Note that the information below is out-of-date (2007), and will soon be updated (I hope!)

Scientific Background

The central aim of my research is to determine the theoretical and neural distinctions between conscious ("explicit") and unconscious ("implicit") expressions of memory. One proposal is that explicit and implicit memory are supported by distinct neural networks in the brain; sometimes called "declarative and "non-declarative" memory systems respectively. Memory problems caused by dementia, and also by healthy ageing, are thought to be primarily impairments of a declarative memory system within the medial temporal lobes (MTL). Some forms of implicit memory (specifically priming), on the other hand, can be preserved in such cases, which is attributed to intact plasticity in other cortical regions. A more complete characterisation of explicit and implicit memory will therefore help our understanding and possible rehabilitation of such memory problems. I attempt such a characterisation by using behavioural, computational and brain imaging techniques.

The Neural Bases of Conscious and Unconscious Memory and Perception

One of the most important claims during the last few decades of cognitive neuroscience is that human memory reflects a collection of distinct neural systems. This claim was originally based on the memory impairments following brain damage or dementia, in which some types of memory are impaired while others are spared. More recently, this evidence has been complemented by dissociable patterns of activity found in the brains of healthy volunteers while they perform various memory tasks, as measured by functional Magnetic Resonance Imaging (fMRI) and Electro- and Magneto-encephalography (EEG/MEG). My research focuses on three hypothetical expressions of memory associated with the repetition of a stimulus: recollection, familiarity and priming. Recollection and familiarity are examples of explicit memory, and differentiated primarily by whether repetition is accompanied by retrieval of contextual detail associated with the initial stimulus presentation (recollection), or by a feeling attributed to recent exposure to the stimulus, in the absence of contextual information (familiarity). Priming is an example of implicit memory, reflecting facilitated processing of a stimulus within a given task, in the absence of conscious associations of that stimulus with the past. These theoretical constructs have proved helpful in explaining many of the behavioural dissociations found in both healthy individuals and patients with amnesia. However our recent modelling work suggests that many of these behavioural dissociations can be explained by a single underlying memory signal (i.e, the distinctions between recollection, familiarity and priming may be quantitative rather than qualitative). Nonetheless, neuroimaging data from our fMRI and EEG/MEG experiments suggest that the three expressions of memory can be associated with distinct patterns of brain activity, consistent with multiple memory signals in the brain. Thus the challenge is to specify more precisely the nature of these multiple potential memory signals, how and when they are experienced consciously (as explicit rather than implicit memory), and how they might be mapped onto a single dimension of evidence in order to make a memory judgment.

Our memory for a stimulus is highly dependent on the manner in which we perceive it when initially encountered. Likewise, our perception of a stimulus is influenced by any memory we might have, explicit or implicit, owing to its prior processing. Thus there is necessarily a close relationship between perception and memory, particularly implicit versus explicit expressions of both. For example, can we encode a lasting memory for a stimulus of which we were not aware? How is our current perception of a stimulus affected by unconscious memories of it? I try to address these questions, focusing on visual objects like faces, and the role of plasticity in ventral temporal visual systems. I am also interested in the relationship between perception and memory in relation to current theories about the function of Medial Temporal Lobe (MTL) regions; specifically that MTL regions like perirhinal cortex and hippocampus are involved in perception of complex objects and spatial relationships respectively, in addition to storing memories for them.

More specific projects are (detailed below):

• Relationship between Recognition Memory and Repetition Priming
• Neural correlates of Recollection and Familiarity in Recognition Memory
• Role of Attention and Awareness in Repetition Priming
• Role of Stimulus-Response Associations in Priming
• The use of Repetition Effects to identify Neural Correlates of Face Processing
• The Neural Basis of fMRI RS and its Use as a Tool to study Perception
• The Philosophy of Functional Neuroimaging (Neophrenology?)
• Methodological Developments in fMRI, EEG/MEG Analysis
• Short-term Memory for Serial Order (Past project)

Note that the opinions stated here are, of course, biased (and should not necessarily be associated with some of the collaborators named!)

Current Projects

Relationship between Recognition Memory and Repetition Priming

A common view in memory research is that explicit memory is functionally and anatomically distinct from implicit memory. For example, explicit memory is believed to be supported by a specialised "declarative" memory system within the medial temporal lobes (MTL), whereas implicit memory is believed to be enabled by other cortical and subcortical structures.

