- Email: [email protected]
Associative and temporal learning: New directions
Behavioural Processes 101 (2014) 1–3
Contents lists available at ScienceDirect
Behavioural Processes journal homepage: www.elsevier.com/locate/behavproc
Editorial
Associative and temporal learning: New directions a r t i c l e Keywords: Associative Brain Frequency Information Learning Temporal Timing
i n f o
a b s t r a c t Associative and temporal learning are fundamental properties of behavior. Despite the temporal dynamics of behavior, traditional associative (trial based) approaches have often ignored (within trial) timing properties of behavior. Therefore, associative and temporal learning are considered different, parallel strategies, whose mechanisms and rules are domain-specific. The rift between the two fields is not surprising considering the difference in questions, measures, and approaches. Some questions explored in this mini-review are as follows: Are the behavioral phenomena appropriately described, measured or quantified? How do animals integrate associative and temporal information? What are the behavioral processes that bridge the associative and temporal fields? How are associative and temporal information instantiated and processed in the brain? A resolution involves finding a more adept way (e.g., computational or biological) to describe the associative and temporal phenomena, for example by transforming them in a more abstract dimension, such as information (e.g., entropy calculation) or frequency (e.g., neural firing). When seen from this neural-computation vantage point, the distinctions between associative and temporal learning vanish, and the question becomes: What are the mechanisms that coexist, cooperate and compete in a brain that processes associative and temporal information in real time? This article is part of a Special Issue entitled: Associative and Temporal Learning. © 2014 Published by Elsevier B.V.
Associative and temporal learning are fundamental properties of behavior. Animals learn to predict appetitive and aversive events using cues from different domains. They learn that some events are good predictors of outcomes, and that this prediction is reliable in a particular context, provided by general environmental cues, by the temporal relationships between external events, and by the timing of their own responses. Despite the temporal dynamics of behavior, traditional associative (trial based) approaches have often ignored (within trial) timing properties of behavior (Rescorla and Wagner, 1972), while classical timing theories often ignored associative properties of behavior (Gibbon, 1977). Not surprising, associative and temporal learning came to be considered different, parallel strategies, whose mechanisms and rules are domain-specific, with research aiming at understanding how they separately control and modulate behavior and cognition, thus providing limited cross-talk among the two fields. So different and incongruent are associative and temporal learning considered, that the temporal coding hypothesis – the notion that animals encode simultaneously the associative and temporal attributes of a given situation (Molet and Miller, 2013) – remains a revolutionary idea in the field. Some take this idea to mean that animals build cognitive maps including associative and temporal information (Taylor et al., 2013). Instead, one can take it to mean that both forms of learning are ultimately based on similar neural mechanisms. Indeed, recent advances in neuroscience promote a new challenge into the investigation of brain and behavior mechanisms: to capture the on-going dynamics of brain and behavior as a function of time. For this reason, it has become necessary to promote a cross-disciplinary investigation of associative and 0376-6357/$ – see front matter © 2014 Published by Elsevier B.V. http://dx.doi.org/10.1016/j.beproc.2014.01.005
temporal learning. Yet, the rift between the two fields is not surprising considering the difference in questions, measures, and approaches, as illustrated by the following outstanding questions: Are the behavioral phenomena appropriately described, measured or quantified? In the associative field, data are collected by integrating behavior (e.g., responses) over large intervals of time, usually over several trials as long as many minutes. It is therefore not surprising that evidence seems to indicate that learning proceeds over trials (Bouton et al., 2013), i.e., over the long intervals used for observation. Instead, in temporal learning, care is given to collecting data in real time, with exquisite sub-second precision. Also not surprisingly, data reveal that animals modulate their behavior in real time, e.g., in response to unexpected events (Buhusi and Matthews, 2013), and that learning proceeds within a (few) trial(s) (Reyes and Buhusi, 2013). These findings are compatible with a dual process account (Delamater et al., 2013). Most