Pigeons use low rather than high spatial frequency information to make visual category
discriminations
Stephen E. G. Lea
University of Exeter, UK
Guido De Filippo
Alma Mater Studiorum Università di Bologna, Italy, and University of Exeter, UK
Ruth Dakin and Christina Meier
University of Exeter, UK
Correspondence address:
S. E. G. Lea
University of Exeter
Psychology (CLES)
Washington Singer Laboratories
Exeter EX4 4QG
United Kingdom
Telephone +44 1392 724612, Fax +44 1392 724623, email s.e.g.lea@exeter.ac.uk
© Copyright 2013 S. E. G. Lea, G. De Filippo, R. Dakin & C. Meier. Moral right asserted.
This version saved 30 January 2013
Under revision for resubmission to the Journal of Experimental Psychology: Animal
Behaviour Processes as a Brief Communication (Tracking no. 2012-0336)
Keywords: Pattern recognition; Spatial frequency analysis; Hybrid stimuli; Pigeon vision
Author Note
We thank Catherine Bryant for developing the initial version of the client program used,
Catriona Ryan for animal care, and her, Andy Wills and Ian McLaren for discussion.
Address correspondence to Stephen Lea, Psychology (CLES), University of Exeter,
Washington Singer Laboratories, Exeter EX4 4QG, UK. Email: s.e.g.lea@exeter.ac.uk
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Abstract
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Pigeons were trained to discriminate photographs of cat faces from dog faces. They were
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then presented with test stimuli involving high- and low-pass spatial frequency filtering.
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Discrimination was maintained with both types of filtered stimuli, though it was increasingly
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impaired the more information was filtered out, and high-pass filtering impaired
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discrimination more than low-pass filtering. The pigeons were then exposed to hybrid stimuli
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in which high-pass filtered dog faces were combined with low-pass filtered cat faces, and
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vice versa. Response to hybrid stimuli was determined more by the low spatial frequency
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content than by the high frequency content, whereas humans viewing the same stimuli at
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corresponding viewing distance respond more strongly to the high-frequency content. These
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results are unexpected given that, compared with humans, pigeons’ behavior tends to be
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controlled by the local details of visual stimuli rather than their global appearance.
Lea et al, Pigeons use low spatial frequencies: Page 2 of 17
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Pigeons use low rather than high spatial frequency information to make visual category
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discriminations
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Consider the stimulus shown at the center of the bottom row of Figure 1. When viewed from
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a distance, it appears to be a cat’s face; but if it is sufficiently magnified on a computer
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screen, a dog’s face becomes the dominant percept. Figure 1 shows how these “hybrid
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stimuli” (Schyns and Oliva, 1994) are constructed. The “parent” cat’s face is subjected to
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low-pass spatial frequency filtering, while the parent dog’s face is high-pass filtered, and the
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two filtered stimuli are added together. The effect depends on our differential sensitivity to
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different bands of spatial frequency. At a small size, our visual systems respond to the lower
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spatial frequencies, so we see the cat’s face. As we enlarge the image, all the spatial
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frequencies are lowered, and eventually those present in the dog’s face reach the range to
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which we are most sensitive, and we see the dog. Oliva, Torralba and Schyns (2006) show
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some striking examples of such stimuli.
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Insert Figure 1 about here
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Analysis of visual input in terms of spatial frequency allows a systematic approach to the
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problem of form perception: all the pattern information present in a visual stimulus is
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preserved in its spatial frequency spectrum, but in a more tractable form. Such spatial
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frequency analysis, and differential treatment of different frequency bands, has been argued
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to be a fundamental property of the visual system of humans (Campbell & Robson, 1968;
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Blakemore & Campbell, 1969). The approach has been extended to other animals including
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birds (Hardy and Jassik-Gerschenfeld, 1979). However, it is not known how non-human
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species would respond to the kind of hybrid stimuli shown in Figure 1. The question is
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particularly interesting in relation to pigeons, since their visual cognition has been shown to
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differ from that of humans in ways that might be explained by different sensitivity to
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particular spatial frequencies. Pigeons tend to respond to local details of stimuli in situations
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where human behavior is more determined by the Gestalt (Cook 1993). For example, faced
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with hierarchical stimuli like those of Navon (1977), which humans classify in terms of
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global properties, pigeons classify them in terms of the elements of which they are composed
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(Cavoto & Cook, 2001). Faced with stimuli in which a small diamond sometimes does, and
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sometimes does not, fit exactly into a notch in the perimeter of a large square, pigeons detect
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the notch with equal ease regardless of whether the diamond is fitted into it, whereas humans
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find the task harder when the diamond fits into the notch, since we then see the image as a
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diamond superimposed on an intact square (Fujita and Ushitani, 2005). If pigeons are trained
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to discriminate between images of cats and dogs, and are then faced with chimeras made up
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of cat heads on dog bodies or vice versa, they classify them in terms of the body (Ghosh, Lea
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& Noury, 2004, Experiment 1) whereas human infants classify them in terms of the head
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(Quinn & Eimas, 1996).
