Intuitive Physics: Across Tasks and AgeCedar Riener, Dennis Proffitt, Timothy Salthouse

Department of Psychology

IntroductionDo you remember the material covered in your high school physics course? Many don’t, but would nonetheless assume that

most adults (including themselves) could accurately predict the behavior of simple physical systems such as a ball rolling off a table or

water moving within a tilted glass. A number of tasks have been adopted to test just this : people’s intuitive understanding of physics.

These tasks, called intuitive physics tasks, have met with surprising results. Instead of demonstrating comprehension of basic physics,

many studies in the field of intuitive physics have shown that a large percentage of adults display a systematic misunderstanding about the

simple principles which govern physical events 1,2.

Much of the previous work in the field of intuitive physics has focused on two major approaches to explain these unexpected

results. The first approach asserts that the systematic errors observed on intuitive physics tasks are due to systematically incorrect implicit

theories of physics3. Jean Piaget showed that young children were not simply failing at certain tasks, but instead showing systematic

biases4. This led him to propose that children went through stages of theoretical development which reflected a non-incremental

reorganization of knowledge. McCloskey observed that when adult participants were given the c-shaped tube task (see Intuitive Physics

Items on right) they behaved as if they possessed something akin to medieval impetus theory5. Those participants that drew a curved path

for a ball exiting a c-shaped tube seemed to hold a belief that the curvilinear “impetus” imparted to the ball by the tube gradually dissipates

after the ball exits the tube.

There are two sources of evidence against a theoretical explanation of intuitive physics performance. First of all, participants

are not internally consistent6. People may give two different responses to the same question when it is presented twice. Secondly, the

surface characteristics of the problem can affect performance. For example, while many participants erroneously report that a ball exiting

a c-shaped tube will continue to curve, very few also believe that water exiting a curved hose will also continue to curve 7.

The second approach has sought to analyze the specific problem or task and explain errors based on characteristics of each task.

McAfee and Proffitt explained errors on the water level task by applying a universal perceptual bias 8. These authors pointed out that the

water-level task is not necessarily a problem which tests one’s knowledge of whether water remains horizontal regardless of the orientation

of the container. Performance on the water level task could reflect how one represents the problem; either based on an environment-

centered coordinate frame or a container-centered coordinate frame. While this explanation explains the errors in the water level task, it

does not apply well to other tasks, such as the c-shaped tube problem. Proffit and Gilden explained errors in natural dynamics tasks as

related to the complexity of the problem, not the principles tested9. This approach is broad in its scope (it applies to many physical events)

but it is limited in its precision, in that it makes a binary distinction between simple and complex problems.

Our research is aimed at answering questions at a more fundamental level. First of all, if there are compelling explanations for

individual physics tasks, do we still have reasons to believe that intuitive physics tasks belong to a single category? In search of evidence

for or against the unity of intuitive physics task, our research addresses the question: Does intuitive physics performance reflect a unitary

and persistent general mechanical reasoning ability? The answer to this question will not help to judge explanations for performance on

individual tasks, or address whether or not participants hold incorrect theories about physics. However, our experiment will gather

evidence which will inform the worth of considering intuitive physics a single literature.

Methods

ConclusionResearch Questions

1. Does intuitive physics performance reflect a unitary mechanical reasoning disposition?

2. Is this disposition persistent across the adult lifespan?

3. How is intuitive physics related to other cognitive abilities?

Sample

204 participants Ages 20-91 Broad cross section of healthy adults

only 7% in school Mean years of education = 16

Assessments Intuitive Physics Items:

5 pairs of standard intuitive physics tasks (see below)

Standardized battery of Mechanical Reasoning Items: 6 items from the Woodcock Johnson Mechanical Reasoning subtest

Standardized Battery of Fluid Intelligence Items: Average of the z-scores from the Woodcock Johnson Analysis-Synthesis and Wechsler Block Design

Standardized Battery of Crystallized Intelligence Items: Average of the z-scores from the Wechsler Vocabulary subtest and the Woodcock Johnson Picture Vocabulary subtest

Intuitive Physics Items

References

Results1. Intuitive physics performance does reflect a unitary disposition.

The intuitive physics items are strongly correlated with the mechanical reasoning items, suggesting that they reflect the same underlying ability.

Cronbach’s coefficient Analogous to testing for measurement error Adjusted for binary values On a scale from 0 to 1

Intuitive Physics coefficient 0.74Nunnally (1978) sets criteria at 0.70

Even if factors do exist, they are weak. 5 factors with eigenvalues greater than 1. Only the first factor accounts for more than 6% of variance (19.1%)

all five together only account for 36.2% of variance

Most of these factors are actually “bloated specifics”

the same problem phrased two different ways

b. Factor analysisa. Tests of Reliability

2. This disposition is persistent across the adult lifespan.

20 40 60 80

Chronological Age

4

8

12

16

Intu

itiv

e P

hys

ics

Per

form

ance

R-Square = 0.00

3. Intuitive physics performance is strongly correlated with fluid intelligence.

-3.00 -2.00 -1.00 0.00 1.00

Fluid Intelligence

4

8

12

16

Intu

itiv

e P

hys

ics

Per

form

ance

R-Square = 0.34

Archimedes Principle Problem

Water Level ProblemPendulum Problem

Falling Object Problem

C- Shaped Tube Problem

On the left is a glass of water sitting on the table. On the right, the glass has been tilted. Assume that the glass and water are now stationery. Draw the line showing the top surface of the water, given that it ends at the indicated point.

