Brain State Dependent Role of Attention in Perceptual Processing and Decision Making

Institute for Research in Fundamental Sciences

Institute for Studies in Theoretical Physics and Mathematics (IPM)

School of Cognitive Sciences (SCS)

Ph.D. Thesis Cognitive Science, Brain and Cognition

Brain State Dependent Role of Attention in Perceptual Processing and Decision Making

Supervisor: Professor Hossein Esteky

March, 2011

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Abstract

Attention to a specific target or location in visual space enhances the baseline activity of the cells representing the target or the spatial location. Attention can also be directed based on the expectations. Attention mediated enhanced baseline activity is correlated with improved object recognition. To explore the relation of visual attention with neural baseline activity, cortical sensory processing and the behavioral choice we recorded the activity of single cells in the inferior temporal cortex of monkeys during two different tasks. The tasks were a passive fixation and a two-alternative forced choice categorization of noisy body and object images. We found enhanced neural activity in categorization task compared to the passive fixation task. Both body and object selective cells showed significantly more response enhancement for their preferred category compared to the non-preferred category. No such response enhancement was observed in trials when the monkeys made a wrong choice in the categorization task. Magnitude of the response enhancement was larger for more noisy stimuli. More importantly, in trials with high baseline activity responses of body selective and object selective cells to body images were enhanced and suppressed, respectively. We also found decreased neural response variability in the categorization compared to the passive task. Larger effects were observed at higher noise levels. By measuring choice probability we found that neural firing rate was correlated with monkeys’ choice, particularly in trials with high baseline activity. We suggest that attentional enhancement of IT cells’ baseline firing rate is correlated with improved neural response reliability and category selectivity. These effects are dependent on the cells’ category selectivity, attentional load and the exact time of baseline activity increase.

Keywords: object recognition, neural baseline activity, visual attention, decision making

Table of Contents

Introduction…………………………………………………………………..….10

The crucial role of “visual object categorization: in everyday life…………10

Where in the brain is category information represented? ……………………11

Anatomy of inferior temporal cortex…………………………….…………23

Attention improves categorization performance, especially in difficult condition…………………………………………………………………….25

Bottom-up vs. top-down attention………………………………….………26

What is attention directed to?………………………………………….……28
1. Object-based attention…………………………………..…..….……29

Sources and targets of attention in the brain…………………………….…31

Attention modulates different response properties…………………..……..31
1. Firing rate modulation………………….……………………………32
  1. Response suppression…………………………………………33

Baseline enhancement………………………………..………34

Reliability increase……………………………………….………….36

Response sensitivity increase……………………………….……….37

Response selectivity modulation…………………………….………38

Synchronization, oscillation and correlated responses across cell population…………………………………………………………….39

Objectives……………………………………………………………………..….42

Method……………………………………………………………………………43

Subjects………………………………………………………….…………43

Stereotactic MRI……………………………………………….…………..43

Head-post implantation surgery.……………………….…………………..44

Stimuli…………………………………………………….………………..46

Tasks…………………………………………………………………………47
1. Active task (two-alternative forced-choice body/object categorization)……………………………………………………….47

Training…………………………………………………….………………50

Eye monitoring………………………………………………….………….52

Craniotomy surgery …………………………………………….………….52

Recording………………………………………………………….……….53
1. Signal amplification and frequency filtering ……………………….57

Data analysis……………………………………………………..…………59
1. Peristimulus time histograms (PSTH), normalizing and smoothing ……………………………………………………………………….67

Results…………………………………………………………………………….68

Conclusion………………………………………………………………………..88

Figures……………………………………………………………………………90

Stimulus set …………………………………….……………..….90

Figure 2. Different noise levels of an exemplar stimulus ……………….…91

Figure 3. Passive task……………………………………………………….92

Figure 4. Active task (two-alternative forced-choice body/object categorization…………………………………………………………….…93

Figure 5. Monkeys’ performance in body/object categorization task. …….95

Figure 6. The pattern of performance decline as a function of noise level was reverse for bodies and objects …………………………………………….. 96

Figure 7. Monkeys’ performance in body/object categorization task for subcategories …………………………………………………………..…..97

Figure 8 .Performance decline between adjacent signal levels in subcategories of bodies and objects ……………………………….………98