importantly, these findings underscore the need to collect and analyze data in a more comprehensive manner in both fields, for example by analyzing changes in behavior within a trial (Buhusi and Matthews, 2013), or from trialto-trial (Reyes and Buhusi, 2013) rather than averaging behavior over many trials. How do animals integrate associative and temporal information? When multiple cues are to be evaluated or used, animals usually mix the associative and temporal solution to the task. For example, when timing two stimuli of different durations, the presentation of both stimuli retrieves a mixed memory of the two intervals (Matell et al., 2013), a phenomenon suggestive of associative (trial based) properties of the two stimuli than their within-trial timing. Similar temporal mixing is observed with sudden changes
2
Editorial / Behavioural Processes 101 (2014) 1–3
in expected (criterion) durations (Sanabria and Oldenburg, 2013), and when manipulating associative (trial-level) variables, such as trial frequency (Jozefowiez et al., 2013). Finally, the presentation of distracting events alters the timing of the target stimulus following both associative (trial-based) and temporal (within-trial) regularities (Buhusi and Matthews, 2013). Such findings indicate that animals freely mix associative and temporal information in an adaptive, task specific manner, thus blurring the distinction between the two. How can these findings be understood within the associative and temporal frameworks? Here are some future directions: Investigate processes that bridge the associative and temporal learning fields: Contingency, expectation, and error learning are obvious candidates, particularly when examined from a perspective that is neither associative nor temporal. Information theory offers one such prominent vantage point (Gallistel et al., 2013): rather than learning about associative or temporal cues, animals extract information from the environment, including, among others, associative and temporal regularities. This informationbased learning transcends associative and temporal domains, and provides exciting new predictions regarding the principles of both associative and temporal learning (Reyes and Buhusi, 2013). Investigate how associative and temporal information are represented and processed in the brain: The biological perspective offers exciting new opportunities, measures, and mechanisms to be investigated. Besides the striatum (Hattori and Sakata, 2013) and the hippocampus (Buhusi and Schmajuk, 1996; Suzuki, 2007b) attention is currently paid to the amygdala (Diaz-Mataix et al., 2013; Raybuck and Lattal, 2013). From this biological vantage point, multiple brain structures (Dickinson, 2012; Suzuki, 2007a) and neurotransmitter systems (Heilbronner and Meck, 2013) interact in the process of prediction error learning (Kirkpatrick, 2013), which also transcends associative and temporal learning. Provide a resolution to the debate about associative and temporal learning: A possible resolution to the debate about associative and temporal learning (Church, 2013) will be found at either/both the biological or/and computational levels, and will likely involve finding a more adept way to describe both the associative and temporal phenomena. For example, temporal information may be represented in the brain by coincidental activation of multiple neural populations firing with different frequencies (Buhusi and Oprisan, 2013; Oprisan and Buhusi, 2014), thus supporting a possible mapping (transformation) of temporal information to an abstract dimension, frequency (Killeen, 2013). Should a similar mapping occur for associative information, it would provide an opportunity to address both associative and temporal phenomena within the same framework, in as much as the information theory or error prediction learning would. Evaluate new predictions: Both the information, error-prediction, and frequency paradigms have the promise to provide new, rather exciting predictions. For example, in the frequency paradigm (Buhusi and Meck, 2005; Killeen, 2013), long-accepted properties of temporal learning, such as time-scale invariance, may be no more than a mere reflection of the contribution to behavior of small fluctuations (noise) in neural firing (Buhusi and Oprisan, 2013; Oprisan and Buhusi, 2014). Similarly, the over-reset effect of emotional distracters on timing (Brown et al., 2007) may be no more than a mere reflection of amygdala–striatal interactions (Oprisan et al., 2013), rather than a defining property of the “internal clock”. Moreover, in the information paradigm (Gallistel et al., 2013), the long-accepted contingency requirement in associative learning may simply reflect information/entropy calculations (Reyes and Buhusi, 2013). Finally, biological correlates of associative notions such as error (Kirkpatrick, 2013) or surprise (Buhusi and Schmajuk, 1996; Roesch et al., 2012) are predicted to co-exist in the brain.