This airplane is carrying a canister of supplies as it flies over a field. The plane drops the canister. Draw the path that the canister will follow before it hits the ground.

The c-shaped tube you see below is lying flat on a table (this is an aerial view). A ball is placed in one end and launched so that it proceeds through the tube and out the other side. Draw the path that the ball will take once it exits the tube, starting at the given point.

The pendulum below is swinging from side to side. The string is cut when the pendulum reaches its highest point (indicated by the scissors). Draw the path of the pendulum bob as it falls to the ground.

The picture on the left shows a styrofoam disc floating in a tub of water. A clay ball is attached to the top of the disc with glue. The water level on the tub is marked with an “X.”

The disc and the ball are then removed from the tub and placed back into the tub, upside down (as shown). Where will the water level be relative to the case on the left? (above, the same, or below the X) (circle one)

X

?

The performance of participants in our study offers compelling evidence that intuitive physics performance is indeed reflective of a general mechanical reasoning ability. Furthermore, this ability is stable across the adult lifespan, as well as highly correlated with measures of fluid cognitive ability.

Before we situate our evidence in the context of the current intuitive physics literature, however, it is important to state that the existence of a mechanical reasoning ability does not rule out in principle either a theory approach or individual problem explanations. Rather, it can be seen as a possible source of confirmation and clarification for certain explanations within these accounts. Future researchers can design intuitive physics studies knowing that a certain amount of the “noise” that they see in their performance data can be attributed to individual differences in a mechanical reasoning ability. By assessing this ability prior to administering the intuitive physics problems, this “noise” could be filtered out, allowing other patterns in the data to be more easily recognizable.

Applying our findings to the two major approaches discussed in the introduction, the evidence for an underlying mechanical reasoning ability seems to lend support to the account discussed by Proffitt and Gilden (’89). Our evidence suggests an expansion on this account. Rather than splitting intuitive physics tasks between easy and hard categories along the lines of problem complexity, perhaps items can be placed on a continuum of difficulty. This scale would complement the scale of intuitive physics performance described in our results.

Finally, we must now question our original motivation for studying intuitive physics tasks. If everyone has some level of this ability, and some problems are harder than other, perhaps the “surprising” errors observed on intuitive physics tasks in the past is only surprising to those who get them right.Mechanical Reasoning Items

The Mechanical Reasoning Items are made up of 6 multiple choice items which require mechanical reasoning using physical concepts such as center of mass, gravity, and collision dynamics.

Fluid and Crystallized Intelligence TestsFluid intelligence was measured by taking an average of the z-scores from the Woodcock Johnson Analysis-Synthesis and Wechsler Block Design subtests. These subtests provide an approximate measure of one’s facility with processing given information, regardless of experience or stored knowledge.

Crystallized intelligence was measured by taking an average of the z-scores from the Woodcock Average of the z-scores from the Wechsler Vocabulary subtest and the Woodcock Johnson Picture Vocabulary subtest. These subtests provide an approximate measure of the level of stored knowledge that an individual has acquired by testing vocabulary and picture recognition.

1. Champagne, A. B., Klopfer, L. E. & Anderson, J. H. (1980). Factors influencing the learning of classical mechanics. American Journal of Physics. 48, 1074-10792. Caramazza, A., McCloskey, M., & Green, B. (1981). Naive beliefs in "sophisticated" subjects: Misconceptions about trajectories of objects. Cognition, 9(2), 117-

123.3. McCloskey, M. (1983) Intuitive physics. Scientific American. 248(4), 122-130.4. Piaget, J., & Inhelder, B. (1956). The child’s conception of space. London: Routledge and Kegan Paul.5. McCloskey, M., & Kohl, D. (1983). Naive physics: The curvilinear impetus principle and its role in interactions with moving objects. Journal of Experimental

Psychology: Learning, Memory, & Cognition, 9(1) 146-156.6. Cooke, N. J., & Breedin, S. D. (1994). Constructing naive theories of motion on the fly. Memory & Cognition, 22(4), 474-493.7. Kaiser, M. K., Jonides, J., & Alexander, J. (1986). Intuitive reasoning about abstract and familiar physics problems. Memory & Cognition, 14(4), 308-312.8. McAfee, E. A., & Proffitt, D. R. (1991). Understanding the surface orientation of liquids. Cognitive Psychology, 23(3)9. Proffitt, D. R., & Gilden, D. L. (1989). Understanding natural dynamics. Journal of Experimental Psychology: Human Perception & Performance, 15(2), 384-393.

* This research was supported by funds from the Virginia Aging Initiative directed by Timothy Salthouse