Running head: SHORTENED VERSION OF TITLE 1 Title of Your Research Study Author(s) First, Middle Initial (if applicable) and Last Name(s) in Starting with the Individual who Made the Biggest Contribution (not alphabetical) Institutional Affiliation(s) Author Note The author note is typically used in manuscripts that will be submitted for publication. The author note may provide additional information regarding the affiliations of the authors. It is also used to acknowledge those who contributed to the study, but not at the level of authorship. Lastly, the author note typically includes contact information for at least one author (see APA guide p. 24, section 2.03 & sample paper on p. 41.) Remember to format the author note using block format (no indents, left or right justification). Abstract The abstract is a brief (usually 100-150 words) summary of your experiment. What was your question? What did you do? What did you find? What is your conclusion/interpretation? Try taking the lead sentence or two (but not word-for-word) from your introduction, results and discussion and integrate them into your abstract. Additionally, add a sentence or two describing your procedure, especially if it differs from those typically used to study the phenomenon. The abstract is page two. Nothing goes on this page except the abstract. Center the word "Abstract" on the page and format in bold-face type. Do not put the title of your paper on this page. Begin typing the abstract on the line directly below the heading. Notice that the abstract is not indented, and is written in block format. It is also double-spaced. Typically, the abstract is one paragraph in length. Keywords: type a few words (or phrases) that would be useful if someone was searching for a study similar to this one. For example, if you studied reaction time in a card sorting task your key words might be “card sorting,” “response time” and decision making. (Note: the word “keyword” is italicized and indented.) Title On the third page, you typically begin your introduction. Notice that the word "INTRODUCTION" does not appear at the top of the page as many of the other headings do. The title used is the same one that appears on the cover page. The first paragraph should contain a description of the phenomena that you are studying. Make a general statement about the phenomenon and how it is typically measured. Also, talk about how one might manipulate or influence the outcome (i.e, what variables could potentially influence the results). Subsequent research should describe previous research that examined the phenomena. These studies serve to provide the rationale for your study. What did the researchers do? What did they find? What did they conclude? Do this for each study cited. Typically, one or more paragraphs are necessary to explain each study. Try to make the transition smooth from one paragraph to the next. Use transition words (see SIGNAL WORDS hand.

1. Running head: SHORTENED VERSION OF TITLE
1
Title of Your Research Study
Author(s) First, Middle Initial (if applicable) and Last Name(s)
in Starting with the
Individual who Made the Biggest Contribution (not
alphabetical)
Institutional Affiliation(s)
Author Note
The author note is typically used in manuscripts that will be
submitted for publication. The author note may provide
additional information regarding the affiliations of the authors.
It is also used to acknowledge those who contributed to the
study, but not at the level of authorship. Lastly, the author note
typically includes contact information for at least one author
(see APA guide p. 24, section 2.03 & sample paper on p. 41.)
Remember to format the author note using block format (no
indents, left or right justification).
Abstract
The abstract is a brief (usually 100-150 words) summary of your
experiment. What was your question? What did you do? What
did you find? What is your conclusion/interpretation? Try
taking the lead sentence or two (but not word-for-word) from
your introduction, results and discussion and integrate them into
your abstract. Additionally, add a sentence or two describing
your procedure, especially if it differs from those typically used

2. to study the phenomenon.
The abstract is page two. Nothing goes on this page except the
abstract. Center the word "Abstract" on the page and format in
bold-face type. Do not put the title of your paper on this page.
Begin typing the abstract on the line directly below the heading.
Notice that the abstract is not indented, and is written in block
format. It is also double-spaced. Typically, the abstract is one
paragraph in length.
Keywords: type a few words (or phrases) that would be useful
if someone was searching for a study similar to this one. For
example, if you studied reaction time in a card sorting task your
key words might be “card sorting,” “response time” and
decision making. (Note: the word “keyword” is italicized and
indented.)
Title
On the third page, you typically begin your introduction.
Notice that the word "INTRODUCTION" does not appear at the
top of the page as many of the other headings do. The title used
is the same one that appears on the cover page.
The first paragraph should contain a description of the
phenomena that you are studying. Make a general statement
about the phenomenon and how it is typically measured. Also,
talk about how one might manipulate or influence the outcome
(i.e, what variables could potentially influence the results).
Subsequent research should describe previous research that
examined the phenomena. These studies serve to provide the
rationale for your study. What did the researchers do? What
did they find? What did they conclude?
Do this for each study cited. Typically, one or more paragraphs
are necessary to explain each study. Try to make the transition

3. smooth from one paragraph to the next. Use transition words
(see SIGNAL WORDS handout). For example, similarly, Jones
et al. found that…or, in contrast, Smith reported that…
Describe studies that used similar experimental procedures to
the ones that you are using and mention the findings.
Describe the present (your) experiment. Define your
experimental question. Describe what you are doing differently
from other studies. Describe your experimental hypothesis (i.e.,
what do you expect to find?).
Method
Participants
This section immediately follows the Introduction. DO NOT
leave extra lines. The only time you start on a new page is if
the heading is by itself at the bottom of the page!!!
Only information related to subjects is presented here. That is,
how many subjects, ages, gender, nature of participation (i.e.,
paid for participation, fulfillment of an academic requirement,
etc.). If you are working with a special population or there
were other criteria for selection, this should also be included.
Materials
Only information related to the stimuli used in the experiment is
presented here. Remember that the stimuli that are described
are for the entire experiment, not just one subject. If you are
using a complex piece of equipment (such as EEG or fMRI) to
perform your study, then you would include an additional
section under the header Apparatus where you would describe
the technical details of the equipment.
Experimental Design

4. If a complex design is used, information about the experimental
design is presented here. If the design is simple, it may be
incorporated into the procedure section. You must describe the
design, within or between-subjects (i.e., how the independent
variable was manipulated with respect to subjects). You must
define the independent variable (note: DO NOT say the
independent variable was…Rather, name the variable) and
describe the levels of the independent variable. You must
describe any control procedures that were used. For example,
the order of conditions (i.e., counterbalanced, Latin Square
design, randomly ordered, etc.) and the assignment of subjects
to conditions (important in between-subjects designs).
Following the description of the control procedures for the
presentation of conditions to subjects (within subject designs)
and/or the assignment of subjects to conditions (between
subjects designs) describe any other control procedures related
to the presentation of stimuli or the order of trials within each
condition. If you do not use an experimental design subheading,
you must provide this information at or near the beginning of
the procedure section.
Procedure
A concise description of the experimental procedures. That is,
what the subject experienced. Organize this section around the
events in each trial. This includes the order and the timing of
different stimuli that were presented. When you get to critical
stimulus events, give the specific details about its/their nature
(how stimuli were presented etc.) Then describe the nature of
the subject's response and the instructions to the subject
regarding task performance. Next, describe how the specific
responses are measured (i.e., response time, reaction time,
number of errors, etc.) This includes a definition of the
dependent variable and how the variable was measured. For
example, if the dependent measure was response time,
operationally define response time.

