Research paper
Does attention play a role in dynamic receptive field adaptation to changing acoustic salience in A1?

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Abstract

Acoustic filter properties of A1 neurons can dynamically adapt to stimulus statistics, classical conditioning, instrumental learning and the changing auditory attentional focus. We have recently developed an experimental paradigm that allows us to view cortical receptive field plasticity on-line as the animal meets different behavioral challenges by attending to salient acoustic cues and changing its cortical filters to enhance performance. We propose that attention is the key trigger that initiates a cascade of events leading to the dynamic receptive field changes that we observe. In our paradigm, ferrets were initially trained, using conditioned avoidance training techniques, to discriminate between background noise stimuli (temporally orthogonal ripple combinations) and foreground tonal target stimuli. They learned to generalize the task for a wide variety of distinct background and foreground target stimuli. We recorded cortical activity in the awake behaving animal and computed on-line spectrotemporal receptive fields (STRFs) of single neurons in A1. We observed clear, predictable task-related changes in STRF shape while the animal performed spectral tasks (including single tone and multi-tone detection, and two-tone discrimination) with different tonal targets. A different set of task-related changes occurred when the animal performed temporal tasks (including gap detection and click-rate discrimination). Distinctive cortical STRF changes may constitute a “task-specific signature”. These spectral and temporal changes in cortical filters occur quite rapidly, within 2 min of task onset, and fade just as quickly after task completion, or in some cases, persisted for hours. The same cell could multiplex by differentially changing its receptive field in different task conditions. On-line dynamic task-related changes, as well as persistent plastic changes, were observed at a single-unit, multi-unit and population level. Auditory attention is likely to be pivotal in mediating these task-related changes since the magnitude of STRF changes correlated with behavioral performance on tasks with novel targets. Overall, these results suggest the presence of an attention-triggered plasticity algorithm in A1 that can swiftly change STRF shape by transforming receptive fields to enhance figure/ground separation, by using a contrast matched filter to filter out the background, while simultaneously enhancing the salient acoustic target in the foreground. These results favor the view of a nimble, dynamic, attentive and adaptive brain that can quickly reshape its sensory filter properties and sensori-motor links on a moment-to-moment basis, depending upon the current challenges the animal faces. In this review, we summarize our results in the context of a broader survey of the field of auditory attention, and then consider neuronal networks that could give rise to this phenomenon of attention-driven receptive field plasticity in A1.

Introduction

How is the cortical representation of sound influenced by attention? Since the work of Hubel, Galambos and colleagues (Hubel et al., 1959), it has been known that the responses of single neurons in auditory cortex can be strongly modulated by attention. In their pioneering study in awake cat auditory cortex, a brief but prescient paper in the style of anecdotal neurophysiology, they observed that the responses of some cells (∼10%) were highly dependent upon whether or not the cat was aroused by the presented sounds, or attended to the acoustic stimuli. As Hubel wrote (Personal communication 2006): “One day I entered the triple soundproof room to see if the cat was still alive, and discovered that rattling the doorknob, or keys, produced clear and lively responses  I found that almost anything I did that made a noise elicited firing as long as the cat appeared interested”. Some of their “attention” units were located in A1, others in higher auditory cortical areas. In their study, they noted several characteristics of sounds that elicited an attentive state in their cats and lead to enhanced neural responses: (i) novelty (i.e. novel sounds were better than repeated sounds), (ii) meaning (i.e. natural sounds were better than clicks or tones), (iii) multi-sensory spatial coherence (i.e. acoustic stimuli presented simultaneously with a matched visual source were better than sounds without a matched visual counterpart). Although the cats were fully awake in the experiments of Hubel and colleagues, they were not behaviorally trained on any auditory task, so it was not possible in this early study to systematically explore the role of goal-directed attention in modulating sensory processing, a challenge left for future research. One caveat, noted by Hubel, is that this study did not control for pinnae movement, nor measure neuronal directionality tuning, thus leaving open the question of whether the observed effects were truly the results of either spatial or feature-based attention.

