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Bayesian Reader                                                                                                                                                              99 
Figure Captions
Figure 1
Figure 1 shows the input distributions centered on two words at times early and late in processing. 
The figure is based on the assumption that there is a lexicon of only two words, and that they vary 
on a single perceptual dimension. Each curve represents the probability density function of  ƒ(I|W
i
for a given SEM. Calculations are based on likelihoods, that is, on the heights of the probability 
density function of each word at the point corresponding to the mean of the input distribution. The 
lower two curves correspond to a time early in processing where the SEM is large. The ratio of the 
heights of these two curves at the position corresponding to the mean of the input distribution is 
close to one, indicating that both words have similar probabilities. For the upper curves, 
corresponding to later in processing, the SEM is much smaller. As P(input|word 2) is now almost 
zero, P(word 1| input) will be close to 1.0. Note that, at any given time, the pdfs for all words are 
the same as they are all determined by the SEM of the input distribution. 
Figure 2
Possible network implementation of the Bayesian Reader. Not all connections shown. See text for 
details. 
Figure 3
Simulation of the effect of word-frequency in word identification. Model RT (in samples) is plotted 
against log word frequency. The figure also shows the regression line for RT against log frequency. 
This line accounts for 99% of the variance. 
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Bayesian Reader                                                                                                                                                              100 
Figure 4
Simulation of the effect of word-frequency in lexical decision. Model RT (in samples) is plotted 
against log word frequency. The figure also shows the regression line for RT against log frequency. 
This line accounts for 99% of the variance. 
Figure 5
Illustration of areas of high- and low neighborhood density in perceptual space. Each dot represents 
a word, and ‘High’ and ‘Low’ correspond to the centers of input distributions in high and low 
density areas of lexical space respectively. 
Figure 6 a-c
Data and simulations of Forster and Shen showing data (in Milliseconds) and simulated lexical 
decision times to words and nonwords as a function of number of neighbors. Panel 
a
shows the 
data, Panel 
shows simulated RT with a response threshold of 0.95, and Panel 
c
shows simulations 
with a threshold of 0.75. 
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Bayesian Reader                                                                                                                                                              101 
Figure 1 
word 1
word 2
input
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Bayesian Reader                                                                                                                                                              102 
Figure 2. 
Layer 2 word output 
units. P(W|I) 
Word Sigma unit 
Layer 1 Gaussian  
word units 
SEM unit, modulating 
Gaussian tuning 
function 
Input units driven by 
vector representing 
the current mean of 
the input samples.
s.
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Bayesian Reader                                                                                                                                                              103 
Figure 3  
200
300
400
500
600
700
800
900
1000
1
10
100
1000
10000
word frequency
Model RT
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Bayesian Reader                                                                                                                                                              104 
Figure 4  
0
100
200
300
400
500
600
700
800
1
10
100
1000
10000
word frequency
Model RT
Bayesian Reader                                                                                                                                                              105 
Figure 5 
Low 
High
h
Bayesian Reader                                                                                                                                                              106 
Figure 6 a-c 
Data
550
600
650
700
750
800
0
1
2
3
Neighbors
words
non-words
Model (p=0.95)
300
400
500
600
700
0
1
2
3
Neighbors
words
non-words
Model (p=0.75)
200
250
300
350
400
450
0
1
2
3
Neighbors
words
non-words
Bayesian Reader                                                                                                                                                              107 
Table 1  
RT
1.0
Log freq
-0.60
1.0
0
Rank freq
-0.63
-0.89
1.0
N
-0.11
0.10
-0.10
1.0
Model (0.99)
0.61
-0.99
0.91
-0.14
Model (0.95)
0.56
-0.89
0.82
-0.25
RT
Log freq
Rank
N
Table 1. 
Correlations of model with data from Balota, Cortese and Pilotti (1999).  Correlations are based on 
1215  4-letter words  Model correlations are shown with thresholds of both 0.99 and 0.95. 
Bayesian Reader                                                                                                                                                              108 
Table 2. Andrews (1989) Experiment 1 
Data 
Model 
Adjusted Model 
High N  Low N 
High N  Low N 
High N  Low N 
High F   
602 
608   
371 
404   
597 
613 
Low F   
693 
733   
570 
651   
693 
732 
Table 2 shows mean lexical decision latencies (in Milliseconds) from Experiment 1 of Andrews 
(1989) (Data), the unadjusted results of the model simulations (Model), and the model output fitted 
to the data by regressing the model against the data. 
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