5. In the next paragraph, describe the remaining important details
of the testing situation and conditions (i.e., the number of trials
of each type, the length of the practice and experimental
portions of the session--were they time-based or performance
based). If practice sessions were performance based, you must
provide the performance criteria.
The last part of this subsection ends with a statement regarding
the treatment of the data including data reduction (means for
each subject, and/or means across subjects), transformation,
statistical tests employed and alpha level. Data reduction and
transformation information is required in psych 213/advanced
experimental courses for instructional purposes. This
information is not always required when simple designs are
employed.
Results
(immediately follows Method – don’t leave extra lines!!!)
Present a statement about the overall results of the manipulation
(i.e., there was an effect or not). For example, “Group-mean
response times varied as a function of the number of
alternatives in a card-sorting task.” Then describe the data
under each condition. Present the descriptive statistics first. If
tables or figures were used, point the reader to a Figure or
Table. For each table or figure, provide a structure statement
(tell the reader how to read the figure or table). For example,
Table 1 displays both the group mean response times and the
mean sort time for each subject under each condition. Then
present a content statement that describes the message that the
data reveals. For example, "The data show that the group-mean
response time under the 2-alternative condition was less than
the group mean response time under the 4-alternative
condition." Do not repeat the information provided in the table

6. or figure in the text. That is, if the table presents the group
mean response times under each condition, do not present the
mean response times in the text. Once the data have been
described, present the results of inferential statistical tests. Tell
the reader what tests were applied and what measures were
subjected to the test. For example "The difference between
group means was found to be significant, t(df)=t value, p<.05.”
Do not provide information about the meaning of the null
hypothesis or the meaning of the alpha level and what chance
factors have to do with the findings. Do not use the word
"prove." You may use the word "significant." Do not use the
word "insignificant." You may say "not significant."
This basic format should be followed for all variables, tables,
figures, and statistical tests. Report the results, but do not
interpret them except with simple statements such as “the data
(the findings, the analyses) suggest that the number of
alternatives affects response time.” The results section should
be used for stating what was found. The discussion section is
used for explaining why you think you found what you did.
Discussion
The discussion immediately follows the results section. Do not
skip spaces following the results.
Restate your experimental question. Describe your findings.
Did you find what you predicted?
Go back to the other research that you cited in the introduction.
Are your findings similar to or different from these studies? If
different, do you have any idea why? What information do you
have to support this?

7. Talk about any procedural differences between your study and
others. How might they have affected the outcome of your
study?
Reiterate your conclusions. Talk about any shortcomings or
limitations to the present study. Suggest ideas for improving
the study and for future research.
References
(The reference page always begins on a new page. Below is a
sample of the formatting)
American Psychological Association (2001). Publication
Manual of the American
Psychological Association (5th ed.). Washington, DC: Author.
Berntsen, D. (1996). Involuntary autobiographical memory.
Applied Cognitive Psychology, 10, 435-454.
Brown, S.W., Newcomb, D.C. & Kahrl, K.G. (1995).
Temporal-signal detection and individual
differences in timing. Perception, 24, 525-538.
Eisler, H. (1996). Time perception from a psychophysicist’s
perspective. In: H. Helfrich (Ed.),
Time and mind (pp.65-86). Seattle: Hogrefe & Huber
Publishers.
Hicks, R.E. & Miller, G.W. (1976). Transfer of time judgments

8. as a function of feedback.
American Journal of Psychology, 89, 303-310.
Hogarth, R.M., Gibbs, B.J., McKenzie, C.R.M. & Marquis, M.
A. (1991). Learning from feedback: Exactingness and
incentives. Journal of Experimental Psychology: Learning,
Memory & Cognition, 17(4), 734-752.
Mace, J.H. (2006). Episodic remembering creates access to
involuntary conscious memory: Demonstrating involuntary
recall on a voluntary recall task. Memory, 14(8), 917-924.
McBurney, D.H. (2001). Research methods (5th ed.). US:
Wadsworth/Thompson Learning
Publishers.
Stolarz-Fantino, S. Fantino, E. & Van Borst, N. (2006). Use of
base rates and case cue information in making likelihood
decision. Memory & Cognition, 34(3), 603-618.
A paper may have more than one table. Number the tables in
the order presented in the paper. Each table is presented on a
SEPARATE page.
Table Title (give the title a name – what does it
describe/summarize?)
Table 1
Summary of Effect of Hours Studied on Test Scores
Less than 10
More than 10
Mean Test Score

9. 70.50
82.30
Standard Deviation
5.10
3.30
Note. The note contains a brief verbal description of the table.
Other information that may facilitate understanding of the
information provided in the table should also be included in the
table note. (See APA guide p. 52)
Figures follow tables. Each figure is presented on a separate
page. The figures are numbered in the order they appear in the
text. Start the figure near the top of the page. The figure
caption is a brief description of the figure. It appears at the
bottom of the figure and is double spaced. The word “Figure”
and the figure number are italicized.
Days
0
1
2
3
4
5
6
7
8
9
10
11
Number of Aggrssive Incidents
0
2
4
6

10. 8
10
Figure 1. The figure shows the number of aggressive incidents
before and after treatment. The time of intervention is
represented by the vertical dashed line. The graph shows a
systematic decrease in aggressive outbursts following
implementation of the treatment.
_1315846468.unknown
1
Name: Lab report 2 Spring 2019
Grade Summary Table (details follow)
Cover Sheet (1)
Abstract (5)
Introduction (30)
Method (15)
Results (19)
Discussion (20)
Reference Page (5)
Figures & Captions (5)
Subtotal (100)

11. Late Deductions (5 points/day)
Paper Total (100)
Cover Sheet (1)
Must have correct running head, title, name, university, and
author’s note to receive full credit
Abstract (5)
Beginning with the next line, write a concise summary of the
key points of your research. (Do not
indent.) Your abstract should contain at least your research
topic, research questions, participants,
methods, results, data analysis, and conclusions. You may also
include possible implications of your
research and future work you see connected with your findings.
Your abstract should be a single
paragraph, double-spaced. Your abstract should be between 150
and 250 words. Your abstract must
have all of these elements in order to receive full credit.
Introduction (30)
Opening Paragraph (2)
Summarize paper 1 (3)

12. Summarize paper 2 (3)
Summarize paper 3 (3)
Concluding Paragraph
Purpose & Description of Study (3)
Comparison to Cited Research (3)
Questions and Expectations (1)
Writing – APA & Quality (12)
2
Method (15)
Participants (1)
Apparatus/Materials (2)
Procedure (4)
Writing – APA & Quality (8)
Results (19)
Opening Statement (2)
F-values reported in APA format (3)
Describe differences between means (3)

13. Refer to figure (1)
Writing – APA & Quality (10)
Discussion (20)
Opening Paragraph (4)
• Restate Experimental Question
• Describe Findings
• Findings vs. Predictions
Comparison to Cited Studies (5)
• Compare/contrast findings to cited studies
• Describe similarities/differences
• Describe any effects owing to procedural
similarities/differences
• Implications of similarities/differences
Conclusions (3)
• Restate Findings
• Limitations/Shortcomings
• Direction for Future Research
Writing – APA & Quality (8)

14. Reference page (5)
All references must be in the correct APA format and must be
appropriately cited in the text of your
paper in order to receive full credit.
3
Figure(s) and caption (5)
Figure(s) must be in APA format. They should include no more
than one or two sentences that describe
the results in general detail (no need to report stats). The
caption must also explain what the error bars
mean.
Description (5)
Task switching
Stephen Monsell
School of Psychology University of Exeter, Exeter, EX4 4QG,
UK

