def create_information(name, threshold, decider):
""" Create a information theory based decision function. """
check_threshold(threshold)
@update_name(name)
def information(trial):
H_a = 0
H_b = 0
l = float(len(trial))
norm_const = np.log2(l) / l
for ii,t in enumerate(trial):
if t == 'A':
H_a += -0.5 * np.log2(0.5)
## For a binary alphabet, b-ary entropy is
## H(A) = sum_i(b*log_2(b))
## where b is the probability a letter
## in the alphabet
## appears at slot i (i.e. = t above).
## In this case b = p(A) = p(b) = 0.5 for all i.
else:
H_b += -0.5 * np.log2(0.5)
# And see if a decision can be made
decision = decider(H_a * norm_const,
H_b * norm_const, threshold, ii+1)
if decision != None:
return decision
else:
# If threshold is never met,
# we end up here...
return _create_d_result('N', None, None, None)
return information
def create_likelihood_ratio(name, threshold, decider):
""" Create a likelihood_ratio function (i.e. sequential probability ratio
test).
Empirical support:
----
1. De Gardelle, V., & Summerfield, C. (2011). Robust averaging during
perceptual judgment. PNAS, 108(32). """
check_threshold(threshold)
@update_name(name)
def likelihood_ratio(trial):
""" Use a version of the sequential ratio test to decide (log_10). """
# Transform threshold to suitable deciban
# equivilant.
dthreshold = threshold * 2.0
## 2 decibans is 99% confidence,
## so use that to map threshold (0-1)
## to the deciban threshold (dthreshold).
cA = 0.0
cB = 0.0
logLR = 0.0
l = float(len(trial))
for ii,t in enumerate(trial):
if t == 'A':
cA += 1
else:
cB += 1
if (cA > 0) and (cB > 0):
logLR += log(cA/cB, 10)
else:
continue
## This implementaion can't decide on all
## A or B trials. Live with this edge case for now?
# A custom decision function
# was necessary:
if fabs(logLR) >= dthreshold:
if logLR > 0:
return _create_d_result('A', logLR, 0, ii+1)
elif logLR < 0:
return _create_d_result('B', logLR, 0, ii+1)
elif logLR == 0:
return _create_d_result('N', logLR, 0, ii+1)
else:
# It should be impossible to get here, however
# just in case something very odd happens....
raise ValueError(
"Something is very wrong with the scores.")
else:
# If threshold is never met,
# we end up here...
return _create_d_result('N', None, None, None)
return likelihood_ratio
def create_naive_probability(name, threshold, decider):
""" Create a decision function using naive (multiplicative)
probability estimates. """
check_threshold(threshold)
@update_name(name)
def naive_probability(trial):
""" Calculate the likelihood of the continuous sequence of either
A or B in <trial>, decide when p_sequence(A) or (B) exceeds
<threshold>. """
## Init
score_A = 0
score_B = 0
lastcat = trial[0]
p = 0.5
# Loop over trial calculating scores.
for ii, t in enumerate(trial[1:]):
if t == lastcat:
# If t is the same,
# decrease the likelihood (p).
p = p * 0.5
# Assign p to a score,
# also reflect it
if t == 'A':
score_A = 1 - deepcopy(p)
else:
score_B = 1 - deepcopy(p)
# And see if a decision can be made
decision = decider(score_A, score_B, threshold, ii+1)
if decision != None:
return decision
else:
# Otherwise reset
lastcat = deepcopy(t)
p = 0.5
else:
# If threshold is never met,
# we end up here...
return _create_d_result('N', None, None, None)
return naive_probability
def create_incremental_lba(name, threshold, decider, k=0.1, d=0.1):
""" Create a version of Brown and Heathcote's (2008) LBA
model modified so A/B updates are exclusive rather than simultaneous.
Input
-----
k - the start point.
d - the drift rate.