Other contemporary experimentalists working during this time period on the awake cat or monkey auditory cortex, such as Katsuki, did not mention the presence of any such “attention” units. However, in a fairly thorough study of responses in awake cat auditory cortex (Evans and Whitfield, 1964), the authors wrote: “About one third of the units responding to sound could be stimulated only by clicks or ‘odd’ sounds, such as the jangling of keys. Many of them gave inconsistent responses unless the attention of the cat was attracted to the source of the sound. These resemble the ‘attention’ units reported by Hubel et al. (1959). Some of these units had very low thresholds, but most required loud ‘startling’ sounds for consistent stimulation”. Curiously, these researchers said almost nothing more about these “attention” neurons in the rest of this paper, or in two subsequent publications on the awake cat auditory cortex, perhaps because “… all of those units which required ‘odd’ sounds to stimulate them, or where much ingenuity and experiment were necessary to obtain the ‘attention’ of the unit, were obtained from cortex which was relatively inactive …”. In the following 20 years, a handful of neurophysiological studies continued the investigation of the effects of auditory attention on cortical processing in the context of behavior (including Hocherman et al., 1976, Pfingst et al., 1978, Benson and Hienz, 1978, Miller et al., 1980). These studies demonstrated increases in cell evoked discharge for an attended stimulus compared to an identical non-attended stimulus and showed that these effects could occur with remarkably short-latency.

However, as Hubel and colleagues (1959) had ruefully noted: “Unfortunately attention is an elusive variable that no one has as yet been able to quantify”. It remains so today. Although there has been considerable research on auditory attention over the past fifty years, using a variety of approaches (psychoacoustic, behavioral, neurophysiological (single unit and EEG), MEG, functional fMRI neuroimaging) the underlying neural mechanisms remain mysterious. Moreover, to make the problem even more challenging, there is clear evidence that attention itself, defined as a top-down selection process that focuses cortical processing resources on the most relevant sensory information in order to maintain goal-directed behavior in the presence of multiple, competing distractions, is hardly a unitary phenomenon, but may be comprised of several distinct behavioral and neural processes (Posner and Peterson, 1990, Desimone and Duncan, 1990, Parasuraman, 1998, Ahveninen et al., 2006, Johnson and Zatorre, 2006).

So, what do we currently know about auditory attention? We know that auditory attention allows us to rapidly direct our acoustic focus towards sounds of interest in our acoustic environment. Attention can be bottom-up (sound-based) or top-down (task-dependent), and top-down control can trump involuntary attention switching to task-irrelevant distractor sounds (Sussman et al., 2003) perhaps through top-down attentional modulation by the prefrontal cortex of the deviance detection system in the auditory cortex (Doeller et al., 2003). Attention provides a top-down salience filter (Hafter et al., 2007) that in conjunction with bottom-up “pop-out” auditory salience (Kayser et al., 2005) is thought to pass only a small part of the incoming acoustic information to higher auditory areas. Attentional mechanisms can modulate neural activity encoding the spatial location and/or the acoustic attributes of the selected targets and the early sensory representation of attended stimuli (Ahveninen et al., 2006). This is illustrated by one of the best-known examples of auditory attention – the “cocktail party effect” (Cherry, 1953, Haykin and Chen, 2005) where we can easily selectively eavesdrop on different speakers in a crowded room brimming with multiple conversations. Cherry speculated on possible cues to its solution, including location, lip-reading, mean pitch differences, different speaking speeds, male/female speaking voices, or accents. However, whatever the cues, or the exact mechanisms involved in deciphering them, it is clear that in order to accomplish this feat of selective attention to a single stream in a natural environment with multiple sound sources, we must already be highly proficient at auditory scene segregation (or ASA). As Bregman’s influential studies emphasized (Bregman, 1990), listeners have to solve the ASA problem in order to extract one or more relevant auditory streams from the mixture of sources that typify their acoustic environment. Sound sources may differ in a variety of acoustic cues (location, instantaneous fundamental frequency, or the patterns of energy envelope modulation in different frequency bands) that facilitate grouping. There is evidence that the brain has a fairly sophisticated pre-attentive automatic scene analysis system that parses the acoustic scene into streams and analyzes stability and novelty, even for task-irrelevant streams (Winkler et al., 2003). This automatic process may correspond to what Bregman referred to as a “bottom-up” or “primitive” grouping. In addition, Bregman suggested a set of top-down grouping processes which he termed “schema-driven” mechanisms, based on acquired expectations from prior experience or knowledge. Recent results (Carlyon, 2004, Cusack et al., 2004, Wrigley and Brown, 2004, Molholm et al., 2005, Snyder et al., 2006) also suggest the presence of two cortical mechanisms of streaming – an automatic “pre-attentive” segregation of sounds and an attention-dependent streaming mechanism. The process of auditory scene analysis sets the stage, and in conjunction with mechanisms for automatic change detection (represented by the evoked potential MMN), seamlessly interacts with the auditory attention system (Naatanen, 1992, Naatanen et al., 2001, Opitz et al., 2005, Sussman, 2005). Thus, an explanation of the cocktail party effect must include an understanding of the interplay between ASA, and our abilities to direct spatial attention to sound sources within the acoustic scene and/or to direct featural attention by focusing on distinctive acoustic vocal features (such as fundamental frequency, timbre, accent, intonation) in order to identify individual speaker voices (Ahveninen et al., 2006).