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To explain these and related results, Ghosh et al (2004) and Goto, Lea, Wills and Milton
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(2011) suggested that, compared with humans, pigeons may be more sensitive to the higher
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spatial frequencies in a stimulus. The present experiment tests this account directly. As in
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Ghosh et al (2004, Experiment 2) and Goto et al (2011), pigeons were trained to discriminate
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between images of the faces of cats and dogs. They were then tested with stimuli that were
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either low-pass or high-pass filtered in the spatial frequency domain, and with hybrid stimuli
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composed as in Figure 1.
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Method
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Subjects
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Eight pigeons, obtained as discards from local fanciers, were kept in an indoor aviary and
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maintained at or above 80% of free feeding weight. They had previously served in another
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experiment on visual pattern discrimination, using similar training and testing procedures but
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with stimuli of completely different appearance.
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Apparatus
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Each pigeon was tested in one of four 71 x 50.5 x 43.5 cm operant chambers. One long wall
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of each chamber was fitted with a 31 x 23.5 cm (15-in.) touch monitor (Model 1547L 1024 x
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768 pixel TFT monitor with CarrollTouch infra-red detector; ELO Touchsystems Inc.),
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mounted 12 cm above the grid floor of the chamber. Effective pecks to target areas were
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followed by an immediate bleep from a 50-ohm loudspeaker, which also played white noise
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into the box. Two 2.8-W white houselights were mounted above and to either side of the
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screen. Two 6 x 5-cm apertures gave access to grain hoppers when solenoids were activated;
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they were located directly below the houselights and 4 cm above the floor of the chamber.
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The hoppers were illuminated by a 2.8-W white light when activated, and contained a 2:1
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mixture of hemp seed and conditioner. The interior of the box was monitored by a video
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camera. The experiment was controlled by a computer (Quadvision Ltd) located in an
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adjacent laboratory area, using the Whisker control server system (Cardinal & Aitken, 2010)
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with client programs written in Microsoft ® Visual Basic 6.0.
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Stimulus materials
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The training stimuli were full-color cat and dog face images on black backgrounds, as used
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by Ghosh et al (2004) and Goto et al (2011). There were ten cat faces and ten dog faces, with
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each cat face paired with a particular dog face for the purposes of producing the hybrid
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transfer stimuli described below. Where possible, paired faces were roughly matched in
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terms of overall brightness, hue, and orientation, though the extent to which this could be
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done was variable. The faces were selected so that the range of variation of luminance and
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hue within categories was much greater than the mean differences of those properties
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between categories. Images fitted within a 100 x 100 pixels (3.1 x 3.1 cm) square, subtending
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approximately 60° of arc at the pigeon’s eye at typical pecking distance. All images are
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shown in the Electronic Supplementary Materials (Fig. E1), and information about their
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spatial frequency content has been reported in the Supplementary Material for Goto et a
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(2011).
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Low-pass spatially filtered versions of a stimulus can be produced by convolving the 2-
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dimensional matrix of its pixel values for each color channel (red, green or blue) with a
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smaller square matrix populated with unit values, called a filter kernel (Walisch, Lusignan,
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Benayoun, Baker, Dickey, and Hatsopoulos, 2009, pp. 87ff). In simple terms, this blends the
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pixel values across the area of the kernel, and thus removes high spatial frequency
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information; but it does so without producing the sharp boundaries (and, therefore, artefactual
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high spatial frequencies) that result from Harmon’s (1973) mosaicization procedure. For the
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present experiment, convolution was carried out using the convn routine within Matlab®
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R2008a with filter kernels of size 5, 10 and 20 pixels, which removed spatial frequencies
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above 0.20, 0.10 and 0.05 cycles/pixel respectively.
