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213
The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced choice tasks
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Rational approximations to rational models: Alternative algorithms for category learning
"... Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible fo ..."
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Cited by 61 (19 self)
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Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models is thus explaining how optimal solutions can be approximated by psychological processes. We outline a general strategy for answering this question, namely to explore the psychological plausibility of approximation algorithms developed in computer science and statistics. In particular, we argue that Monte Carlo methods provide a source of “rational process models” that connect optimal solutions to psychological processes. We support this argument through a detailed example, applying this approach to Anderson’s (1990, 1991) Rational Model of Categorization (RMC), which involves a particularly challenging computational problem. Drawing on a connection between the RMC and ideas from nonparametric Bayesian statistics, we propose two alternative algorithms for approximate inference in this model. The algorithms we consider include Gibbs sampling, a procedure
Integrated Neural Processes for Defining Potential Actions and Deciding between Them: A Computational Model
, 2006
"... To successfully accomplish a behavioral goal such as reaching for an object, an animal must solve two related problems: to decide which object to reach and to plan the specific parameters of the movement. Traditionally, these two problems have been viewed as separate, and theories of decision making ..."
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Cited by 56 (2 self)
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To successfully accomplish a behavioral goal such as reaching for an object, an animal must solve two related problems: to decide which object to reach and to plan the specific parameters of the movement. Traditionally, these two problems have been viewed as separate, and theories of decision making and motor planning have been developed primarily independently. However, neural data suggests that these processes involve the same brain regions and are performed in an integrated manner. Here, a computational model is described that addresses both the question of how different potential actions are specified and how the brain decides between them. In the model, multiple potential actions are simultaneously represented as continuous regions of activity within populations of cells in frontoparietal cortex. These representations engage in a competition for overt execution that is biased by modulatory influences from prefrontal cortex. The model neural populations exhibit activity patterns that correlate with both the spatial metrics of potential actions and their associated decision variables, in a manner similar to activities in parietal, prefrontal, and premotor cortex. The model therefore suggests an explanation for neural data that have been hard to account for in terms of serial theories that propose that decision making occurs before action planning. In addition to simulating the activity of individual neurons during decision tasks, the model also reproduces key aspects of the spatial and temporal statistics of human choices and makes a number of testable predictions.
Ongoing spontaneous activity controls access to consciousness: A neuronal model for inattentional blindness
- PLoS Biology
, 2005
"... Even in the absence of sensory inputs, cortical and thalamic neurons can show structured patterns of ongoing spontaneous activity, whose origins and functional significance are not well understood. We use computer simulations to explore the conditions under which spontaneous activity emerges from a ..."
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Cited by 50 (5 self)
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Even in the absence of sensory inputs, cortical and thalamic neurons can show structured patterns of ongoing spontaneous activity, whose origins and functional significance are not well understood. We use computer simulations to explore the conditions under which spontaneous activity emerges from a simplified model of multiple interconnected thalamocortical columns linked by long-range, top-down excitatory axons, and to examine its interactions with stimulus-induced activation. Simulations help characterize two main states of activity. First, spontaneous gamma-band oscillations emerge at a precise threshold controlled by ascending neuromodulator systems. Second, within a spontaneously active network, we observe the sudden ‘‘ignition’ ’ of one out of many possible coherent states of high-level activity amidst cortical neurons with long-distance projections. During such an ignited state, spontaneous activity can block external sensory processing. We relate those properties to experimental observations on the neural bases of endogenous states of consciousness, and particularly the blocking of access to consciousness that occurs in the psychophysical phenomenon of ‘‘inattentional blindness,’ ’ in which normal subjects intensely engaged in mental activity fail to notice salient but irrelevant sensory stimuli. Although highly simplified, the generic properties of a minimal network may help clarify some of the basic cerebral phenomena underlying the autonomy of consciousness. Citation: Dehaene S, Changeux JP (2005) Ongoing spontaneous activity controls access to consciousness: A neuronal model for inattentional blindness. PLoS Biol 3(5): e141.
A contextbased theory of recency and contiguity in free recall
- Psychological Review
, 2008
"... The authors present a new model of free recall on the basis of M. W. Howard and M. J. Kahana’s (2002a) temporal context model and M. Usher and J. L. McClelland’s (2001) leaky-accumulator decision model. In this model, contextual drift gives rise to both short-term and long-term recency effects, and ..."