I have concentrated on tests of visual recognition memory and repetition priming, believed to tax declarative and nondeclarative memory systems respectively, and which can be closely matched in their experimental procedures and scoring. Both tests have the benefit of a long pedigree of laboratory study, and their procedural simplicity facilitates their adaptation to the scanning environment (e.g, affording a high level of experimental control that is difficult to achieve with more naturalistic tests of memory).

While some mnemonic processes, such as the fluency of perceptual or conceptual processing of stimuli, are likely to contribute to both tests, a key question is whether more than one such memory process is needed to explain functional (behavioural) differences in such tasks. Together with Chris Berry and David Shanks, we developed a computational model (an extension of signal-detection theory) that assumes only a single memory signal (Berry et al, 2008b). By adding the important assumption of independent task-specific (non-mnemonic) noise, this model does surprisingly well at reproducing many "dissociations" between recognition memory and repetition priming that have been reported, such as the effect of attentional manipulations at study (Berry et al, 2006a) and impairments in amnesia (Berry et al, 2008a). It also explains the relationship between recognition memory (d'), priming (d' or RTs) and measures of fluency (RTs). A property key to explaining these dissociations is a greater measurement noise in typical priming tasks, which can make them less sensitive than recognition memory tasks (Berry et al, 2006b).

More recently however, using a variant of the "CID-R" paradigm in which priming and recognition are measured trial-by-trial, we have obtained a pattern of data more difficult to reproduce by our single-process model. One explanation involves the possibility that, while familiarity and priming may share a common cause, recollection is a truly distinct additional process. (While support for such "dual-process" models of recognition memory has been claimed on the basis of other data, such as ROCs, interpretation of such data is still a matter of debate).

Whereas computational modelling questions some of the distinctions between recollection, familiarity and priming, imaging data appear more consistent with these distinctions. For example, a number of fMRI studies of recognition memory (see below) suggest that different brain regions are associated with recollection and familiarity (though none has yet met the strict criteria I proposed for a qualitative difference; Henson, 2006). Using ERPs and a procedure invented by Jacoby & Whitehouse, we have also found at least four different spatiotemporal effects associated with recollection, familiarity and (immediate, subliminal) repetition priming (Woollams et al, 2008). Jason Taylor and I are currently replicating this paradigm with MEG, to provide more precise spatial information about the different effects.

I have also used the word-stem completion paradigm to contrast explicit and implicit memory, which can be matched in just about every way except whether or not the completion instructions refer to prior exposure to the target words (Schacter's "retrieval intentionality" criterion). In an fMRI study together with Bjoern Schott, Alan Richardson-Klavehn and Emrah Duzel, using a modified version of the word-stem completion paradigm, we found differences between recognition and priming at both encoding (Schott et al, 2006) and retrieval (Schott et al, 2005). Increased activity in the hippocampus for example was related only to successful recognition memory (not priming), whereas decreased activity (repetition suppression) in cortical regions like fusiform were related only to priming. In another fMRI study with Christiane Thiel, we contrasted the effects of lorezapam and scopolamine (versus placebo) on priming in the word-stem completion task (Thiel et al, 2001). The resulting priming effects were consistent with those above in being associated with repetition suppression in certain brain regions under conditions believed to have minimal contamination by declarative memory.

Thus at the moment, I think there are good reasons for distinguishing recollection from familiarity/priming, but fewer reasons to suppose that familiarity and priming are distinct processes (as some suggest); rather familiarity and priming may reflect (as others suggest) the same cause, ie fluency at one or more stages of perceptual/lexical/semantic processing, differing in only whether or not that fluency is attributed to the past. (This attribution is itself likely to be a complex process, but one important factor is whether the fluency is expected or unexpected; an idea I am developing in terms of a "predictive theory" of memory.)

