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What are flexible representations?
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Behavioural Processes 77 (2008) 437–439
Commentary
What are flexible representations? Commentary on Melchers, Shanks and Lachnit Evan J. Livesey, Justin A. Harris ∗ School of Psychology, The University of Sydney, NSW 2006, Australia Received 26 September 2007; accepted 26 September 2007
Keywords: Associative learning; Elemental processing; Configural processing
Melchers et al. (2007) review evidence pertaining to how human and non-human animals solve nonlinear discriminations such as negative patterning. They conclude that the most appropriate theoretical framework to describe this learning is one in which the underlying representational processes are flexible. By this they mean that the events that are being discriminated can be described either as simple elemental representations, corresponding to the sum of their component features, or as configural representations, where some or all of an event’s representation is qualitatively distinct from the representations of its component features. The conclusion is an attractive one in certain respects, in that it suggests that while the initial encoding of stimuli as parts and wholes is quite complex, the learning processes which they support might remain elegantly simple and still explain a sizable proportion of the complex behaviour observed in the study of human and animal learning. However, in this commentary we raise two issues in connection with this analysis, and ultimately argue that researchers still have some way to go before concluding that this form of representational flexibility is either necessary or desirable. Throughout their paper, Melchers et al. equate certain discriminations with the specific solution provided by configural representations. For example, they state that “Negative patterning . . . biconditional discrimination, and the feature-neutral task . . . are typical problems that can only be solved configurally.” (p. 413–427). However, a clear distinction needs to be made between the discrimination itself and the means by which that discrimination is solved. A discrimination involving compound stimuli may, in some circumstances, have a tractable linear solution (i.e., the outcomes of the components combine
∗
Corresponding author. E-mail address: [email protected] (J.A. Harris).
0376-6357/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.beproc.2007.09.006
linearly to give the outcome to the compound) and in other circumstances may necessitate a nonlinear solution. This is quite different from saying that the discrimination must be solved by elemental or configural learning because a solution to a nonlinear discrimination need not involve configural processing of the stimuli (just as a solution to a linear problem need not be elemental). Unfortunately, this also means that manipulations that improve the subject’s performance on a subsequent nonlinear problem do not necessarily imply a shift towards configural processing. It is true that nonlinear discriminations are intractable to those associative models that assume there is a one-to-one relationship between the representation of an event and the separate components of that event (e.g., between a compound of two stimuli and the individual stimuli themselves). Consequently, most viable models of associative learning (elemental or configural) have in common the assumption that stimulus representations involve a nonlinear combination of stimulus elements. Configural models such as Pearce’s (1987, 1994) are nonlinear by default because each configuration is represented uniquely and only one configuration is involved in learning at a given moment. Hybrid elemental-configural models (e.g., Wagner and Brandon, 2001) provide a solution to nonlinear discriminations because they break the one-to-one relationship by adding, subtracting, or replacing the identities of features of the component stimuli. But even purely elemental models can solve nonlinear discriminations by assuming quantitative nonlinearity in the processing of stimulus elements. According to two recent elemental models (Harris, 2006; McLaren and Mackintosh, 2002), the elements representing a compound, AB, are the same as those representing the individual stimuli, A and B, but the strength of any element’s activation by the compound will often differ from its activation by A or B alone (or, if the element is common to A and B, its activation in the compound will differ from the simple sum
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E.J. Livesey, J.A. Harris / Behavioural Processes 77 (2008) 437–439
of its activation by both stimuli). This means that most complex discriminations, including negative and positive patterning, biconditional and feature-neutral, can be solved relying solely on elemental representations. To explain how this can work, we will describe how the elemental model proposed by Harris (2006) can solve negative patterning (A + B + vs. AB−). The model follows the approach of stimulus sampling theories by assuming that each conditioned stimulus (CS) is represented by a number of perceptual elements that individually gain or lose associative strength, and that compete with one another for attention on the basis of their salience (Atkinson and Estes, 1963; Blough, 1975; Bush and Mosteller, 1951; Rescorla, 1976). The key feature of the model that introduces nonlinearity into the representational process is that attention itself affects element activation strength—any element that captures attention receives a further boost to its strength. Because attention is assumed to have limited capacity, an