Questions for Emergent Simulation
All questions are due upon completion of all the Labs
In the following assignments the question numbers correspond to these in
the text book for easier identification of the subject area. Please use them as
a guide in finding proper exercises to prepare your answers. Since these
questions are strongly related to the text book material, please read the
corresponding section of the text to verify your understanding of the
question and possible explanation based on the Emergent models.
Try to keep your answer short (one paragraph for each question or in case of
subquestions (a), (b), (c) one paragraph for each one). Please use
illustrations from the simulator sparingly – only if you have to illustrate your
answer. Since many of these questions are correlated with the lab exercises,
work on them systematically during the semester, so you have less work at
the end when all the questions are due.
Students must work independently and present their own results answering
questions according to their own understanding of the obtained results;
however collaboration between students in the lab preparatory work (setting
tolls, finding information about the problems and related data or discussing
the underlying theory) is strongly encouraged.
Finally, through these questions I can verify that you did a decent work
during the lab, so they will influence your grade from the lab as well (in
addition to a separate grade from the questions).
Question 2.3
(a) How does the response of the unit change when you change g_bar_l? Why?
(b) How does this differ from changes to g_bar_e?
(c) Use the same technique you used in the previous question to compute the exact
amount of leak current necessary to put the membrane potential exactly at threshold when
the g_bar_e value is at the default of .4 (show your math).
Question 2.4
What can you conclude about the relationship between the resting potential and the leak
reversal potential?
Question 2.6
Describe and explain the effects on the spike rate of decreasing g_bar_e to .38, and of
increasing it to .42.
Question 3.1
How did the lack of bias weights affect the hidden unit activities, and their relation to the
number of active units in the input patterns?
Question 3.3
(a) What happens generally to the hidden activations with the reduction in leak value?
(b) How does this affect the cluster plot of hidden unit activities?
(d) If the goal of the network is to have the same hidden representation for each version
of the same digit, and different representations for different digits, how does changing the
units' excitability (via the leak current) affect the success of the network, and why?
Question 3.13
Why do you think kWTA can use a fast update rate where unit-based inhibition cannot?
Question 5.1
Explain why the obtained pattern of strong and weak weights resulted from the CPCA
Hebbian learning algorithm.
Question 5.3
Explain why the delta rule weights solve the problem, but the Hebbian ones do not (don't
forget to include the bias weights bias.wt in your analysis of the delta rule case).
Question 5.6
How fast does GeneRec learn this EASY task described on p. 171 compared to the
Hebbian rule? Be sure to run several times in both, to get a good sample.
Question 6.1
Report the summary statistics from the batch text log (Batch_1_Textlog for your batch
run). Does this indicate that your earlier observations were generally applicable?
Question 6.2
Explain the results in terms of the weight patterns, the unique pattern statistic, and the
general effects of Hebbian learning in representing the correlational structure of the input.
Question 6.4
Interpret the cluster plot you obtained (especially the clusters with events at zero
distance) in terms of the correspondence between hidden states and the current node
versus the current letter. Remember that current node and current letter information is
reflected in the letter and number before the arrow.
Question 8.2
Which different properties of edges are encoded differently by different hidden units?
There are four main ones, with one very obvious one being orientation - different hidden
units encode edges of different orientations (e.g., horizontal, vertical, diagonal). Describe
three more such properties or dimensions.
Question 8.4
Explain the significance of the level of conjunctive representations and spatial invariance
observed in the V2 receptive fields, in terms of the overall computation performed by the
network.
Question 8.6
Based on this latest display, do V4 units appear to code for entire objects, or just parts of
different objects? Explain.
Question 8.7
For those units that were significantly active, based on the number of different locations
for which the unit was active (i.e., the area of colored pixels in the display), would you
say that these units exhibited at least some degree of spatial invariance? Explain.
Question 8.9
By what mechanism does the spatial cue influence the subsequent processing of the target
in the valid and invalid cases?
Question 9.2
(a) Report the average testing statistic (avg_tst_se) for a batch run of 5 simulated subjects.
(b) How do these results compare to the human data presented in figure 9.4?
(c) Looking at the training graph log, roughly how many epochs does the network take to
reach its maximum error on the AB list after the introduction of the AC list?
Question 9.4
Report the average testing error (avg_tst_se) for the batch run, and the number of
epochs it takes the network to reach its maximum error on the AB list after the
introduction of the AC list.
Question 9.8
Report the number of times the network responded "a" instead of "b" for the "b" test trials.
Question 9.12
Explain how the AC unit accurately predicts future reward, and at what point it does so
(note that the external reward is visible as the activation state of the AC unit on the first
plus phase of the recall trial).
Question 9.13
Describe what happens to the network's internal representations and output (gaze, reach)
responses over the delay and choice trials. You should observe the network making the
error.
Question 10.1
(a) Do you think the initial phonological activation is caused by the "direct" input via
orthography or the "indirect" input via semantics?
(b) Check for any cases where this initial phonological pattern is subsequently altered
when the later input arrives, and describe what you find.
Question 10.6
(a) Is there evidence in the model for a difference between concrete and abstract words in
the number of semantic errors made?
(b) Explain why this occurs in terms of the nature of the semantic representations in the
model for these two types of words (recall that concrete words have richer semantics with
more overall units).
Question 10.9
(a) Do the most active units code for the appropriate inflectional phonological pattern?
(b) Describe the steps you took to reach this answer.
Question 10.11
(a) Report the cluster plot and cosine matrix results.
(b) Comment on how well this matches your intuitive semantics from having read this
textbook yourself.
Question 10.12
Think of another example of a word that has different senses (that is well represented in
this textbook), and perform an experiment similar to the one we just performed to
manipulate these different senses. Document and discuss your results.
Question 11.1
(a) At which layers in the network are the differences greatest?
(b) Can you explain this in terms of the error signals as they propagate through the
network?
Question 11.2
(a) Describe what happens in the network during the conflict color naming condition,
paying particular attention to the activations of the hidden units.
(b) Explain how this leads to the observed slowing of reaction time (settling).
Question 11.4
Explain why PFC lesions do not affect learning in the IDS task in the network (focus on
the advantages of the PFC, and why the demands of the task do not require these
advantages).
Question 11.6
Explain why the dorsolateral (dimensional) lesion has no effect on the intradimensional
reversal.
Question 11.8
Explain why the absence of the dimension-level (dorsolateral) PFC units impairs
extradimensional shift performance in this way.