Working Memory as part of a functional system
Working memory in the
functional framework
Working memory is constantly
involved in problem-solving,
however working memory
functions also make use of
stored information shown in the
gray boxes at the bottom of the
functional framework diagram.
Working Memory
A schematic of brain
areas in the frontal and
parietal lobes that
support working memory
processes.
So What are these
areas?
Working Memory
Brain regions supporting working memory overlap with those supporting attention,
conscious events, and episodic recall. This widespread activation primarily includes
frontal and parietal cortex.
Explicit Problem Solving
Problem-solving can be thought of as fnding a path through a maze of choice-points
between possible sub-goals toward a fnal goal.
A puzzle called the Towers of Hanoi is frequently used to investigate brain areas
involved in explicit problem-solving. The task is to move the disks between the rods,
one step at a time, until that they match the goal.
Explicit Problem Solving
Brain areas activated during solving of the Towers of Hanoi include the dorsolateral
prefrontal cortex.
Explicit Problem Solving
Flexibility is an important aspect of successful explicit problem-solving. People may get
‘fxedd in problem-solving strategies: a standard task used to investigate exibility in
problem-solving strategies is the Wisconsin Card Sorting Task WCSs.
The basic task is for the subject to sort cards, however they are not instructed as to
what the rules are for sorting -- they must extract the rules based on experimenter
feedback regarding whether their sorting is correct or not.
Wisconsin Card Sorting Task
 Row of 4 example cards
set out
• Patient is given a deck of 64
different cards
 Told to place each card
under the one it best
matches
 Told correct or incorrect
after each card
 Must deduce what the
underlying rule is.
Correct!
Explicit Problem Solving
Brain areas involved in adapting to new rules -- or task switching -- overlap with
areas active for other executive tasks in frontal and parietal lobes.
Explicit Problem Solving
A current model for brain areas
involved in explicit problem-solving:
on the outer surface of each
hemisphere, peak activity during
problem-solving appears in the
dorsolateral prefrontal cortex
DLPFCs.
During task con ict or errors, we fnd
high activity in the forward anteriors
part of cingulate cortex ACCs.
Mental Workload and Cortical Activity
Effortful tasks show a wide
spread of brain activity,
even beyond the executive
regions of the frontal
cortex.
In a classic fMRI study by
Smith and Jonides, memory
load was varied using an nback task. In this task, the
subject must hold in mind
several trials in order to
report the item that was
presented in the npreceding trials.
Mental Workload and Cortical Activity
Effortful tasks show a wide spread of brain
activity, even beyond the executive regions
of the frontal cortex
Results showed a dramatically
expanded cortical activity as a
function of memory load.
Semantic Memory
Semantic memory plays a key role
in problem-solving. How and where
are concepts represented in the
brain?
A recent summary of semantic
memory location in the left
hemisphere provides evidence that
semantic working memory involves
constantly looping activity between
the temporal and frontal lobes.
Short term vs Long term memory
Working memory constantly activates long-term storage
Cowan 2001s suggests that working memory may be thought of as active and timelimited neuronal activity playing on long-term patterns of structural connectivity.
Language Supporting Functions
Abstract concepts, prototypes, and networks
How is semantic knowledge represented in the brain? Do we carry pictures in
our heads that represent the perceptual world around us? Evidence suggests
that we tend to use visual images that are prototypical reminders of categories
like chair or movie star.
Logic & Probability Meet
Probabilistic Categories
Members of a category do not possess members that have necessary
and suffi cient features
Implications that follow:
1. Some members of the category are better members (more
representative) then other members
2. Category boundaries are graded and “fuzzy”
3. Learning a category does not involve learning a “rule”
4. Abstraction is moving higher in a natural taxonomy of
category types
E. Rosch (1976) Natural
Categories
E. Rosch revives the concerns of Wittgenstein:
“The world is structured because real-world attributes do not occur
independently of one another. Creatures with features are likely to
wings then creatures with fur, and objects with the visual apperance
of chairs are more likely to have functional sit-on-ableness then objects
with the appearance of cats. That is combinations of attributes
of real objects do not occur uniformly. Some pairs, triples, or n-tuples
are quite probable , appearing in combination sometimes with one,
sometimes with another attribute; others are rare; others logically
cannot or empirically donot occur. “
Kinds of Categories
Stimulus
Complexity
Posner &
Keele
(1968)
Prototype Results
More errors with high distortion or average distance
from prototype
More errors with new patterns then old patterns
More errors with new patterns then Prototype
And after several days Prototype no worse then old
patterns and is with many subjects better than old or
new patterns.
“The Genesis of Abstraction”.
HOMA:
Protoids
The era
of Prototype
Studies
Homa
Homa (1975,1978,1980)
Learning About Categories in the Absence of Memory, by Larry R.
Squire and Barbara J. Knowlton © 1995
National Academy of Sciences.
Abstract
A fundamental question about memory and cognition concerns how
information is acquired about categories and concepts as the result of
encounters with specifi c instances. We describe a profoundly amnesic
patient (E.P.) who cannot learn and remember specifi c instances-i.e.,
he has no detectable declarative memory. Yet after inspecting a series
of 40 training stimuli, he was normal at classifying novel stimuli
according to whether they did or did not belong to the same category
as the training stimuli. In contrast, he was unable to recognize a single
stimulus after it was presented 40 times in succession....
....E.P. could also properly classify the prototype of the category even
after 1 week, but has trouble with novel exemplars..
Using Existing Knowledge
Knowledge comes in networks
Basic level
animals in zoos
Mental representations, including
words, concepts, and images, are
thought to be organized in the brain
in elaborate networks of knowledge.
Ideas appear to be represented in the
cortex in terms of complex webs of
learned connectivities, rather than
localized fling systems with neatly
arranged conceptual categories.
Overlapping semantic networks for
concepts ‘tigerd and ‘elephantd.
Using Existing Knowledge
Conceptual defcits
Some of what we have learned
about cortical representation of
concepts comes from patients
with brain damage in regions
that are involved in semantic
memory and conceptual
representation.
The specifc defcits that these
patients have following brain
damage informs us as to the
role of those brain regions in
semantic memory.
Patient EW (Caramazza & Shelton 1998)
• 100% (or close to) on all tasks involving
semantic processing except if they involve
animals when generally 50 – 70%
• However such restricted category effects do not
yet form a syndrome with a clear anatomical
basis. So EW said to have a left fronto-parietal
lesion.
• Possible exception – selective impairment of fruit
and vegetable knowledge – BUT Crutch &
Warrington (Cog Neuro 2003) argue that this is
Selective Conceptual Defcits
Conceptual defcits
Patient EW had a selective defcit in naming animals
but not in naming members of other categories, such
as faces.
Her defcits were not only in recognizing pictures of
animals, but also in recognizing spoken animal
names, indicating that visual and sound
representations of animals might be located in the
same brain areas.
The fact that her defcit was limited to the animal
category indicates that different semantic categories -like animals and faces -- may be stored in differing
brain regions.
What is the Representation of Concepts in the brain?
Are they in separate areas of the brain?
How are different sensory modalities related?
Where is the Basic Level? How represented?
What kind of information goes into Working Memory?
How does Language access conceptual information?
What if you know more than one language? Are
they stored in the same place in the brain? Do
concepts in different languages interact?