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Abstract
This paper outlines the model-based theory of causal reasoning. It postulates that
the core meanings of causal assertions are deterministic and refer to temporally-ordered
sets of possibilities:
A causes B to occur means that given
A,
B occurs, whereas
A enables B to occur means that given
A, it is possible for
B to occur. The paper shows how mental models represent such assertions, and how these
models underlie deductive, inductive, and abductive reasoning yielding explanations.
It reviews evidence both to corroborate the theory and to account for phenomena sometimes
taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating
that mental models for causal inference are implemented within lateral prefrontal
cortex.
1. An oculomotor delayed-response task was used to examine the spatial memory functions of neurons in primate prefrontal cortex. Monkeys were trained to fixate a central spot during a brief presentation (0.5 s) of a peripheral cue and throughout a subsequent delay period (1-6 s), and then, upon the extinction of the fixation target, to make a saccadic eye movement to where the cue had been presented. Cues were usually presented in one of eight different locations separated by 45 degrees. This task thus requires monkeys to direct their gaze to the location of a remembered visual cue, controls the retinal coordinates of the visual cues, controls the monkey's oculomotor behavior during the delay period, and also allows precise measurement of the timing and direction of the relevant behavioral responses. 2. Recordings were obtained from 288 neurons in the prefrontal cortex within and surrounding the principal sulcus (PS) while monkeys performed this task. An additional 31 neurons in the frontal eye fields (FEF) region within and near the anterior bank of the arcuate sulcus were also studied. 3. Of the 288 PS neurons, 170 exhibited task-related activity during at least one phase of this task and, of these, 87 showed significant excitation or inhibition of activity during the delay period relative to activity during the intertrial interval. 4. Delay period activity was classified as directional for 79% of these 87 neurons in that significant responses only occurred following cues located over a certain range of visual field directions and were weak or absent for other cue directions. The remaining 21% were omnidirectional, i.e., showed comparable delay period activity for all visual field locations tested. Directional preferences, or lack thereof, were maintained across different delay intervals (1-6 s). 5. For 50 of the 87 PS neurons, activity during the delay period was significantly elevated above the neuron's spontaneous rate for at least one cue location; for the remaining 37 neurons only inhibitory delay period activity was seen. Nearly all (92%) neurons with excitatory delay period activity were directional and few (8%) were omnidirectional. Most (62%) neurons with purely inhibitory delay period activity were directional, but a substantial minority (38%) was omnidirectional. 6. Fifteen of the neurons with excitatory directional delay period activity also had significant inhibitory delay period activity for other cue directions. These inhibitory responses were usually strongest for, or centered about, cue directions roughly opposite those optimal for excitatory responses.(ABSTRACT TRUNCATED AT 400 WORDS)
An information-processing model is outlined that predicts that performance on non-routine tasks can be impaired independently of performance on routine tasks. The model is related to views on frontal lobe functions, particularly those of Luria. Two methods of obtaining more rigorous tests of the model are discussed. One makes use of ideas from artificial intelligence to derive a task heavily loaded on planning abilities. A group of patients with left anterior lesions has a specific deficit on the task. Subsidiary investigations support the inference that this is a planning impairment.
[1]1Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory Washington, DC, USA
[2]2Beckman Institute for Advanced Science and Technology, University of Illinoi at Urbana-Champaign Urbana, IL, USA
[3]3Department of Psychology, Princeton University Princeton, NJ, USA
[4]4Department of Psychology, New York University New York, NY, USA
Author notes
Edited by: John J. Foxe, Albert Einstein College of Medicine, USA
Reviewed by: Britt Anderson, University of Waterloo, Canada; Aaron P. Blaisdell, University
of California Los Angeles, USA
*Correspondence: Sangeet S. Khemlani, Navy Center for Applied Research in Artificial
Intelligence, Naval Research Laboratory, Washington, DC 20375, USA e-mail:
skhemlani@
123456gmail.com
;
Aron K. Barbey, Beckman Institute for Advanced Science and Technology, University
of Illinoi at Urbana-Champaign, Urbana, IL 61801, USA e-mail:
barbey@
123456illinois.edu
;
Philip N. Johnson-Laird, Department of Psychology, New York University, New York,
NY 10003, USA e-mail:
phil@
123456princeton.edu
This article was submitted to the journal Frontiers in Human Neuroscience.
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