School of Psychology, Birkbeck College

Course PSYC044U Psychobiology II WEEK 8
March 15 2007

gifThis is just the first 8 pages of the longer paper handout. Web versions of many of the other pages in the paper handout are accessible from the side index. If you need to print out the handout, then all the pages are in this 'pdf' file, but this is quite large and may be difficult to download over a telephone modem.

[top of page 1 of handout]
Perceptual Processes, Attention and Discrimination Learning
Differential reactions to certain classes of stimulus input are characteristic products of most forms of learning, and discriminatory abilities can be studied using the procedures of habituation. “Discrimination learning” however, refers to procedures in which one stimulus, or set of stimuli, is associated with certain motivational outcomes, and another with different outcomes.

In many of the experiments to be discussed, there is a simple rule, such as —

“when the light is green, responses will be rewarded; but when the light is red, no rewards are available.”

This is a “successive discrimination” — where only one stimulus is presented at a time. “GO/ NO GO” procedures are the most basic form of successive discrimination, where a single response is rewarded with one cue, but not with another. Many more complex procedures are possible, such as “press right when there's a picture of a knife, press left when there's a picture of a fork”. This could be used to measure whether animal subjects can discriminate between pictures of several different object categories (e.g. Bhatt et al, 1988)

It is also possible to use choice procedures: the simplest is “simultaneous discrimination” — e.g. “when presented with both a circle and a square, direct a response to the square”.

The stimuli used in discrimination procedures are often called discriminative stimuli. (In a GO/ NO GO procedure they may be referred to as S+ and S–, or positive and negative stimuli).

Theoretical Issue
Generally, the relative complexity of animal perceptual learning. This can be divided into two sub-areas:

1.  Pattern recognition: category and “concept” learning; the formation of internal descriptions and representations; rote learning explanations including feature analysis and "exemplar" or template theories of stimulus classification. Theoretical models of visual object recognition deal with these issues (Gauthier and Palmeric, 2002; Riesenhuber and Poggio, 2000, 2002; Peissig & Tarr, 2007). These models are intended to explain human object recognition but have relied heavily on neurophysiological evidence obtained using rhesus monkeys (Desimone et al., 1984; Hubel and Wiesel, 1979; Perrett et al., 1992; Muhammed et al., 2006). However, evidence from human brain imaging (e.g. Vogels et al.,2002; Martin 2007) and even more direct human neurophysiological evidence (Lee et al., 2000; Krieman et al., 2000, 2002; Quiroga et al., 2005) is increasingly available. (see also page 2)


2.  The contrast between the conditioning/extinction theory of discrimination learning (Spence, 1937) and two-stage attentional accounts. (Lieberman, 2000; pages 384-6).

The underlying theoretical question for discrimination learning is whether it can be explained in terms of a simple conditioning theory (the Hull/Spence theory or the “conditioning-extinction theory”), or whether certain of the phenomena require more complex cognitive processes for their explanation.
The standard answer is that some discrimination learning procedures tap more complex processes. E.g. (From Mackintosh, 1983).
“...... the special procedures of discriminative experiments make available a set of stimuli whose representation calls on processes not normally studied in simple conditioning experiments” (p.272-4)



Sample Essay

Consider the evidence for visual pattern recognition and categorization in animal learning experiments.











1. Pattern recognition and stimulus catergorization.

It is often emphasised that sensory processes, especially vision, must allow xs to recognise real objects in the natural world and that this requires complex information processing (Sutherland, 1968; Bernays & Wcislo, 1996; Logothetis & Sheinberg, 1996; Riesenhuber and Poggio, 2000; Tarr and Cheng, 2003; Miller et al., 2003).

“Imagine waiting for incoming passengers at the arrival gate at the airport. Your visual system can easily find faces and identify whether one of them is your friend's. As with other tasks that our brain does effortlessly, visual recognition has turned out to be difficult for computers. In its general form, it is a very difficult computational problem, which is likely to be significantly involved in eventually making intelligent machines. Not surprisingly, it is also an open and key problem for neuroscience.

