The “tweaking principle” for task switching
Salva Ardid
1
2
Xiao-Jing Wang
0
2
0
Center for Neural Science, New York University
,
New York, New York 10003
,
USA
1
Department of Biology and Centre for Vision Research, York University
,
Toronto, Ontario
,
Canada
,
M3J 1P3
2
Department of Neurobiology and Kavli Institute for Neuroscience, Yale University
,
New Haven, Connecticut 06510
,
USA
-
A hallmark of executive control is the brains agility to
shift between different tasks depending on the behavioral
rule currently in play [1]. Humans and other animals
exhibit a remarkable ability to flexibly select an appropriate
response to a sensory input, and rapidly switch to another
sensory-response mapping when task rule or goal changes.
An increasing number of monkey experiments have been
performed using task-switching paradigms, combined with
single-neuron recording from sensory, parietal, and
prefrontal cortical areas. Physiological evidence from these
studies suggests that modulation of neural activity by task
rule is typically weak [2]. By contrast, most previous
models commonly assume that a rule signal is similarly strong
as sensory stimulation in affecting activity of cortical
neurons [3]. How can small rule modulation explain large
(binary) behavioral changes in task switching?
In this work, we propose a solution to this puzzle,
which we refer to as the tweaking hypothesis [4]. The
core idea is that network reconfiguration underlying task
switching can be realized by very weak top-down signals
from rule neurons in prefrontal cortex. This is because a
weak input can be greatly amplified through
reverberating attractor dynamics in categorization and decision
circuits, ultimately leading to circuit selection in favor of
one sensory-motor mapping over another.
We tested the tweaking hypothesis by developing a
neural circuit model for task switching that consists of
several basic and interacting circuit modules for sensory
coding, rule representation, categorization of stimulus
features, and action selection, respectively [4]. The model
was validated by reproducing salient single-neuron
physiological observations [2] and behavioral effects
associated with task switching [1,5,6]. Notably, the model
identifies specific circuit mechanisms, in terms of neural
dynamics and reward-dependent synaptic plasticity, that
explain salient and widely observed behavioral effects
associated with task switching [4]: (i) Switch cost:
response time and error rate increase in trials following a
task switch. Switch cost splits into a component that
decreases with a longer time for preparation and a
residual component that remains [5]. (ii) Task-response
interaction: on task repeat trials, the response time is
shorter if the same motor response is repeated; by
contrast on switch trials, response time is shorter if an
alternative motor response is selected [5,6]. (iii) Congruency
effect: response times and the error rate are larger when
the stimulus is incongruent compared to when it is
congruent, which depends on whether the mapped
behavioral response is different or the same, according to
alternative rules [5,6].
This work represents a neural circuit model for task
switching and sheds insights in the brain mechanism of
a fundamental cognitive capability; in particular, that
category-selective neurons play an essential role in
resolving the sensory-motor conflicts that typically appear in
task-switching paradigms [4].
Acknowledgements
This work was supported by the Office of Naval Research Grant
N00014-131-0297, The Swartz Foundation Fellowship (Salva Ardid), and John Simon
Guggenheim Foundation Fellowship (Xiao-Jing Wang).
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