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High-pass filtered stimuli can then be produced by subtracting the low-pass filtered stimuli
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from the originals, leaving only the high spatial-frequency information. However, this
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procedure also results in a very low intensity image, so to restore the typical intensity, neutral
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grey was added to the high-pass images by adding half the maximal value to all pixel values,
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as recommended by Walisch et al. In order to retain the same general appearance in the
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transfer as in the training stimuli, the grey was then removed from the area of the original
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image’s black background.
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Hybrid images were constructed by adding a low-pass filtered dog face image to the high-
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pass filtered image of the cat face with which it was paired (or vice versa), after removal of
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the neutral grey from the high-pass image.
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Figure 1 shows the test stimuli made from one cat/dog pair with each filter cut-off frequency;
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the complete sets of test stimuli are shown in the Electronic Supplementary Material (Figs.
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E2-E4). It should be noted that, because the parent stimuli were 100-pixel square bitmap
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images, even the parent stimuli had already been subjected to spatial frequency filtering, in
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the sense that no information at a spatial frequency above 0.5 cycles/pixel (the Nyquist limit,
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see Walisch et al., 2011, chap. 7) or below 0.01 cycles/pixel (the reciprocal of the overall
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width of the image) could be represented in them.
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To establish that discrimination and transfer could not be based on the frequently highly
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salient dimensions of brightness and color, mean values of red, blue and green intensities at
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each pixel were calculated for each training and transfer stimulus. These means were then
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converted to measures of overall luminance (the sum of the pixel values for the three
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channels), redness (the red pixel value minus the green pixel value) and blueness (the blue
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pixel value minus the mean of the red and green pixel values). The measures were expressed
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as percentages of their maximum possible values. The relation of these percentage values to
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perceived brightness and hue can be assumed to be monotonic.
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The training cat faces had somewhat higher overall luminance on average than the training
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dog faces (35.1% and 31.3% of maximum values, respectively), but the difference was small
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compared with the range within each category (27% for cats and 25% for dogs). The
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differences in mean redness and blueness between cat and dog faces were less than 1%,
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compared with within-category ranges of 10% to 17%.
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The effects of filtering on overall luminance and color were similar for both cat and dog face
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stimuli. High-pass filtered stimuli had somewhat higher luminance than the training stimuli
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(by a mean of 4.5%), and low-pass filtered stimuli somewhat lower (by a mean of 3.5%).
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The high pass filtered stimuli were somewhat less saturated than the training stimuli (both
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redness and blueness closer to zero by approximately 3%), and the low pass stimuli
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correspondingly more saturated. Filtered stimuli did not differ from training stimuli in hue.
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Hybrid stimuli tended to have slightly lower luminance than training stimuli (by a mean of
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4.6%) but did not differ from them in hue.
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To assess whether the cat and dog images could be discriminated on the basis of overall
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luminance or hue, the rho statistic of Herrnstein, Loveland and Cable (1976) was used to
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assess the overlap between the two categories. This statistic, which was also used to assess
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the birds’ discrimination performance, takes a value of 0.5 for random assortment, and 1.0 for
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perfect separation of two categories. For overall luminance, the rho value for the
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discriminability of cat and dog faces was 0.66, in line with the higher luminance of the cat
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faces. But since the criterion of successful learning was set at a rho of 0.80, it was impossible
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for a pigeon relying on brightness alone to learn the discrimination to the required level. Rho
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values for redness and blueness were .59 and .51, so virtually no discrimination could be
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made on the basis of hue alone.