Figure 9. Behavioral d́ (d́ = Z “hit rate” – Z “false alarm”) in different visual signals ……………………………………………………………….…….. 99

Figure 10. Cumulative d́ in signal level of 90 ……………………….……100

Figure 11. Reaction time in different signal conditions in correct and wrong trials……………………………………………………………………….101

Figure 12. Reaction time in subcategory level……………………………102

Figure 13. Relation between reaction time and performance in different signal levels…………………………………………………………….…103

Figure 14. Mean number of microsaccades in different noise levels……..104

Figure 15. Mean number of microsaccades in correct and wrong trials of different signal levels……………………………………………………..105

Figure 16. Reaction time in trials with and without microsaccades in different signal levels…………………………………………………..…106

Figure 17. Normalized mean firing rate of body cells across different visual signals and behavioral conditions…………………………………….…..107

Figure 18. Normalized mean firing rate of non-body cells across different visual signals and behavioral conditions…………………………………..109

Figure 19.Response modulation index (RMI) as a function of task difficulty……………………………………………………………….….111

Figure 20. Mean response modulation onset across body image signal levels in body cells’ correct trials…………………………………………….….112

Figure 21. Attentional enhancement of IT cells’ body-object discriminability (d’) was observed only in correct trials and degree of enhancement depended on task difficulty………………………………………………………..…114

Figure 22. Mean d’ modulation in correct (blue) and wrong (black) compared to passive condition in body cells…………………………………………116

Figure 23. Mean d’ modulation in correct (blue) and wrong (black) compared to passive condition in non-body cells……………………………………117

Figure 24. Temporal pattern of baseline firing rate modulation in active compared to passive condition…………………………………………….118

Figure 25. Temporal pattern of p-values of t-tests measuring significant increase of baseline rate in active compared to passive condition……..…119

Figure 26. Frequency distribution of proportion of HBTs in body (top) and non-body (bottom) cells during active task…………………………….…120

Figure 27. Baseline dependent enhancement of body and suppression of non-body cells’ responses to presentation of body images in correct condition……………………………………………………………….….121

Figure 28. Baseline dependent enhancement of body and suppression of non-body cells’ responses to presentation of body images in wrong condition………………………………………………………………..…122

Figure 29. Temporal dynamic of body and non-body cells’ RMI to presentation of body images in correct and wrong conditions for HBTs………………………………………………………………………123

Figure 30. Temporal dynamic of body and non-body cells’ RMI to presentation of body images in correct and wrong conditions for LBTs…124

Figure 31. P-values of t-tests measuring significant enhancement of body and suppression of non-body cells’ responses in HBTs as time window to define high baseline activity varied over time…………………………….125

Figure 32. P-values of t-tests measuring significantly larger RMI values of body and smaller RMI values of non-body cells’ in HBTs vs. LBTs as time window to define high vs. low baseline activity varied over time…….….126

Figure 33. Body cells’ RMI values of high and low baseline trials across body stimulus signal levels…………………………………………….…127

Figure 34. Baseline dependent modulation of body and non-body cells’ responses to presentation of object images…………………………….…129

Figure 35. Baseline dependent modulation of body and non-body cells’ responses to presentation of object images…………………………….…130

Figure 36. Temporal dynamics of body and non-body cells’ RMI to presentation of object images in correct and wrong conditions for HBTs………………………………………………………………………131

Figure 37. Temporal dynamics of body and non-body cells’ RMI to presentation of object images in correct and wrong conditions for LBTs………………………………………………………………………132

Figure 38. Frequency distribution of adjusted RMI values for body and non-body cells in HBTs and LBTs………………………………………….…133

Figure 39. Rate-matched fano factor modulation index (FFMI) of body and non-body cells to presentation of body images in correct condition for HBTs vs. LBTs………………………………………………………………..…134

Figure 40. Rate-matched fano factor modulation (FFMI) of body and non-body cells to presentation of object images in correct conditions for HBTs and LBTs. …………………………………………………………………135

Figure 41a. Frequency distribution of normalized d’ modulation difference in LBTs vs. HBTs for body and non-body cells…………………………..136

Figure 41b. The impact of task specific attentional modulation on firing rate depends on cells’ category selectivity……………………………………..137

Figure 42. The impact of task specific attentional modulation on firing rate depends on cells’ category selectivity………………………………….…138