element’s ability to capture attention is determined not only by its own salience but also by the salience of other elements active at the same time. From this it follows that when two stimuli are presented simultaneously, the increase in total number of elements will mean an increase in the competition for attention. Consequently, some elements that capture attention when their stimulus is presented on its own will not capture attention when the stimulus is presented in compound with another stimulus. These elements will be strongly active during single-stimulus presentations but will be weakly active during compound presentations, and therefore their activation strength will be correlated with reinforcement in a negative patterning discrimination. An associative rule, such as that proposed by Rescorla and Wagner (1972), ensures that excitatory associative strength is distributed among these elements so that relatively little conditioned responding to the single CSs generalises to their compound. From this description it should be clear that evidence that subjects can solve nonlinear discriminations does not necessarily imply configural encoding. Our second point is that evidence from studies with human participants almost certainly over-emphasises flexibility in associative systems. As Melchers et al. have succinctly described, there is a common belief amongst many learning researchers that “the underlying mechanisms that govern associative learning in animals are the same as those that come into play in many situations in which humans try to make sense of the causal texture of their own environment” (p. 413–427). However, this by no means suggests that all phenomena observed in humans should be expected to manifest in some form in animal learning. Our ability to use working memory and other executive functions to manipulate what we have learned before making an overt response adds an extra level of complexity and flexibility to human judgments. Separating flexibility in stimulus encoding from flexibility in responding is thus a difficult task. Moreover, the nature of many of the experimental tasks used to study human associative learning means that the way the events are represented is inherently ambiguous because of the symbolic (verbal) format in which information is presented. That is, evidence for flexibility may arise from representational ambiguity.
Considering the need for caution when interpreting results only found in human studies, we certainly agree with Melchers et al.’s suggestion for future research. To give the dynamic coding hypothesis further credence, researchers must concern themselves with the search for representational flexibility in animal learning. Although there is an abundance of animal learning results that point more towards elemental or configural learning under different procedural circumstances and with different subjects, there is little evidence that stimulus processing in non-human animals changes from elemental to configural (or vice versa) as a consequence of previous experience. Alvarado and Rudy (1992) reported evidence for such a change using simultaneous visual discrimination learning with rats. However, Williams and Braker (2002) found that previous experience with a negative patterning discrimination had minimal impact on how rats were encoding stimuli used in subsequent tasks. Resolving this issue will require some very carefully designed experiments, and on this point we do not wholly agree with some of those suggested by Melchers et al. For instance, they argue that finding differences in the difficulty of a nonlinear problem according to whether the stimuli are from the same or different modalities will constitute evidence of representational flexibility. However, at least one elemental model (Harris, 2006) predicts that a nonlinear problem using within-modality stimuli should be easier than one with between-modality stimuli, while Pearce’s (1987, 1994) configural model predicts the reverse, that using withinmodality stimuli should make the task more difficult. Thus, if a difference in difficulty were obtained, it could be interpreted as evidence for a static representational system (either elemental or configural, depending on the result) rather than a dynamic one in which encoding changes according to the stimuli. If representational flexibility were unambiguously shown in animals, as well as humans, the final problem for the Melchers et al. hypothesis would be to demonstrate that the observed changes should actually be attributed to dynamic elemental and configural encoding. A competing explanation might be one described in terms of attentional flexibility, where the salience of different representational components gradually shifts, rather than changes in the intrinsic structure of those representations that seem to be implied by an elemental/configural account. Within an elemental theory that incorporates nonlinearities in stimulus representation, changes in attention may well have a similar end result. A mechanism that forces the activation weights of certain elements to behave less linearly, or increases the salience of those elements that already behave in a nonlinear fashion, would make it easier to solve a patterning or biconditional problem. While the models proposed by McLaren and Mackintosh (2002) and Harris (2006) do not incorporate mechanisms to provide these results, to posit changes of this nature certainly would not run contrary to the overriding aims of those models. References Alvarado, M.C., Rudy, J.W., 1992. Some properties of configural learning: an investigatino of the transverse-patterning problem. J. Exp. Psychol.: Anim. Behav. Processes 18, 145–153.