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Cited by 43 (19 self)
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The authors present a new model of free recall on the basis of M. W. Howard and M. J. Kahana’s (2002a) temporal context model and M. Usher and J. L. McClelland’s (2001) leaky-accumulator decision model. In this model, contextual drift gives rise to both short-term and long-term recency effects, and contextual retrieval gives rise to short-term and long-term contiguity effects. Recall decisions are controlled by a race between competitive leaky accumulators. The model captures the dynamics of immediate, delayed, and continual distractor free recall, demonstrating that dissociations between short- and long-term recency can naturally arise from a model in which an internal contextual state is used as the sole cue for retrieval across time scales.
A dynamic interactive theory of person construal
, 2011
"... A dynamic interactive theory of person construal is proposed. It assumes that the perception of other people is accomplished by a dynamical system involving continuous interaction between social categories, stereotypes, high-level cognitive states, and the low-level processing of facial, vocal, and ..."
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Cited by 35 (8 self)
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A dynamic interactive theory of person construal is proposed. It assumes that the perception of other people is accomplished by a dynamical system involving continuous interaction between social categories, stereotypes, high-level cognitive states, and the low-level processing of facial, vocal, and bodily cues. This system permits lower-level sensory perception and higher-order social cognition to dynamically coordinate across multiple interactive levels of processing to give rise to stable person construals. A recurrent connectionist model of this system is described, which accounts for major findings on (a) partial parallel activation and dynamic competition in categorization and stereotyping, (b) top-down influences of high-level cognitive states and stereotype activations on categorization, (c) bottom-up category interactions due to shared perceptual features, and (d) contextual and cross-modal effects on categorization. The system’s probabilistic and continuously evolving activation states permit multiple construals to be flexibly active in parallel. These activation states are also able to be tightly yoked to ongoing changes in external perceptual cues and to ongoing changes in high-level cognitive states. The implications of a rapidly adaptive, dynamic, and interactive person construal system are discussed.
Simple neural networks that optimize decisions
- Int. J. Bifurc. Chaos
"... We review simple connectionist and ¯ring rate models for mutually inhibiting pools of neurons that discriminate between pairs of stimuli. Both are two-dimensional nonlinear stochastic ordinary di®erential equations, and although they di®er in how inputs and stim-uli enter, we show that they are equi ..."
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Cited by 34 (15 self)
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We review simple connectionist and ¯ring rate models for mutually inhibiting pools of neurons that discriminate between pairs of stimuli. Both are two-dimensional nonlinear stochastic ordinary di®erential equations, and although they di®er in how inputs and stim-uli enter, we show that they are equivalent under state variable and parameter coordinate changes. A key parameter is gain: the maximum slope of the sigmoidal activation func-tion. We develop piecewise-linear and purely linear models, and one-dimensional reductions to Ornstein-Uhlenbeck processes that can be viewed as linear ¯lters, and show that reac-tion time and error rate statistics are well approximated by these simpler models. We then pose and solve the optimal gain problem for the Ornstein-Uhlenbeck processes, ¯nding ex-plicit gain schedules that minimize error rates for time-varying stimuli. We relate these to time courses of norepinephrine release in cortical areas, and argue that transient ¯ring rate changes in the brainstem nucleus locus coeruleus may be responsible for approximate gain optimization.
Biasing simple choices by manipulating relative visual attention
- Judgment and Decision Making
, 2008
"... Several decision-making models predict that it should be possible to affect real binary choices by manipulating the relative amount of visual attention that decision-makers pay to the two alternatives. We present the results of three behavioral experiments testing this prediction. Visual attention i ..."
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Cited by 30 (4 self)
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Several decision-making models predict that it should be possible to affect real binary choices by manipulating the relative amount of visual attention that decision-makers pay to the two alternatives. We present the results of three behavioral experiments testing this prediction. Visual attention is controlled by manipulating the amount of time subjects fixate on the two items. The manipulation has a differential impact on appetitive and aversive items. Appetitive items are 6 to 11 % more likely to be chosen in the long fixation condition. In contrast, aversive items are 7 % less likely to be chosen in the long fixation condition. The effect is present for primary goods, such as foods, and for higher-order durable goods, such as posters.
Human cognition and a pile of sand: A discussion on serial correlations and self–organized criticality.
- Journal of Experimental Psychology: General,
, 2005
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