15. Everyday life requires frequent shifts between cognitive
tasks. Research reviewed in this article probes the con-
trol processes that reconfigure mental resources for a
change of task by requiring subjects to switch fre-
quently among a small set of simple tasks. Subjects’
responses are substantially slower and, usually, more
error-prone immediately after a task switch. This
‘switch cost’ is reduced, but not eliminated, by an
opportunity for preparation. It seems to result from
both transient and long-term carry-over of ‘task-set’
activation and inhibition as well as time consumed by
task-set reconfiguration processes. Neuroimaging
studies of task switching have revealed extra activation
in numerous brain regions when subjects prepare to
change tasks and when they perform a changed task,
but we cannot yet separate ‘controlling’ from ‘con-
trolled’ regions.
A professor sits at a computer, attempting to write a paper.
The phone rings, he answers. It’s an administrator,

16. demanding a completed ‘module review form’. The pro-
fessor sighs, thinks for a moment, scans the desk for the
form, locates it, picks it up and walks down the hall to the
administrator’s office, exchanging greetings with a col-
league on the way. Each cognitive task in this quotidian
sequence – sentence-composing, phone-answering, con-
versation, episodic retrieval, visual search, reaching and
grasping, navigation, social exchange – requires an
appropriate configuration of mental resources, a pro-
cedural ‘schema’ [1] or ‘task-set’ [2]. The task performed
at each point is triggered partly by external stimuli (the
phone’s ring and the located form). But each stimulus
affords alternative tasks: the form could also be thrown in
the bin or made into a paper plane. We exercise intentional
‘executive’ control to select and implement the task-set,
or the combination of task-sets, that are appropriate to
our dominant goals [3], resisting temptations to satisfy
other goals.
Goals and tasks can be described at multiple grains or
levels of abstraction [4]: the same action can be described
as both ‘putting a piece of toast in one’s mouth’ and
‘maintaining an adequate supply of nutrients’. I focus here
on the relatively microscopic level, at which a ‘task’
consists of producing an appropriate action (e.g. conveying
to mouth) in response to a stimulus (e.g. toast in a
particular context). One question is: how are appropriate
task-sets selected and implemented? Another is: to what
extent can we enable a changed task-set in advance of the
relevant stimulus – as suggested by the term ‘set’?
Introspection indicates that we can, for example, set
ourselves appropriately to name a pictured object aloud
without knowing what object we are about to see. When an
object then appears, it is identified, its name is retrieved
and speech emerges without a further ‘act of intention’: the

17. sequence of processes unfolds as a ‘prepared reflex’ [5,6].
Many task-sets, which were initially acquired through
instruction or trial and error, are stored in our memories.
The more we practice a task, or the more recently we have
practised it, the easier it becomes to re-enable that task-
set. At the same time, in the absence of any particular
intention, stimuli we happen to encounter evoke ten-
dencies to perform tasks that are habitually associated
with them: we unintentionally read the text on cereal
packages or retrieve the names of people we pass in the
street. More inconveniently, stimuli evoke the tendency to
perform tasks habitually associated with them despite a
contrary intention. The standard laboratory example of
this is the Stroop effect [7]: we have difficulty suppressing
the reading of a colour name when required to name the
conflicting colour in which it is printed (e.g. ‘RED’ printed
in blue). Brain damage can exacerbate the problem, as in
‘utilization behaviour’, which is a tendency of some
patients with frontal-lobe damage to perform the actions
afforded by everyday instruments, such as matches,
scissors and handles, even when these actions are
contextually inappropriate [8].
Hence the cognitive task we perform at each moment,
and the efficacy with which we perform it, results from
a complex interplay of deliberate intentions that are
governed by goals (‘endogenous’ control) and the avail-
ability, frequency and recency of the alternative tasks
afforded by the stimulus and its context (‘exogenous’
influences). Effective cognition requires a delicate, ‘just-
enough’ calibration of endogenous control [9] that is
sufficient to protect an ongoing task from disruption
(e.g. not looking up at every movement in the visual
field), but does not compromise the flexibility that allows
the rapid execution of other tasks when appropriate

18. (e.g. when the moving object is a sabre-toothed tiger).
To investigate processes that reconfigure task-set, we
need to induce experimental subjects to switch between
tasks and examine the behavioural and brain correlates of
changing task. Task-switching experiments are not new
(Box 1), but the past decade has seen a surge of interest,
stimulated by the development of some novel techniques
for inducing task switches and getting subjects to prepare
for them (Box 2), and some surprising phenomena revealed
thereby, as well as by the broader growth of interest in
control of cognition (e.g. [10]).Corresponding author: Stephen
Monsell ([email protected]).
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Task switching: basic phenomena
In a task-switching experiment, subjects are first pre-
trained on two or more simple tasks afforded by a set of
stimuli (Figs 1 and 2 provide examples). Each task
requires attention to, and classification of, a different
element or attribute of the stimulus, or retrieval from
memory or computation of a different property of the
stimulus. Then, a stimulus is presented on each of a series
of trials and the subject performs one of the tasks. There
are several methods for telling the subject which task to
perform (Box 2), but in all cases the task sometimes

19. changes from one trial to the next, and sometimes does not.
Thus, we can examine performance or brain activation on
and following trials when the task changes for evidence of
extra processing demands that are associated with the
need to reconfigure task-set. We can also examine the
effects of localized brain damage, transient magnetic
stimulation (TMS) or pharmacological interventions on
behavioural indices of switching efficiency. Four phenom-
ena of primary interest (of which the first three are
illustrated in Figs 1 and 2) are described below.
Switch cost (task-repetition benefit)
Generally, responses take longer to initiate on a ‘switch trial’
than on a ‘non-switch’ or task-repetition trial, often by a
substantial amount (e.g. 200 ms relative to a baseline of
500 ms). Also, the error rate is often higher after a task switch.
Preparation effect
If advance knowledge is given of the upcoming task and
time allowed to prepare for it, the average switch cost is
usually reduced.
Residual cost
Preparation generally does not eliminate the switch cost.
In the examples shown, the reduction in switch cost seems
to have reached a substantial asymptote, the ‘residual
cost’, after ,600 ms of preparation. Substantial residual
costs have been reported even when 5 s or more is allowed
for preparation (e.g. [11,12]).
Mixing cost

20. Although performance recovers rapidly after a switch
(Fig. 1), responses remain slower than when just one task
must be performed throughout the block: there is a long-
term as well as a transient cost of task switching.
These phenomena have been demonstrated with a wide
range of different tasks and they are modulated by
numerous other variables. What explains them?
Sources of the switch cost
Time taken by control operations
To change tasks, some process or processes of ‘task-set
reconfiguration’ (TSR) – a sort of mental ‘gear changing’ –
must happen before appropriate task-specific processes
can proceed. TSR can include shifting attention between
stimulus attributes or elements, or between conceptual
criteria, retrieving goal states (what to do) and condition –
action rules (how to do it) into procedural working memory
(or deleting them), enabling a different response set and
adjusting response criteria. TSR may well involve inhi-
bition of elements of the prior task-set as well as activation
of the required task-set.
An account of the switch cost that appeals intuitively is
that it reflects the time consumed by TSR. The preparation
effect then suggests that, if sufficient time is allowed, TSR
can, to some extent, be accomplished under endogenous
control, before the stimulus onset. The residual cost is
more perplexing. Rogers and Monsell [13] suggest that
Box 1. Early research on task-set and task switching
The intentional and contextual control of ‘set’ (‘Einstellung’)
was