There may be a similarity between attention in the auditory and visual modalities, where a two-component framework for attentional selection (top-down and bottom-up) has also emerged from psychophysical and behavioral studies. Two sets of mechanisms are thought to operate in parallel in both modalities: using either bottom-up, automatic, image-based saliency cues or top-down, attentional, task-dependent cues. Another fundamental similarity (and duality) common to both modalities is that attention can either be spatial or feature-based. We will continue to explore the comparison between visual and auditory attention in the final section.

Overall enhancement of human auditory cortex activity by selective attention has been shown by functional MRI (Grady et al., 1997, Jancke et al., 1999, Jancke et al., 2003, Rama and Courtney, 2005, Voisin et al., 2006), PET (Zatorre et al., 1999, Hugdahl et al., 2000, Alho et al., 2003, Johnson and Zatorre, 2005), EEG (Hillyard et al., 1973) and MEG (Woldorff et al., 1993, Ozaki et al., 2004, Ahveninen et al., 2006). Auditory attention can selectively be directed to a rich variety of features including spatial location, auditory pitch, frequency or intensity, to tone duration or FM direction or slope, to speech vs. nonspeech streams. What is the neural locus of these auditory attentional effects? Although some human imaging studies have shown clear attentional modulatory effects in A1, as well as other primary and secondary auditory cortical regions, other studies (Petkov et al., 2004) report greater effects of auditory attention in higher auditory association areas, at least in a dual task paradigm (comparing responses when one sensory modality is attended and the other is ignored). Petkov and colleagues suggest that there may be two distinct types of auditory cortical pathways, one of which faithfully transmits acoustic information for all incoming stimuli and is unaffected by attentional bias, and another which is attentionally labile, is strongly modulated by attention and analyzes the acoustic features of behaviorally relevant sounds. Although intriguing, it is possible that this distinction would evaporate if subjects were tested with other auditory task conditions besides pitch discrimination in a dual task context (Petkov et al., 2004) which might reveal additional attentional modulatory effects in complementary cortical areas (Ahveninen et al., 2006). The work of Brechmann and Scheich (2005) demonstrates that attentional focus on different features of the same acoustic stimuli leads to differential hemispheric activation of auditory cortex. There is also some evidence for hemispheric specialization of the attentional system – for example a study by Zatorre and colleagues (1999) suggests that auditory attention to either spatial location or tonal frequency activates a common network of right hemisphere cortical regions. A recent MEG/fMRI paper (Ahveninen et al., 2006) provides further evidence for the presence of dual selective-attention effects on sound localization and identification. Additional evidence for lateralization is provided by a recent ERP study (Alain et al., 2006) that observed plastic changes in event-related potentials during rapid perceptual learning while listeners were trained to distinguish between two phonetically distinct vowels. These changes occurred in right auditory cortex and right anterior superior temporal gyrus/inferior prefrontal cortex and were dependent upon auditory attention to the phonetic discrimination task.

In general, bimodal selective attention usually leads to widespread increased activity in relevant sensory cortices while simultaneously leading to decreased activity in irrelevant sensory cortices (Johnson and Zatorre, 2006). Other association cortical areas in the attentional network (Posner and Peterson, 1990) are also activated in auditory attention – such the posterior parietal cortex (Cohen et al., 2005, Shomstein and Yantis, 2004, Shomstein and Yantis, 2006), and right inferior frontal and dorsolateral prefrontal cortex (Voisin et al., 2006). Moreover, neuroimaging studies of the thalamus (Frith and Friston, 1996) and physiological (McAlonan et al., 2006) and neuroanatomical (Sakoda et al., 2004) studies of the thalamic reticular nucleus suggest that the different thalamic nuclei may play important roles in attentional modulation and in helping direct the shifting focus of attention (Crick, 1984). Most recently, physiological studies by Otazu and Zador (2006) have observed an attention-driven overall enhancement of spontaneous activity in the medial geniculate thalamus, which may play a role in generating selective responses in auditory cortex. There is also evidence for auditory attentional modulation of activity in the superior colliculus in mammals, in parallel with the demonstration of top-down gain control in the avian midbrain (Winkowski and Knudsen, 2006). And a recent study (Perez-Gonzalez et al., 2005) has shown the presence of novelty detector neurons in the inferior colliculus, that may contribute to a subcortical attentional, arousal or orienting responses. In sum, these results suggest that auditory attention involves a wide range of auditory cortical and subcortical structures, and also integrates into a multi-sensory attentional network that includes parietal and frontal cortical regions (Bidet-Caulet et al., 2005, Foxe et al., 2005, Peers et al., 2005, Serences and Yantis, 2006, Raz and Buhle, 2006). Looking at the whole set of brain areas involved in the control of auditory attention reveals a richly interconnected network, that includes the computation of early auditory features, the location of acoustic items of interest, recognition of auditory objects, and the planning of actions.