In summary, the two fields are, and will likely remain isolated, until findings from other fields, like biology and computation, will provide an appropriate vantage point for re-evaluating the relationship between the two. Interestingly, both fields seems at the brink of embracing new paradigms/perspectives: Temporal learning seems ready to abandon the internal-clock paradigm in favor of the frequency paradigm (Buhusi and Meck, 2005), while at the associative end, research is directed at validating the information paradigm (Gallistel et al., 2013), and at finding biological correlates of error-prediction learning (Kirkpatrick, 2013). When seen from such vantage points, the distinctions between associative and temporal learning vanish, and the question becomes: What are the mechanisms that coexist, cooperate and compete in a brain that processes associative and temporal information in real time? As both associative and temporal learning embrace this new direction, this is certainly a very exciting time for the two fields.
References Bouton, M.E., Woods, A.M., Todd, T.P., 2013. Separation of time-based and trialbased accounts of the partial reinforcement extinction effect. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.08.006. Brown, B.L., Richer, P., Doyere, V., 2007. The effect of an intruded event on peakinterval timing in rats: isolation of a postcue effect. Behav. Process. 74 (3), 300–310, http://dx.doi.org/10.1016/j.beproc.2006.11.004. Buhusi, C.V., Matthews, A.R., 2013. Effect of distracter preexposure on the reset of an internal clock. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.09.003. Buhusi, C.V., Meck, W.H., 2005. What makes us tick? Functional and neural mechanisms of interval timing. Nat. Rev. Neurosci. 6 (10), 755–765, http://dx.doi.org/10.1038/nrn1764. Buhusi, C.V., Oprisan, S.A., 2013. Time-scale invariance as an emergent property in a perceptron with realistic, noisy neurons. Behav. Process. 95, 60–70, http://dx.doi.org/10.1016/j.beproc.2013.02.015. Buhusi, C.V., Schmajuk, N.A., 1996. Attention, configuration, and hippocampal function. Hippocampus 6 (6), 621–642, http://dx.doi.org/10.1002/ (SICI)1098-1063(1996)6:6<621::AID-HIPO6>3.0.CO;2-J. Church, R.M., 2013. A resolution of the debate about associative and temporal learning. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.08.011. Delamater, A.R., Desouza, A., Rivkin, Y., Derman, R., 2013. Associative and temporal processes: a dual process approach. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.09.004. L., Tallot, L., Doyere, V., 2013. The amygdala: a Diaz-Mataix, potential player in timing CS–US intervals. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.08.007. Dickinson, A., 2012. Associative learning and animal cognition. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 367 (1603), 2733–2742, http://dx.doi.org/ 10.1098/rstb.2012.0220. Gallistel, C.R., Craig, A.R., Shahan, T.A., 2013. Temporal contingency. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.08.012. Gibbon, J., 1977. Scalar expectancy theory and Weber’s law in animal timing. Psychol. Rev. 84 (3), 279–325, http://dx.doi.org/10.1037/0033-295X.84.3.279. Hattori, M., Sakata, S., 2013. Brain electrophysiological activity with temporal processing in rats. Behav. Process., correlates http://dx.doi.org/10.1016/j.beproc.2013.09.011. Heilbronner, S.R., Meck, W.H., 2013. Dissociations between intertiming and intertemporal choice following administration val fluoxetine, cocaine, or methamphetamine. Behav. Process., of http://dx.doi.org/10.1016/j.beproc.2013.09.013. Jozefowiez, J., Polack, C.W., Machado, A., Miller, R.R., 2013. Trial frequency effects in human temporal bisection: implications for theories of timing. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.07.023. P.R., 2013. Finding time. Behav. Process., Killeen, http://dx.doi.org/10.1016/j.beproc.2013.08.003. Kirkpatrick, K., 2013. Interactions of timing and prediction error learning. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.08.005. Matell, M.S., Kim, J.S., Hartshorne, L., 2013. Timing in a variable interval procedure: evidence for a memory singularity. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.08.010. Molet, M., Miller, R.R., 2013. Timing: an attribute of associative learning. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.05.015. Oprisan, S.A., Buhusi, C.V., 2014. What is all the noise about in interval timing? Philos. Trans. R. Soc. Lond. B: Biol. Sci., http://dx.doi.org/10.1098/rstb.2012.0459 (in press). Oprisan, S.A., Dix, S., Buhusi, C.V., 2013. Phase resetting and its implications for interval timing with intruders. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.09.005. Raybuck, J.D., Lattal, K.M., 2013. Bridging the interval: theand neurobiology of trace conditioning. Behav. Process., ory http://dx.doi.org/10.1016/j.beproc.2013.08.016.