21. discussed in 19th and early 20th century German experimental
psychology. In 1895, von Kries used as examples the way the
clef sign
changes the action performed to play a note on the musical
stave, and
the way the current state of a game changes how one sets
oneself to
respond to an opponent’s behaviour [58]. Exner and the
Wurzburg
school described the ‘prepared reflex’, and, in 1910, Ach
described
experiments on overlearned responses competing with the
acqui-
sition of a novel stimulus – response mapping, see [6]. Until
recently,
in the English-language literature, ideas about control of task-
set have
been stimulated mainly by the observation of impairments of
control,
both in everyday action and as a result of neurological damage,
see
[2], despite some experimentation on normal executive function
in

22. cognitive laboratories [5].
The invention of the task-switching paradigm is credited to
Jersild
[59] who had students time themselves working through a list of
items, either repeating one task or alternating between two.
Some
task pairs (adding 3 to vs. subtracting 3 from numbers) resulted
in
dramatic alternation costs; others (adding 3 to a number vs.
writing
the antonym of an adjective) did not. Jersild’s paradigm was
revived,
and his results replicated using discrete reaction-time
measurements,
by Biederman and Spector [60]. Despite this work and some
pioneering task-cueing studies (e.g. [61 – 63]) it was only in the
mid
1990s that the present surge of research on task switching
developed.
Box 2. Task switching paradigms
There are several methods of telling a subject which task to do
on each
trial. Jersild’s method (Box 1), which is still sometimes used

23. (e.g. [39]),
compares the duration of blocks of trial in which the subject
alternates
tasks as rapidly as possible with blocks in which they repeat
just one
task. This contrast of alternated and repeated tasks can also be
used
with discrete reaction-time measurement (e.g. [14]). However,
this
comparison confounds switch costs and mixing costs. Also, the
alternation blocks impose a greater working memory load – to
keep
track of the task sequence and maintain two tasks in a state of
readiness – and might promote greater effort and arousal. These
problems are avoided by the alternating-runs paradigm [13], in
which
the task alternates every N trials, where N is constant and
predictable
(e.g. Fig. 1, predictable condition, and Fig. 2), so that one can
compare
task-switch and task-repetition trials within a block. An
alternative is to
give the subjects short sequences of trials [20,27] with a

24. prespecified
task sequence (e.g. colour – shape – colour). Either way, one
can
manipulate the available preparation time by varying the
stimulus –
response interval, but this also varies the time available for any
passive dissipation of the previous task-set.
In the task-cueing paradigm [63,64], the task is unpredictable,
and
a task cue appears either with or before the stimulus (e.g. Fig.
1,
random condition). It is now possible to manipulate
independently
the cue – stimulus interval (allowing active preparation) and the
response – cue interval (allowing passive dissipation).
Alternatively,
in the intermittent-instruction paradigm, the series of trials is
interrupted occasionally by an instruction that indicates which
task
to perform on the trials following the instruction [65]. Even
when the
instruction specifies continuing with the same task, there is a
‘restart’

25. cost after the instruction [29], but this is larger when the task
changes;
the difference yields a measure of switch cost.
Review TRENDS in Cognitive Sciences Vol.7 No.3 March 2003
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part of TSR cannot be done until exogenously triggered
by stimulus attributes that are associated with the
task; Rubinstein et al. [14] characterize this part as
retrieval of stimulus – response rules into working
memory. An alternative account, from De Jong [15],
makes no distinction between endogenous and exogen-
ously-triggered TSR. It proposes that, although sub-
jects attempt TSR before stimulus onset (given the
opportunity), they succeed on only a proportion of
switch trials. If they succeed they are as ready for the
changed task as on a task-repetition trial. If they ‘fail
to engage’, the whole TSR process must be performed
after stimulus onset. This idea of TSR as a probabil-
istic all-or-none state change is supported by the fit of
a discrete-state mixture model to the distribution of
reaction times (RTs) on prepared switch trials [15,16].
But why should TSR be all-or-none? One rationale is
that TSR includes an attempt to retrieve either the
goal or the task rules from memory; retrieval attempts
either succeed or fail [17,18].

26. Fig. 1. Predictable and unpredictable task switching. In this
experiment (Ref. [42], Exp. 2), the tasks were to classify the
digit as either odd/even or high/low, with a left or
right key-press. (a) For some subjects, the task was cued by the
background colour (as illustrated) and for others by the
background shape; the colour or shape changed at
the beginning of every trial. The response – stimulus interval in
different blocks was 50 ms, 650 ms and 1250 ms, during which
subjects could prepare for the next stimulus.
In some blocks, the task changed predictably every four trials
(with a ‘clock hand’ rotating to help keep track of the
sequence): the ‘switch cost’ was limited to the first trial
of the changed task (b). In other blocks, the task varied
randomly from trial to trial and recovery from a task switch was
more gradual. In both cases, the switch cost was
reduced by ,50% by extending the time available for preparation
to 650 ms (the ‘preparation effect’); a further increase had little
effect (the ‘residual cost’). These data
demonstrate that, at least in normal, young adults, even with
complete foreknowledge about the task sequence, switch costs
are large, and that recovery from a task switch
is characteristically complete after one trial. When the task is
unpredictable, recovery might be more gradual, but a few
repetitions of a task results in asymptotic readiness
for it. (Data redrawn with permission from Ref. [42].)
TRENDS in Cognitive Sciences
(a)

27. Predictable task sequence
Random task sequence
Trial
Cue (50, 650,
or 1250 ms)
Stimulus
(until response)
8
6 8 1 3 8 4
2 7 9 1 8 2
(b)
500
600
700
800
900
1000
50
650

28. 1250
Predictable Random
0.0
2.0
4.0
6.0
1 2 3 4
Position in run
0.0
2.0
4.0
6.0
1 2 3 4
500
600
700
800
900
1000

29. E
rr
o
rs
(
%
)
M
e
a
n
c
o
rr
e
ct
R
T
(
m
s)
Fig. 2. Preparation effect and residual cost. (a) In this
experiment (Ref. [13], Exp. 3), the stimulus is a character pair
that contains a digit and/or a letter. The tasks were to clas-

30. sify the digit as odd/even, or the letter as consonant/vowel. The
task changed predictably every two trials and was also cued
consistently by location on the screen (rotated
between subjects). (b) The time available for preparation
(response – stimulus interval) varied between blocks. Increasing
it to ,600 ms reduced switch cost (the ‘prep-
aration effect’), but compared with non-switch trials there was
little benefit of any further increase, which illustrates the
‘residual cost’ of switching. (Data redrawn with per-
mission from Ref. [13].)
TRENDS in Cognitive Sciences
600
650
700
750
800
850
900
0 500 1000 1500
Response–stimulus interval (ms)
Switch trial
Non-switch trial

31. M
e
a
n
c
o
rr
e
ct
R
T
(
m
s)
(a) (b)
G7 #E
4A L9
Letter task
(switch)
Letter task
(non-switch)
Digit task
(switch)

32. Digit task
(non-switch)
Review TRENDS in Cognitive Sciences Vol.7 No.3 March
2003136
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Transient task-set inertia
Consider Stroop stimuli. It is well-known that incongru-
ence between the colour in which the word is displayed and
the colour it names interferes much more with naming the
display colour than with naming the word, an asymmetry
of interference that is attributable to word naming being
the more practised, and hence ‘stronger’, task-set [19].
Surprisingly, if subjects must switch between this pair of
tasks, switching to the stronger task results in the larger
switch cost [20 – 22]. In another striking example, bilingual
subjects named digits more slowly in their second langu-
age on non-switch trials, but on switch trials named more
slowly in their first language [23]. This surprising
asymmetry of switch costs eludes explanation in terms of
the duration of TSR. How could it take longer to
reconfigure for the more familiar task? Allport et al. [20]
propose that one must apply extra inhibition to the
stronger task-set to enable performance of the weaker.
This inhibition then carries over to the next trial;
overcoming the inhibition prolongs response selection.
Subsequent work reveals some problems with this
account. For example, the surprising asymmetry of switch
costs can be reversed by manipulations that produce only a