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Procedure
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We used a multiple simultaneous discrimination procedure (Huber, Apfalter, Steurer, &
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Prossinger, 2005; Wills et al., 2009, Experiment 2b). Sessions started with the presentation
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of a circular observing key, 80 pixels (2.5 cm) in diameter, centered on the vertical midline of
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the touchscreen and 238 pixels (7 cm) above its base. A peck to this key removed it, and an
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array of ten stimuli was then presented at random non-overlapping locations within a
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rectangle of 735 x 450 pixels (23 x 14 cm) centered on the vertical midline of the touchscreen
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and starting 56 pixels (1.8 cm) above its base. An example of the appearance of the screen at
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this stage is shown in Figure E5 in the Electronic Supplementary Materials. When the pigeon
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pecked a positive stimulus twice in succession, it disappeared, and a circular reward key of
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diameter 80 pixels (2.5 cm) appeared, centered 100 pixels (3.1 cm) from the right or left of
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the touchscreen and 168 pixels (5.3 cm) above its base. The left reward key appeared if the
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stimulus that had been pecked was centered in the left half of the touchscreen, and the right
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reward key otherwise. A peck on the reward key led to the presentation and illumination of
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the corresponding grain hopper for 2.5 s. If the pigeon pecked a negative stimulus twice in
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succession, this also disappeared, but no reward key appeared, and all keys became
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ineffective for 2.5 s. Once a stimulus had disappeared, it did not reappear. When all positive
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stimuli had disappeared, any remaining negative stimuli also disappeared, and after the feeder
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operation an inter-trial interval of 3 s ensued. The observing key was then presented again.
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One session was run per day, two to five days per week. Because of the pigeons’ previous
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experience, minimal pretraining was required; pecking at cat and dog face stimuli was
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established across 1 to 5 sessions by presenting, first, white hexagons of the same size as the
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training stimuli, and subsequently desaturated versions of the training stimuli, increasing the
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saturation from array to array until stable pecking was achieved.
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Training sessions consisted of the presentation of ten arrays, each including five cat faces and
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five dog faces. The cat and dog faces that were paired for the purposes of producing transfer
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stimuli were never presented in the same array, but within each pair of arrays, all ten faces of
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each type were presented. Four pigeons had cat faces as positive stimuli, and the other four
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had dog faces as positive. Performance on each array was assessed by the rho statistic of
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Herrnstein, Loveland and Cable (1976), based on the rank order of removal of the 10 stimuli,
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with any negative stimuli left after all positives had been removed being assigned the mean of
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the remaining ranks. Training was continued for each pigeon for at least 5 sessions, and then
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until it had recorded two consecutive sessions with a mean rho across the ten arrays of 0.80 or
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above; between 5 and 11 sessions were given.
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Test sessions were then given. One type of test stimulus (high-pass, low-pass, or hybrid), at a
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single filter cut-off frequency, was used in each session. All pigeons were first tested with
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the stimuli constructed using a filter cut-off frequency of 0.10 cycles/pixel; half of them were
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then tested with the 0.05 and then the 0.20 cycles/pixel filtered stimuli, and the other half had
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these two sets of tests in the opposite order. All pigeons had the high- and low-pass tests
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before the hybrid-stimulus tests, but half the pigeons had the high-pass tests before the low-
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pass tests and the others had them the other way round.
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Test sessions consisted of eleven arrays. Odd-numbered arrays contained only training
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stimuli. In even-numbered arrays, two positive and two negative stimuli were replaced by
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test stimuli. In the high-pass and low-pass tests, these stimuli were associated with the
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contingencies appropriate to the stimuli (cat or dog faces) from which they had been derived.
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One test session was given with each of these kinds of stimulus at each filter cut-off
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frequency, and each stimulus was presented in its degraded form once within that session.
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Two hybrid-stimulus test sessions were given for each cut-off frequency. Each session
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included all the possible hybrid stimuli, half of them associated with the contingencies
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appropriate to cats and half with the contingencies appropriate to dogs; between the wo test
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sessions, the contingencies associated with each hybrid stimulus were reversed. In all arrays,
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training contingencies of reinforcement were in effect for the training stimuli. After each test
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session, a training session was run, to confirm that performance remained at or above a mean
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rho of 0.8 (it always did).
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Data Analysis
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Test results are summarized as the mean ranks at which each kind of stimulus (including
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training stimuli) were removed from test arrays, ranking the first stimulus removed as 10.
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Consistency across pigeons of performance trends as a function of filter cutoff frequency are
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assessed using the Page’s test, an extension of the Spearman rank correlation coefficient
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(Siegel and Castellan, 1988, pp. 184ff). All significance values reported are two-tailed.
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Results
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Figure 2a shows the mean ranks at which the pigeons pecked the stimuli in tests involving
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low-pass filtering. Discrimination between the degraded positive stimuli and the degraded
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negatives was measured by the difference of mean ranks. It was significant for all cut-off
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frequencies (Wilcoxon T ≤ 1, P≤0.02). However it was progressively degraded as the cut-off
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frequency for the filter decreased. There was a consistent trend across the pigeons for
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discrimination to get worse as filter cut-off frequency decreased, and this was significant
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(Page’s L = 109, P<.001).