Neural correlates of Recollection and Familiarity in Recognition Memory

In collaboration with Mick Rugg, we used several experimental manipulations to attempt to separate the fMRI correlates of familiarity and recollection, including Remember/Know judgments (Henson et al, 1999a), confidence judgments (Henson et al, 2000), deep/shallow study tasks (Henson et al, 2005), and modified source memory tasks (Rugg et al, 2002), including memory for emotional context (Smith et al, 2004; Smith et al, 2005; Maratos et al, 2001). In general, these experiments have supported the recollection/familiarity distinction, though the data are far from conclusive. Important findings include the association of posterior cingulate and inferior parietal cortex with recollection (for review, see Rugg et al, 2002), and the association of anterior medial temporal cortex (outside the hippocampus) with familiarity (for meta-analysis, see Henson et al, 2003). Somewhat surprisingly perhaps, my take on the literature in 2005 was that there was not yet clear evidence of functional dissociations within the MTL, at least between hippocampus and surrounding rhinal/parahippocampal cortex (Henson, 2005).

A further common finding has been the activation of prefrontal cortex during recognition memory. After considering a large number of similar findings in our laboratories, Tim Shallice, Paul Fletcher and I hypothesised that these activations reflect a number of distinct control processes that work to optimise memory performance; for example, in monitoring retrieved information in relation to the task goals (Henson et al, 1999b; Henson et al, 2002; for review, see Fletcher & Henson, 2002). Indeed, even a manipulation as simple as the ratio of old to new items in a recognition memory test dissociated prefrontal activations, which we attributed to control processes, from parietal activations, which we attributed to "true" explicit memory retrieval (Herron et al, 2004). This reinforced the importance of subtle procedural changes, such as what participants perceive as the targets in a memory test (issues that are well-known in the ERP literature, but often over-looked in behavioural studies). More recently however, we do find regions in anterior prefrontal cortex (BA 10) that seem to correlate specifically with contextual retrieval, and differentially according to the nature of that context (Simons et al, 2008). Another type of retrieval control process we have investigated is "retrieval orientation" – a state in which one is attempting to retrieve a specific type of information – using both EEG (Hornberger et al, 2006a) and fMRI (Hornberger et al, 2006b; see also Schott et al, 2005).

Note that the recognition memory paradigm has limitations. Firstly, the paradigm may only tax a subset of memory retrieval processes. The provision of a strong retrieval cue (a so-called "copy cue") minimises the need for participants to engage in elaborative search strategies (as is required, for example, when freely recalling studied items). Unfortunately, recall paradigms are more difficult to adapt to the scanning environment, since they normally require spoken responses, which can cause movement-related artefacts in fMRI and EEG/MEG data. By developing methods to minimise such artefacts in fMRI, we successfully investigated proactive interference in a cued recall paradigm (Henson et al, 2002), the results of which supported a role for prefrontal cortex in monitoring during memory retrieval.

The above experiments concentrated on the test phase of the recognition memory task. In collaboration with Leun Otten, we conducted fMRI experiments focused on the study (encoding) phase, investigating neural activity that predicts subsequent memory. We showed that the hippocampus appears to predict subsequent memory regardless of the study task (Otten et al, 2001). Other regions, like anterior left inferior prefrontal cortex, appear to predict subsequent memory only during tasks that require semantic elaboration (see Rugg et al, 2002, for review). We were also able to distinguish subsequent memory effects that occur on an item-by-item level from those that reflect longer-lasting "states" (e.g, periods of sustained attention; Otten et al, 2002).

More recently, and together with Audrey Duarte, we have investigated how the neural correlates of recollection (Duarte et al, 2008) and familiarity (Duarte et al, in press) change with age. We have also begun to contrast recollection and familiarity for different types of stimuli (e.g, faces vs scenes), given increasing evidence from patients of differential involvement of MTL structures in memory for spatial vs nonspatial stimuli (Lee et al., 2006; Taylor et al, 2007; Barense et al, in press).

Role of Attention and Awareness in Repetition Priming

While some have argued that priming effects can occur in the absence of attention, or are not affected by attentional manipulations, or at least are less sensitive to attentional manipulations than, for example, recognition memory, such claims are either questionable, or in the latter case, can be explained by the lower sensitivity of typical priming tasks (Berry et al, 2006a; Berry et al, 2006b). Thus like others, I believe that spatial and temporal attention is necessary for priming effects, and in three fMRI experiments, we failed to find any evidence of repetition suppression for faces, houses or objects in the ignored hemifield (Eger et al, 2004; Henson & Mouchlianitis, 2007; Thoma & Henson, 2007).