33. modest reduction in the asymmetry of Stroop-like inter-
ference between the tasks [22,24]. However, this pattern
can be accommodated by a model that combines transient
persistence of task-set activation (or inhibition) with the
assumption that executive processes apply the minimum
endogenous-control input that enables the appropriate
task, given the anticipated interference [22]. The detection
of cross-task interference during a trial might also prompt
the ramping-up of endogenous control input, which would
lead to greater TSI on a switch trial following an
incongruent stimulus [9].
Other observations support the transient carry-over of
task-set activation from trial to trial. Several researchers
[25,26] report evidence that, with preparation held
constant, a longer delay after the last performance of the
previous task improves performance on the switch trial.
This suggests dissipating activation of the competing task-
set. Sohn and Anderson [18] fit data on the interaction
between preparation interval and foreknowledge with a
model that assumes exponential decay of task-set acti-
vation following a trial, and an endogenous preparation
process whose probability of success increases throughout
the preparation interval. There is also evidence for
persistence of inhibition applied to a task-set in order to
disengage from it: so, for example, responses are slower on
the last trial of the sequence Task A, Task B, Task A, than
the sequence Task C, Task B, Task A [27,28].
Associative retrieval
Even when performing only one task (e.g. word naming),
responses are slower if subjects have performed another
task afforded by the same stimuli (e.g. colour naming) in
the previous few minutes [20,21,29]. This long-term
priming has been attributed to associative retrieval of

34. task-sets that are associated with the current stimulus
[29,30], and seems likely to be the source of the mixing
cost. Allport and colleagues found this priming to be
magnified on a switch trial or when performance was
merely resumed after a brief pause, which suggests that
associative interference may contribute also to switch
costs [21,29]. Further experiments [30] demonstrated that
this priming can be quite stimulus-specific. In these
experiments, each stimulus was a line drawing of one
object with the name of another superimposed (e.g. a lion
with the word APPLE). In the first block, subjects named
the object, ignoring the word. Later, they showed larger
switch costs for naming the word in stimuli for which they
had previously named the picture, even if only once and
several minutes before.
All of the above?
Initial theorising tended to try to explain switch costs in
terms of just one mechanism (e.g. [13,20]). Although
single-factor models of task switching continue to be
proposed [31] most authors now acknowledge a plurality of
causes, while continuing to argue over the exact blend. For
example, although long-term effects of task priming imply
associative activation of competing task-sets by the
stimulus, the contribution this makes to the transient
switch cost observed with small sets of stimuli, all recently
experienced in both tasks, is uncertain. Moreover, residual
switch costs occur even with ‘univalent’ stimuli (i.e. those
associated with only one task) for which there should be no
associative competition [13,26], and switch costs some-
times do not occur for bivalent stimuli where there should
be massive associative competition, such as switching
between prosaccades and antisaccades to peripheral
targets [32]. Transient carry-over of task-set activation

35. or inhibition is now well established as an important
contributor to switch costs, especially the residual cost, but
it remains unclear whether the effect is to slow task-
specific processes (e.g. response selection) or to trigger
extra control processes (ramping up of control input when
response conflict is detected). A combination of both
mechanisms is likely. Something of a consensus has
developed around the idea that the preparation effect, at
least, reflects a time-consuming, endogenous, task-set-
reconfiguration process, which, if not carried out before the
stimulus onset, must be done after it.
Issues for further research
Unfortunately, the foregoing consensual account of the
preparation effect is not without problems. First, there are
studies in which the opportunity for preparation with
either full [33] or partial [34] foreknowledge of the
upcoming task does not reduce the switch cost, even
though it improves overall performance. Second, in task-
switching experiments, to know whether TSR is necessary,
a subject must discriminate and interpret an external cue
(with unpredictable switching), retrieve the identity of the
next task from memory (with predictable switching), or
both (many predictable switching experiments provide
external cues as well). The contribution of these processes
to switch costs has been neglected. Koch [35] has reported
that, with predictable switching, a preparation interval
reduces the switch cost only when there is an external cue
to help subjects remember which task is next. Logan and
Bundesen [36] found that changing the cue when repeat-
ing the task produced nearly as much of a preparation
Review TRENDS in Cognitive Sciences Vol.7 No.3 March 2003
137

36. http://tics.trends.com
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effect as changing both cue and task. Hence, processes of
interpreting the cue and/or determining whether TSR is
required might contribute much of the preparation effect.
It is even possible that, in some cases, these processes are
so demanding that they constitute a separate task, thus
vitiating the distinction between ‘switch’ and ‘non-switch’
trials.
Another intriguing issue is the role of language.
Introspection indicates that in both everyday life and
task-switching experiments people to some extent verbal-
ize what they intend to do next (‘er…colour’) and how (‘if
red, this key’). Goschke [9] found that requiring subjects to
say an irrelevant word during a 1.5 s preparation interval
abolished the reduction in switch cost observed when the
subject either named the task (‘colour’ and ‘letter’) or said
nothing. He attributed this to interference with verbal
self-instruction, extending to TSR the Vygotskian view
[37] that self-instruction using language is fundamental to
self-regulation. Others have found that irrelevant con-
current articulation (e.g. saying ‘one – two – one – two…’) –
which is known to interfere with phonological working
memory – impairs performance disproportionately in task
alternation compared to single task blocks [38,39]. It is
also suggested that the association claimed between
damage to the left prefrontal cortex and switching deficits
(see below) reflects impaired verbal mediation caused by
left hemisphere damage, rather than a more general
control deficit [40]. However, subjects in these studies were
relatively unpractised. Traditional theories of skill acqui-
sition [41] assign language a relatively transitory role in

37. task-set learning. A task-set, especially if acquired via the
verbal instructions of another person, may be represented
initially via verbal self-instruction, but after sufficient
practice, control shifts from declarative (including verbal)
representations to a learned, procedural representation.
Standard examples are learning to shift gear or tie a knot.
Hence, we might expect that any cost or benefit of verbal
self-instruction in reconfiguring a task-set would vanish
with practice.
Experiments on task switching have thrown up
numerous other puzzling observations. Why does an
opportunity for preparation often reduce switch costs
without reducing Stroop-like interference from the other
task [13,25,42]? Why are switch costs larger when the
response is the same as the previous trial [13]? We are
unlikely to make sense of the increasingly complex set of
variables that are known to influence switch costs without
either computational simulation [43,44] or mathematical
modelling [18,22,45,46] of their interactions. Progress in
disentangling the complex causation of switch costs is
necessary to interpret the effects of ageing [47 – 49] and
brain damage [50,51] on, and individual differences [52] in,
task-switching costs, and their association and dis-
sociation with behavioural indices of other control func-
tions. Even without a full understanding of their
causation, the substantial magnitude of switch costs
should also be an important consideration in the design
of human – machine interfaces that require operators to
monitor multiple information sources and switch between
different activities under time pressure, such as in air-
traffic control.
Brain correlates of task switching
At first glance, task switching lends itself well to the