Editorial / Behavioural Processes 101 (2014) 1–3 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, vol. II. Appleton-Century-Crofts, pp. 64–99. Reyes, M.B., Buhusi, C.V., 2013. What is learned during simultaneous temporal acquisition? An individual-trials analysis. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.09.008. Roesch, M.R., Esber, G.R., Li, J., Daw, N.D., Schoenbaum, G., 2012. Surprise! Neural correlates of Pearce–Hall and Rescorla–Wagner coexist within the brain. Eur. J. Neurosci. 35 (7), 1190–1200, http://dx.doi.org/10.1111/j.1460-9568.2011.07986.x. Sanabria, F., Oldenburg, L., 2013. Adaptation of timing behavior to a regular change in criterion. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.07.018. Suzuki, W.A., 2007a. Integrating associative learning signals across the brain. Hippocampus 17 (9), 842–850, http://dx.doi.org/10.1002/hipo.20321.
3
Suzuki, W.A., 2007b. Making new memories: the role of the hippocampus in new associative learning. Ann. N. Y. Acad. Sci. 1097, 1–11, http://dx.doi.org/10.1196/annals.1379.007. Taylor, K.M., Joseph, V., Zhao, A.S., Balsam, P.D., 2013. Temporal maps in appetitive Pavlovian conditioning. Behav. Process., http://dx.doi.org/10.1016/ j.beproc.2013.08.015.
Catalin V. Buhusi USTAR BioInnovations Center, Department of Psychology, Utah State University, 2810 Old Main Hill, Logan, UT 84322-2810, United States
Contents lists available at ScienceDirect
Behavioural Processes journal homepage: www.elsevier.com/locate/behavproc
Editorial
Associative and temporal learning: New directions a r t i c l e Keywords: Associative Brain Frequency Information Learning Temporal Timing
i n f o
a b s t r a c t Associative and temporal learning are fundamental properties of behavior. Despite the temporal dynamics of behavior, traditional associative (trial based) approaches have often ignored (within trial) timing properties of behavior. Therefore, associative and temporal learning are considered different, parallel strategies, whose mechanisms and rules are domain-specific. The rift between the two fields is not surprising considering the difference in questions, measures, and approaches. Some questions explored in this mini-review are as follows: Are the behavioral phenomena appropriately described, measured or quantified? How do animals integrate associative and temporal information? What are the behavioral processes that bridge the associative and temporal fields? How are associative and temporal information instantiated and processed in the brain? A resolution involves finding a more adept way (e.g., computational or biological) to describe the associative and temporal phenomena, for example by transforming them in a more abstract dimension, such as information (e.g., entropy calculation) or frequency (e.g., neural firing). When seen from this neural-computation vantage point, the distinctions between associative and temporal learning vanish, and the question becomes: What are the mechanisms that coexist, cooperate and compete in a brain that processes associative and temporal information in real time? This article is part of a Special Issue entitled: Associative and Temporal Learning. © 2014 Published by Elsevier B.V.