"""
k = float(k)
d = float(d)
## Just in case
## they're int
check_threshold(threshold)
@update_name(name)
def incremental_lba(trial):
""" Use Brown and Heathcote's (2008) LBA model, modified so A/B updates
are exclusive rather than simultanous, to make the decision. """
# Init
score_A = k
score_B = k
for ii, t in enumerate(trial):
if t == 'A':
# An incremental version of LBA.
# that recognizes the balistic updates
# are exclusive. A and B updates happen
# at each time step. In this form
# updates are exclusive to A or B
# but still ballistic.
score_A += d
else:
score_B += d
decision = decider(score_A, score_B, threshold, ii+1)
if decision != None:
return decision
else:
# If threshold is never met,
# we end up here...
return _create_d_result('N', None, None, None)
return incremental_lba
def create_abscount(name, threshold, decider):
""" Create a decision function that use the counts of A and B to
decide.
This implements the counting model (so far as I can tell, I can't find a
copy, only other references to it) of Audely and Pike (1965) Some
stocastic models of choice, J of Math. and Stat. Psychology, 18,
207-225. """
check_threshold(threshold)
@update_name(name)
def abscount(trial):
""" Return a category (A, B, or N (neutral)) for <trial>
based on number of As versus Bs. """
import random
score_A = 0
score_B = 0
l = float(len(trial))
for ii, t in enumerate(trial):
# Update scores based on t
if t == 'A':
score_A += 1
else:
score_B += 1
# Norm them
score_A_norm = score_A / l
score_B_norm = score_B / l
# print("({0}). {1}, {2}".format(ii, score_A_norm, score_B_norm))
# And see if a decision can be made
decision = decider(score_A_norm, score_B_norm, threshold, ii+1)
if decision != None:
return decision
else:
# If threshold is never met,
# we end up here...
return _create_d_result('N', None, None, None)
return abscount
def create_race(name, threshold, decider):
""" Create a race to threshhold model as described in
Rowe et al (2010). Action selection: a race model for the selection and
non-selected actions distinguishes the contribution of premotor and
prefrontal areas.
Input
-----
"""
check_threshold(threshold)
@update_name(name)
def race(trial):
""" A race to threshold model. """
return race
def create_relcount(name, threshold, decider):
""" Create a decision function that use the relative difference
in A and B counts to decide.
A variation on Audely and Pike (1965, and abscount()) that uses the
relative ration of A/B instead of an absolute counter. """
check_threshold(threshold)
@update_name(name)
def relcount(trial):
""" Return a category (A, B, or N (neutral)) for <trial>
based on proportion of As to Bs. """
cA = 0.0
cB = 0.0
for ii, t in enumerate(trial):
# Update scores based on t
if t == 'A':
cA += 1
else:
cB += 1
if (cA > 0) and (cB > 0):
score_A = cA / (cA + cB)
score_B = 1 - score_A
# And see if a decision can be made
decision = decider(score_A, score_B, threshold, ii+1)
if decision != None:
return decision
else:
continue
else:
# If threshold is never met,
# we end up here...
return _create_d_result('N', None, None, None)
return relcount
def create_urgency_gating(name, threshold, decider, gain=0.4):
""" Create a urgency gating function (i.e. implement:
Cisek et al (2009). Decision making in changing
conditions: The urgency gating model, J Neuro, 29(37)
11560-11571.) """
check_threshold(threshold)
@update_name(name)
def urgency_gating(trial):
""" Decide using Cisek's (2009) urgency gating algorithm. """
l = float(len(trial))
for ii, t in enumerate(trial):
urgency = ii ## urgency is elapsed "time",
## i.e. a index of trial length
pA_ii = _p_response(trial, ii, 'A')
pB_ii = _p_response(trial, ii, 'B')
print("pA_ii: {0}".format(pA_ii))
print("pB_ii: {0}".format(pB_ii))
score_A = fabs(gain * urgency * (pA_ii - 0.5))
score_B = fabs(gain * urgency * (pB_ii - 0.5))
print("score_A: {0}".format(score_A))
print("score_B: {0}".format(score_B))
decision = decider(score_A, score_B, threshold, ii+1)
if decision != None:
return decision
else:
# If threshold is never met,
# we end up here...
return _create_d_result('N', None, None, None)
return urgency_gating
def create_blca(name, threshold, decider, length=10, k=0.1, wi=0.1, leak=0.1, beta=0.1):
""" Creates a ballistic leaky competing accumulator model based on,
Usher and McClelland (2001), The time course of perceptual choice: the leaky
competing accumulator model. Psychological Review, 108, 550-592, on which
this code is based.
