Anthony Stigliani - Revision history

imported>Kweb: /* Experimental Design */

2014-04-16T05:26:07Z

Experimental Design

← Older revision		Revision as of 05:26, 16 April 2014
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	= Experimental Design =		= Experimental Design =
	fMRI-A has been measured using a variety of stimulus presentation parameters and experimental designs, each of which has advantages and disadvantages. Some studies use blocked designs in which adaptation is observed across several presentations of the same stimulus, while others use event-related designs in which adaptation is observed across two ~~repeated~~ presentations. In general, block designs produce larger effect sizes (Grill-Spector et al., 2006), but event-related designs are more appropriate in certain circumstances such as when the predictability or full counterbalancing of stimuli is important. <br>		fMRI-A has been measured using a variety of stimulus presentation parameters and experimental designs, each of which has advantages and disadvantages. Some studies use blocked designs in which adaptation is observed across several presentations of the same stimulus, while others use event-related designs in which adaptation is observed across two presentations of the same stimulus. In general, block designs produce larger effect sizes (Grill-Spector et al., 2006), but event-related designs are more appropriate in certain circumstances such as when the predictability or full counterbalancing of stimuli is important. <br>

	== Block Design ==		== Block Design ==
	[[File:block_design.jpg\|320px\|thumb\|right]]		[[File:block_design.jpg\|320px\|thumb\|right]]

	In blocked fMRI-A designs, ~~several~~ stimuli in the same experimental condition are presented in groupings~~, which~~ are ideally counterbalanced across conditions over the course of the experiment. In an experiment examining the the sensitivity of neural representations to changes in viewpoint, an identical condition block would consist of several sequential presentations of the same face shown from the same viewpoint each time. Transformation conditions blocks would consist of several presentations of the same shown from different viewpoints, and different condition blocks would contain several images of different faces.		In blocked fMRI-A designs, a series of stimuli belonging to the the same experimental condition are presented in groupings that are ideally counterbalanced across conditions over the course of the experiment. In the aforementioned experiment examining the the sensitivity of neural representations to changes in viewpoint, for example, an identical condition block would consist of several sequential presentations of the same face shown from the same viewpoint each time. Transformation conditions blocks would consist of several presentations of the same face shown from different viewpoints, and different condition blocks would contain several images of different faces.

	'''Advantages'''		'''Advantages'''
	*Generally produces ~~largest~~ effect sizes (see Grill-Spector et al., 2006)		*Generally produces larger effect sizes (see Grill-Spector et al., 2006)
	*Relatively simple to generate stimulus sequences and model fMRI-A effects		*Relatively simple to generate stimulus sequences and model fMRI-A effects
	'''Disadvantages'''		'''Disadvantages'''
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	[[File:pairedadaptation_design.jpg\|320px\|thumb\|right]]		[[File:pairedadaptation_design.jpg\|320px\|thumb\|right]]

	In paired adaptation designs, fMRI response to pairs of stimuli are the units of analysis which allows both stimuli to be presented in one TR and modeled as ~~one~~ event. With ~~regards~~ to the aforementioned face perception study, identical condition events would consist of two sequential presentations of the same image. Transformation condition events would consist of sequential ~~presentation~~ of the same face from different viewpoints, and different condition events would consist of images of two different faces.		In paired adaptation designs, fMRI response to pairs of stimuli are the units of analysis which allows both stimuli to be presented in one TR and modeled as a single experimental event. With regard to the aforementioned face perception study, identical condition events would consist of two sequential presentations of the same image. Transformation condition events would consist of sequential presentations of the same face from different viewpoints, and different condition events would consist of images of two different faces.