Associative and temporal learning are fundamental properties of behavior. Animals learn to predict appetitive and aversive events using cues from different domains. They learn that some events are good predictors of outcomes, and that this prediction is reliable in a particular context, provided by general environmental cues, by the temporal relationships between external events, and by the timing of their own responses. Despite the temporal dynamics of behavior, traditional associative (trial based) approaches have often ignored (within trial) timing properties of behavior (Rescorla and Wagner, 1972), while classical timing theories often ignored associative properties of behavior (Gibbon, 1977). Not surprising, associative and temporal learning came to be considered different, parallel strategies, whose mechanisms and rules are domain-specific, with research aiming at understanding how they separately control and modulate behavior and cognition, thus providing limited cross-talk among the two fields. So different and incongruent are associative and temporal learning considered, that the temporal coding hypothesis – the notion that animals encode simultaneously the associative and temporal attributes of a given situation (Molet and Miller, 2013) – remains a revolutionary idea in the field. Some take this idea to mean that animals build cognitive maps including associative and temporal information (Taylor et al., 2013). Instead, one can take it to mean that both forms of learning are ultimately based on similar neural mechanisms. Indeed, recent advances in neuroscience promote a new challenge into the investigation of brain and behavior mechanisms: to capture the on-going dynamics of brain and behavior as a function of time. For this reason, it has become necessary to promote a cross-disciplinary investigation of associative and 0376-6357/$ – see front matter © 2014 Published by Elsevier B.V. http://dx.doi.org/10.1016/j.beproc.2014.01.005
temporal learning. Yet, the rift between the two fields is not surprising considering the difference in questions, measures, and approaches, as illustrated by the following outstanding questions: Are the behavioral phenomena appropriately described, measured or quantified? In the associative field, data are collected by integrating behavior (e.g., responses) over large intervals of time, usually over several trials as long as many minutes. It is therefore not surprising that evidence seems to indicate that learning proceeds over trials (Bouton et al., 2013), i.e., over the long intervals used for observation. Instead, in temporal learning, care is given to collecting data in real time, with exquisite sub-second precision. Also not surprisingly, data reveal that animals modulate their behavior in real time, e.g., in response to unexpected events (Buhusi and Matthews, 2013), and that learning proceeds within a (few) trial(s) (Reyes and Buhusi, 2013). These findings are compatible with a dual process account (Delamater et al., 2013). Most importantly, these findings underscore the need to collect and analyze data in a more comprehensive manner in both fields, for example by analyzing changes in behavior within a trial (Buhusi and Matthews, 2013), or from trialto-trial (Reyes and Buhusi, 2013) rather than averaging behavior over many trials. How do animals integrate associative and temporal information? When multiple cues are to be evaluated or used, animals usually mix the associative and temporal solution to the task. For example, when timing two stimuli of different durations, the presentation of both stimuli retrieves a mixed memory of the two intervals (Matell et al., 2013), a phenomenon suggestive of associative (trial based) properties of the two stimuli than their within-trial timing. Similar temporal mixing is observed with sudden changes
2
Editorial / Behavioural Processes 101 (2014) 1–3
in expected (criterion) durations (Sanabria and Oldenburg, 2013), and when manipulating associative (trial-level) variables, such as trial frequency (Jozefowiez et al., 2013). Finally, the presentation of distracting events alters the timing of the target stimulus following both associative (trial-based) and temporal (within-trial) regularities (Buhusi and Matthews, 2013). Such findings indicate that animals freely mix associative and temporal information in an adaptive, task specific manner, thus blurring the distinction between the two. How can these findings be understood within the associative and temporal frameworks? Here are some future directions: Investigate processes that bridge the associative and temporal learning fields: Contingency, expectation, and error learning are obvious candidates, particularly when examined from a perspective that is