38. subtractive methodology of neuroimaging and electro-
physiology. We can compare event-related activation in
trials that differ only in whether they do or do not follow
another of the same task. Numerous brain regions, usually
in medial and lateral regions of the prefrontal cortex, but
sometime in parietal lobes, cerebellum and other sub-
cortical regions, are reported to be more active on switch
than on non-switch trials. As one example, left dorso-
lateral prefrontal cortex has been reported to be more
active when subjects switch the attribute attended to
[53,54], and this appears consistent with evidence that
patients with left frontal damage have behavioural
abnormalities in switching between attributes [50,51].
Regrettably, as we have learned from behavioural
studies, task switch and repeat trials are likely to differ
in ways other than the occurrence of TSR. There may be
extra interference at the levels of both task-set and
stimulus – response mapping. The greater difficulty of
switch trials is likely to elicit general arousal and extra
error-monitoring. Moreover, even if region X contains an
executive ‘module’ that reconfigures the behaviour of
regions A, B and C, we would expect to see differential
activation, not only of the controlling region X, but also of
areas A, B and C, much as we see modulation of activation
in striate and extrastriate cortex when visual attention is
shifted endogenously [55]. Differential activation evoked
by stimuli on switch and repeat trials does not differentiate
between the ‘source’ and the ‘target’ of the control.
One approach is to try to isolate the brain activity that is
associated with preparing for a task switch. By stretching
out the preparation interval to 5 s [11], 8 s [12] and 12.5 s
[54], one can try to separate modulations of the blood-
oxygen-level-dependent (BOLD) signal that are linked to
preparatory activity from changes associated with process-

39. ing of the stimulus on switch trials. Some have reported
that preparation for a switch evokes extra activation in
regions that are different from those that undergo extra
activation to a switch-trial stimulus [11,54] whereas
others have not [12]. However, long preparation intervals
might either require extra processing to maintain prepar-
ation, or encourage subjects to postpone preparation. To
deal with this, Brass and von Cramon [56] compared
activation in trials with a task cue followed by a stimulus
1.2 s later, trials in which the stimulus was omitted, trials
in which the cue was delayed until the stimulus onset, and
null trials. Cue-only trials caused activation in the left
inferior frontal junction and the pre-SMA region that
correlated with the behavioural cueing benefit in cue-
stimulus trials. When the cue was delayed, this activation
was also delayed. Hence this activity seems to be cue-
related, but it is unclear (as in behavioural studies)
whether it is associated with interpreting the cue or the
consequent TSR.
In a study focusing on the medial frontal cortex,
Rushworth et al. [57] interrupted a series of stimuli
every 9 – 11 trials with a ‘stay/shift’ cue. When the cue
indicated whether to maintain or reverse the left/right
response rule in the following trials, a larger BOLD signal
was evoked in the pre-SMA region by ‘shift’ than by ‘stay’
Review TRENDS in Cognitive Sciences Vol.7 No.3 March
2003138
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cues. When the cue specified whether to maintain or

40. switch the stimulus dimension (colour versus shape) used
to direct attention for a perceptual detection task, a
more posterior ‘hot-spot’ was seen. To determine
whether these activations were functionally essential,
brief trains of TMS pulses were applied to these
regions. TMS following a shift, but not a stay, cue
substantially prolonged RT to the upcoming stimulus,
but only for the response-rule reversal. Hence activity
in the pre-SMA region is, apparently, needed to reverse
a stimulus – response assignment. We do not know
whether this activity reflects the source or the target of
an ‘act of control’, or both.
Acknowledgements
Thanks to Hal Pashler, Nachshon Meiran, Ulrich Mayr and an
anonymous
reviewer for their comments on an earlier draft of this article.
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Research Focus
Within the, Update section of TICS, Research Focus articles
highlight important and interesting
recent research developments in cognitive science. The authors
of the original research papers
discussed may be invited to write a Response to the Research
Focus article
If you know of any research just published that you think should
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http://www.trends.comTask switchingTask switching: basic
phenomenaSwitch cost (task-repetition benefit)Preparation
effectResidual costMixing costSources of the switch costTime
taken by control operationsTransient task-set inertiaAssociative
retrievalAll of the above&quest;Issues for further researchBrain
correlates of task switchingAcknowledgementsReferences

51. I on,
1969,
iraal ol Experimental Psychology
i9, Vol. 81, No. 1, 141-145
LOOKING AT UPSIDE-DOWN FACES1
ROBERT K. YIN 2
Massachusetts Institute of Technology
Memory for faces was compared with memory for other classes
of familiar
and complex objects which, like faces, are also customarily seen
only in one
orientation (mono-oriented). Performance was tested when the
inspection
and test series were presented in the same orientation, either
both upright or
both inverted, or when the two series were presented in opposite
orientations.
The results show that while all mono-oriented objects tend to be
more diffi-
cult to remember when upside-down, faces are
disproportionately affected.
These findings suggest that the difficulty in looking at upside-
down faces
involves two factors: (a) a general factor of familiarity with
mono-oriented
objects; and (6) a special factor related only to faces.
It is a well-known fact that pictures of hu-

52. man faces, when viewed upside-down, are
extremely difficult to recognize (Arnheim,
1954, p. 86; Attneave, 1967, p. 26; Kohler,
1940, p. 60). Kohler not only noted this,
but also speculated that the difficulty was
attributable to the loss of "facial expression"
in the inverted picture. More recently, in-
vestigators have examined this phenomenon
in several ways. Brooks and Goldstein
(1963) showed that recognition of inverted
faces is worse than that of upright faces
when children are asked to identify snapshots
of their classmates. That memory for in-
verted faces is poorer than memory for up-
right faces among adults has been shown in
a paired-associate task (Goldstein, 1965)
and a recognition task (Hochberg & Galper,
1967).
These studies have not indicated the ex-
tent to which the difficulty in viewing an up-
side-down face is related specifically to the
face. An alternative hypothesis would be
that any set of objects customarily seen in
one orientation, i.e., mono-oriented, might
be more difficult to recognize when inverted.
Some evidence for this hypothesis was re-
1 This study was supported by a grant from
the John A. Hartford Foundation, Inc. (New
York, N. Y.) to H.-L. Teuber and a predoctoral
award to the author from the National Science
Foundation. The author gratefully acknowledges
the advice and encouragement of H.-L. Teuber
throughout all phases of this work.