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Insert Figure 2 about here
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Figure 2b shows a similar but converse picture with the high-pass filtered stimuli. The
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degradation of discrimination was more severe than with the low-pass filtered stimuli, but
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discrimination between degraded positive and degraded negative stimuli was significant at all
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cut-off frequencies (Wilcoxon T≤2, P≤0.02). There was a consistent trend across the pigeons
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for discrimination to get worse as filter cut-off frequency increased, and this was significant
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(Page’s L = 109, P<.001).
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Figure 2c shows the mean ranks at which the pigeons pecked the hybrid stimuli. As the filter
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cut-off frequency increased, the mean rankings of the test stimuli approached those of the
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stimulus which contributed the low spatial frequencies. With the 0.10 and 0.20 cycles/pixel
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filter cut-off frequencies, the pigeons had a significant tendency to discriminate the hybrid
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stimuli according to their low-frequency content (Wilcoxon T=0, P<0.01). All eight pigeons
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ranked hybrids containing low-frequency information from their positive stimuli above those
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containing low-frequency information from their negative stimuli. When the filter cut-off
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frequency was 0.05 cycles/pixel, however, there was no significant discrimination (Wilcoxon
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T=13). As cut-off frequency increased, the tendency for the pigeons to respond early in an
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array to hybrids containing high frequencies from the positive stimulus declined, and this
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decline was consistent across pigeons and significant (Page’s L = 106, P<.05). Conversely,
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the tendency to respond early in an array to hybrids containing low frequencies from the
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positive stimulus increased as cut-off frequency increased, and this increase was consistent
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across pigeons and significant (Page’s L = 107, P<.01).
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Discussion
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Both high-pass and low-pass filtering degraded discrimination, and accordingly we can
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conclude that the pigeons had learned to use both low and high spatial frequencies to
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discriminate the cat and dog face images. Furthermore, significant discrimination remained
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with both the highest frequency high-pass filter and the lowest frequency low-pass filter used
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here, confirming that both high and low frequency information were sufficient to allow some
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discrimination, and that the pigeons had learned to use both kinds of information. Goto et al
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(2011) drew a similar conclusion from less direct tests of the influence on discrimination of
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information at different spatial frequencies.
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With the cut-off frequencies used here, high-pass filtering produced more degradation than
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low-pass filtering. The hybrid stimuli allow a direct test of whether high or low spatial
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frequencies were more important for the classification of these stimuli. As is to be expected,
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the higher the cut-off frequency, the more the stimuli tended to be treated as belonging to the
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category from which their low frequencies were taken. However, at all but the lowest cut-off
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frequency, the hybrids were treated like their low frequency parents. Thus, within the range
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available in our stimuli, the pigeons were indeed more influenced by low than by high spatial
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frequencies, even though the latter were sufficient for them to make the discrimination when
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high spatial frequency information was all that was present.
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This result is surprising given that, compared with humans, pigeons tend to attend
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preferentially to small details of visual stimuli, which must necessarily be defined in terms of
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high spatial frequencies. It is true that, if circumstances require it, pigeons can attend to the
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global features of stimuli (e.g. Goto, Wills and Lea, 2004). But in the present experiment, we
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showed that the pigeons had learned about both high and low spatial frequency information,
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so if frequency biases explained their dominant attention to details of visual stimuli, we
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would have expected them to be more influenced by the high-frequency information than
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humans. Yet to the pigeons, the hybrids produced with the 0.05 cycles/pixel cut-off filter
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were completely ambiguous (Figure 5), whereas inspection of Figure 1shows that to the
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human eye, these hybrids unambiguously resemble the species contributing the high spatial
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frequencies (see also Figure E4a in the Electronic Supplementary Material, where the full set
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of these stimuli is shown). To confirm that the impression given by Figures 1 and E4a is
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correct, we conducted tests with a sample of human participants using the hybrid stimuli from
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the present experiment at a variety of viewing distances, and at all distances the 0.05
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cycles/pixel filtered stimuli were categorized according to their high-frequency content in
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over 90% of trials. It seems obvious that if a sufficiently low filter cut-off frequency was
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used to construct the hybrids, the pigeons would respond to them according to their high
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frequency content, but it also seems that a substantially lower frequency would be required
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than for humans.