However, one can attend to a location in space (and time) but still not be aware of a stimulus, such as when it is presented briefly between a forward and backward mask (ie, subliminal). Together with Sid Kouider, we used such sandwich masking of faces and showed reliable behavioural priming that was not easily explicable by measures of participants' ability to see the prime (and which could not be explained by stimulus-response learning; see below). We also showed concurrent repetition suppression in occipital and fusiform face areas using fMRI (Kouider et al, in press), demonstrating that modulation of activity in such ventral stream areas can occur without awareness (but with attention). This suppression even occurred across different views of faces and for both familiar and unfamiliar faces. An EEG version of this paradigm (Henson et al, 2008) revealed two subliminal repetition effects, an early one (100-150ms post-prime onset), which was sensitive to view but not familiarity (much like the fMRI data), and a later one (300-500ms post-prime onset), which was sensitive to familiarity (much like the behavioural priming). More recently, we replicated this with MEG, and are trying to relate the generators to the fMRI data. We are also extending this work to subliminal semantic priming, though here stimulus-response learning seems to dominate (see below). More generally, such masked priming paradigms would appear a useful way to investigate the extent of unconscious processing in the brain that does not rely on the more typical attentional manipulations.

Role of Stimulus-Response Associations in Priming

The behavioural phenomenon of priming is often conceptualised in terms of the facilitation of one or more computational stages associated with a given stimulus and task, such as more rapid perceptual identification of that stimulus and/or more rapid retrieval of semantic information relevant to the task (the "abstractionist" account). For reviews, see (Henson, 2003; Henson, 2008). However, it is also likely that the response made to the first presentation of a stimulus can become directly associated with that stimulus, such that the priming produced by repetition of the stimulus can potentially be caused by rapid retrieval of the response, circumventing many of the prior computations (possibly one example of an "episodic" account). If priming is to be used as a tool to investigate different computations associated with a cognitive function (e.g, word recognition), the contribution of such S-R learning needs to be controlled. More recently, the fMRI phenomenon of repetition suppression (RS), which is often associated with priming, has been used as a tool to localise different computations within the brain (see below). However, a recent study by Dobbins et al (2004) argued that in many cases RS does not reflect local computations, but rather a bypassing of those regions/computations owing to response retrieval. They found that RS was diminished when such response retrieval was disrupted (by reversing the task); indeed, RS was, surprisingly, abolished in brain regions associated with visual object processing.

Given that other studies of ours had found RS in fusiform regions under conditions that seemed difficult to explain in terms of response learning (e.g, Henson et al, 2000; Henson et al, 2003), Aidan Horner and I repeated the Dobbins et al paradigm in a series of experiments and found that stimulus-response associations indeed dominate priming in this paradigm (Horner & Henson, in press). In fact, we found evidence of simultaneous bindings between stimuli and multiple levels of response representation (from the specific finger press, to the "yes/no" decision, to an abstract classification). It is even possible that RT priming in such speeded classification tasks is nothing but S-R learning (though priming in other paradigms, particularly identification of degraded stimuli, would seem more difficult to explain by S-R learning). We also replicated Dobbins et al's finding that RS in prefrontal regions is sensitive to the task switches believed to modulate S-R associations Horner & Henson, in press). However, in our data, the RS in perceptual (fusiform) regions appeared invariant to such task switches, suggesting that S-R associations cannot explain RS in all brain regions (or such regions are not always bypassed by retrieval of such associations).

S-R learning also appears to play a role in other types of priming, such as negative priming and subliminal priming. Doris Eckstein and I have studied the role of S-R learning in subliminal "semantic" (categorical) priming of faces, and shown it to be sufficient to explain all of the priming we observe. Again however, while this could be used to question the existence of truly unconscious semantic access, others have reported subliminal semantic priming that is more difficult to explain by S-R learning, reinforcing the general picture that S-R learning is an important factor, but not the only factor, in priming.