	'''Advantages'''		'''Advantages'''
	*Stimuli are less predictable than in block designs		*Stimuli are less predictable than in block designs
	*More conditions of interest can ~~generally~~ be included in the experiment because epochs ~~are~~ shorter		*More conditions of interest can often be included in the experiment because individual epochs have shorter durations
	*More suitable for implementing parametric designs		*More suitable for implementing parametric designs
	'''Disadvantages'''		'''Disadvantages'''
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	[[File:laggedadaptation_design.jpg\|320px\|thumb\|right]]		[[File:laggedadaptation_design.jpg\|320px\|thumb\|right]]

	In lagged adaptation designs, pairs of stimuli are repeated over the course of a run (or in different runs) but not on sequential trials. There is some evidence that different neural mechanisms ~~explain~~ fMRI-A observed with many intervening stimuli (Weiner et al., 2010). In this type of design, trials ~~containing~~ a single stimulus presentation are modeled as novel or repeated, and the number of intervening stimuli ~~should~~ be balanced across different stimulus conditions.		In lagged adaptation designs, pairs of stimuli are repeated over the course of a run (or in different runs) but not on sequential trials. There is some evidence that different neural mechanisms account for fMRI-A observed with many intervening stimuli (Weiner et al., 2010). In this type of design, trials consisting of a single stimulus presentation are modeled as novel or repeated, and the number of intervening stimuli must be balanced across different stimulus conditions.

	'''Advantages'''		'''Advantages'''
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	=== Continuous-carryover design ===		=== Continuous-carryover design ===
	[https://cfn.upenn.edu/aguirre/wiki/public:carry-over_designs Continuous carry-over designs] use serially balanced stimulus sequences which simultaneously counterbalance main effects and first-order carry-over effects for all stimuli in the sequence. This allows fMRI-A and multi-voxel pattern analysis (MVPA) to be performed in the same dataset because the first order carry-over effects measured with fMRI-A are independent of the counterbalanced main effects required to perform MVPA. This type of design is well suited to model fMRI-A when the stimuli of interest fill a continuous dimensional space such as shape or color. ~~This type of~~ design ~~has~~ recently ~~been~~ used distinguish brain regions that represent color as a continuous dimension and regions that represent colors in a more categorical manner ([https://cfn.upenn.edu/aguirre/wiki/_media/public:presentations:coloradaptposter_vss.pdf Persichetti et al., 2013]). <br>		[https://cfn.upenn.edu/aguirre/wiki/public:carry-over_designs Continuous carry-over designs] use serially balanced stimulus sequences which simultaneously counterbalance main effects and first-order carry-over effects for all stimuli included in the sequence. This allows fMRI-A and multi-voxel pattern analysis (MVPA) to be performed in the same dataset because the first order carry-over effects measured with fMRI-A are independent of the counterbalanced main effects required to perform MVPA. This type of design is well suited to model fMRI-A when the stimuli of interest can be modeled to fill a continuous dimensional space such as shape or color. For example, a continuous carry-over design was recently used to distinguish between brain regions that represent color as a continuous dimension and regions that represent colors in a more categorical manner ([https://cfn.upenn.edu/aguirre/wiki/_media/public:presentations:coloradaptposter_vss.pdf Persichetti et al., 2013]). <br>

	'''Advantages'''		'''Advantages'''

imported>Kweb: /* Measuring fMRI-A */

2014-04-16T05:18:06Z

Measuring fMRI-A

imported>Kweb: /* Background */

2014-04-16T05:10:56Z

Background

← Older revision		Revision as of 05:10, 16 April 2014
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	[[File:repetition_supression.jpg\|thumb\|300px\|right\|'''Figure 1.''' Response of an example IT neuron to first the presentation of stimulus A (left), the first presentation of stimulus B (center), and repeated presentation of stimulus A (right) (figure courtesy of Grill-Spector et al. 2006).]]		[[File:repetition_supression.jpg\|thumb\|300px\|right\|'''Figure 1.''' Response of an example IT neuron to first the presentation of stimulus A (left), the first presentation of stimulus B (center), and repeated presentation of stimulus A (right) (figure courtesy of Grill-Spector et al. 2006).]]