neither associative nor temporal. Information theory offers one such prominent vantage point (Gallistel et al., 2013): rather than learning about associative or temporal cues, animals extract information from the environment, including, among others, associative and temporal regularities. This informationbased learning transcends associative and temporal domains, and provides exciting new predictions regarding the principles of both associative and temporal learning (Reyes and Buhusi, 2013). Investigate how associative and temporal information are represented and processed in the brain: The biological perspective offers exciting new opportunities, measures, and mechanisms to be investigated. Besides the striatum (Hattori and Sakata, 2013) and the hippocampus (Buhusi and Schmajuk, 1996; Suzuki, 2007b) attention is currently paid to the amygdala (Diaz-Mataix et al., 2013; Raybuck and Lattal, 2013). From this biological vantage point, multiple brain structures (Dickinson, 2012; Suzuki, 2007a) and neurotransmitter systems (Heilbronner and Meck, 2013) interact in the process of prediction error learning (Kirkpatrick, 2013), which also transcends associative and temporal learning. Provide a resolution to the debate about associative and temporal learning: A possible resolution to the debate about associative and temporal learning (Church, 2013) will be found at either/both the biological or/and computational levels, and will likely involve finding a more adept way to describe both the associative and temporal phenomena. For example, temporal information may be represented in the brain by coincidental activation of multiple neural populations firing with different frequencies (Buhusi and Oprisan, 2013; Oprisan and Buhusi, 2014), thus supporting a possible mapping (transformation) of temporal information to an abstract dimension, frequency (Killeen, 2013). Should a similar mapping occur for associative information, it would provide an opportunity to address both associative and temporal phenomena within the same framework, in as much as the information theory or error prediction learning would. Evaluate new predictions: Both the information, error-prediction, and frequency paradigms have the promise to provide new, rather exciting predictions. For example, in the frequency paradigm (Buhusi and Meck, 2005; Killeen, 2013), long-accepted properties of temporal learning, such as time-scale invariance, may be no more than a mere reflection of the contribution to behavior of small fluctuations (noise) in neural firing (Buhusi and Oprisan, 2013; Oprisan and Buhusi, 2014). Similarly, the over-reset effect of emotional distracters on timing (Brown et al., 2007) may be no more than a mere reflection of amygdala–striatal interactions (Oprisan et al., 2013), rather than a defining property of the “internal clock”. Moreover, in the information paradigm (Gallistel et al., 2013), the long-accepted contingency requirement in associative learning may simply reflect information/entropy calculations (Reyes and Buhusi, 2013). Finally, biological correlates of associative notions such as error (Kirkpatrick, 2013) or surprise (Buhusi and Schmajuk, 1996; Roesch et al., 2012) are predicted to co-exist in the brain.
In summary, the two fields are, and will likely remain isolated, until findings from other fields, like biology and computation, will provide an appropriate vantage point for re-evaluating the relationship between the two. Interestingly, both fields seems at the brink of embracing new paradigms/perspectives: Temporal learning seems ready to abandon the internal-clock paradigm in favor of the frequency paradigm (Buhusi and Meck, 2005), while at the associative end, research is directed at validating the information paradigm (Gallistel et al., 2013), and at finding biological correlates of error-prediction learning (Kirkpatrick, 2013). When seen from such vantage points, the distinctions between associative and temporal learning vanish, and the question becomes: What are the mechanisms that coexist, cooperate and compete in a brain that processes associative and temporal information in real time? As both associative and temporal learning embrace this new direction, this is certainly a very exciting time for the two fields.
