The Silence of the Lambs

Film Information

The Silence of the Lambs is a 1991 American psychological horror film directed by Jonathan Demme and written by Ted Tally, adapted from Thomas Harris’s 1988 novel. It stars Jodie Foster as Clarice Starling, a young FBI trainee who is hunting a serial killer, “Buffalo Bill” (Ted Levine), who skins his female victims. To catch him, she seeks the advice of the imprisoned Dr. Hannibal Lecter (Anthony Hopkins), a brilliant psychiatrist and cannibalistic serial killer. The film also features performances from Scott Glenn, Anthony Heald, and Kasi Lemmons.

All information in this section came from Wikipedia.

Clip Information

Clarice Starling, a young female FBI agent, meets Dr. Hannibal Lector to talk about recent killings by a man known as Buffalo Bill. Dr. Lector is in prison because he is a murderer himself.

Abbrev	Film	Clip Start	Clip Stop	Duration
SilenceLambs	The Silence of the Lambs (1991)	00:14:14.500	00:18:48.500	274

Characteristic	Value
Format	MPEG-4
File Size	71.0 MiB
Duration	274.024
Frame Rate	23.976
Video Width	1920
Video Height	1040
Video BitRate	2.0 MB/s
Audio Channels	2
Audio SamplingRate	48000
Audio BitRate	129.0 kB/s

Subtitles

The following wordcloud shows the words used in this clip, scaled by number of occurrences and colored by sentiment (orange = negative, green = positive, grey = neutral or unsure). Note that the words have been stemmed and lemmatized and stopwords have been removed.

The table below shows all subtitles in this clip with the start and stop time of each subtitle’s appearance in seconds.

Start	End	Subtitle
8.111	10.613	You use Evyan skin cream.
13.367	16.118	And sometimes you wear L'Air du Temps,
17.621	19.038	but not today.
21.458	23.751	Did you do all these drawings, Doctor?
26.338	29.507	That is the Duomo, seen from the Belvedere.
31.093	32.635	You know Florence?
33.720	35.971	All that detail just from memory, sir?
36.014	39.934	Memory, Agent Starling, is what I have instead of a view.
39.976	41.143	Well,
41.228	46.065	perhaps you'd care to lend us your view on this questionnaire, sir.
46.149	48.484	Oh, no, no, no.
48.819	50.611	You were doing fine.
50.654	53.823	You had been courteous and receptive to courtesy.
53.907	58.494	You had established trust with the embarrassing truth about Miggs.
58.578	62.748	And now this ham-handed segue into your questionnaire.
64.418	65.835	It won't do.
66.169	68.129	I'm only asking you to look at this, Doctor.
68.171	70.297	Either you will or you won't.
70.340	71.424	Yeah.
72.092	74.176	Jack Crawford must be very busy indeed,
74.261	77.096	if he is recruiting help from the student body.
77.180	80.015	Busy hunting that new one, Buffalo Bill.
80.058	82.184	What a naughty boy he is.
82.686	86.105	Do you know why he's called Buffalo Bill?
86.189	89.358	Please tell me. The newspapers won't say.
90.652	93.279	Well, it started as a bad joke in Kansas City Homicide.
93.363	97.116	They said, "This one likes to skin his humps."
98.702	102.621	Why do you think he removes their skins,
102.706	104.206	Agent Starling?
104.249	106.459	Enthrall me with your acumen.
107.210	108.752	It excites him.
109.087	112.840	Most serial killers keep some sort of trophies from their victims.
112.883	114.216	I didn't.
114.301	115.426	No.
115.510	117.178	No, you ate yours.
119.723	121.765	You send that through now.
148.793	149.835	Oh, Agent Starling,
149.920	153.422	you think you can dissect me with this blunt, little tool?
153.965	155.007	No.
155.425	156.967	I thought that your knowledge...
157.052	159.303	You're so ambitious, aren't you?
159.679	160.930	You know what you look like to me
161.014	163.432	with your good bag and your cheap shoes?
163.517	165.267	You look like a rube.
165.602	169.730	A well-scrubbed, hustling rube with a little taste.
170.565	172.942	Good nutrition has given you some length of bone,
173.026	176.362	but you're not more than one generation from poor white trash,
176.446	178.364	are you, Agent Starling?
178.448	181.283	And that accent you've tried so desperately to shed,
181.326	183.035	pure West Virginia.
183.620	185.913	What does your father do? Is he a coal miner?
185.956	187.998	Does he stink of the lamp?
188.083	190.709	And, oh, how quickly the boys found you.
190.794	194.338	All those tedious, sticky fumblings in the back seats of cars,
194.422	198.175	while you could only dream of getting out, getting anywhere,
198.260	201.971	getting all the way to the FBI.
205.267	207.142	You see a lot, Doctor.
208.103	213.023	But are you strong enough to point that high-powered perception at yourself?
214.276	215.568	What about it? Why don't you...
215.652	219.238	Why don't you look at yourself and write down what you see?
220.156	221.907	Maybe you're afraid to.
231.501	234.295	A census-taker once tried to test me.
234.629	238.048	I ate his liver with some fava beans,
238.133	240.175	and a nice Chianti.
249.102	251.895	You fly back to school now, little Starling.
252.522	254.064	Fly, fly, fly.
269.122	272.875	I bit my wrist so I could die.

Holistic Ratings

A total of 80 participants watched this film clip and then provided holistic ratings on how the entire clip made them feel. These holistic ratings were completed using five Positive Affect items (i.e., alert, determined, enthusiastic, excited, inspired) and five Negative Affect items (i.e., afraid, distressed, nervous, scared, upset), each rated on an ordinal scale from 0 to 4. The plot below shows the

Dynamic Ratings

A total of 79 participants watched this film clip and used the CARMA software to provide continuous (i.e., second-by-second) ratings of how it made them feel. These continuous ratings were made on a single emotional valence scale ranging from -4 (very negative) to 4 (very positive).

Time Series

We can plot the distribution of all valence ratings per second of the film clip to get a sense of how its emotional tone changes over time. The solid black line represents the mean of all ratings and the yellow, green, and purple ribbons represent the central 50%, 70%, and 90% of the ratings, respectively.

Inter-Rater Reliability

A Bayesian generalizability study was used to decompose the variance in ratings of this video clip into the following components: timepoint variance (in average ratings of each second, across raters), rater variance (in average ratings from each rater, across seconds), and residual variance (including second-by-rater interactions and measurement error). The lower and upper columns in the table below represent the boundaries of the 95% equal-tail credible interval. Note that we dropped the first 10 seconds of each clip (as rater “warmup” time).

Component	Term	Estimate	Lower	Upper	Percent
Rater	Variance	0.940	0.715	1.344	0.478
Timepoint	Variance	0.273	0.233	0.331	0.139
Residual	Variance	0.754	0.739	0.769	0.383

From these variance components, we can estimate inter-rater reliability of the ratings. There are many formulations of the two-way intraclass correlation (ICC), but the most relevant to our purposes here is the balanced average-measures consistency formulation or ICC(C,k).

Term	Estimate	Lower	Upper	Raters	Error
ICC(C,k)	0.967	0.961	0.972	79	Relative

Below, we can also visualize the posterior distributions of each of these parameters. Values with higher posterior density are more probable.