E.J. Livesey, J.A. Harris / Behavioural Processes 77 (2008) 437–439 Atkinson, R.C., Estes, W.K., 1963. Stimulus sampling theory. In: Luce, R.D., Bush, R.B., Galanter, E. (Eds.), Handbook of Mathematical Psychology, vol. 3. Wiley, New York, pp. 121–268. Blough, D.S., 1975. Steady state data and a quantitative model of operant generalization and discrimination. J. Exp. Psychol.: Anim. Behav. Processes 104 (1), 3–21. Bush, R.R., Mosteller, F., 1951. A model for stimulus generalization and discrimination. Psychol. Rev. 58, 413–423. Harris, J.A., 2006. Elemental representations of stimuli in associative learning. Psychol. Rev. 113, 584–605. McLaren, I.P.L., Mackintosh, N.J., 2002. Associative learning and elemental representation: II. Generalization and discrimination. Anim. Learn. Behav. 30, 177–200. Melchers, K.G., Shanks, D.R., Lachnit, H., 2007. Stimulus coding in human associative learning: flexible representations of parts and wholes. Behav. Processes. Pearce, J.M., 1987. A model for stimulus generalization in Pavlovian conditioning. Psychol. Rev. 94 (1), 61–73.
439
Pearce, J.M., 1994. Similarity and discrimination: a selective review and a connectionist model. Psychol. Rev. 101 (4), 587–607. Rescorla, R.A., 1976. Stimulus generalization: some predictions from a model of Pavlovian conditioning. J. Exp. Psychol.: Anim. Behav. Processes 2 (1), 88–96. Rescorla, R.A., Wagner, A.R., 1972. A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. In: Black, A.H., Prokasy, W.F. (Eds.), Classical Conditioning II: Current Research and Theory. Appleton-Century-Crofts, New York, pp. 64–99. Wagner, A.R., Brandon, S.E., 2001. A componential theory of Pavlovian conditioning. In: Mowrer, R.R., Klein, S.B. (Eds.), Handbook of Contemporary Learning Theories. Lawrence Erlbaum Associates, Inc., Mahwah NJ, USA, pp. 23–64. Williams, D.A., Braker, D.S., 2002. Input coding in animal and human associative learning. Behav. Processes 57, 149–161.
Behavioural Processes 77 (2008) 437–439
Commentary
What are flexible representations? Commentary on Melchers, Shanks and Lachnit Evan J. Livesey, Justin A. Harris ∗ School of Psychology, The University of Sydney, NSW 2006, Australia Received 26 September 2007; accepted 26 September 2007
Keywords: Associative learning; Elemental processing; Configural processing
Melchers et al. (2007) review evidence pertaining to how human and non-human animals solve nonlinear discriminations such as negative patterning. They conclude that the most appropriate theoretical framework to describe this learning is one in which the underlying representational processes are flexible. By this they mean that the events that are being discriminated can be described either as simple elemental representations, corresponding to the sum of their component features, or as configural representations, where some or all of an event’s representation is qualitatively distinct from the representations of its component features. The conclusion is an attractive one in certain respects, in that it suggests that while the initial encoding of stimuli as parts and wholes is quite complex, the learning processes which they support might remain elegantly simple and still explain a sizable proportion of the complex behaviour observed in the study of human and animal learning. However, in this commentary we raise two issues in connection with this analysis, and ultimately argue that researchers still have some way to go before concluding that this form of representational flexibility is either necessary or desirable. Throughout their paper, Melchers et al. equate certain discriminations with the specific solution provided by configural representations. For example, they state that “Negative patterning . . . biconditional discrimination, and the feature-neutral task . . . are typical problems that can only be solved configurally.” (p. 413–427). However, a clear distinction needs to be made between the discrimination itself and the means by which that discrimination is solved. A discrimination involving compound stimuli may, in some circumstances, have a tractable linear solution (i.e., the outcomes of the components combine
∗
Corresponding author. E-mail address: [email protected] (J.A. Harris).