References Bouton, M.E., Woods, A.M., Todd, T.P., 2013. Separation of time-based and trialbased accounts of the partial reinforcement extinction effect. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.08.006. Brown, B.L., Richer, P., Doyere, V., 2007. The effect of an intruded event on peakinterval timing in rats: isolation of a postcue effect. Behav. Process. 74 (3), 300–310, http://dx.doi.org/10.1016/j.beproc.2006.11.004. Buhusi, C.V., Matthews, A.R., 2013. Effect of distracter preexposure on the reset of an internal clock. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.09.003. Buhusi, C.V., Meck, W.H., 2005. What makes us tick? Functional and neural mechanisms of interval timing. Nat. Rev. Neurosci. 6 (10), 755–765, http://dx.doi.org/10.1038/nrn1764. Buhusi, C.V., Oprisan, S.A., 2013. Time-scale invariance as an emergent property in a perceptron with realistic, noisy neurons. Behav. Process. 95, 60–70, http://dx.doi.org/10.1016/j.beproc.2013.02.015. Buhusi, C.V., Schmajuk, N.A., 1996. Attention, configuration, and hippocampal function. Hippocampus 6 (6), 621–642, http://dx.doi.org/10.1002/ (SICI)1098-1063(1996)6:6<621::AID-HIPO6>3.0.CO;2-J. Church, R.M., 2013. A resolution of the debate about associative and temporal learning. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.08.011. Delamater, A.R., Desouza, A., Rivkin, Y., Derman, R., 2013. Associative and temporal processes: a dual process approach. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.09.004. L., Tallot, L., Doyere, V., 2013. The amygdala: a Diaz-Mataix, potential player in timing CS–US intervals. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.08.007. Dickinson, A., 2012. Associative learning and animal cognition. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 367 (1603), 2733–2742, http://dx.doi.org/ 10.1098/rstb.2012.0220. Gallistel, C.R., Craig, A.R., Shahan, T.A., 2013. Temporal contingency. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.08.012. Gibbon, J., 1977. Scalar expectancy theory and Weber’s law in animal timing. Psychol. Rev. 84 (3), 279–325, http://dx.doi.org/10.1037/0033-295X.84.3.279. Hattori, M., Sakata, S., 2013. Brain electrophysiological activity with temporal processing in rats. Behav. Process., correlates http://dx.doi.org/10.1016/j.beproc.2013.09.011. Heilbronner, S.R., Meck, W.H., 2013. Dissociations between intertiming and intertemporal choice following administration val fluoxetine, cocaine, or methamphetamine. Behav. Process., of http://dx.doi.org/10.1016/j.beproc.2013.09.013. Jozefowiez, J., Polack, C.W., Machado, A., Miller, R.R., 2013. Trial frequency effects in human temporal bisection: implications for theories of timing. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.07.023. P.R., 2013. Finding time. Behav. Process., Killeen, http://dx.doi.org/10.1016/j.beproc.2013.08.003. Kirkpatrick, K., 2013. Interactions of timing and prediction error learning. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.08.005. Matell, M.S., Kim, J.S., Hartshorne, L., 2013. Timing in a variable interval procedure: evidence for a memory singularity. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.08.010. Molet, M., Miller, R.R., 2013. Timing: an attribute of associative learning. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.05.015. Oprisan, S.A., Buhusi, C.V., 2014. What is all the noise about in interval timing? Philos. Trans. R. Soc. Lond. B: Biol. Sci., http://dx.doi.org/10.1098/rstb.2012.0459 (in press). Oprisan, S.A., Dix, S., Buhusi, C.V., 2013. Phase resetting and its implications for interval timing with intruders. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.09.005. Raybuck, J.D., Lattal, K.M., 2013. Bridging the interval: theand neurobiology of trace conditioning. Behav. Process., ory http://dx.doi.org/10.1016/j.beproc.2013.08.016.
Editorial / Behavioural Processes 101 (2014) 1–3 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, vol. II. Appleton-Century-Crofts, pp. 64–99. Reyes, M.B., Buhusi, C.V., 2013. What is learned during simultaneous temporal acquisition? An individual-trials analysis. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.09.008. Roesch, M.R., Esber, G.R., Li, J., Daw, N.D., Schoenbaum, G., 2012. Surprise! Neural correlates of Pearce–Hall and Rescorla–Wagner coexist within the brain. Eur. J. Neurosci. 35 (7), 1190–1200, http://dx.doi.org/10.1111/j.1460-9568.2011.07986.x. Sanabria, F., Oldenburg, L., 2013. Adaptation of timing behavior to a regular change in criterion. Behav. Process., http://dx.doi.org/10.1016/j.beproc.2013.07.018. Suzuki, W.A., 2007a. Integrating associative learning signals across the brain. Hippocampus 17 (9), 842–850, http://dx.doi.org/10.1002/hipo.20321.
3
Suzuki, W.A., 2007b. Making new memories: the role of the hippocampus in new associative learning. Ann. N. Y. Acad. Sci. 1097, 1–11, http://dx.doi.org/10.1196/annals.1379.007. Taylor, K.M., Joseph, V., Zhao, A.S., Balsam, P.D., 2013. Temporal maps in appetitive Pavlovian conditioning. Behav. Process., http://dx.doi.org/10.1016/ j.beproc.2013.08.015.
Catalin V. Buhusi USTAR BioInnovations Center, Department of Psychology, Utah State University, 2810 Old Main Hill, Logan, UT 84322-2810, United States