Figure 43. Comparison of RMI values of correct vs. wrong trials of LBTs and HBTs……………………………………………………………….…139

Figure 44. Comparison of rate modulation in body and non-body cells population across trials of body images with different baseline spike counts……………………………………………………………………..140

Figure 45. Comparison of rate modulation in body and non-body cells population across trials of object images with different baseline spike counts………………………………………………………………….….142

Figure 46. Baseline dependent correlation of neural activity and behavioral choice…………………………………………………………………..…143

Figure 47. Correlation between CP and cells’ body/object discrimination power………………………………………………………………………144

Figure 48. CP values of body cells plotted against the HBTs proportion in active task………………………………………………………………….145

Figure 49. RMI values of body cells plotted against the HBTs proportion in active task…………………………………………………………………146

Figure 50. Attentional modulation of baseline and evoked response in 30 low baseline cells…………………………………………………………147

Figure 51. Attentional modulation of baseline and evoked response in 30 low baseline cells…………………………………………………………148

Figure 52. Percent of HBT in active is plotted vs. percent of HBT in passive for 14 body and 16 non-body cells………………………………………..149

Figure 53. RMI of low baseline body and non-body cells in different stimulus and choice conditions……………………………………………150

Figure 54. RMI of low baseline body and non-body cells in different stimulus and choice conditions……………………………………………151

Figure 55. Percent of HBT is active vs. percent of HBT in passive………152

Figure 56. A combination of baseline firing rate and evoked response modulation in active compared with passive conditions affects monkeys’ performance…………………………………………………………….…153

Figure 57. Polar plots of IT cells activity show that baseline dependent differential response of IT cell subpopulations determines monkey’s choice…………………………………………………………………..…155

Appendix1: Stimulus set……………………………………………….……….158

Appendix2: List of abbreviations………………………………………………164

References………………………………………………………………………..166

Introduction

The crucial role of “visual object categorization” in everyday life

Our normal life relies on ability of visual object recognition or determining the identity of a seen object. We recognize different familiar or novel objects in everyday life. We do this with no or little effort, despite the fact that these objects may vary in form, color, illumination, view, size or texture from time to time. Based on both behavioral and neural data there are different levels of object recognition. When we see Einstein’s face, first we detect it as a “face” (supraordinate level), perceive as a “human face” (ordinate level) and then “Einstein’s face” (subordinate level). Spector and Kanwisher explored the sequence of processing steps in object recognition by asking human subjects to do three different tasks: object detection, categorization and identification. Accuracy and reaction time were similar for object detection and categorization showing that “as soon as you know it is there, you know what it is” (Spector and Kanwisher, 2005). On the other hand, lower accuracy and longer reaction time was observed for identification compared to categorization, introducing them as different steps of object recognition. Compatible with behavioral data firing patterns of single cells in inferior temporal cortex, a cortical area involved in object recognition, convey the information about categorization and identification with different latencies. Earliest part of the response (~120 ms after stimulus presentation) represents information about categorization while more detailed information about members of category started ~50 ms later (Sugase et al., 1999). Therefore, visual cortex processes information from global to fine in a hierarchical fashion. It has been suggested that categorization relies on the “presence or absence of features”, whereas identification is based on “configurational judgments”.

“Visual object categorization” or our ability to classify objects by giving meaning to our environment enables us to interact normally and efficiently with objects and events. There are some defined classes of objects in our mind. They usually share some major common properties in their appearance, while at the same time there are lots of differences among their members. For example, trees usually grow from the earth, they have roots, stem and usually green leaves. While they have similar properties, each of the species has a set of specific characteristics. But we call

all of them trees, and also easily classify any new member as tree, even if we have not seen something like it before. This fascinating ability of categorization objects is vital for our survival. We know special traits for different object categories. We have learned how to treat and interact with any of them, depending on their characteristics. For example, classifying a rod-shaped moving object as “snake” makes us to run away as fast as possible. We perform this task easily and rapidly under very different conditions and even in noisy environment. Behavioral studies in human have shown that they can recognize animals in a cluttered picture which is presented only for 20ms with reaction times less than 400ms and 95% accuracy (Thorpe et al., 1996; Keysers et al., 2001). Monkeys showed even faster reaction times (Fabre-Thorpe et al., 1998). Monkeys could categorize food and trees with reaction times less than 250ms (Vogels, 1999a). Single cell studies in macaque inferior temporal (IT) cortex have revealed that category response latency is less than 100ms from stimulus onset (Vogels, 1999b; Kiani et al., 2005; Perrett et al., 1982).

