Cell Language Theory, The: Connecting Mind And Matter

356 The Cell Language Theory: Connecting Mind and Matter

b2861 The Cell Language Theory: Connecting Mind and Matter “6x9”

almost perfectly symmetric, i.e., Gaussian. It seems possible that the

shape of the pitch histograms of various readings reflects the emotion of

the reader and that this emotional aspects of the reading sounds may be

quantified using PDE. To the extent that a pitch histogram of reading a

text, a sentence, or a phrase fits PDE, to that extent it may be inferred that

the reader’s mind exercised selection on his or her emotional states.

8.3.17 Decision-Time Histograms (Figure 8.6(q))

It is known that, when a person is presented with a problem to be solved

with a binary decision, the more difficult the problem is, the longer it takes

the person to come to a decision [342–346]. The drift-diffusion model

(DDM) of decision-making is a widely employed theoretical model in

behavioral neurobiology [342–346]. DDM accurately reproduces (i.e.,

simulates) the decision-time histograms (see Experimental in Figure 8.6q),

which was in turn reproduced by PDE almost exactly (see Planckian).

Figure 8.7(a) depicts the two essential features of DDM, i.e., (a) the

Gaussian-distributed drift rates (i.e., the rates of evidence accumulation in

the brain), which can be represented as tan a, where a is the arctangent of

the drift rate, D/t, with D being the decision threshold and t the decision

time; and (b) the nonlinear relation between the independent variable of

the Gaussian distribution and the decision times. Because of these two

features, the Gaussian-distributed drift rates can produce a right-long-

tailed decision-time histogram, as shown in Figure 8.7(a), where the right-

long-tailed distribution was derived from the Gaussian distribution based

on two simple operations: (a) transform x of the Gaussian distribution to

D/tan a and (b) preserve the y-coordinates of the Gaussian distribution

unchanged.

In Figure 8.7(c), the Planckian distribution (and hence experimentally

observed decision-time histograms, since PDE faithfully reproduce them)

was almost perfectly reproduced by the Planckian distribution law derived

from the Gaussian distribution by transforming the Gaussian x-coordinate

to D/tan a, and keeping the y-coordinate invariant. This indicates that the

Planckian distribution can be alternatively derived from the Gaussian dis-

tribution based on DDM. That is, DDM can be viewed as the bridge

between the Gaussian and Planckian distributions.

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