Note: To keep notation consistent with other models in this framework,
the notation differs from that in Usher and McClelland.
Input
----
length - Length of the trial
k - Start point
wi - Initial connection weight (same for both A and B)
leak - Leak rate
beta - Inhibition strength
"""
check_threshold(threshold)
@update_name(name)
def blcba(trial):
""" Decide using Usher and McClelland's (2001) ballistic leaky
competing accumulator model. """
score_A = k
score_B = k
# impulse = wi * (1.0 / length)
for ii, t in enumerate(trial):
# As score_A + score_B = 1, if one is 1, the
# other must be zero.
if t == 'A':
score_A += wi * 1 - (k * score_A) - beta * score_B
score_B += wi * 0 - (k * score_B) - beta * score_A
else:
score_A += wi * 0 - (k * score_A) - beta * score_B
score_B += wi * 1 - (k * score_B) - beta * score_A
# print("{2} - {3}. score_A: {0}, score_B: {1}".format(
# score_A, score_B, ii, t))
# Scores must be postive,
# reset if otherwise
if score_A < 0:
# print("Reset A")
score_A = 0
elif score_B < 0:
score_B = 0
# print("Reset B")
decision = decider(score_A, score_B, threshold, ii+1)
if decision != None:
# print("hit")
return decision
else:
# If threshold is never met,
# we end up here...
return _create_d_result('N', None, None, None)
return blcba
def create_snr(name, threshold, decider, mean_default=False):
""" Creates a model based on Gardelle et al's mean / SNR model however
it has been modified for this paradigm - means and SDs are calculated
online, after each piece of information is delivered.
NOTE: As SD is 0 until the first piece of contrary data is experienced,
decisions can't be made for all A or all B trials. This is thought
to be an artifact. To compensate, is set mean_default to True. In this case
when SD is zero, the mean alone is used to update.
Note: This was not the best model in that paper (LLR was) but it was close
and in my opinion could be a useful/interesting 'bad model' to approximate
LLR, in some cases anyway (see 'Robust Averaging Across Elements in the
Decision Space' section in their results for an important caveat).
Also, we know the brain tracks averages and variances
in economic tasks.
See:
De Gardelle, V., & Summerfield, C. (2011). Robust averaging during
perceptual judgment. PNAS, 108(32).
"""
check_threshold(threshold)
@update_name(name)
def snr(trial):
""" Gardelle et al's mean / SNR model. """
# Init
l = len(trial)
meanA = 0
meanB = 0
M2A = 0 # Second mean (needed for online)
M2B = 0
score_A = 0.0
score_B = 0.0
for ii, t in enumerate(trial):
n = ii + 1 ## reindex needed
## so n is the A/B count
# Calculate the mean and sd
# using online algortimns outlined
# in Donald E. Knuth (1998). The Art of Computer
# Programming, volume 2: Seminumerical Algorithms,
# 3rd edn.
x = 1.0 / l
if t == 'A':
delta = x - meanA
meanA += delta/n
# Can't update if any
# of the denom are 0
try:
M2A += delta * (x - meanA)
sdA = sqrt(M2A / (n - 1))
score_A += meanA/sdA
except ZeroDivisionError:
if mean_default:
score_A += meanA
else:
pass
else:
delta = x - meanB
meanB += delta/n
try:
M2B += delta * (x - meanB)
sdB = sqrt(M2B / (n - 1))
score_B += meanB/sdB
except ZeroDivisionError:
if mean_default:
score_B += meanB
else:
pass
# print("{2} - {3}. score_A: {0}, score_B: {1}".format(
# score_A, score_B, ii, t))
# And see if a decision can be made
decision = decider(score_A, score_B, threshold, n)
if decision != None:
return decision
else:
# If threshold is never met,
# we end up here...
return _create_d_result('N', None, None, None)
return snr