	Repetition-related reductions in neural firing were first observed in electrophysiological recordings of neurons in macaque inferior temporal (IT) cortex (e.g., Li et al., 1993). Instead of illustrating a general reduction in neural firing when any two stimuli are shown in quick succession, response attenuation in this region was only observed only when identical visual stimuli were presented sequentially (see ''Figure 1''). Similar effects of stimulus repetition are observed in the human brain with fMRI-A, and some regions in ventral temporal cortex show adapted responses even when the repeated stimulus undergoes various transformations such as changes in position in the visual field or retinal size (e.g., Grill-Spector et al., 1999). This raises the ~~questions~~ of what counts as the "same" stimulus, but this depends on the particular question being asked. In studies of object recognition, individual items are typically object exemplars or faces, but fMRI-A has also observed in left inferior frontal cortex when different objects from the same category are shown sequentially as compared to when objects from different categories are presented in succession (Vuilleumier et al. 2002), which allows different hypotheses to be tested. Outside of this body of research, fMRI-A has been used to address a range of topics from the orientation tuning of neurons in visual cortex (Fang et al., 2005) to the representation of numerosity in parietal cortex (Piazza et al., 2004). The definition of an item varies greatly across these studies, but the power of ~~this~~ approach lies in its ability examine how different transformations affect the neural representation of individual items or stimuli as defined by the experimenter. <br>		Repetition-related reductions in neural firing were first observed in electrophysiological recordings of neurons in macaque inferior temporal (IT) cortex (e.g., Li et al., 1993). Instead of illustrating a general reduction in neural firing when any two stimuli are shown in quick succession, response attenuation in this region was only observed only when identical visual stimuli were presented sequentially (see ''Figure 1''). Similar effects of stimulus repetition are observed in the human brain with fMRI-A, and some regions in ventral temporal cortex show adapted responses even when the repeated stimulus undergoes various transformations such as changes in position in the visual field or retinal size (e.g., Grill-Spector et al., 1999). This raises the question of what counts as the "same" stimulus, but to some extent this depends on the particular question being asked. In studies of object recognition, individual items are typically object exemplars or faces, but fMRI-A has also observed in left inferior frontal cortex when different objects from the same category are shown sequentially as compared to when objects from different categories are presented in succession (Vuilleumier et al. 2002), which allows different hypotheses to be tested. Outside of this body of research, fMRI-A has been used to address a range of topics from the orientation tuning of neurons in visual cortex (Fang et al., 2005) to the representation of numerosity in parietal cortex (Piazza et al., 2004). The definition of an item varies greatly across these fMRI-A studies, but the power of the approach lies in its ability examine how different transformations affect the neural representation of individual items or stimuli as defined by the experimenter. <br>

	As illustrated in ''Figure 2'', several neural mechanisms have been proposed to explain fMRI-A including general neural fatigue, ~~the~~ sharpening of neural representations, and response facilitation resulting in reduced processing time (Grill-Spector et al., 2006), but recent evidence suggests that different mechanisms may operate across different brain regions and time scales (Weiner et al., 2010). In the fatigue model, all neurons that are responsive to a given stimulus show a reduction in firing rate when the stimulus is repeated. The sharpening model posits that fMRI-A reflects a sparser pattern of activation with repeated exposure to a stimulus, and the facilitation model holds that repetition leads to faster neural processing, which results in reduced fMRI response. While these models implicitly assume that fMRI-A reflects reductions in the spiking rates of neurons, both the inputs and outputs to a brain region contribute to BOLD signal, and it is important to consider that some repetition-related response reductions could be inherited from earlier regions (see Logothetis, 2008). <br>		As illustrated in ''Figure 2'', several neural mechanisms have been proposed to explain fMRI-A including general neural fatigue, sharpening of neural representations, and response facilitation resulting in reduced processing time (Grill-Spector et al., 2006), but recent evidence suggests that different mechanisms may operate across different brain regions and time scales of repetition (Weiner et al., 2010). In the fatigue model, all neurons that are responsive to a given stimulus show a reduction in firing rate when the stimulus is repeated. The sharpening model posits that fMRI-A reflects a sparser pattern of activation with repeated exposure to a stimulus, and the facilitation model holds that repetition leads to faster neural processing, which results in reduced fMRI response as a result of a shorter duration of signal accumulation. While these models implicitly assume that fMRI-A reflects reductions in the spiking rates of individual neurons, both the inputs and outputs to a brain region contribute to BOLD signal, and it is important to consider that some repetition-related response reductions could be inherited from earlier regions (see Logothetis, 2008). <br>