Where in the brain is category information represented?

Neural mechanisms of and cortical areas involved in visual object categorization are among the hottest areas in field of cognitive neuroscience. Exploring the underlying mechanisms of visual categorization in the activity of single neurons of a special cortical area is based on what Santiago Ramon Cajal proposed by “Neuron Doctrine” over a century ago. He showed that nervous system is not one continuous web but a network of discrete cells. According to “Neuron Doctrine” individual neurons are the basic structural and functional units of the nervous system. This finding led to a new view of brain function called “Cellular Connectionism”. Based on this view, individual neurons are the signaling units of the brain; they are generally arranged in functional groups and connect to one another in a precise fashion and different behaviors are produced by different brain regions interconnected by specific neural pathways (Kandel, 2000).

Visual cortices are regions of the brain dedicated to the process of visual information. There is a “feed-forward flow of visual information” in these cortical areas. Visual information after reaching the eyes extends from the retina to the primary visual cortex (V1) and then the secondary visual cortex (V2). After V2, visual information goes through two different visual pathways:

Dorsa visual pathway or “what” pathway, involved in motion detection and visumotor tasks

Ventral visual pathway or “where” pathway, involved in object recognition

Understanding and recognition of shape of visual objects are completed in ventral visual pathway of the brain. Across the ventral visual pathway, there is a flow of visual information from the lower level visual areas (V1 & V2) into mid level (V4) and then to the high level visual area (IT) (Merigan & Maunsell, 1993). There is also a hierarchical organization even along the subareas of IT cortex. These intrinsic connections in the IT cortex were studied by Fujita & Fujita (1996). They showed that these connections were distributed in an anisotropic manner (fibers go through anteroposterior direction more than mediolateral direction) around the injection of the tracer showing the continuous feed-forward flow of visual information even in these subareas. Along with this feed-forward flow of visual information there is a hierarchical processing of the visual information. Reflected light from visual stimuli after entering the eyes is converted into electrical signals by photoreceptors and ganglion cells in the retina which respond optimally to contrast and small spots of light in their small receptive fields resulting in decomposition of visual stimuli into a pattern of small spots. Progressive convergence of input from retina and LGN (lateral geniculate nucleus) to the primary visual cortex (V1) leads to some feature abstraction. The outline of a visual image is decomposed into spots in retina and then recomposed into short line segments of various orientations by simple and complex cells in V1 cortex (Hubel & Wiesel, 1962). The visual pathway extends from V1 to V2. V2 neurons continue the analysis of contours begun by V1 neurons. Response of many V2 neurons to illusory lines just as real edges shows that the feature abstraction is in progress through the visual stream (Kandel, 2000). To clarify the progressive abstraction of visual information processing from V2 to downstream cortices, Kobatake & Tanaka (1994) defined an index based on the ratio of the maximum neural response to simple stimuli to the maximum neural response to all other stimuli in their image set (both simple and complex stimuli). The distribution of this ratio shifted from 1 toward 0 step by step from V2 to anterior IT. They showed that in macaque monkeys, the best stimulus of cells in V2 were just simple shapes, in V4 and posterior IT were both simple and complex features and the cells selective to complex features were intermingled in single penetrations with cells that responded maximally to some simple features. They also found that neurons of anterior IT were just selective to complex features. They suggested that local neuronal networks in V4 and posterior IT play an essential role in the abstraction of simple features into complex object features. These findings are consistent with “Feature Detection Theory”, one of the main theories in object recognition. According to this theory, the object perception proceeds by recognizing individual features, such as back, seat, arms and base of a chair, and assembling them into a coherent pattern, or chair.

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عنوان :پایان نامه رشته زبان انگلیسی : Brain State Dependent Role of Attention in Perceptual Processing and Decision Making

Institute for Research in Fundamental Sciences

Institute for Studies in Theoretical Physics and Mathematics (IPM)

School of Cognitive Sciences (SCS)

Ph.D. Thesis Cognitive Science, Brain and Cognition

Brain State Dependent Role of Attention in Perceptual Processing and Decision Making

Supervisor: Professor Hossein Esteky

March, 2011