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 problem-solving has clear, conscious goals and clearly defned steps for getting from a starting point to a solution.

Mental arithmetic is an example of explicit problem-solving

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

• 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

Row of 4 example cards

set out

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 n-back task. In this task, the subject must hold in mind several trials in order to report the item that was presented in the n-preceding 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 storageCowan 2001s suggests that working memory may be thought of as active and time-limited 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 necessaryand 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 occurindependently 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 objectswith the appearance of cats. That is combinations of attributesof real objects do not occur uniformly. Some pairs, triples, or n-tuplesare 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 ComplexityPosner & 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 HOMA:ProtoidsThe eraof PrototypeStudies

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

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.

Basic levelanimals in zoos

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 involvingsemantic processing except if they involveanimals when generally 50 – 70%

• However such restricted category effects do notyet form a syndrome with a clear anatomicalbasis. So EW said to have a left fronto-parietallesion.

• Possible exception – selective impairment of fruitand vegetable knowledge – BUT Crutch &Warrington (Cog Neuro 2003) argue that this isthrough selective loss of colour knowledge

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? Arethey stored in the same place in the brain? Do concepts in different languages interact?