Dennis M. Shaffer

How Dogs Navigate to Catch Frisbees

Using micro-video cameras attached to the heads of 2 dogs, we examined their optical behavior while catching Frisbees. Our findings reveal that dogs use the same viewer-based navigational heuristics previously found with baseball players (i.e., maintaining the target along a linear optical trajectory, LOT, with optical speed constancy). On trials in which the Frisbee dramatically changed direction, the dog maintained an LOT with speed constancy until it apparently could no longer do so and then simply established a new LOT and optical speed until interception. This work demonstrates the use of simple control mechanisms that utilize invariant geometric properties to accomplish interceptive tasks. It confirms a common interception strategy that extends both across species and to complex target trajectories.

Evaluating the Effectiveness of a Personal Response System in the Classroom

We evaluated the effectiveness of the use of an electronic personal response system (or “clickers”) during an introductory psychology lecture on perceptual constancy. We graphed and projected student responses to questions during the lecture onto a large-screen display in Microsoft PowerPoint. The distributions of answers corresponded well to results found in the literature. Students rated the lecture as more interactive, interesting, and entertaining. Students in the clicker lecture also performed significantly better on exam questions concerning the lecture compared to another group of students who did not use the clickers.

Baseball outfielders maintain a linear optical trajectory when tracking uncatchable fly balls.

The authors investigated whether behavior of fielders pursuing uncatchable fly balls supported either (a) maintenance of a linear optical trajectory (LOT) with monotonic increases in optical ball height or (b) maintenance of optical acceleration cancellation (OAC) with simultaneous lateral alignment with the ball. Past work supports usage of both LOT and OAC strategies in the pursuit of catchable balls headed to the side. When balls are uncatchable, fielders must choose either optical linearity or alignment at the expense of the other. Fielders maintained the LOT strategy more often and for a longer period of time than they did the OAC alignment strategy. Findings support the LOT strategy as primary when pursuing balls headed to the side, whether catchable or not.

Sugar and space? Not the case: Effects of low blood glucose on slant estimation are mediated by beliefs.

There is a current debate concerning whether peoples physiological or behavioral potential alters their perception of slanted surfaces. One way to directly test this is to physiologically change peoples potential by lowering their blood sugar and comparing their estimates of slant to those with normal blood sugar. In the first investigation of this (Schnall, Zadra, & Proffitt, 2010), it was shown that people with low blood sugar gave higher estimates of slanted surfaces than people with normal blood sugar. The question that arises is whether these higher estimates are due to lower blood sugar, per se, or experimental demand created by other aspects of the experiment. Here evidence was collected from 120 observers showing that directly manipulating physiological potential, while controlling for experimental demand effects, does not alter the perception of slant. Indeed, when experimental demand went against behavioral potential, it produced judgmental biases opposite to those predicted by behavioral potential in the low blood sugar condition. It is suggested that low blood sugar only affects slant judgments by making participants more susceptible to judgmental biases.

Escalating Slant Increasing Physiological Potential Does Not Reduce Slant Overestimates

A wealth of evidence has shown that people verbally overestimate the slant of hills by 15° to 20° (Bhalla & Proffitt, 1999; Creem-Regehr, Gooch, Sahm, & Thompson, 2004; Proffitt, Bhalla, Gossweiler, & Midgett, 1995). Over the past 15 years, one of the most widely accepted explanations to account for conscious overestimates of slant has been that people’s perception of slant relates the incline of hills to physiological potential, or how much motor effort the hills would require to climb. Consistent with this explanation, research has shown that decreased physiological potential due to fatigue, poor health, increasing age, or the burden of a weighted backpack leads to further exaggerations of the steepness of hills (Bhalla & Proffitt, 1999; Proffitt et al., 1995). Similarly, hills look steeper from the top than from the bottom, presumably because they are more difficult and dangerous to descend than to ascend (Proffitt et al., 1995; Stefanucci, Proffitt, Clore, & Parekh, 2008). More recently, the evidence favoring a physiological explanation has been disputed. For example, demand characteristics have been shown to explain the effects of weighted backpacks (Durgin et al., 2009). Other work has shown that older people and college-age students estimate slopes the same (i.e., achieve the same estimates), contradicting the work showing the effects of increasing age (Durgin, Hajnal, Li, Tonge, & Stigliani, 2010; Norman, Crabtree, Bartholomew, & Ferrell, 2009). Further, Ross (2006) found that downhill slopes and uphill slopes appear to be equally steep, and Li and Durgin (2009) determined that estimates of downhill slopes depend on where one stands relative to the edge when looking down from the top. Li and Durgin suggested, too, that overestimates of uphill slopes in previous work may be explained by a scale expansion of perceived optical slant. In the present study, we investigated whether increasing observers’ physiological potential—that is, making it easier for them to traverse a slanted surface―would lead them to provide lower estimates of slant. Participants were asked to verbally estimate the slant of a flight of stairs and an escalator. According to the physiological-potential account, estimates would be lower for stairs than for hills, because stairs are easier to climb than similarly sloped hills, and estimates would be lowest of all for the escalator, because traversing an escalator requires no motor effort.