The use of Repetition Effects to identify Neural Correlates of Face Processing

Much is known about how humans perceive faces, and much is known about neural responses to faces in nonhuman primates. One way to map the neuroanatomy of face processing in humans is to use the technique of fMRI repetition suppression (RS; see below). I began by contrasting repetition effects for familiar and unfamiliar faces, assuming that only the former would have long-term perceptual representations (e.g, FRUs in the Bruce & Young model). In fact, we found RS for familiar (famous) faces, but repetition enhancement (RE) for unfamiliar (previously-unseen) faces in face-responsive regions. A similar interaction between familiarity (pre-experimental exposure) and repetition (experimental exposure) was found for other stimuli such as symbols, prompting us to suggest that RS reflects modification of existing representations, whereas RE reflects the (initial) formation of new representations (Henson et al, 2000).

Subsequent experiments showed this story cannot be so simple, particularly given that fMRI RS appears highly sensitive to the task (Henson et al, 2002) and the lag between repetitions (Henson et al, 2004a), among other factors. Indeed, RE is not always seen for unfamiliar faces; sometimes (particularly for immediate repetitions with no intervening items) unfamiliar faces show RS (Henson et al, 2004b) (though a consistent pattern across six experiments using long-lag repetition is greater RS for familiar than unfamiliar faces (Henson et al, 2000; 2002; 2004b [above]; Henson et al 2003; Thiel et al, 2002; Kouider et al, in press).

Switching to immediate repetition paradigms, we have continued to use RS to explore the sensitivity of different brain regions to different aspects of faces, concentrating on the fusiform face area (FFA), occipital face area (OFA) and superior temporal sulcus (STS). For example, with we found that FFA is sensitive to identity but not expression changes, while STS is sensitive to expression but not identity changes (Winston et al, 2004); that FFA appears to reflect the categorical perception of identity (using morphs of familiar faces), whereas OFA is more sensitive to image properties (Rotsthein et al, 2005). Furthermore, a more anterior FFA region showed some generalisation of RS across different photographs of the same person, but only when that person is familiar, whereas RS in more posterior fusiform and occipital regions was sensitive to the specific photograph (Eger et al, 2005). Finally, a recent long-duration adaptation paradigm implicated a more anterior STS region in gaze perception (Calder et al, 2007). The above results are generally consistent with popular models that postulate parallel routes for processing identity (FFA) and expression/eye-gaze (STS), but sharing an initial stage of structural encoding (OFA). Our more recent work however shows that activity in these regions also depends on featural versus configural processing strategies (Cohen-Kadosh et al, 2009); and note that some of these fMRI RS results may be specific to short-lags (e.g, reflect the influence of a short-lived visual iconic store, or "pictorial codes" in the Bruce & Young model).

More recently, Elias Mouchlianitis and I have been testing possible laterality differences in visual-object representations in ventral temporal regions (as proposed by Marsolek, 2005) by using divided visual field presentation of faces, and testing priming within versus across different views. Preliminary results replicated the well-known right-hemisphere advantage in face-processing, which is ameliorated by priming, but little evidence for interactions between hemisphere (visual field) and effects of view on priming.

The Neural Basis of fMRI RS and its Use as a Tool to study Perception

The most common finding in fMRI/PET studies of priming is a reduced haemodynamic response for repeated vs initial presentations of stimuli. This reduction has been termed "repetition suppression" (RS) (or in the context of immediate or sustained presentations, "fMR-adaptation"). It is often assumed that RS reflects facilitation of the processes performed by a set of neurons owing to performance of the same processes in the recent past. If so, RS can be used as a tool to map the brain regions associated with different processes (much like priming has been used behaviourally). For instance, if a brain region shows RS when a particular view of an object is repeated, but not when the same object is repeated from a different view, then it can be inferred that the processes performed by that brain region are view-specific rather than true "object-based" representations. Indeed, it has been claimed and demonstrated in at least one case that this use of RS affords higher spatial resolution than more typical categorical subtractions of different stimulus-types (Naccache & Dehaene, 2001).

We have applied this logic to the study of visual object processing: For example, fusiform RS appeared to generalise over mirror-reflections of objects (Eger et al, 2004), and to a lesser extent over split pictures of objects (Thoma & Henson, 2007), though in both cases only when prime and probe are attended. We also replicated an intriguing laterality in fusiform concerning the degree of generalisation over view changes of everyday objects (Vuilleumier et al, 2002). However, when we slowed down object recognition (like James et al, 2000), we only found RS in fusiform AFTER the point of identification, raising concerns that even with normal object presentation, fusiform RS may be a consequence rather than a cause of priming (Eger et al, 2007).