0376-6357/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.beproc.2007.09.006
linearly to give the outcome to the compound) and in other circumstances may necessitate a nonlinear solution. This is quite different from saying that the discrimination must be solved by elemental or configural learning because a solution to a nonlinear discrimination need not involve configural processing of the stimuli (just as a solution to a linear problem need not be elemental). Unfortunately, this also means that manipulations that improve the subject’s performance on a subsequent nonlinear problem do not necessarily imply a shift towards configural processing. It is true that nonlinear discriminations are intractable to those associative models that assume there is a one-to-one relationship between the representation of an event and the separate components of that event (e.g., between a compound of two stimuli and the individual stimuli themselves). Consequently, most viable models of associative learning (elemental or configural) have in common the assumption that stimulus representations involve a nonlinear combination of stimulus elements. Configural models such as Pearce’s (1987, 1994) are nonlinear by default because each configuration is represented uniquely and only one configuration is involved in learning at a given moment. Hybrid elemental-configural models (e.g., Wagner and Brandon, 2001) provide a solution to nonlinear discriminations because they break the one-to-one relationship by adding, subtracting, or replacing the identities of features of the component stimuli. But even purely elemental models can solve nonlinear discriminations by assuming quantitative nonlinearity in the processing of stimulus elements. According to two recent elemental models (Harris, 2006; McLaren and Mackintosh, 2002), the elements representing a compound, AB, are the same as those representing the individual stimuli, A and B, but the strength of any element’s activation by the compound will often differ from its activation by A or B alone (or, if the element is common to A and B, its activation in the compound will differ from the simple sum
438
E.J. Livesey, J.A. Harris / Behavioural Processes 77 (2008) 437–439
of its activation by both stimuli). This means that most complex discriminations, including negative and positive patterning, biconditional and feature-neutral, can be solved relying solely on elemental representations. To explain how this can work, we will describe how the elemental model proposed by Harris (2006) can solve negative patterning (A + B + vs. AB−). The model follows the approach of stimulus sampling theories by assuming that each conditioned stimulus (CS) is represented by a number of perceptual elements that individually gain or lose associative strength, and that compete with one another for attention on the basis of their salience (Atkinson and Estes, 1963; Blough, 1975; Bush and Mosteller, 1951; Rescorla, 1976). The key feature of the model that introduces nonlinearity into the representational process is that attention itself affects element activation strength—any element that captures attention receives a further boost to its strength. Because attention is assumed to have limited capacity, an element’s ability to capture attention is determined not only by its own salience but also by the salience of other elements active at the same time. From this it follows that when two stimuli are presented simultaneously, the increase in total number of elements will mean an increase in the competition for attention. Consequently, some elements that capture attention when their stimulus is presented on its own will not capture attention when the stimulus is presented in compound with another stimulus. These elements will be strongly active during single-stimulus presentations but will be weakly active during compound presentations, and therefore their activation strength will be correlated with reinforcement in a negative patterning discrimination. An associative rule, such as that proposed by Rescorla and Wagner (1972), ensures that excitatory associative strength is distributed among these elements so that relatively little conditioned responding to the single CSs generalises to their compound. From this description it should be clear that evidence that subjects can solve nonlinear discriminations does not necessarily imply configural encoding. Our second point is that evidence from studies with human participants almost certainly over-emphasises flexibility in associative systems. As Melchers et al. have succinctly described, there is a common belief amongst many learning researchers that “the underlying mechanisms that govern associative learning in animals are the same as those that come into play in many situations in which humans try to make sense of the causal texture of their own environment” (p. 413–427). However, this by no means suggests that all phenomena observed in humans should be expected to manifest in some form in animal learning. Our ability to use working memory and other executive functions to manipulate what we have learned before making an overt response adds an extra level of complexity and flexibility to human judgments. Separating flexibility in stimulus encoding from flexibility in responding is thus a difficult task. Moreover, the nature of many of the experimental tasks used to study human associative learning means that the way the events are represented is inherently ambiguous because of the symbolic (verbal) format in which information is presented. That is, evidence for flexibility may arise from representational ambiguity.
