Phil/Psych 256, Week 9

Brains and Emotions

Transition: challenges for cognitive science

Challenges: brains, emotions, consciousness, bodies, physical world, dynamic systems, social world, dynamic systems.

How to react?

Positions in the philosophy of mind

What is a mental state, e.g. thinking of spring?

1. Dualism: mental state = non-material state of spiritual mind. E.g. Descartes, Eccles, religious views.

2. Idealism: everything is mental. Pan-psychism: everything is conscious, at least to a degree.

3. Identity theory: mental state = brain state. E.g. Smart 1950s.

4. Functionalism: mental state = functional state of an information processing system. There is an underlying physical state (functionalism is a kind of materialism) but the physical state places no constraints on mental states.

Argument from multiple instantiation: thinking could proceed just as well in different kinds of computers and in extraterrestrial brains, as in our. So the actual physical instantiation (hardware) is much less important than the software program. Putnam, Fodor, Johnson-Laird.

5. Eliminative materialism: do not try to equate mental states with anything, since our theory of mental states is just part of folk psychology which is largely false. Instead, replace talk of mental states with theories drawn from human neuroscience. Reject functionalism because it is crucial that thinking is based in human brains. Paul and Pat Churchland.

6. Integrative (methodological) materialism: folk psychology is a rough but not worthless approximation. Our views of mental states can change as better computational and neuropsychological theories are developed. In principle, different kinds of hardware could run different kinds of software, but in practice the physical instantiation matters a lot. Need to study the brain, as well as different kinds of computers, and Martians too if they come along.

7. Mysterian materialism: mental states are physical states, but are far too weird and complicated to be explained scientifically.


Methods for investigating brains

1. Dissection: discover the structure of the brain.

2. Single cell recording

3. Electroencephalography (EEG)

4. Psychological experiments on people with brain damage, e.g. HM

5. Brain scanning

6. Transcranial magnetic stimulation

7. Use machine learning to interpret brain scans: brain image analysis.

8. Cumulative cognitive neuroscience: Yarkoni et al. 2011

9. Optogenetics

Brain scanning


1. CAT: Computerized axial tomography

2. PET : Positron emission tomography

3. MRI: Magnetic resonance imaging

4. fMRI: Functional magnetic resonance imaging

5. Diffusion tensor imaging

6. MEG: Magnetoencephalography

7. NIRS: Near infra-red spectroscopy

History of brain mapping


Head is inserted into a giant circular magnet

The magnet realigns atoms and protons

Machine sends a radio pulse that causes atoms to release energy

A computer detects energy release and produces an image

Functional MRI measures brain function by measuring changes in blood volume

Possible to map functions down to a few millimeters

Medical applications

Limitations:of fMRI

Makes strong assumptions about relation between brain activity and blood flow

Numerous calculations required

Low temporal resolution because of sluggish metabolic response

Neurologically realistic computational models

1. Spiking neurons.

A spike train is a chain of pulses (action potentials) emitted by a single neuron. A spike usually lasts 1-2 ms. A spike train can be characterized by a set of times, t1 ... tn, at which the neuron spikes, or by a series of ones and zeros, where 1 means that the neuron spikes at a particular time and 0 means that it is not spiking.
Whether a neuron N spikes at time t is a function of:
• the magnitude of the input to N, i.e. the electrical signals that N is receiving from other neurons
• the firing threshold, which is the minimal amount of input needed to fire
• refractoriness: after a neuron has fired, it takes a while before it can spike again.
A background signal (e.g. from the brain stem) may provide a phase pattern that allows different neurons to become synchronized or correlated with each other.

2. Brain areas

The brain is organized into regions. E.g. the GAGE model of Wagar and Thagard organizes neurons into groups representing cortex, hippocampus, amygdala, and nucleus accumbens. Neurons are not connected to every other neuron.

3. Theoretical neuroscience

The innateness question

1. Empiricism: the mind is a blank slate, and everything is learned from experience.

2. Nativism: many concepts and rules are innate, e.g. universal grammar.

Evolutionary psychology: the brain has many innate modules derived from natural selection.

3. Neural constructivism: the brain has evolved, not to have innate specialized circuits, but to be able to acquire flexible representations through interactions with complex physical and social environments.

Molecular biology and biochemistry

Molecules matter to mental computation:

  • Individual cells can perform computations internally.
  • Neuronal signaling via synapses depends on many different kinds of chemical neurotransmitters.
  • Long distance signaling takes place by hormones.
  • Brain chemicals have temporal as well as topographic effects: coordination and synchrony.
  • Drug effects.
  • Your brain on ecstasy.
  • Practical applications




    Basic Emotions

    - happy, sad, anger, fear, disgust. Maybe surprise.


    How do these feel different? Are they confusable?


    E.g. anger increases heart rate. Fear and sad increase skin temp.

    Note also blood pressure, neural activity.

    Cognitive aspects

    Oatley: emotions are tied in with goals.

    Marvin Minsky: The Emotion Machine.

    Emotions are both cognitive and physiological

    Thagard and Aubie on emotional consciousness.

    Emotions as semantic pointers


    Analogical understanding using emotions.

    Put yourself in another's shoes and have similar experiences. E.g. Olympics: skaters fall.

    Question: what are the limits of empathy? Can you know what it is like to be a bat? What it is like to be of the opposite sex? What it is like to be a Maori?

    Better question: what makes it hard to understand what it is like to be a member of the opposite sex?

    Key ingredients in mapping:

    situation + goals --> emotions (cf. Oatley)

    Empathy as analogical mapping:

     Source (me)  Target (you)
     my situation  your situation
     my goals  your goals
     situation + goals -> emotion  situation + goals -> emotion?
     my emotion  your emotion?

    As in all analogies, my empathic understanding of you is based on


    How far can the computational/representational understanding of mind (CRUM) go in helping us to understand emotions? Can it be tied in with the phenomenological and the physiological? We need an integrated theory of emotions that ties all of these together. Then emotions extend and supplement CRUM, but do not require its abandonment.

    Emotional Coherence (Thagard)

    1. Elements have positive or negative valences.

    2. Elements can have positive or negative emotional connections to other elements.

    3. The valence of an element is determined by the valences and acceptability of all the elements to which it is connected.

    Connectionist implementation: Units have valences as well as activations. Parallel constraint satisfaction operates on emotion as well as belief.

    Emotions as semantic pointers

    Other emotion articles by Thagard.

    Practical applications of emotion

    1. Critical thinking
    2. Politics
      fMRI being used to investigate reactions to candidates, e.g. amygdala, insula, striatum
      Drew Westen: Political Brain
    3. Neuromarketing
    4. Design: make things beautiful and thus more useful
    5. Relationships: love, trust, oxytocin

    Key points in Oatley

    Phil/Psych 256

    Computational Epistemology Laboratory.

    Paul Thagard

    This page updated Nov. 4, 2015