In light of this review of auditory attention, a number of key questions emerge, including the following: (1) what is the relationship between auditory spatial attention and auditory feature-based attention? (2) what are the contributions of different neural loci (including multiple subcortical and auditory cortical areas) to these different forms of auditory attention? – and what are the network dynamics for this widely distributed set of structures modulated by auditory attention? (3) what is the relation between arousal, vigilance and attention? (4) what is the neural basis of the pre-attentive components of automatic change detection, represented by the MMN? And how does it integrate with attention on a cellular and network level? (5) what role does attention play in modulating neuronal receptive fields in A1? (6) what are the neural mechanisms underlying attentional effects such as STRF shape changes? (7) what is the time course of task-related plasticity compatible with the time course of attention? (8) what is the relationship between learning and attention? how does task training shape the direction of attention? We will touch on some of these questions in this review, and leave most of the others for future work. In Section 2, our discussion will focus on the results of some of our recent experimental studies (relevant to (5) above) to explore the possible role of attention in modulating A1 receptive field properties.

The adaptive functions of the cerebral cortex rely upon flexibility and plasticity of information processing networks. Many previous studies have demonstrated that local and global properties of the auditory cortex (specifically in A1) are extraordinarily plastic in response properties to a variety of training procedures (see Weinberger, 1998, Weinberger, 2001, Weinberger, 2003a, Weinberger, 2003b, Weinberger, 2007, Fritz et al., 2005b). Receptive fields and frequency response profiles of A1 neurons can be attentionally gated to adaptively assume different states or filter properties depending upon the behavioral demands of the ongoing task demands. An important study by Polley and colleagues (2006) shows that differential cortical plasticity arises during perceptual learning when animals attend to different features of the same acoustic stimulus set. Attention may also be instrumental in shifting from one cortical state to another. A recent study by Blake and colleagues (2006) demonstrates that a combination of acoustic stimuli and reward are insufficient to evoke cortical plasticity in the absence of an active, behavioral link between the two, and emphasizes the importance of forging dynamic links between sensory stimuli and motor actions during task learning (Cohen et al., 2005). Of course it is also important to note the presence of a vast literature on varieties of receptive field plasticity in A1 that can be induced in the absence of attention (including Bakin and Weinberger, 1996, Kilgard et al., 2001a, Kilgard et al., 2001b, Kilgard et al., 2002, Kilgard and Merzenich, 2002, Ohl and Scheich, 1996, Ohl et al., 1997, Suga et al., 2002, Suga and Ma, 2003).

Section snippets

Goal-directed selective attention and rapid task-related plasticity

We began our current research on the effects of auditory attention on primary auditory cortex in the ferret (Kowalski et al., 1995, Kowalski et al., 1996a, Kowalski et al., 1996b), because we thought it might be valuable to study the impact of attention by examining dynamic changes in receptive field shape under different auditory attention conditions, in which the animal needs to focus on different salient acoustic features or cues in order to perform the task. To quantify these

Discussion and speculation

Approaching the question of the neural basis of selective goal-directed attention at an oblique angle, the experiments described above suggest that rapid auditory task-related plasticity is an ongoing process that occurs as the animal switches between different tasks and dynamically adapts auditory cortical STRFs in response to changing acoustic demands and attentional focus on salient acoustic cues. Rapid plasticity modifies STRF shape in a manner consistent with enhancing the behavioral

Acknowledgements

We would like to thank Bob Galambos and David Hubel for discussions of the early studies of auditory attention, Nima Mesgarani for assistance with task development and software programming, David Klein for computational analysis, Henry Heffner for continuing generous advice and guidance on behavioral training, Michael Brosch for comments on the manuscript, Pingbo Yin for discussion on auditory featural attention, Kevin Donaldson for help with ferret care and training. We are also grateful for

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