The main computational difficulty is the problem of variability. A vision system needs to generalize across huge variations in the appearance of an object such as a face, due for instance to viewpoint, illumination or occlusions. At the same time, the system needs to maintain specificity. It is important here to note that an object can be recognized at a variety of levels of specificity: a cat can be recognized as ‘my cat’ on the individual level, or more broadly on the categorical level as ‘cat’, ‘mammal’, ‘animal’ and so forth.” (Riesenhuber and Poggio, 2000; page 1199).






[middle of page 2 of handout]

“The ability to readily adapt to novel situations requires something beyond storing specific stimulus-response associations. Instead, many animals can detect basic characteristics of events and store them as generalized classes. Because these representations are abstracted beyond specific details of sensory inputs and motor outputs, they can be easily generalized and adapted to new circumstances. Explorations of neural mechanisms of sensory processing and motor output have progressed to the point where studies can begin to address the neural basis of abstract, categorical representations. Recent studies have revealed their neural correlates in various cortical areas of the non-human primate brain.”

(Abstract of Miller et al., 2003)










[bottom of page 2 of handout]
“Visual recognition of objects is an impressively difficult problem that biological systems solve effortlessly.” (Tarr and Cheng, 2003)






"Visual object recognition is of fundamental importance to most animals." (Logothetis and Sheinberg, 1996)








[bottom of page 2 of handout]
Herrnstein (1990) suggested that 5 kinds or levels of categorization can be observed in animal discrimination learning.

1. Simple discrimination refers to sensitivity to precisely defined physical stimulus characteristics, as in absolute wavelength or brightness discriminations.

2. Categorization by rote. This can be regarded as a list of simple discriminations, as in Vaughan and Greene (1984).

3. Open-ended categories. Herrnstein's example is learning to recognize acorns, in the sense that new examples of an acorn to not have to be learned separately. It can arise from perceptual similarity.

4. Concepts. Herrnstein's definition of a concept is an open-ended or rote-learned category in which a change to one member of the stimulus category is reflected in reactions to the other members. His informal example is an animal which discovers that one acorn tastes bitter, and thereafter associates bitterness with acorns in general.

5. Abstract relations. These have been studied in animals in the case of oddity or matching relationships, which are abstract in the sense that they are independent of the precise physical characteristics of any stimulus category. However Herrnstein says that “It is at the level of abstract relations that a large gap opens up between human categorizations and categorization by other animals.” (Herrnstein, 1990; p.133; see Lieberman, 2000, pp. 508-514).



2. Attentional processes.

The Conditioning/Extinction Theory (Spence, 1937).

This can be illustrated in the context of relational learning (discrimination in terms of “larger than”, “greener than”, etc.) and the peak shift result (Hanson, 1959). The theory assumes minimal analysis of incoming stimuli, and that absolute stimulus inputs become actively associated with response outputs, or have inhibitory associations with response outputs.

Evidence for a separate attentional process

The conditioning/extinction theory does not include any attentional processes. Theories such as that of Sutherland and Mackintosh (1971) do. In this “the suggestion was that animals might learn to attend to the relevant stimuli of a discrimination problem”. (An alternative way of referring to the same idea is to talk about “changes in the associability of stimuli”: Mackintosh, 1983, p. 251). There are two main kinds of evidence for attentional processing in discrimination learning: transfer effects; and conditional discriminations. (See Lieberman, 2000; pp. 384-398; Reynolds, 1961). A third strand of evidence comes from visual search: when searching for an “X” among a display of other letters it is usual to assume that human subjects are selectively attending to X's, and similar quantitative evidence can be obtained with animal subjects (e.g. Blough, P.M., 1989; Blough, D.S, 2000; Zentall and Riley, 2000).