	[[File:fMRI-A_models.jpg\|thumb\|600px\|center\|'''Figure 2.''' Neural models of fMRI-A. Different neural mechanisms have been proposed to explain repetition-related reductions in fMRI signal. The fatigue model posits a general reduction in neural response with stimulus repetition. The sharpening model holds that repetition results in activation of a sparser neural representation, and the facilitation model attributes fMRI-A to ~~an overall~~ reduction in processing time (figure ~~courtesy of~~ Grill-Spector et al., 2006).]]		[[File:fMRI-A_models.jpg\|thumb\|600px\|center\|'''Figure 2.''' Neural models of fMRI-A. Different neural mechanisms have been proposed to explain repetition-related reductions in fMRI signal. The fatigue model posits a general reduction in neural response with stimulus repetition. The sharpening model holds that repetition results in activation of a sparser neural representation, and the facilitation model attributes fMRI-A to a reduction in processing time (figure adapted from Grill-Spector et al., 2006).]]

	= Measuring fMRI-A =		= Measuring fMRI-A =

imported>Kweb: /* Paired adaptation design */

2013-07-07T01:16:51Z

Paired adaptation design

← Older revision		Revision as of 01:16, 7 July 2013
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	'''Advantages'''		'''Advantages'''
	*Stimuli are less predictable than in block designs		*Stimuli are less predictable than in block designs
	*More conditions of interest can generally be included in the experiment because ~~events~~ are shorter		*More conditions of interest can generally be included in the experiment because epochs are shorter
	*More suitable for implementing parametric designs		*More suitable for implementing parametric designs
	'''Disadvantages'''		'''Disadvantages'''

imported>Kweb: /* Block Design */

2013-07-07T01:15:55Z

Block Design

← Older revision		Revision as of 01:15, 7 July 2013
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	*Relatively simple to generate stimulus sequences and model fMRI-A effects		*Relatively simple to generate stimulus sequences and model fMRI-A effects
	'''Disadvantages'''		'''Disadvantages'''
	*Stimuli are ~~highly~~ predictable based upon the first few stimulus in a block		*Stimuli are somewhat predictable based upon the first few stimulus presentations in a block
	*More difficult to implement parametric designs in many cases		*More difficult to implement parametric designs in many cases

imported>Kweb: /* An Example from Face Perception */

2013-07-07T01:13:12Z

An Example from Face Perception

← Older revision		Revision as of 01:13, 7 July 2013
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	[[File:fMRI-A_schematic2.jpg\|400px\|thumb\|center\|Figure adapted from Grill-Spector & Malach (2001)]]		[[File:fMRI-A_schematic2.jpg\|400px\|thumb\|center\|Figure adapted from Grill-Spector & Malach (2001)]]
	<br>		<br>
	When the same face is shown from different views, fMRI response would differ depending upon the sensitivity of the underlying neural representation to changes in viewpoint. If the majority of neurons in a voxel respond to the face in a viewpoint-specific manner, the response would ~~remain adapted~~. If, on the other hand, most neurons responded in a viewpoint-invariant manner, fMRI signal should ~~recover to~~ levels observed in epochs of ~~different~~ faces.		When the same face is shown from different views, fMRI response would differ depending upon the sensitivity of the underlying neural representation to changes in viewpoint. If the majority of neurons in a voxel respond to the face in a viewpoint-specific manner, the response would show a recovery from adaptation because different populations of neurons are activated by the different views. If, on the other hand, most neurons responded in a viewpoint-invariant manner, fMRI signal should remain adapted at levels observed in epochs of identical faces.
	<br>		<br>
	[[File:fMRI-A_schematic3.jpg\|400px\|thumb\|center\|Figure adapted from Grill-Spector & Malach (2001)]]		[[File:fMRI-A_schematic3.jpg\|400px\|thumb\|center\|Figure adapted from Grill-Spector & Malach (2001)]]

