NEUROBIOLOGY OF DECISION-MAKING, CSHL, May 2005

Choice, decision and action investigated with visually guided

saccades.

Jeffrey D. SchallWith

Leanne Boucher, Gordon Logan & Tom Palmeri

• Choice – action in the context of alternatives to satisfy a goal, desire or preference• Action – movements with consequences that can be explained by referring to preferences, goals and beliefs• Decision – deliberation when alternatives are vague, payoffs are unclear and habits are reversed

Definitions

“I look forward to playing and hopefully I can get to that point where I can make that decision.” — Michael Jordan on his anticipated return to professional basketball. Associated Press, 19 July 2001.

"I feel that way right now. Ask me in two or three months and I may change. I don't think I will. I'm pretty sure that's my decision." — Michael Jordan on his retirement from professional basketball. Associated Press, 17 July 1998.

• Distinguish two uses of “decision”• As characteristic of behavior (e.g., Decision Theory)

• But measures of outcome do not specify mechanism• As process producing behavior

• Mechanism with particular architecture

• Decision as process has two distinct meanings• Decide to -- Alternative actions (can be identified with choosing)• Decide that -- Alternative categories (not identified with choosing)

Further defining “decision”

• The properties of neurons do not reveal function

• Formal (computational) theories of performance explain function

• But distinct models cannot be distinguished from behavior testing, e.g., diffusion or race

• Properties of neurons might provide constraints to distinguish between models …

• … if and only if the neural activity measured is the instantiation of the cognitive process in question, which constitutes a linking proposition

Necessity of formal linking propositions

Teller DY. 1984. Vision Research 24:1233-1246Schall JD. 2004. Ann Rev Psychol 55:23-50

Linking propositions for decision making

Time from stimulus (sec)A

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Hanes & Schall (1996) described neural activity that looked like an accumulator.

They identified this activity with form of sequential sampling models.

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Linking propositions for decision makingRT = Decision time + Residual timeResidual time = Encoding time + Preparation time

Stimulus encoding Sequential sampling Response preparation

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Countermanding task

NO STOP SIGNAL Trials

Reaction Time

STOP SIGNAL Trials

Stop Signal Delay

CANCELLED

NON-CANCELLED

Countermanding performance

Countermanding paradigm: Race model

Logan, G.D. & Cowan, W.B. (1984) On the ability to inhibit thought and action: A theory of an act of control. Psychological Review 91:295-327. Hanes DP and Schall JD (1995) Countermanding saccades in macaque.Visual Neuroscience 12:929-937

Reaction Time

Stop Signal DelayCANCELLED

“GO”

“GO”

“STOP”

NON-CANCELLED

“GO”

“STOP”

Visual Cortex

LGN

RF

Saccade

Thalamus

Cerebellum

SCsSCi

Frontal cortex

(DLPFC, ACC, SEF)Parietal

Cortex (LIP)

Retina

Temporal Cortex (TEO)

FEF

Basal Ganglia

Munoz DP, Schall JD (2003) Concurrent distributed control of saccade initiation in the frontal eye field and superior colliculus. In The Oculomotor System: New Approaches for Studying Sensorimotor Integration. Edited by WC Hall, AK Moschovakis. CRC Press, Boca Raton, FL. Pages 55-82.

Saccades are produced by a distributed network

Countermanding physiology

No stop trials

Non-canceled trials

No stop trials

Canceled trials

STOP SSRT STOP SSRT

Hanes, D.P., W.F. Patterson, J.D. Schall (1998) The role of frontal eye field in countermanding saccades: Visual, movement and fixation activity. Journal of Neurophysiology 79:817-834.

Pare M, Hanes DP (2003) Controlled movement processing: superior colliculus activity associated with countermanded saccades. Journal of Neuroscience 23:6480-6489.

1 - The race model of countermanding performance assumes that the GO and the STOP processes have independent finish times (Logan & Cowan, 1984).

Mapping the race model onto neural processes

2 – Saccades are produced by a network of interacting neurons.

Paradox – How can a network of interacting neurons produce behavior that looks like the outcome of race between independent processes?

Explore properties of simple network of GO and STOP units.Mapping the race model onto neural processes

Constrained by the characteristics of countermanding behavior and by the form of activation of neurons

L.Boucher, G.D.Logan, T.J.Palmeri, J.D.Schall. An interactive race model of countermanding saccades. Program No. 72.10. 2003 Abstract Viewer/Itinerary Planner.

GOSTOPSTOPGOGO

dta

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GO, GO

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Complete independenceSTOPGO

L.Boucher, G.D.Logan, T.J.Palmeri, J.D.Schall. An interactive race model of countermanding saccades. Program No. 72.10. 2003 Abstract Viewer/Itinerary Planner.