1. Attention and visual search. In this paradigm, the results of visual search experiments using both naturalistic and artificial stimuli have been interpreted as evidence for attentional mechanisms (e.g. Bond, 1983; Blough, P.M., 1989; Blough and Blough, 1997, Blough, 2000; Blough, 2002). Visual search phenomena have been used as a paradigm for theories of selective attention in human vision (Treisman and Gelade, 1990; Treisman and Gormican, 1988) and some broadly similar results have been obtained in pigeons. For instance, Blough (1989) found that the reaction times of birds searching for a target letter increased with the number of alternative letters in a display, and decreased if the particular target to be searched for was indicated by a cue preceding the trial; and Cook (1992) found that pigeons were more accurate in searching for single features than for conjunctive mixtures of two features (colour and shape). (See Roberts, 1998, p. 33 and pp. 43-50; also Buracas and Albright, 1999; Bond and Kamil, 2002; Dukas, 2002)

2. Transfer effects. The idea here is to show that training with a certain set of stimuli and a particular response output requirement facilitates the learning of a new response task with related stimuli. If this occurs, one conclusion is that the facilitation happens because the initial training “switched in” attention to an appropriate kind of stimulus input. Such transfer effects have been demonstrated in serial reversal learning, transfer from easy to difficult versions of the same problem, transfer to similar problems using the same dimension, and, in the case of “oddity” learning, transfer to similar problems using different stimulus dimensions. (See Lieberman, 2000, pp. 386-387 or Walker, 1987, pp 260-274 for transfer effects.)

3. Conditional discriminations. A very general result is that animals appear to become more sensitive to a particular stimulus modality, or a dimension within a modality, when it is used for discrimination training (e.g. Pavlov, 1927; Jenkins and Harrison, 1960). A basic proposition in attentional theories is that there is in some sense more processing of a stimulus feature when the its inputs have motivational significance: this would explain the above transfer effects (e.g. Sutherland and Mackintosh, 1971). In more elaborate experiments, one stimulus can be used as a cue to signal that attention should be paid to another – that is, the attentional process can be switched on and off (e.g. Yarczower, 1971: 'pay attention to the tilt of a line when the background is red': Lieberman, 2000; pp. 384-5).


[top of page 4 of handout]
Overall Conclusion

Animal perceptual systems have evolved to do useful things in natural environments. But it appears that, at least in higher vertebrates, there are substantial capacities for perceptual recognition of non-natural stimuli. Categorization and pattern recognition, involving grouping of sets of stimuli in functional relations to responses, appear to be consequences of perceptual learning.            








Further note: Hearing

Most of the evidence to be considered for pattern recognition involves the visual modality, but similar theoretical questions arise also for hearing. For bird species which learn by vocal imitation in the wild, it should be expected that laboratory procedures will reveal a highly sophisticated perceptual learning system (e.g. Chaiken et al., 1997; Hausberger et al., 2000). Other species possess perceptual learning mechanisms which are sufficiently general to apply to complex stimuli of kinds which would not be found in natural environments. Porter and Neuringer (1984) tested the auditory discrimination of different kinds of classical music by pigeons: the data suggested that organ and orchestral music of the same loudness can be differentiated in this relatively unvocal species, but also that unknown characteristics of Stravinsky's 'Rite of Spring' (orchestral) generalized to a piece of modern organ music. A number of experiments indicate that both non-human primates (e.g Waters and Wilson, 1976; Hauser et al., 2001; Ramus et al., 2000) and mammals such as rodents and cats (e.g. Kuhl and Miller, 1975; Eggermont, 1995) can readily discriminate human speech sounds.





Main Sources

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Roberts, W.A (1998) Principles of Animal Cognition. Boston: McGraw-Hill. (2x 3wk loan, 2x1wk loan, 1 SLC at BK: Chaper 2 “Perception and Attention”, pp. 28-63 & Chapter 11 “Concept Learning, pp 335-58)

Walker, S.F. (1984). Learning Theory and Behaviour Modification. Methuen: London. pp. 57-62

Walker, S.F. (1987). Animal Learning: An Introduction. Routledge & Kegan Paul: London. Chapter 8

Further Reading

Pearce (1997) Animal Learning and Cognition 2nd Edition. Hove: Psychology Press. 156.315 PEA in new section at Birkbeck. (Chapter 5, Discrimination Learning: one SL copy and one loan copy at BK).

Walker, S.F. (1985). Animal Thought. Routledge & Kegan-Paul: London pp. 237-319

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