Lea et al, Pigeons use low spatial frequencies: Page 13 of 17
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Figure 2 shows that, with both the low- and high-pass filtered stimuli, generalization
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decrement was shown primarily with the positive rather than the negative stimuli. This is
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probably a consequence of the multiple-simultaneous discrimination procedure, which
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resembles a visual search task in putting the emphasis on the subject finding positive stimuli
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among an array of negatives, with positives becoming increasingly rare as the trial continues.
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It is possible that the process of removing the neutral grey from the background area of the
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high-pass filtered stimuli introduced additional low-frequency information into these stimuli,
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by emphasizing the outlines of the faces. It is hard to see how this would produce the results
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we found, since it seems more likely to have made the high-pass transfer stimuli artefactually
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easier to discriminate, which is not what we found. Nevertheless, we checked the effect of
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this manipulation by repeating the experiment with four of the birds (two from the cat-
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positive and two from the cat-negative group) using the same stimuli, but with the black
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backgrounds replaced with neutral grey. The results are available in the Electronic
287
Supplementary Material (Fig. E6); they were effectively identical to those reported in Figure
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2.
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Surprisingly, therefore, it appears that, relative to humans, pigeons come more under the
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control of low rather than high spatial frequencies, even though they had clearly learned to
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discriminate the stimuli on the basis of both high or low spatial frequencies. Thus
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preferential attention to high spatial frequencies cannot be used to account for pigeons’
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greater attention to small details of visual stimuli, and it seems we must look elsewhere for an
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account of pigeons’ attention to details. A possible explanation would be the greater area of
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the bird retina that is provided with a dense matrix of primary receptors (e.g. Galifret, 1968).
296
At least as regards perception of hybrid stimuli, spatial frequency analysis does not seem to
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provide a clear account of the differences between human and pigeon visual cognition. The
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idea of separate channels for different spatial frequencies within the vertebrate visual system
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has been a powerful one, and it continues to be deployed, not least in relation to the visual
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systems of birds (e.g. Pinto and Baron, 2010). It seems, however, that further investigation of
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the psychophysical and cognitive roles of such channels is required.
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Figure captions
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Figure 1. Examples of the stimuli used. “Parent” stimuli were used in training. Test stimuli
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were produced from them by low-pass spatial filtering (left column), high-pass spatial
352
filtering (right column), and by combining low-pass and high-pass filtered stimuli from
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different parents to make hybrids (center column). Filter cut-off frequencies of 0.05, 0.10
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and 0.20 cycles/pixel were used. Stimuli were shown in color.
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Figure 2. Response to modified positive and negative stimuli in test sessions. Data are mean
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ranks of pecking a stimulus within test arrays (first stimulus pecked has rank 10). High ranks
357
imply that the stimulus was treated as positive. Data for parent stimuli are for those
358
presented within test arrays. Error bars show ranges across the 8 pigeons. Panel (a) shows
359
data for low-pass filtered stimuli; panel (b) shows data for high-pass filtered stimuli; and
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panel (c) shows data for the two different kinds of hybrid stimuli. Note that in panel (c) data
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for the parents of the two types of hybrid are the same, but with inverted valence.
Lea et al, Pigeons use low spatial frequencies: Figure 1 of 2
Figure 1
Examples of the stimuli used. “Parent” stimuli were used in training. Test stimuli were
produced from them by low-pass spatial filtering (left column), high-pass spatial filtering
(right column), and by combining low-pass and high-pass filtered stimuli from different
parents to make hybrids (centre column). Filter cut-off frequencies of 0.05, 0.10 and 0.20
cycles/pixel were used. Images were shown in color.
Lea et al, Pigeons use low spatial frequencies: Figure 2 of 2
Figure 2
(a)
(b)
(c)
.
Response to modified positive and negative stimuli in test sessions. Data are mean ranks of
pecking a stimulus within test arrays (first stimulus pecked has rank 10). High ranks imply
that the stimulus was treated as positive. Data for parent stimuli are for those presented
within test arrays. Error bars show ranges across the 8 pigeons. Panel (a) shows data for
low-pass filtered stimuli; panel (b) shows data for high-pass filtered stimuli; and panel (c)
shows data for the two different kinds of hybrid stimuli. Note that in panel (c) data for the
parents of the two types of hybrid are the same, but with inverted valence.