imported>Kweb: /* Continuous-carryover design */

2013-06-07T19:03:08Z

Continuous-carryover design

← Older revision		Revision as of 19:03, 7 June 2013
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	=== Continuous-carryover design ===		=== Continuous-carryover design ===
	[https://cfn.upenn.edu/aguirre/wiki/public:carry-over_designs Continuous carry-over designs] use serially balanced stimulus sequences which simultaneously counterbalance main effects and first-order carry-over effects for all stimuli in a sequence. This allows fMRI-A and multi-voxel pattern analysis (MVPA) to be performed in the same dataset because the first order carry-over effects measured with fMRI-A are independent of the counterbalanced main effects required to perform MVPA. This type of design is well suited to model fMRI-A when the stimuli of interest fill a continuous dimensional space such as shape or color. This type of design has recently been used distinguish brain regions that represent color as a continuous dimension and regions that represent colors in a more categorical manner ([https://cfn.upenn.edu/aguirre/wiki/_media/public:presentations:coloradaptposter_vss.pdf Persichetti et al., 2013]).<br>		[https://cfn.upenn.edu/aguirre/wiki/public:carry-over_designs Continuous carry-over designs] use serially balanced stimulus sequences which simultaneously counterbalance main effects and first-order carry-over effects for all stimuli in the sequence. This allows fMRI-A and multi-voxel pattern analysis (MVPA) to be performed in the same dataset because the first order carry-over effects measured with fMRI-A are independent of the counterbalanced main effects required to perform MVPA. This type of design is well suited to model fMRI-A when the stimuli of interest fill a continuous dimensional space such as shape or color. This type of design has recently been used distinguish brain regions that represent color as a continuous dimension and regions that represent colors in a more categorical manner ([https://cfn.upenn.edu/aguirre/wiki/_media/public:presentations:coloradaptposter_vss.pdf Persichetti et al., 2013]). <br>

	'''Advantages'''		'''Advantages'''

imported>Kweb: /* Block Design */

2013-06-07T19:01:44Z

Block Design

← Older revision		Revision as of 19:01, 7 June 2013
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	== Block Design ==		== Block Design ==
			[[File:block_design.jpg\|320px\|thumb\|right]]

	In blocked fMRI-A designs, several stimuli in the same experimental condition are presented in groupings, which are ideally counterbalanced across conditions over the course of the experiment. In an experiment examining the the sensitivity of neural representations to changes in viewpoint, an identical condition block would consist of several sequential presentations of the same face shown from the same viewpoint each time. Transformation conditions blocks would consist of several presentations of the same shown from different viewpoints, and different condition blocks would contain several images of different faces.		In blocked fMRI-A designs, several stimuli in the same experimental condition are presented in groupings, which are ideally counterbalanced across conditions over the course of the experiment. In an experiment examining the the sensitivity of neural representations to changes in viewpoint, an identical condition block would consist of several sequential presentations of the same face shown from the same viewpoint each time. Transformation conditions blocks would consist of several presentations of the same shown from different viewpoints, and different condition blocks would contain several images of different faces.

	~~[[File:block_design.jpg\|320px\|thumb\|center]]~~

	'''Advantages'''		'''Advantages'''

imported>Kweb: /* Lagged adaptation design */

2013-06-07T19:00:47Z

Lagged adaptation design

← Older revision		Revision as of 19:00, 7 June 2013
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	=== Lagged adaptation design ===		=== Lagged adaptation design ===
			[[File:laggedadaptation_design.jpg\|320px\|thumb\|right]]

	In lagged adaptation designs, pairs of stimuli are repeated over the course of a run (or in different runs) but not on sequential trials. There is some evidence that different neural mechanisms explain fMRI-A observed with many intervening stimuli (Weiner et al., 2010). In this type of design, trials containing a single stimulus presentation are modeled as novel or repeated, and the number of intervening stimuli should be balanced across different stimulus conditions.		In lagged adaptation designs, pairs of stimuli are repeated over the course of a run (or in different runs) but not on sequential trials. There is some evidence that different neural mechanisms explain fMRI-A observed with many intervening stimuli (Weiner et al., 2010). In this type of design, trials containing a single stimulus presentation are modeled as novel or repeated, and the number of intervening stimuli should be balanced across different stimulus conditions.