53. 2 Requests for reprints should be sent to Robert
K. Yin, Department of Psychology, Massachusetts
Institute of Technology, Cambridge, Massachusetts
02139.
ported by Henle (1942), who showed that
alphabetic letters were correctly perceived
more frequently than their mirror reversals
by 5"s familiar with the letters, and by Ghent
(1960), who found that young children are
markedly dependent on familiar orientation
for recognizing realistic figures. In addition,
Dallett, Wilcox, and D'Andrea (1968) re-
ported that memory for upright magazine
pictures was better than that for the same
pictures when presented upside-down. The
investigators did not indicate, however, the
extent of homogeneity among the pictures or
the degree to which the pictures were of ob-
jects that are customarily mono-oriented.
The present experiments were designed to
test whether a general impairment on mono-
oriented objects when inverted could account
for the difficulty with viewing upside-down
faces. More specifically, performance on up-
right and inverted tasks for faces was com-
pared with that for other classes of every-
day objects having a priori properties similar
to faces in being mono-oriented, familiar,
complex, and not easily verbalized, i.e., ob-
jects that are not distinguished from each
other by the use of simple labels.
To test performance, a forced-choice recog-
nition memory task was used. In this task,

54. 5s were shown individual pictures (an in-
spection series) and then presented with
pairs of pictures (a test series). In the
test series they indicated the one of the
pair they thought they had seen in the in-
spection series. Three experiments were
conducted. In the first and third, the
141
142 ROBERT K. YIN
orientation of the materials in both the in-
spection and test series was the same (both
upright or both inverted). In the second,
the orientations were opposite (inspected
upright and tested upside-down or inspected
upside-down and tested upright).
EXPERIMENT I
Method
Subjects.—There were 26 paid volunteers, 13
men and 13 women, ranging from 18 to 31 yr, of
age (mean age = 21.7 yr.). These were under-
graduate and graduate students attending summer
schools in the Boston area and represented a wide
variety of geographical origins and academic
interests.
Materials.—There were 64 different pictures,
all black and white, within each of four types of
materials: faces, houses, airplanes, and men in
motion. All pictures were pasted on a 3 X 5 in.

55. card for presentation.
The faces were studio pictures of adult males,
chosen to be similar with respect to general age,
expression, and lack of outstanding distinguishing
features, such as glasses, beards, or unique marks.
All poses were full face, and the pictures were
trimmed just under the chin to eliminate as much
clothing as possible. The houses were generally
of the same architecture, but were not as uniform
as the faces in orientation of view or size of pic-
ture. In addition, since all were actual photo-
graphs, the pictures included trees and other nat-
ural features, although they were trimmed to
minimize the presence of distinguishing features,
such as fences, front stoops, and roof markings.
Neither the airplanes nor the men in motion
were real photographs, but were caricatures. The
planes were sideview silhouettes of all types and
models (military, commercial, and private) of
planes found in the world today. The men-in-mo-
tion pictures consisted of the same cartoon stick
figure engaged in various everyday movements and
postures, with no other objects present in any of
the pictures.
Procedure.—Each S1 looked at an inspection
series of 40 pictures, presented singly and turned
TABLE 1
MEAN ERRORS, Exp. I
Material
Faces

56. Houses
Airplanes
Men in motion
Presentation
Test and Inspection
series upright
M
.89
2.23
3.65
2.35
SD
1.09
1.60
1.69
1.27
Test and inspection
series inverted
M
4.35
3.42
3.85
3.27
SD
1.41

57. 1.36
2.03
1.58
by E at a rate of 3 sec. per picture. Then a test
series, consisting of 24 pairs of pictures, was pre-
sented. Each pair contained 1 old picture (an
exact duplicate of a picture in the inspection series)
and a new picture (one not previously shown),
and S had merely to indicate which picture in each
pair was the old one. The S proceeded at his own
rate in the test series. Since only 24 pairs were
in the test series, there were 16 pictures in the
inspection series which did not recur in the test
series.
Each inspection and test series constituted a block
and was a mixed list, containing two different types
of materials, 20 of each in the inspection series
and 12 pairs of each in the test series. The order
of presentation of the 40 inspection series pictures
was randomized, with the two exceptions that
neither of the two materials was shown for more
than four consecutive cards and that there were
always at least 2 of the nonrecurring pictures, one
of each type of material, at either end of the series.
The order of the 24 test series pairs was dictated
by the order of pictures in the inspection series, so
that there was a constant lag between each inspec-
tion picture and its occurrence in the test series.
All 5s went through four such blocks of inspec-
tion and test series, viewing two blocks rightside-
up (both series upright) and two upside-down
(both series inverted). Thus each S performed
in all experimental conditions, viewing the four

58. materials in two presentations. The order of
presentation among the blocks was balanced in the
following manner: (a) Each 5" was shown all four
materials (two blocks) first; half of the 5"s saw
these two blocks upside-down first, the other half
rightside-up first; (6) the mixing of the materials
was such that roughly one-third of the ,9s had
blocks consisting of faces-houses or airplanes-
men-in-motion, one-third had blocks of houses-air-
planes or faces-men-in-motion, and the remaining
third had blocks of airplanes-faces or houses-men-
in-motion; (c) the blocks were alternated so that
each picture was shown rightside-up as often as it
was upside-down; and (d) the sexes were balanced
with regard to all of these conditions.
Results
The mean errors, with their standard de-
viations, appear in Table 1. An analysis of
variance of the error scores showed that
there were significant differences as a func-
tion of presentation, F (1, 25) = 90.90, p <
.0005, materials, F (3, 75) = 6.63, p < .001,
and their interaction, F (3, 75) = 9.18, p <
.0005.
Although all materials were more difficult
in the inverted presentation, the extent to
which each type of material contributed to
this effect varied. Using t tests for matched
pairs, two-tailed, the effect of inversion was
RECOGNITION OF INVERTED FACES 143

59. greatest for faces, t (25) = 8.48, p < .001,
significant but not as great for the houses,
t (25) = 3.01, p < .01, and the men in mo-
tion, t (25) = 2.15, p < .05, and not signif-
icant for the airplanes, t (25) < 1.
The materials also differed in their overall
difficulty. Although this finding is not of
primary interest here, the major reason for
it was that the airplanes tended to be the
most difficult material in either presentation.
Of greater interest is the fact that the
Presentations X Materials interaction was
significant. Further analysis showed that
this was due mainly to the faces, which were
easier than all the other materials when
viewed upright, * (25) = 7.31, p < .001,
but more difficult than the rest when viewed
upside-down, t (25) = 2.53, p < .02. Ex-
amination of the individual scores produced
added evidence of the existence of a dif-
ference between faces and the other ma-
terials. In general, those who did better in
the inverted orientation also tended to be
the ones who did better in the upright
orientation. However, for faces, the reverse
was true. Taking the average inverted score
for houses, airplanes, and men in motion,
and arbitrarily assigning all 5"s to a better
group (n = 14, average error = 2.88) and
a worse group (» = 12, average error =
4.25), the better group is also better on the
upright task (average error = 2.36), while
the worse group is still the worse one (aver-

60. age error = 3.19). Using a t test for inde-
pendent samples, two-tailed, the difference
between the two groups in their upright
scores is significant at the p < .05 level,
* (24) = 2.46.
On the other hand, arbitrarily assigning all
5*8 by their score on the inverted-face task to
a better group (« = 14, average error =
3.29) and a worse group (» = 12, average
error = 5.58), we find that the better group
is now the worse one on the upright-face task
(average error =1.29), while the worse
group is the better one (average error =
.42). This difference on the upright-face
task is significant at the p < .05 level,
t (24) = 2.14.
Sex differences.—Men and women did
not differ in their total upright or inverted
scores. There were differences between ma-
TABLE 2
MEAN ERRORS, EXP. II
Material
Faces
Houses
Airplanes
Men in motion
Presentation
Up-Down

61. M
3.81
2.86
3.19
4.05
SD
1.71
1.83
1.94
1.79
Down-Up
M
5.14
3.43
4.14
4.24
5D
1.39
1.47
1.58
1.72
terials, however, in that the men's average
upright and inverted score for airplanes was
better than that of the women, t (24) =
2.26, p < .05, while the women's average
upright and inverted score for houses was
better, but not significantly, than that of the

62. men, t (24) = 1.91, p < .10. In both cases
the t test was for independent samples and
two-tailed. There were no sex differences
for either the faces or the men in motion.
Order of presentation.—There were no
differences when the groups were character-
ized by viewing order, upright first or in-
verted first, or by the mixture of the ma-
terials in the different blocks.
EXPERIMENT II
Experiment II required 5"s to make a
mental inversion of the materials, presenting
the inspection and test series in opposing
orientations.
Method
Subjects.—There were 21 paid volunteers, 13
men and 8 women, ranging from 18 to 26 yr. of
age (mean age = 21.1 yr.). The general nature
of the sample was the same as that of Exp. I.
Materials and procedure.—The materials were
the same as those used in Exp. I, and the overall
procedure was exactly the same with one excep-
tion: For each 5 the two presentations were up-
down (inspection series presented upright and test
series inverted) or down-up (inspection series pre-
sented inverted and test series upright). As in
Exp. I, each 5 performed in all experimental con-
ditions, viewing the four materials in both pres-
entations.