Complete independenceReproduces countermanding behavior…

STOPGO

L.Boucher, G.D.Logan, T.J.Palmeri, J.D.Schall. An interactive race model of countermanding saccades. Program No. 72.10. 2003 Abstract Viewer/Itinerary Planner.

Stop signal delay (ms)

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L.Boucher, G.D.Logan, T.J.Palmeri, J.D.Schall. An interactive race model of countermanding saccades. Program No. 72.10. 2003 Abstract Viewer/Itinerary Planner.

Complete independence

Stop Signal SSRT

Stop Signal SSRT

… but does not produce correct activations.

The GO process isnever interrupted!

STOPGO

Key insight – the inhibition of STOP on GO cannot be uniform and instantaneous; it must be late and potent

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Delayed potent STOPReproduces countermanding behavior…

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Delayed potent STOP… and reproduces neural activation

The GO process is not modulated in non-canceled trials

The GO process is modulated within SSRTin canceled trials

0 100 200 3000 100 200 300

Activa

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Countermanding performance is produced by pool of neurons the prepare movements (GO process) and pool of neurons that interrupt preparation (STOP process).

The STOP process is composed of an early (afferent) stochastic stage and a late potent interruption stage.

Specific conclusions

Target Stop signal

D GO D STOP

0

Time from stimulus(ms)

SSRT

50 250200150100 300

Redundant but distinct models cannot be distinguished based on behavior data (Moore, 1956, in Automata Studies, ed. CE Shannon, J McCarthy. Princeton Univ. Press)

Properties of neurons can distinguish between alternative architectures… but only if neurons instantiate the processes in question.

GO process identified with pool of “movement” neurons.STOP process identified with pool of “movement inhibition” neurons.

General conclusions

Stochastic response preparation process necessary to explain countermanding performance.

If so, response preparation must be more or less stochastic during all tasks.

Therefore, the proper form of response preparation variability must be incorporated into sequential sampling models of perceptual or memory decisions.

This and much other evidence indicates that RT is the expression of at least two distinct but not necessarily discrete stages of processing – encoding+categorization (decide that) and response preparation (decide to).

General conclusions continued

General conclusions continued

It is possible now to determine the duration of intermediate stages with invasive measures of neural states.

However, this depends on proper linking propositions.

Information about process durations and transitions is necessary to elucidate how stimulus ambiguity, prior probability and reward history influence choices.

"[Since] we cannot break up the reaction into successive acts and obtain the time of each act, of what use is the reaction time?" – R.S. Woodworth (1938) in Experimental Psychology [quoted in Luce (1986)]

Rees et al. Nature Neuroscience 3, 716 - 723 (2000)

An empirical basis for distinguishing between choosing and deciding

It is deciding when anterior cingulate cortex is engaged.

Area MT

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Motion strength

Anterior cingulate cortex

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Motion strength

Monkey C Monkey A1 Monkey A2 Parameter Independent Interactive Independent Interactive Independent Interactive

μGO 4.62 4.64 5.49 5.42 5.12 4.99

σGO 18.53 18.22 23.11 22.81 22.91 22.84

μSTOP 12.11 11.56 27.42 21.45 27.60 23.08

σSTOP 13.22 18.22 140.60 140.97 140.90 141.07

βGO 0.00 0.00429 0.00 0.000265 0.00 0.0399

βSTOP 0.00 0.00694 0.00 0.00561 0.00 0.0399

DSTOP 1 71 27 15 27 50

χ2 22.85 24.23 89.74 91.16 106.41 96.91 cancel time — -18 — -25 — -23

STOPinterrupt — 11 — 35 — 10 SSRT 103 102 77 77 76 85

5 6 7 8 9 10 11 12 1330

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X = 2.332

150 200 250 300 350 400

3.990.746.812.33

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Time from stimulus (ms)Time from stimulus (ms)

Reaction time (ms)Reaction time (ms)150 200 250 300 350 400150 200 250 300 350 400

Stop signal delay (ms)Stop signal delay (ms)

X =2X =

2

X = 2.492X = 2.35

2

6.240.747.535.24

4.590.747.575.24

100 200 300Time from stimulus (ms)

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Fixation cell activity from FEF & SC

Hanes, D.P., W.F. Patterson, J.D. Schall (1998) The role of frontal eye field in countermanding saccades: Visual, movement and fixation activity. Journal of Neurophysiology 79:817-834.

Pare M, Hanes DP (2003) Controlled movement processing: superior colliculus activity associated with countermanded saccades. Journal of Neuroscience 23:6480-6489.