	~~[[File:laggedadaptation_design.jpg\|320px\|thumb\|center]]~~

	'''Advantages'''		'''Advantages'''

imported>Kweb: /* Event-Related Designs */

2013-06-07T18:56:26Z

Event-Related Designs

← Older revision		Revision as of 18:56, 7 June 2013
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	== Event-Related Designs ==		== Event-Related Designs ==
	=== Paired adaptation design ===		=== Paired adaptation design ===
	[[File:pairedadaptation_design.jpg\|320px\|thumb\|~~center~~]]		[[File:pairedadaptation_design.jpg\|320px\|thumb\|right]]

	In paired adaptation designs, fMRI response to pairs of stimuli are the units of analysis which allows both stimuli to be presented in one TR and modeled as one event. With regards to the aforementioned face perception study, identical condition events would consist of two sequential presentations of the same image. Transformation condition events would consist of sequential presentation of the same face from different viewpoints, and different condition events would consist of images of two different faces.		In paired adaptation designs, fMRI response to pairs of stimuli are the units of analysis which allows both stimuli to be presented in one TR and modeled as one event. With regards to the aforementioned face perception study, identical condition events would consist of two sequential presentations of the same image. Transformation condition events would consist of sequential presentation of the same face from different viewpoints, and different condition events would consist of images of two different faces.

← Older revision		Revision as of 05:18, 16 April 2014
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	#'''Transformation condition''' Response when the same stimulus is repeated but varied along one dimension		#'''Transformation condition''' Response when the same stimulus is repeated but varied along one dimension

	In a typical fMRI-A design, a ~~"basic" or "~~baseline" fMRI-A effect is defined as reduced fMRI signal in the identical condition compared to the different condition. Response to the transformation ~~conditoin~~ is used to assess the sensitivity of a neural representation to the varied dimension.		In a typical fMRI-A design, a baseline fMRI-A effect is defined as reduced fMRI signal in the identical condition compared to the different condition. Response to the transformation condition is then used to assess the sensitivity of a neural representation to the varied dimension.

	== Measuring a Dynamic Range of fMRI-A ==		== Measuring a Dynamic Range of fMRI-A ==
	BOLD response to the first two conditions must be measured in order to define a dynamic range in which to compare response to the ~~third~~ condition ~~of interest~~. If fMRI signal in a voxel is reduced when an identical stimulus is repeated relative to presentation of different stimuli, this suggests that underlying neural representation in this region is stimulus-specific. This inference is based on the notion that ~~sequential~~ presentation of different stimuli ~~activate~~ different populations of neurons in ~~the~~ voxel, and therefore the neither population becomes adapted. However, if there are populations of neurons that respond selectively to the repeated stimulus, subsequent presentations of the same item will activate the same population of neurons repeatedly resulting in an adapted fMRI response. <br>		BOLD response to the first two conditions must be measured in order to define a dynamic range in which to compare response to the transformation condition. If fMRI signal in a voxel is reduced when an identical stimulus is repeated relative to presentation of different stimuli, this suggests that underlying neural representation in this region is stimulus-specific. This inference is based on the notion that presentation of different stimuli leads to activation of different populations of neurons in a voxel, and therefore the neither population becomes adapted. However, if there are populations of neurons that respond selectively to the repeated stimulus, subsequent presentations of the same item will activate the same population of neurons repeatedly resulting in an adapted fMRI response. <br>