Considering the need for caution when interpreting results only found in human studies, we certainly agree with Melchers et al.’s suggestion for future research. To give the dynamic coding hypothesis further credence, researchers must concern themselves with the search for representational flexibility in animal learning. Although there is an abundance of animal learning results that point more towards elemental or configural learning under different procedural circumstances and with different subjects, there is little evidence that stimulus processing in non-human animals changes from elemental to configural (or vice versa) as a consequence of previous experience. Alvarado and Rudy (1992) reported evidence for such a change using simultaneous visual discrimination learning with rats. However, Williams and Braker (2002) found that previous experience with a negative patterning discrimination had minimal impact on how rats were encoding stimuli used in subsequent tasks. Resolving this issue will require some very carefully designed experiments, and on this point we do not wholly agree with some of those suggested by Melchers et al. For instance, they argue that finding differences in the difficulty of a nonlinear problem according to whether the stimuli are from the same or different modalities will constitute evidence of representational flexibility. However, at least one elemental model (Harris, 2006) predicts that a nonlinear problem using within-modality stimuli should be easier than one with between-modality stimuli, while Pearce’s (1987, 1994) configural model predicts the reverse, that using withinmodality stimuli should make the task more difficult. Thus, if a difference in difficulty were obtained, it could be interpreted as evidence for a static representational system (either elemental or configural, depending on the result) rather than a dynamic one in which encoding changes according to the stimuli. If representational flexibility were unambiguously shown in animals, as well as humans, the final problem for the Melchers et al. hypothesis would be to demonstrate that the observed changes should actually be attributed to dynamic elemental and configural encoding. A competing explanation might be one described in terms of attentional flexibility, where the salience of different representational components gradually shifts, rather than changes in the intrinsic structure of those representations that seem to be implied by an elemental/configural account. Within an elemental theory that incorporates nonlinearities in stimulus representation, changes in attention may well have a similar end result. A mechanism that forces the activation weights of certain elements to behave less linearly, or increases the salience of those elements that already behave in a nonlinear fashion, would make it easier to solve a patterning or biconditional problem. While the models proposed by McLaren and Mackintosh (2002) and Harris (2006) do not incorporate mechanisms to provide these results, to posit changes of this nature certainly would not run contrary to the overriding aims of those models. References Alvarado, M.C., Rudy, J.W., 1992. Some properties of configural learning: an investigatino of the transverse-patterning problem. J. Exp. Psychol.: Anim. Behav. Processes 18, 145–153.
E.J. Livesey, J.A. Harris / Behavioural Processes 77 (2008) 437–439 Atkinson, R.C., Estes, W.K., 1963. Stimulus sampling theory. In: Luce, R.D., Bush, R.B., Galanter, E. (Eds.), Handbook of Mathematical Psychology, vol. 3. Wiley, New York, pp. 121–268. Blough, D.S., 1975. Steady state data and a quantitative model of operant generalization and discrimination. J. Exp. Psychol.: Anim. Behav. Processes 104 (1), 3–21. Bush, R.R., Mosteller, F., 1951. A model for stimulus generalization and discrimination. Psychol. Rev. 58, 413–423. Harris, J.A., 2006. Elemental representations of stimuli in associative learning. Psychol. Rev. 113, 584–605. McLaren, I.P.L., Mackintosh, N.J., 2002. Associative learning and elemental representation: II. Generalization and discrimination. Anim. Learn. Behav. 30, 177–200. Melchers, K.G., Shanks, D.R., Lachnit, H., 2007. Stimulus coding in human associative learning: flexible representations of parts and wholes. Behav. Processes. Pearce, J.M., 1987. A model for stimulus generalization in Pavlovian conditioning. Psychol. Rev. 94 (1), 61–73.
439
Pearce, J.M., 1994. Similarity and discrimination: a selective review and a connectionist model. Psychol. Rev. 101 (4), 587–607. Rescorla, R.A., 1976. Stimulus generalization: some predictions from a model of Pavlovian conditioning. J. Exp. Psychol.: Anim. Behav. Processes 2 (1), 88–96. Rescorla, R.A., Wagner, A.R., 1972. A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. In: Black, A.H., Prokasy, W.F. (Eds.), Classical Conditioning II: Current Research and Theory. Appleton-Century-Crofts, New York, pp. 64–99. Wagner, A.R., Brandon, S.E., 2001. A componential theory of Pavlovian conditioning. In: Mowrer, R.R., Klein, S.B. (Eds.), Handbook of Contemporary Learning Theories. Lawrence Erlbaum Associates, Inc., Mahwah NJ, USA, pp. 23–64. Williams, D.A., Braker, D.S., 2002. Input coding in animal and human associative learning. Behav. Processes 57, 149–161.