Given the prevalence of RS in the study of cortical representations, it is critical to relate this haemodynamic phenomenon to its assumed underlying neural cause. At least three different neural models of RS have been advanced (Henson, 2003), and in 2006, Kalanit Grill-Spector, Alex Martin and I were asked to compare and contrast our favoured models in a TICS paper (Grill-Spector et al, 2006). It is likely that all three models (the fatigue, sharpening and facilitation models) apply to some extent under different experimental conditions and in different brain regions. I have concentrated on dynamical models (which we called facilitation models), from considering data from human EEG and MEG in the same paradigms (Henson & Rugg, 2003). Some key assumptions are that RS can reflect a shorter duration of neural activity (Henson & Rugg, 2001), and that EEG/MEG repetition effects (at least for long-lags; Henson et al, 2004) tend to onset relatively late, ie after the initial stimulus-specific responses (such as the N/M170 evoked component that is maximal for faces; Henson et al, 2003). One possibility is that these repetition effects (and RS) reflect re-entrant feedback from higher levels of the visual processing hierarchy more anterior in the ventral stream (or possibly even parietal regions in the dorsal stream; Eger et al, 2007).

Based on the work of Karl Friston, we implemented a simple neural network model, based on predictive coding, that reproduced aspects of this proposal, eg earlier repetition effects in higher than lower regions (Henson & Friston, 2006). Further development of this model however requires more constraints, which may be best obtained from the neural data recorded in nonhuman primates. However, one important implication of this model, if correct, is that the regions showing maximal RS may not be the regions where the critical dimensions of interest are represented - they may be downstream of the critical regions (or more likely, given that repetition causes synaptic changes between all regions in this model, one could argue that the representations are not stored in any one, single region).

The Philosophy of Functional Neuroimaging (Neophrenology?)

Many people have criticised functional neuroimaging as "neophrenology", in reference to the field of phrenology from the C19th, which is now largely ignored (see book by William Uttal; see also writings of Mike Page). I (among others, eg Tim Shallice) have argued that this is not the case. More specifically, I have argued that functional neuroimaging does not only seek to "localise" functions in the brain, but can also inform psychological-level theories (Henson, 2005). For example, in relation to the debate concerning different memory systems (see above), I would argue that functional neuroimaging provides additional data that can inform the debate between, eg single- vs dual-process models of recognition memory (a debate that has so far proven difficult to resolve on the basis of behavioural data alone). In particular, I distinguished two types of psychological inference one can make from fMRI data, which have become known more succinctly as "forward" and "reverse" inference (the latter introduced by Russ Poldrack), related to the simple ideas of "dissociations" and "associations". However, such inferences still require additional "bridging" assumptions (much like the field of cognitive neuropsychology), and certainly more thought than is typically given in functional neuroimaging papers. For example, I argued that the claim of "qualitatively" different patterns of activity over the brain, necessary for a "forward inference", requires criteria more stringent than even a double-dissociation between brain regions and experimental conditions; criteria that resemble the "reversed associations" promoted in psychological research (Henson, 2006a).

Max Coltheart is one of a number of researchers who have questioned whether functional neuroimaging has yet told us anything new about psychological theories, and critiqued some of the examples I gave in the 2005 paper above (see also papers by Mike Page). In my reply (Henson, 2006b), I pointed out that his criticisms nearly always related to psychological issues in the design of the imaging experiments, and not the more theoretical question of whether a perfectly designed (from the psychological perspective) imaging experiment could ever be informative. I also questioned whether some of his examples of theoretically important conclusions from behavioural data were not open to the same questions, and pondered the more fundamental question about whether experimental dissociations and fractionation are appropriate for systems as complex and nonlinear as the brain.

On a more specific issue, I have argued together with Karl Friston against the use of functional localiser sessions to identify regions of interest (ROIs) over which the BOLD signal is averaged (Friston et al, 2006; Friston & Henson, 2006). Note that we did not argue against the functional definition of regions per se, only that these are normally better identified by orthogonal contrasts within the same experimental session, and that they may be better used as search volumes in order to allow for functional heterogeneity within an ROI.