Naive beliefs in baseball: systematic distortion in perceived time of apex for fly balls.

When fielders catch fly balls they use geometric properties to optically maintain control over the ball. The strategy provides ongoing guidance without indicating precise positional information concerning where the ball is located in space. Here, the authors show that observers have striking misconceptions about what the motion of projectiles should look like from various perspectives and that they estimate when the physical apex of a fly ball occurs to be far later than actual, irrespective of baseball experience. Their estimations are consistent with the highest point they are looking at as the ball approaches, not with the physical apex. These findings introduce a new and robust effect in intuitive perception in which people confuse their perceptual perspective with the physical situation that they mentally represent.

A linear optical trajectory informs the fielder where to run to the side to catch fly balls

P. McLeod, N. Reed, and Z. Dienes (2002) argued that the linear optical trajectory (LOT) strategy incorrectly cues fielders to run forward for balls headed beyond them. The authors of this article explain that the downward optical curvature found for balls landing beyond the fielders initial position occurs because the balls reorient the direction the fielder is facing during pursuit. Thus, when downward optical curvature begins, the ball is headed to land in front of where the fielder is facing and running. This investigation of open-loop failure conditions has led to new insights such as the reorientation of the fielder, and it supports the use of maintaining matching rates of vertical and lateral optical ball movement consistent with primacy of the LOT control mechanism even when interception is unachievable.

Evidence for a generic interceptive strategy.

In the present work, we first clarify a more precise definition of instantaneous optical angles in control tasks such as interception. We then test how well two interceptive strategies that have been proposed for catching fly balls account for human Frisbee-catching behavior. The first strategy is to maintain the ball’s image along a linear optical trajectory (LOT). The second is to keep vertical optical ball velocity decreasing while maintaining constant lateral optical velocity. We found that an LOT accounted for an average of over 96% of the variance in optical Frisbee movement, while maintenance of vertical and lateral optical velocities was random. This work confirms a common interception strategy used across interceptive tasks, extending to complex target trajectories.

Anchoring In Action: Manual Estimates Of Slant Are Powerfully Biased Toward Initial Hand Orientation And Are Correlated With Verbal Report

People verbally overestimate hill slant by approximately 15° to 25°, whereas manual estimates (e.g., palm board measures) are thought to be more accurate. The relative accuracy of palm boards has contributed to the widely cited theoretical claim that they tap into an accurate, but unconscious, motor representation of locomotor space. In the current work, 4 replications (total N = 204) carried out by 2 different laboratories tested an alternative anchoring hypothesis that manual action measures give low estimates because they are always initiated from horizontal. The results of all 4 replications indicate that the bias from response anchoring can entirely account for the difference between manual and verbal estimates. Moreover, consistent correlations between manual and verbal estimates given by the same observers support the conclusion that both measures are based on the same visual representation. Concepts from the study of judgment under uncertainty apply even to action measures in information rich environments.

Response: On Catching Fly Balls

this problem was stimuonly that lated by statements [for examples, see (1, the time 2)] that an outfielder moves at an approxiactually sp mately constant speed while he runs to pose that t intercept the ball, and that he does not slow and we ha appreciably before the catch. This is also a puting the prediction of the proposals made by ers speed. McBeath et al. (1). However, such behavior ble our rei is completely at odds with our own impresadequacy sions of how outfielders actually move. even more From many hours spent observing major for cx and league outfielders, it was our belief that in linearly w many (or even most) cases they run relawas signil tively quickly to the spot where the ball will variation ( land and are usually moving only slowly, or while it in not at all, when they make the catch (3). with time, This belief is supported by new, quantitaball lande tive measurements of the motion of outmoved so fielders as they move to catch fly balls, the end of which we now describe. Our measurements for many do not support the basic premise of the contradict LOT model, namely that the fielder moves LOT mode so as to keep the ratio tan ox/tan ( constant. keeps the The movements of several consenting keep iI co outfielders, as they judged and caught fly himself ur balls hit by a live batter, were recorded. experimen From the measured time of flight and landAs we I ing point of the ball, we calculated its trais not emp jectory (4). This was combined with the consider w trajectory of the fielder to derive the angles to the situat and , as functions of time. The particular run, that results shown below were obtained from fielder beg observing a skilled high school baseball outtion is hc fielder. Similar findings were obtained for much to s. other fly balls with the same fielder, with a plished wi recreational softball player, and several othball perper er fielders of different skill levels. V= 0 an( Typically, the outfielder slowed considdirectly at erably toward the end of his run (Fig. 2). pose that This is quite different from the LOT pretan 3 and diction for the same fly ball (Fig. 2B). role of vp i: Strictly speaking, the LOT model asserts Brancazio