	== Assessing Sensitivity and Invariance ==		== Assessing Sensitivity and Invariance ==
	After measuring the ~~basic~~ fMRI-A effect in a voxel, fMRI response to the transformation condition is compared to the identical and different conditions. If the neural representation of a stimulus is sensitive to the varied dimension, fMRI signal should be comparable to conditions in which different stimuli are presented because different populations of neurons are activated when this feature of the stimulus is manipulated. If the representation is invariant or tolerant to changes in the dimension of interest, fMRI signal should remain at levels associated with repetition of an identical stimulus because the neurons are ~~essentially~~ blind to changes in ~~this particular~~ dimension and the same population responds to both variants of the stimulus. While comparisons between these three (or more) conditions are proposed to assess neural population tuning at sub-voxel resolution, it is important to realize that voxels likely contain a mixture of neurons that are sensitive and tolerant to changes in the dimension of interest. Nevertheless, adapted (or recovered) responses ~~just~~ reflect the response profile of the majority of stimulus-responsive neurons in a voxel.		After measuring the baseline fMRI-A effect in a given voxel, fMRI response to the transformation condition is compared to the identical and different conditions. If the neural representation of a stimulus is sensitive to the varied dimension, fMRI signal should be comparable to conditions in which different stimuli are presented because different populations of neurons are activated when this feature of the stimulus is manipulated. If the representation is invariant or tolerant to changes in the dimension of interest, fMRI signal should remain at levels associated with repetition of an identical stimulus because the neurons are figuratively blind to changes in the varied dimension and the same population responds to both variants of the stimulus. While comparisons between these three (or more) conditions are proposed to assess neural population tuning at sub-voxel resolution, it is important to realize that voxels likely contain a mixture of neurons that are sensitive and tolerant to changes in the dimension of interest. Nevertheless, adapted (or recovered) responses can be interpreted to reflect the response profile of the majority of stimulus-responsive neurons in a voxel.

	== An Example from Face Perception ==		== An Example from Face Perception ==
	As an example of how fMRI-A can be used to assess the nature of neural representations, imagine two hypothetical populations of neurons involved in representing a face. In one population, different sets of neurons are activated when the face is viewed from different angles (viewpoint-specific neurons). In the other population, the same set of neurons ~~respond~~ across all viewpoints (viewpoint-invariant neurons). <br>		As an example of how fMRI-A can be used to assess the nature of neural representations, imagine two hypothetical populations of neurons involved in representing a specific face. In one population, different sets of neurons are activated when the face is viewed from different angles (viewpoint-specific neurons). In the other population, the same set of neurons responds across all viewpoints (viewpoint-invariant neurons). <br>
	<br>		<br>
	[[File:fMRI-A_schematic1.jpg\|400px\|thumb\|center\|Figure adapted from Grill-Spector & Malach (2001)]]		[[File:fMRI-A_schematic1.jpg\|400px\|thumb\|center\|Figure adapted from Grill-Spector & Malach (2001)]]
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	[[File:fMRI-A_schematic2.jpg\|400px\|thumb\|center\|Figure adapted from Grill-Spector & Malach (2001)]]		[[File:fMRI-A_schematic2.jpg\|400px\|thumb\|center\|Figure adapted from Grill-Spector & Malach (2001)]]
	<br>		<br>
	When the same face is shown from different views, fMRI response would differ depending upon the sensitivity of the underlying neural representation to changes in viewpoint. If the majority of neurons in a voxel respond to the face in a viewpoint-specific manner, the ~~response~~ would show a recovery from adaptation because different populations of neurons are activated by the different views. ~~If, on~~ the other hand, most neurons responded in a viewpoint-invariant manner, fMRI signal should remain adapted at levels observed in epochs of identical ~~faces~~.		When the same face is shown from different views, fMRI response would differ depending upon the sensitivity of the underlying neural representation to changes in viewpoint. If the majority of neurons in a voxel respond to the face in a viewpoint-specific manner, the fMRI signal in that voxel would show a recovery from adaptation because different populations of neurons are activated by the different views. On the other hand, if most neurons responded in a viewpoint-invariant manner, fMRI signal should remain adapted at levels observed in epochs of identical images.
	<br>		<br>
	[[File:fMRI-A_schematic3.jpg\|400px\|thumb\|center\|Figure adapted from Grill-Spector & Malach (2001)]]		[[File:fMRI-A_schematic3.jpg\|400px\|thumb\|center\|Figure adapted from Grill-Spector & Malach (2001)]]