Methodological Developments in fMRI, EEG/MEG Analysis

My use of neuroimaging has necessitated several methodological developments, guided by the work of Karl Friston and the Statistical Parametric Mapping (SPM) group. My early work coincided with the advent of event-related fMRI, including issues like how to model the neural-haemodynamic mapping (Josephs & Henson, 1999), how to model the BOLD impulse response (for which three degrees of freedom seem sufficient; Henson et al, 2000; Henson et al, 2001; Henson, 2004), how to accommodate different slice acquisition times (Henson et al, 1999), how to characterise the BOLD impulse response latency (Henson & Rugg, 2001; Henson et al, 2002), how to apply Parametric Empirical Bayesian methods (Friston et al, 2002) and how to record speech in the fMRI scanner (Henson & Josephs, 2002).

A particular interest has been in how to optimise experimental designs for fMRI (Josephs & Henson, 1999; Friston et al, 1999; Mechelli et al, 2003a; Mechelli et al, 2003b), summarised in this webpage: http://imaging.mrc-cbu.cam.ac.uk/imaging/DesignEfficiency. A lot of the above ideas related to convolution models for fMRI, design efficiency, etc, can also be found in contributions to chapters in the SPM book (Henson, 2006; Henson & Friston, 2006; Penny & Henson, 2006a; Penny & Henson, 2006b), in example datasets I put on the SPM website and in the SPM manual http://www.fil.ion.ucl.ac.uk/spm/data/, in published guidelines (Poldrack et al, 2008), or technical notes I wrote (e.g, about ANOVAs and single-case studies; Henson & Penny 2003; Henson, 2006; see http://www.mrc-cbu.cam.ac.uk/people/rik.henson/personal/analysis.html).

More recently, I have been helping extend SPM to the analysis of EEG and MEG data. This concerns the use of Random Field Theory to correct for multiple comparisons in statistical maps across space-time (Henson et al, 2008) or frequency-time (Henson et al, 2005), and distributed solutions to the inverse problem (reconstruction of the cortical sources of EEG/MEG data), based on the work of Karl Friston using Parametric Empirical Bayesian methods (Friston et al, 2006; Henson et al, 2007; Friston et al, 2008). I have also examined the question of forward models for MEG (Henson et al, 2009; Mattout et al, 2007) and group-localisation of results. This has included contributing to the SPM5 code. My future ambitions in this area concern methods for proper data fusion of fMRI and EEG/MEG (e.g, Henson et al, in press; Kilner et al, 2005), and the analysis of effective connectivity between timecourses of cortical regions reconstructed from MEG/EEG (e.g, Chen et al, 2009).

Past Projects

Short-term Memory for Serial Order

My past research, stemming from my PhD (http://www.mrc-cbu.cam.ac.uk/people/rik.henson/personal/thesis.html) concerns short-term memory, in particular memory for serial order (e.g, the order of digits in a novel telephone number). This involved finding stricter empirical evidence against previous "chaining" models (Henson et al, 1996) and development of a new computational model - the "Start-End Model" (Henson, 1998a) - together with empirical tests of its predictions (Henson, 1999a) (for which I was awarded the first BPS prize for doctoral research, Henson, 2001). Two key issues in this model relate to the representation of position within a sequence, which appears to be relative to the start and end of that sequence (Henson, 1999b) and the representation of repeated items, for which effects like the Ranschburg effect support both type and token representation (Henson, 1998b). Some of the ideas were extended to examine the effects of ageing (Maylor & Henson, 2000) and development (McCormack et al, 2000) on short-term memory.

Together with Neil Burgess and Graham Hitch, we considered how relative position within a sequence could be coded by temporal oscillators (Henson & Burgess, 1997), and performed some behavioural tests for the existence of such oscillators (Henson et al, 2003). We also considered how such a model of short-term memory for serial order might map onto the brain using fMRI (Henson et al, 2000).

During my MSc, I also briefly examined the short-term memory capacities of auto-associative neural networks (palimpsests) together with David Willshaw (Henson, 1993; Henson & Willshaw, 1995).