63. Results
Table 2 contains the mean errors with
their standard deviations. An analysis of
variance of the errors shows that there were
144 ROBERT K. YIN
significant differences as a function of pres-
entation, F (1, 20) = 11.67, p < .01, and
materials, F (3, 60) = 4.37, p < .01, but not
of their interaction, F (3, 60) = 1.09.
Although all materials were worse in the
down-up presentation than the up-down
presentation, faces were the most affected.
Using t tests for matched pairs, two-tailed,
the difference in presentation was significant
for the faces, t (20) = 3.12, p < .01, but not
for the houses, t (20) = 1.44, airplanes,
t (20) = 1.59, or men in motion, t (20) < 1.
The materials again differed in overall dif-
ficulty, this time mainly because the houses
were easiest in both presentations.
Sex differences.—As in Exp. I, men and
women did not differ in their total scores.
Men tended to do better on airplanes in both
presentations, but there were no differences
for the other materials.
Order of presentation.—There were no
differences due to order of presentation.

64. Comparison of results between Experi-
ments I and II.—In general, for each ma-
terial the up-down performance (Exp. II)
tended to be worse than the upright per-
formance (Exp. I) by about the same
amount that the down-up (Exp. II) was
worse than the inverted (Exp. I). This
consistent decline reflects the added dif-
ficulty imposed by the necessity for inverting
the pictures mentally.
With the faces, however, the up-down
performance was disproportionately worse
than that of the upright. This is apparent
if for each material, one compares the up-
down and down-up difference from Exp. II
with the upright-inverted difference from
Exp. I. Using t tests for independent sam-
ples, two-tailed, the difference between these
differences is significant for faces, t (45) =
3.55, p < .001, but not for houses, t (45) =
1.09, airplanes, t (45) = —.99, or men in
motion, t (45) = 1.26. Thus, while all the
materials tended to become more difficult in
Exp. II, the upright faces were dispropor-
tionately affected.
The major finding from the first two
experiments is that faces are different from
the other materials in two ways. First,
although all the materials were more dif-
ficult when viewed upside-down, faces were
especially difficult (Exp. I). Second, al-
though all the materials were more dif-
ficult when 5" was required to make a mental

65. inversion, the upright face was again dis-
proportionately affected (Exp. II).
At least two interpretations of these re-
sults may be made. The first is that there
is something special about faces that makes
them particularly difficult even when com-
pared with other mono-oriented objects. The
second is that the difference between faces
and the other materials is due solely to dif-
ferences in degree of difficulty among the
materials when presented upright. Accord-
ing to this interpretation, the easier a ma-
terial when upright, the more it will be
affected by inversion, and thus the dis-
proportionate difficulty in remembering
upside-down faces merely reflects the fact
that the faces were the easiest material when
viewed rightside-up.
To try to differentiate between these two
interpretations of the results, a third experi-
ment was designed in which memory for
faces was compared with memory for an-
other class of objects which, while meeting
all the previous criteria in being mono-
oriented, complex, familiar, and not easily
verbalized, would also be as easy to remem-
ber as faces in the upright presentation. In
addition, since the faces used in the first
two experiments were studio pictures, the
third experiment also investigated the pos-
sibility that the difficulty in remembering
faces could be attributed solely to the spe-
cial effects of light and shadow inherent in
such pictures. Therefore an artist's line

66. drawings of adult male faces, made to speci-
fication so that they were similar to the
studio pictures but with all light and shadow
cues eliminated, were used.
EXPERIMENT III
Method
Subjects.—There were 23 paid volunteers, all
male undergraduates attending the regular school
session.
Materials.—There were 36 different pictures, all
black and white, of two types of materials: artist's
sketches of faces and drawings of faceless figures
clothed in different period costumes. The sketches
were cropped very severely, so that no hair, ears,
or chin lines were present. The costumed figures
RECOGNITION OF INVERTED FACES 145
were also cropped so that only the faceless head
and torso of each figure were shown.
Procedure.—The procedure was the same as that
of Exp. I, except that the inspection and test
series were both shorter. The inspection series
contained only 18 pictures, while the test series
contained 18 pairs of pictures. Each block of
inspection and test series was composed of equal
numbers of faces and costumes, and each 5 viewed
two blocks, one rightside-up and the other upside-
down.

67. Results
For faces, the upright errors were M =
1.35, SD = 1.13, and the inverted errors
were M = 2.69, SD = 1.40. For the cos-
tumes, the upright errors were M = .48, SD
— .71, and the inverted errors were M — .78,
SD = .78. Using t tests for matched pairs,
two-tailed, the difference between upright
and inverted errors was significant for the
faces, t (22) = 4.00, p < .001, and strong
but not quite significant for the costumes,
t (22) = 1.91, p < .10. More important,
performance for the costumes was better
than that for the faces in the upright pres-
entation, t (22) = 3.14, p < .01, as well as
in the inverted presentation, t (22) = 5.31,
p < .001. Thus the faces, although not the
easier material in the upright presentation,
were still more affected by inversion when
compared with the costumes.
DISCUSSION
The results of the third experiment indicate
that upside-down faces are difficult to remember
even when the differences between materials are
such that the faces are not the easiest to re-
member in the upright presentation. In addi-
tion, the difficulty is not limited to studio photo-
graphs, but can also be shown to exist with line
drawings.
The data from all three experiments support
the interpretation that the inverted face is
especially difficult to remember because of two

68. factors: a general factor of familiarity with
mono-oriented objects and a special factor in-
volving only the faces. The general factor is
seen as affecting all of the materials used, mak-
ing them more difficult to recognize when up-
side-down; the special factor relates to the
disproportionate difficulty created by the in-
verted face.
It is interesting to speculate what such a
special factor might involve, even though this
question is unanswerable from the present ex-
periments. One clue may be provided by verbal
reports from 5s when they are asked how they
tried to remember the various materials. They
seemed to use two alternative strategies, either
searching for some distinguishing feature or
attempting to get a general impression of the
whole picture. The first tended to be used for
most of the materials; the second was used
mostly for faces, with 5 trying to remember
some personal impression made by the face.
None of the 5s, however, reported being able
to use the second strategy when looking at the
inverted face. Whatever the relevant variables,
further investigation into the difficulty with in-
verted faces may by implication tell us some-
thing about how people recognize normal (i.e.,
upright) faces and how we distinguish one face
from another.
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(Received September 11, 1968)